Neuroplasticity - Dialogues in Clinical Neuroscience

Neuroplasticity

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Dialogu se

Editor-in-chiefJean-Paul MACHER, MD, Rouffach, France

Editorial BoardManfred ACKENHEIL, MD, München, GermanyCésar CARVAJAL, MD, Santiago de Chile, ChileMarc-Antoine CROCQ, MD, Rouffach, FranceMichael DAVIDSON, MD, Tel Hashomer, IsraelMargret R. HOEHE, MD, Berlin, GermanyBarry D. LEBOWITZ, PhD, Rockville, Md, USADeborah J. MORRIS-ROSENDAHL, PhD, Johannesburg, South AfricaRajesh M. PARIKH, MD, Bombay, IndiaDavid RUBINOW, MD, Bethesda, Md, USAPierre SCHULZ, MD, Chêne-Bourg, SwitzerlandCarol A. TAMMINGA, MD, Baltimore, Md, USA

International ConsultantJorge-Alberto COSTA E SILVA, MD, Rio de Janeiro, Brazil

Publication Director / Directeur de la PublicationJean-Philippe SETA, MD, Neuilly-sur-Seine, France

ear Colleagues,

The known properties of the central nervous system are quite remarkable and wemay confidently assume that many more fascinating aspects of the brain remain to bediscovered. Until recently, the causation of mental disorders was always explained interms of abnormalities involving familiar biological concepts, such as monoamine neu-rotransmission, receptor regulation, and molecular biology.

The appearance of a novel explanatory model, accounting for some previouslyunexplained phenomena, is of tremendous interest. It has long been known that somedisorders involve regional modifications that can be evidenced by studying brain struc-ture. Neurotrophic factors preventing cell death have been shown to exist and, morerecently, the process of hippocampal neurogenesis has been described.

Neuroplasticity is the process that underlies neurogenesis: it leads to protein syn-thesis and constitutes a defense mechanism against the deleterious effects of stress. Plasticmodifications of neurons and synapses have been observed thanks to the developmentof neuroimaging techniques, which can reach as far as the cellular level.The observationsrelating to neuroplasticity have led to:• New diagnostic markers.• A better understanding of certain pathogenetic mechanisms.• The proof of activity of certain compounds.

We believe that it is important to give a progress report on the concept of neuro-plasticity and its influence on the understanding of the mechanisms of depression. Weare grateful to Dr David R. Rubinow from the National Institute of Mental Health inBethesda, Md, for bringing together the most qualified authors in the field to discuss thistopic in this issue of Dialogues in Clinical Neuroscience.

Yours sincerely,

Jean-Paul Macher, MD Marc-Antoine Crocq, MD

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Dialogues in Clinical Neuroscience is a quarterly publication that aims toserve as an interface between clinical neuropsychiatry and the neuro-sciences by providing state-of-the-art information and original insights intorelevant clinical, biological, and therapeutic aspects. Each issue addresses aspecific topic, and also publishes free contributions in the field of neuro-science as well as other non–topic-related material. All contributions arereviewed by members of the Editorial Board and submitted to expert con-sultants for peer review.

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ISSN 1294-8322

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EditorialJean-Paul Macher, Marc-Antoine Crocq

In this issueDavid R. Rubinow

State of the artStructural plasticity of the adult brain: how animal models help usunderstand brain changes in depression and systemic disorders related to depressionBruce S. McEwen

Basic researchStructural plasticity of the adult brainFred H. Gage

Regulation of cellular plasticity and resilience by mood stabilizers:the role of AMPA receptor traffickingJing Du, Jorge A. Quiroz, Neil A. Gray, Steve T. Szabo,Carlos A. Zarate Jr, Husseini K. Manji

Pharmacological aspectsNeural plasticity: consequences of stress and actions of antidepressant treatment Ronald S. Duman

Cellular consequences of stress and depression Eberhard Fuchs, Gabriele Flügge

Clinical researchCellular abnormalities in depression: evidence from postmortembrain tissueCraig A. Stockmeier, Grazyna Rajkowska

Neuroplasticity in mood disordersWayne C. Drevets

Cellular plasticity and resilience and the pathophysiology of severe mood disordersDennis S. Charney, George DeJesus, Husseini K. Manji

ISSUE COORDINATED BY: David R. RUBINOW

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ISSUE COORDINATED BY: David R. RUBINOW

Free papersTexture analysis of the brain: from animal models to human applicationsJean-François J. Nedelec, Olivier Yu, Jacques Chambron,Jean-Paul Macher

Problems in texture analysis with magnetic resonance imagingLothar R. Schad

Texture analysis methodologies for magnetic resonance imagingAndrzej Materka

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I n t h i s i s s u e . . .

For many of us, a central tenet of our neurobiological train-ing was that the structure of the brain was fixed, as was thenumber of neurons in the adult brain. This belief was notonly incorrect, but also restricted our understanding of avariety of fundamental processes including learning, adap-tation and maladaptation to stress, development of sus-ceptibility to disease, and resilience. This issue of Dialoguesin Clinical Neuroscience describes the flexible, adaptiveresponses of the brain—neuroplasticity—and the relevanceof neuroplastic changes to the pathophysiology of neu-ropsychiatric illness, the mechanism of action of psy-chotropic medications, and the transduction of environ-mental factors to changes in brain function.

In the State of the art article, Bruce S. McEwen (page 119)provides an introduction to allostasis—adaptation tostress—and an overview of the structural and cellular con-sequences of stress, its molecular mediators in altering brainstructure, and the kinetics of stress-induced structuralremodeling. The compelling models described make appar-ent the complexity and dynamic nature of adaptation, aswell as the ontogeny of susceptibility to psychiatric illness.

In the first Basic research article, Fred H. Gage (page 135)reviews an element of neuroplasticity, neurogenesis, ie, thegeneration of new neurons, and describes the multiplesteps involved in the process of neurogenesis: differentia-tion, commitment, survival, and functional integration. Thiscapacity for self-repair represents one of the therapeuticfrontiers in the treatment of neuropsychiatric illness.

In the second Basic research article, Jing Du and col-leagues (page 143) review the evidence suggesting therole of glutamatergically mediated synaptic plasticity inboth the pathophysiology and treatment of affective ill-ness. While focusing on AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate) and GluR1 (glutamate)receptor trafficking, these authors provide an excellentintroduction to glutamate receptor pharmacology andintracellular signaling as a way of demonstrating both themechanisms of action of mood stabilizers and targets forsubsequent drug development.

In one of the Pharmacological aspects articles, RonaldS. Duman (page 157) discusses the effects of stress andantidepressants on neuroplasticity, particularly as these

effects relate to depression. He focuses on two mediatingsystems that appear to link, at a molecular level, neuro-plasticity, stress, depression, and pharmacotherapy: theneurotrophin BDNF (brain-derived neurotrophic factor),and the cAMP-CREB (cyclic adenosine monophosphate[cAMP]–cAMP-response element binding protein) cas-cade. He suggests that the cellular and molecular under-pinnings of structural and functional plasticity offerpromising clues to the pathophysiology of depression andtargets for drug development. In the other Pharmaco-logical aspects article, Eberhard Fuchs and GabrieleFlügge (page 171) describe the pharmacology of thestress response by focusing on changes in monoaminesand monoamine receptors in several animal stress mod-els. They provide a basis for understanding depression asan impairment of synaptic and structural plasticity, withconsequent implications for its treatment.

In the first Clinical research article, Craig A. Stockmeierand Grazyna Rajkowska (page 185) describe in detail theneural and glial abnormalities identified in several criticalbrain regions in affective illness. This comprehensive reviewof postmortem studies discusses the possible functionalimplications of abnormalities of cell morphology and dis-tribution and introduces the circuitry that is described inmore detail in the second article by Wayne C. Drevets(page 199). While focusing on neuroimaging studies, heprovides a synthesis of identified neuropathological andimaging abnormalities in affective illness, highlightingthose neural circuits strongly implicated as dysfunctional inaffective disorder. Elucidation of this circuitry at functionaland structural levels will also help illuminate substrates forcomponent processes common to a variety of neuropsy-chiatric disorders.

Indeed, in the last article, Dennis S. Charney and his col-leagues (page 217) suggest that studies of neuroplastici-ty will result in a new psychiatric nosology, as well as newtherapeutic targets. Thus, not only will the therapeuticarmamentarium be expanded as we better understandthe mechanics of neuroplasticity, but this better under-standing will also lead to a reconceptualization of howpsychiatric illness is acquired, how it is optimally treated(with attention to both structural and functional ele-ments), and, perhaps most importantly, how resilience isexpressed at cellular and organismic levels.

David R. Rubinow, MD

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C o n t r i b u t o r s

Author affiliations: Harold and MargaretMilliken Hatch Laboratory of Neuroen-docrinology, The Rockefeller University,New York, NY, USA

Bruce S. McEwen, PhD

Author affiliations: Laboratory of Genetics,The Salk Institute, La Jolla, Calif, USA

Fred H. Gage, PhD

Author affiliations: Laboratory of Molecu-lar Pathophysiology, National Institute ofMental Health, Bethesda, Md, USA

Husseini K. Manji, MD, FRCPC

Author affiliations: Division of MolecularPsychiatry, Departments of Psychiatry andPharmacology, Yale University School ofMedicine, New Haven, CT, USA

Ronald S. Duman, PhD

Author affiliations: Clinical NeurobiologyLaboratory, German Primate Center, Göt-tingen, Germany

Eberhard Fuchs, PhD

Author affiliations: The University of Mis-sissippi Medical Center, Department of Psy-chiatry and Human Behavior, Jackson, Miss,USA

Craig A. Stockmeier, PhD

Author affiliations: Mood and Anxiety Dis-orders Program, NIH NIMH/MIB, Bethesda,Md, USA

Wayne C. Drevets, MD

Author affiliations: National Institute ofMental Health, Bethesda, Md, USA

Dennis S. Charney, MD

Author affiliations: FORENAP, Rouffach,France

Jean-François J. Nedelec, PhD

Author affiliations: Department of Bio-physics and Medical Radiation Physics, Ger-man Cancer Research Centre, Heidelberg,Germany

Lothar R. Schad, PhD

Author affiliations: Institute of Electronics,Technical University of Lodz, Lodz, Poland

Andrzej Materka, MSEE, PhD, DSc

hen we experience a stressful event, the initialresponse of the brain, body, and behavior is a protectiveone, and hormones, cytokines, and other mediators, suchas the neurotransmitters, are used to survive and adaptto the challenge. However, repeated stressful experienceshave deleterious effects, in part because the very samemechanisms that help protect in the short term are noweither mismanaged and/or overused.1 And, over weeks,months, and years, the dysregulation and overactivity ofthese systems can promote changes that appear to bedeleterious, and stressful experiences have been reportedto be a major risk factor in the occurrence of depressivedisorders. For example, in the brain, the overactivity ofstress hormones in the blood and endogenous excitatory

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Structural plasticity of the adult brain:how animal models help us understand brainchanges in depression and systemic disordersrelated to depression Bruce S. McEwen, PhD

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Copyright © 2004 LLS SAS. All rights reserved www.dialogues-cns.org

The brain interprets experiences and translates them into behavioral and physiological responses. Stressful events arethose which are threatening or, at the very least, unexpected and surprising, and the physiological and behavioralresponses are intended to promote adaptation via a process called “allostasis.” Chemical mediators of allostasis includecortisol and adrenalin from the adrenal glands, other hormones, and neurotransmitters, the parasympathetic and sym-pathetic nervous systems, and cytokines and chemokines from the immune system. Two brain structures, the amygdalaand hippocampus, play key roles in interpreting what is stressful and determining appropriate responses. The hip-pocampus, a key structure for memories of events and contexts, expresses receptors that enable it to respond to gluco-corticoid hormones in the blood. It undergoes atrophy in a number of psychiatric disorders; it also responds to stressorswith changes in excitability, decreased dendritic branching, and reduction in number of neurons in the dentate gyrus.The amygdala, which is important for “emotional memories,” becomes hyperactive in posttraumatic stress disorder anddepressive illness. In animal models of stress, there is evidence for growth and hypertrophy of nerve cells in the amyg-dala. Changes in the brain after acute and chronic stressors mirror the pattern seen in the metabolic, cardiovascular, andimmune systems, that is, short-term adaptation (allostasis) followed by long-term damage (allostatic load), eg, athero-sclerosis, fat deposition obesity, bone demineralization, and impaired immune function. Allostatic load of this kind is seenin major depressive illness and may also be expressed in other chronic anxiety and mood disorders. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:119-133.

Keywords: structural plasticity; brain; allostasis; allostatic load; stress; depres-sion; anxiety

Author affiliations: Harold and Margaret Milliken Hatch Laboratory ofNeuroendocrinology, The Rockefeller University, New York, NY, USA

Address for correspondence: Bruce S. McEwen, PhD, The Rockefeller University,Box 165, 1230 York Avenue, New York, NY 10021, USA(e-mail: [email protected])

amino acid neurotransmitters in the brain suppress neu-rogenesis in dentate gyrus (DG) and causes debranchingof dendrites in hippocampus and medial prefrontal cor-tex, whereas chronic stress causes neurons in amygdalato show dendritic growth.2-5 The hippocampus containsreceptors for adrenal steroids, which regulate excitabil-ity and morphological changes (Figure 1). Along withmany other brain regions, the amygdala also containsadrenal steroid receptors, which influence function in thisstructure as well (Table I).

Acute stress induces formation of spine synapses in CA1region of hippocampus6 and chronic stress also increasesspine synapse formation in hippocampus and amygdala.7

The contrasting changes of dendrites in amygdala andhippocampus after chronic restraint stress (CRS) offersan unprecedented opportunity for understanding under-lying mechanisms, as will be discussed below.CRS for 21 days or longer impairs hippocampal-depen-dent cognitive function8,9 and enhances amygdala-depen-dent unlearned fear and fear conditioning,10 which areconsistent with the opposite effects of stress on hip-pocampal and amygdala structure. CRS also increasesaggression between animals living in the same cage(Table II).11 Psychosocial stress suppresses neurogenesisand causes dendritic shrinkage,12-15 and one of these stressmodels, the tree shrew, is considered to be a model ofhuman depressive illness.16

Indeed, in major depression and a number of other moodand anxiety disorders, there are reports of hippocampalvolume loss and enlargement of the amygdala.17,18 Studiesin the tree shrew have shown that treatment with anti-


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Selected abbreviations and acronymsCGRP calcitonin gene–related peptideCRS chronic restraint stressDG dentate gyrusGR glucocorticoid receptorIGF-1 insulin-like growth factor–1MR mineralocorticoid receptorNMDA N-methyl-D-aspartatePSA-NCAM polysialated neural cell adhesion moleculetPA tissue plasminogen activator

Figure 1. The hippocampus is a target for adrenal steroids. GR, glucocorticoid receptor; MR, mineralocorticoid receptor; Sch, Schaffer colateral; MF,mossy fiber; CC, corpus callosum.

CA1

CA3

Dentategyrus

Perfusionpathway

Sch

MF

depressant, antiseizure, and mood-stabilizing drugs pre-vents stress-induced hippocampal structural changes.14,15,19

Besides reduced neurogenesis in DG, there is also evi-dence for reduced size of principal neuron cell bodies inhippocampus, which is consistent with reduced size of thedendritic tree.20 Synaptic reorganization is also a likelyconsequence of these rather drastic structural changes,and the animal models cited above provide evidence thatsynapses can be rapidly formed as a result of stress.Takentogether, such structural changes seem likely to play amajor role in the volume loss in the human hippocampusand the related effects on cognitive function and affect.18

This article will review underlying mechanisms and con-sider their applicability to furthering our understandingof the pathophysiology of mood and anxiety disorders.

Allostasis and mechanisms for behavioral adaptation

The amygdala and hippocampus are both involved in con-textual fear conditioning and in passive avoidance learn-ing. In fear conditioning, glucocorticoids enhance learnedfear21 and they play an important role in forming the mem-ory of context in contextual fear conditioning, but not ofthe actual effect of footshock in rats that are already famil-iar with the context where the shock is administered.22,23

This suggests that the hippocampal role in contextual fearconditioning is enhanced by moderate levels of glucocor-ticoids, but the fear conditioning is either not so dependenton glucocorticoids or is so strong that glucocorticoid influ-ences are hard to demonstrate.Yet there is evidence for aninfluence of glucocorticoids on the flow of informationwithin the amygdala.Glucocorticoids potentiate serotonin inhibition of the pro-cessing of excitatory input to the lateral amygdala from thethalamus, suggesting that there is a mechanism for con-taining, or limiting, the sensory input that is important for

fear conditioning.24 Thus, adrenal steroids may regulate thenature of the signals that reach the amygdala and allow forgreater discrimination of the most salient cues for learning.Moreover, in passive avoidance, both catecholamines andglucocorticoids play a role in facilitating learning.25,26

Catecholamines work outside of the blood–brain barrierand their effects can be blocked by β-adrenergic–blockingagents, which do not cross the blood–brain barrier.26

Glucocorticoids enter the brain, and local implants ofexogenous corticosterone into hippocampus, amygdala,and nucleus tractus solitarii are all able to enhance passiveavoidance learning.25

Adrenal steroids also play a supporting role in the learn-ing of a spatial navigation task in mice.27 Adrenalectomyimpairs the acquisition of the memory of hidden platformlocation in the Morris water maze, and glucocorticoidadministration restores the normal learning curve; how-ever, in mice in which the glucocorticoid receptor (GR) isdeleted and replaced with a GR that lacks the DNA bind-ing domain, glucocorticoids do not improve task acquisi-tion.27 This finding illustrates a role for GRs acting uponthe genome in a task that is known to depend on the hip-pocampus. Interestingly, other actions of glucocorticoidsvia GRs are known to involve the protein–protein inter-actions that are not prevented in mice carrying the GRdefective in the DNA binding domain.28

Other evidence for glucocorticoid actions supports aninverted U-shaped dose–response curve in which low tomoderate levels of adrenal steroids enhance acquisition oftasks that involve the hippocampus, whereas high levels ofglucocorticoids disrupt task acquisition.22,29-31 Adrenalsteroids have biphasic effects upon excitability of hip-pocampal neurons, which may underlie their biphasicactions on memory and recall.30,32-34

Adaptive structural plasticity

One of the ways that stress hormones modulate functionwithin the brain is by changing the structure of neurons.Within the hippocampus, the input from the entorhinalcortex to the DG is ramified by the connections between

Structural plasticity of the adult brain - McEwen Dialogues in Clinical Neuroscience - Vol 6 . No. 2 . 2004

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Table I. Distribution of adrenal steroid receptors in brain regions. GR, glu-cocorticoid receptor; MR, mineralocorticoid receptor.

Hippocampus MR and GR

Amygdala GR and some MR

Septum GR and some MR

Hypothalamus GR mostly; low levels of MR

Cerebral cortex GR mostly; low levels of MR

Midbrain GR mostly; low levels of MR

Brain stem GR mostly; patches of MR

Cerebellum GR mostly

Table II. Cumulative effects of restraint stress on behavior.

• Cognitive impairment, spatial recognition memory

(hippocampus)

• Increased anxiety and enhanced fear conditioning

(amygdala)

• Increased aggression (amygdala)

the DG and the CA3 pyramidal neurons. One granuleneuron innervates, on average, 12 CA3 neurons; and eachCA3 neuron innervates, on the average, 50 other CA3neurons via axon collaterals, as well as 25 inhibitory cellsvia other axon collaterals (Figure 2).35 The net result is a600-fold amplification of excitation as well as a 300-foldamplification of inhibition, which provide some degreeof control of the system.As to why this system exists, theDG-CA3 system is believed to play a role in the memoryof sequences of events, although long-term storage ofmemory occurs in other brain regions.36,37

Neurogenesis in the DG

There is structural plasticity within the DG-CA3 system,in that new neurons continue to be produced in the DGthroughout adult life38 and CA3 pyramidal cells undergoremodeling of their dendrites,2 as will be discussed fur-ther below.39

The subgranular layer of the DG contains cells that haveproperties of astrocytes (eg, expression of glial fibrillaryacidic protein) and give rise to granule neurons.40 Afteradministration of bromodeoxyuridine (BrdU) to labelDNA of dividing cells, these newly born cells appear asclusters in the inner part of the granule cell layer, wherea substantial number of them will go on to differentiateinto granule neurons within as little as 7 days. The newgranule neurons appear to be quite excitable and capa-

ble of participating in long-term potentiation. In the adultrat, 9000 new neurons are born per day and survive witha half-life of 28 days.41

There are many hormonal and neurochemical modula-tors of neurogenesis and cell survival in the DG.15,38,42-44

Neurogenesis in the adult DG is enhanced by the hor-mone insulin-like growth factor–1 (IGF-1) and by sero-tonin and a number of antidepressant drugs. Estradiolaccelerates cell proliferation in female rats. IGF-1 is themediator of the ability of exercise to increase cell prolif-eration in the DG. Lack of IGF-1 and insulin in diabeteshas the opposite effect and decreases cell proliferation.Neurogenesis and/or survival of newly born cells isincreased by putting mice in a complex (“enriched”)environment.45 It is also increased by a form of classicalconditioning that activates the hippocampus (“trace con-ditioning”) prolongs the survival of newly born DG neu-rons.46,47 On the other hand, certain types of acute stressand many chronic stressors suppress neurogenesis or cellsurvival in the DG, and the mediators of these inhibitoreffects include excitatory amino acids acting via N-methyl-D-aspartate (NMDA) receptors and endoge-nous opioids.2,48-50 Chronic stress has even more potenteffects on neurogenesis and neuronal survival. CRS for21 days suppressed neurogenesis and CRS for 42 dayscauses the number of DG neurons to decrease along withtotal DG volume (Figure 3).51

Remodeling of dendrites

Another form of structural plasticity is the remodeling ofdendrites in the hippocampus.39 CRS causes retractionand simplification of dendrites in the CA3 region of thehippocampus (Figure 4).2 Such dendritic reorganizationcan also be seen in rats undergoing adaptation of psy-chosocial stress in the visible burrow system (VBS).TheVBS is an apparatus with an open chamber where thereis a food and water supply and several tunnels and cham-bers.52 Rats can be observed from above by a video cam-era in this apparatus. In the VBS, male rats housed withseveral females establish a dominance hierarchy withinseveral days. Over the course of the next week, a few sub-ordinate males may die and others (showing scars frombite marks) will show enlarged adrenals, low testos-terone, and many changes in brain chemistry. The domi-nant shows the fewest scars and has the highest level oftestosterone, but also has somewhat larger adrenal glandsthan cage control rats.


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Figure 2. Why is the CA3 so vulnerable? Feed-forward excitability servesmemory functions but increases vulnerability for excitotoxicity.DG, dentate gyrus.

Regarding changes in brain structure, it was the dominantrats that had a more extensive pattern of debranching ofthe apical dendrites of the CA3 pyramidal neurons in thehippocampus, compared with the subordinate rats, whichshowed reduced branching compared with the cage con-trols.53 What this result emphasizes is that it is not adrenalsize or presumed amount of physiological stress per se thatdetermines dendritic remodeling, but a complex set ofother factors that modulate neuronal structure.We referto the phenomenon as “dendritic remodeling” and we gen-erally find that it is a reversible process. In hibernatinghamsters, it occurs in a matter of hours and reverses itselfjust as quickly when hibernating animals are aroused fromtorpor (A. M. Magarinos, B. S. McEwen, P. Pevet, unpub-lished data). Below we consider mechanisms involved instructural remodeling.The role of adrenal steroids in the structural remodelingdescribed above reflects may interactions with neuro-chemical systems in the hippocampus, including serotonin,γ-aminobutyric acid (GABA), and excitatory amino acids(Figure 5).2 Probably the most important interactions arethose with excitatory amino acids such as glutamate.Excitatory amino acids released by the mossy fiber path-way play a key role in the remodeling of the CA3 regionof the hippocampus, and regulation of glutamate releaseby adrenal steroids may play an important role.54-57 Wehave found that the glutamate transporter, Glt-1, is ele-vated by CRS in hippocampus, particularly in the CA3region, providing another indication that elevated gluta-

mate levels are an important component of structural plas-ticity.We previously showed that NMDA receptor block-ade and the Na/Ca channel blocker, phenytoin, both blockCRS- and glucocorticoid-induced remodeling of dendritesin CA3.58-60 Recent evidence indicates that presynapticreceptors containing kainate receptor subunits such asGluR6 are important for the feed-forward actions of glu-tamate on mossy fiber terminals,61 and one study showedthat a number of kainate receptor subunit mRNAs areregulated biphasically by adrenal steroids.57 In particular,preferential mineralocorticoid receptor (MR) occupancyby low corticosterone (CORT) levels enhanced mRNAlevels for KAR2, GluR6, and GluR7.57 This agrees withour finding that MR activation by aldosterone in adrena-


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Figure 3. A single restraint stress does not suppress cell proliferation. Repeated restraint stress for 21 days suppresses cell proliferation. Repeated restraintstress for 42 days reduces volume of the dentate gyrus (DG) and the number of neurons in the DG.Reproduced from reference 51 with permission: Pham K, Nacher J, Hof PR, McEwen BS. Repeated restraint stress suppresses neurogenesis and induces biphasicPSA-NCAM expression in the adult rat dentate gyrus. Eur J Neurosci. 2003;17:879-886. Copyright © 2003, Blackwell Publishing, Inc.

Figure 4. Hippocampal CA3 pyramidal neurons are remodeled by 21-drestraint stress. A. Control. B. 21 days’ chronic restraint stress.

A B

lectomized (ADX) rats restored levels of [3H]kainatebinding in the mossy fiber region of CA3.56 However, fur-ther studies are needed.Because excitatory amino acids play a key role along withcirculating glucocorticoids, the activation of the CREB(cyclic adenosine monophosphate response element–bind-ing protein) system is a likely candidate mediator, andrecent evidence indicates that phosphorylation of CREBis chronically activated in rats subjected to CRS. CREBhas been linked to regulation of synaptic plasticity and par-ticularly neurogenesis.62 It is possible that CREB isinvolved in activity-dependent synapse formation, whichis evident as a result of long-term potentiation.63,64

However, the role of glucocorticoids in activation of theCREB system has not been thoroughly investigated.Nevertheless, treatment with the mood stabilizer lithiumprevented CRS-induced structural remodeling of thestress-induced elevation of Glt-1 and CREB phosphory-lation (G. E. Wood, L. T. Young, B. S. McEwen, unpub-

lished data), providing further evidence that CRS-inducedstructural plasticity and the molecular markers Glt-1 andphosphoCREB are useful in study of psychiatric illnesses.Structural changes in dendrites and spine synapses arethe result of modifications in the microtubule system ofthe cytoskeleton,65 and new evidence shows that post-translational modification of tubulin65 and phosphoryla-tion of the microtubule associated protein tau66 takeplace along with changes in the actin cytoskeleton,67

under conditions in which reorganization of dendritesand synaptic connections occur. Overall, cytoskeletalchanges, such as increased paired-helical-like phospho-rylation of tau66 and reduced tyrosinated tubulin,65 areconsistent with increased cytoskeletal rigidity. However,this needs much careful study.The Rac/Rho guanosine triphosphatases (GTPases) andrelated proteins such as the guanosine triphosphate(GTP) exchange factor, kalirin, have been shown to playa key regulatory role in cytoskeletal modifications indeveloping and adult neurons.67,68 Except for one relevantstudy on seizures,65 there are no studies thus far of theeffects of chronic stress on these pathways or of the mod-ifications of the cytoskeleton itself.Besides glucocorticoids and excitatory amino acids, neu-rotrophins and gp130 cytokines are implicated in struc-tural plasticity along with extracellular proteases such astissue plasminogen activator (tPA) and neuropsin. Brain-derived neurotrophic factor (BDNF) plays a major rolein activity-dependent synaptic and dendritic remodel-ing,69-73 and is implicated in hippocampal-dependentmemory formation.74 BDNF also regulates tPA releasefrom neurons75 and tPA is released from nerve terminalsin hippocampus and other brain areas such as amyg-dala.76-78 It has been suggested that tPA may play a role inthe processing of proBDNF into active forms.79 The activ-ity of tPA is associated with structural plasticity andincreased fear,77 motor learning,80 and enhancement oflong-term potentiation.81 Activity of tPA is an importantmediator of structural plasticity and enhanced fear in theamygdala resulting from acute restraint stress. For exam-ple, plasminogen (inactive zymogen) leads to plasmin(active serine protease). Using tPA knockout mice, wehave found that in medial and central amygdala77:• tPA is released under stress and initiates neural remod-

eling.• This release is plasminogen-independent (extracellular

signal–regulated kinase [ERK1/2]; guanosine triphos-phate–activating protein [GAP-43]).


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Figure 5. Glucocorticoids increase glutamate levels, N-methyl-D-aspartate(NMDA) receptors, calcium currents, 5-hydroxytryptamine (5-HT)turnover, and 5-HT2 receptors, decrease 5-HT1A receptors, andalter subunit expression of GABAA receptors. A. Cross-sectionof dorsal hippocampus. B. Blow-up of CA3 region. C. CA3 neu-rons highlighting stratum lucium (SL), where mossy fiber termi-nals form synaptic contacts .GABA, γ-aminobutyric acid; DG,dentate gyrus; SR, stratum radiatum; SP, stratum pyramidale;SO, stratum oriens.

• Glutamate levels ++• NMDA receptors ++• Ca++ currents ++• 5-HT turnover ++• 5-HT2 receptors ++• 5-HT1A receptors --• GABAA receptors +/-


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• tPA induces termination of its own action via plas-minogen-activator inhibitor–1 (PAI-1).

• tPA activity is required for increased anxiety in the ele-vated plus maze.

We are presently studying the long-term effects of stress.Neuropsin is another protease that is induced in hip-pocampus by NMDA-mediated excitation in seizuresand leads to proteolysis of the presynaptic adhesion mol-ecule, L1.82

The gp130 cytokines are expressed in hippocampusunder stimulation by seizures, along with their receptors,which are constitutively expressed.83 Leukemia inhibitoryfactor (LIF) is particularly interesting because it inter-feres with neurotrophin signaling84 and causes dendriticretraction in cell culture.85 However, it has not yet beendetermined whether acute or chronic stress increases LIFexpression, and it is conceivable that increased expres-sion of LIF might play a role in dendritic shortening.The ability of neuronal processes to expand or contract,and newly formed neurons to make connections, is depen-dent on the extracellular environment in which poly-sialated neural cell adhesion molecule (PSA-NCAM)plays an important role.86 PSA-NCAM is associated withregions of the brain that show structural plasticity such asthe inner granule cell layer of the DG and the mossy fiberterminals of CA3.87 CRS for 21 days causes increasedPSA-NCAM expression in the DG proliferative zone eventhough cell proliferation is suppressed, and these changeshave disappeared after CRS for 42 days.51 This raised ques-tions about the role of PSA-NCAM in adaptive structuralplasticity, which need to be investigated. Removal of thePSA residue by endoneuraminidase (EndoN)88 is a pow-erful tool for manipulating this system, since PSA removalabolishes plasticity of suprachiasmatic neurons to envi-ronmentally induced phase shifting of the diurnal rhythm.89

We now turn to the important question of whether chronicstress increases or decreases vulnerability of the hip-pocampus to damage from other insults.

Permanent damage as a result of stress

The remodeling of the hippocampus in response to stressis largely reversible if the CRS is terminated at the end of3 weeks.10 After 3 weeks of CRS, neurogenesis is reducedin DG and dendrites are shorter and less branched,51,59,60

and there is an increase in PSA-NCAM expression in theDG that is consistent with increased mobility of neuronalprocesses even in the face of reduced DG neuron pro-

duction. Continuation of CRS for a total of 6 weeks abol-ishes the upregulation of PSA-NCAM and results in a sig-nificant 6% reduction in DG volume and 13% reductionin granule neuron number.51 We do not yet know whetherstructural changes occurring after 6 weeks of CRS arereversible or whether they can be accelerated by antide-pressant or antiepileptic drugs that block the effects ofstress and glucocorticoids on remodeling. Nor do we knowwhether the structural changes occurring with CRSincrease or decrease the vulnerability of the hippocampusto damage by excitotoxicity.It is well established that glucocorticoids exacerbatedamage to the hippocampus caused by ischemia90 andseizures.91,92 Glucocorticoids exacerbate excitotoxic dam-age and do so, at least in part, by facilitating traffickingof immune cells to the injury site,93 and, there, cytotoxicT cells are able to produce cytotoxic death of neurons.94

However, the phenomenon of ischemic preconditioning95

reveals that prior stimulation of the hippocampus caninduce a protective mechanism that may reduce the dam-age produced by a full-scale ischemic event. It is not clearwhether the same mechanisms might be operative whenstress is applied and whether they might affect theresponse to excitotoxicity in response to seizures, but thispossibility needs to be kept in mind if it turns out that priorCRS has a protective effect on subsequent responses toexcitotoxic challenge.Protective agents may also involve substances that areupregulated in the brain in response to damage or threatof damage. One of the prominent features of excitotoxicdamage or removal of adrenal steroids is the robust induc-tion of calcitonin gene–related peptide (CGRP) in termi-nals and cell bodies in hippocampus and in mossy cells.The increased expression of CGRP in mossy cells is espe-cially prominent after bilateral ADX under conditions inwhich there is apoptosis of granule cells, and the CGRPimmunoreactivity is enhanced within the inner third of themolecular layer of the DG. The neuroimmune peptide,CGRP, is one of the most diverse and influentialimmunoregulators of the periphery.This important neu-ropeptide has multiple functions including: its actions as apotent vasodilator96 and an immune modulator,97-102 as wellas a neural and immune developmental regulator, a mod-ulator of hormone release involved in growth and devel-opment, and a stimulator of sympathetic outflow, which ismediated by CRF and an inducer of apoptosis (reviewedin reference 103). Some of the different functional rolesfor CGRP may not be independent, but may be part of a

cascade of events that constitute the healing response toinjury. A number of studies have shown that CGRP isexpressed following various kinds of trauma and plays animportant role in the acute phase response that may be ofparticular relevance to the outcome of the regional injuryresponse in the central nervous system (CNS).103,104

In recent studies, the expression of CGRP within the hip-pocampus increases in four separate models of CNSinjury: ADX,105 intrahippocampal colchicine injection,105

trimethyltin ingestion,106 and kainic acid injections. Ineach case, the expression of this peptide was limited tothe specific region of damage and in association with thesurviving neuronal population. Although the upregula-tion of CGRP may be associated with neuronal cell sur-vival,107 other studies have shown that both microglia andastrocytes express CGRP receptors and that exposure tophysiological levels of CGRP induces c-fos in microgliaand astrocytes and increases plasminogen activators.108

The role of CGRP may then not only protect againstimmune system damage to neurons, but may also partic-ipate in plasticity and healing.

Protective and damaging effects of the mediators of adaptation

Individual differences in the progression of a number ofdisorders that accumulate with time can be conceptual-ized as an accumulation of wear and tear of daily expe-riences, lifestyle, and major life stressors, which interactwith the genetic constitution and predisposing early lifeexperiences.109-111 The neuroendocrine system, autonomicnervous system, and immune system are mediators ofadaptation to the challenges of daily life, referred to as“allostasis,” meaning “maintaining stability throughchange.”112 Physiological mediators, such as adrenalinfrom the adrenal medulla, glucocorticoids from theadrenal cortex, and cytokines from cells of the immunesystem, act upon receptors in various tissues and organsto produce effects that are adaptive in the short term, butcan be damaging if the mediators are not shut off whenno longer needed. When release of the mediators is notefficiently terminated, their effects on target cells are pro-longed, leading to other consequences that may includereceptor desensitization and tissue damage.This processhas been named “allostatic load,”113,114 which refers to theprice the tissue or organ pays for an overactive or ineffi-ciently managed allostatic response.Therefore, allostaticload refers to the “cost” of adaptation.

The brain is the master controller of the three systemsnoted above and is also a target of these systems, subjectto both protection and damage. Allostasis also appliesnot only to circulating hormones, but also to organs andtissues of the body. In the nervous system, neurotrans-mitters are released by neuronal activity, and they pro-duce effects locally to either propagate or inhibit furtherneural activity. Neurotransmitters and hormones are usu-ally released during a discrete period of activation andthen are shut off, and the mediators themselves areremoved from the intracellular space by reuptake ormetabolism in order not to prolong their effects. Whenthat does not happen, however, there is allostatic loadand the brain is at increased risk for damage.115,116

The processes of allostasis and allostatic load have beendescribed and measured for metabolic and cardiovascularchanges that are associated with obesity, type 2 diabetes,and cardiovascular disease.117 However, the same type ofelevated and prolonged secretion of glucocorticoids dur-ing aging has also been associated with impairment of cog-nitive function in rodents118-120 and humans.121-123 Moreover,the endogenous excitatory amino acid neurotransmittersappear to play a major role in these changes,119 eventhough they are also an essential part of normal synapticneurotransmission and plasticity.

Allostatic states in depressive illness

Stress hormones are elevated in major depressive illness.In particular the diurnal rhythm is distorted.124 Normallylow evening levels of cortisol are increased in a subset ofdepressed patients125,126 and the stress hormone axis inmajor depression is resistant to suppression by the syn-thetic glucocorticoid dexamethasone.127 It is also notewor-thy that androgen levels are elevated in women with majordepression, which undoubtedly reflects adrenal hyperac-tivity.128 IGF-1 levels are also reported to be elevated inmajor depression, and this may reflect elevated growthhormone release as a result of the hypercortisolemia.129

Each of these patterns of elevation constitutes an “allo-static state,” and represents a pathway for the developmentof allostatic load in the brain and in other organs through-out the body. Regarding the brain, we already noted thestudies showing that hippocampal volume loss in majordepressive illness is related to duration of the depressionrather than to age per se of the patients.130-132 Not all stud-ies report such changes (see, for example, references 133and 134); the reasons for these different results are beyond


126

the scope of this discussion, but they may be explained bydifferences in the duration of depression, as well as genderand age. It should be noted that hippocampal size inelderly twins shows only 40% genetic contribution, withthe predominant influence being environmental.135 Thisemphasizes the importance of experimental factors andallostatic load in determining hippocampal volume.Hippocampal atrophy has been found in relation todepression in the elderly,136 with an association detectedwith presence of the ApoE4 genotype.137 In subjects witha long-term history of depression, Sheline and colleaguesdescribed magnetic resonance imaging (MRI) evidencefor discontinuities that might represent sites of damage.130

Although some recent postmortem studies on brains fromdepressed individuals did not show neuron loss in hip-pocampus,138,139 the duration of the depression and the sub-type of depression were not carefully controlled.Thus, thepossibility that neural damage may ultimately occur inmajor depression cannot be disregarded, particularly whendepression lasts a long time. However, in a recent study inyoung depressed subjects, hippocampal volume was notsmaller in first-episode depression, but declined rapidlyover several years.140 The key, unanswered question iswhether such changes can be prevented or even reversed.It is important to note that other brain regions besideshippocampus are affected in depressive illness andundergo structural changes. One region is the prefrontalcortex, and structural imaging141 showed loss of volumein familial pure depressive disorder, whereas autopsystudies142-144 have shown loss of volume and glial cells, aswell as neuronal density in both unipolar and bipolar dis-order. There is one animal study showing that chronicglucocorticoid treatment induces loss of dendrites in therat prefrontal cortex.4 However, much more work needsto be done on this brain region.Depressive illness is associated with a hyperactivation ofthe amygdala,145,146 and more recently, with an actualenlargement of the amygdala in the first episode of majordepression.147 This is reminiscent of the increased dendriticbranching reported in rats after repeated immoblizationstress (see above and reference 148). Since the amygdalaintegrates information related to fear and strong emotions,and also sends outputs via the central nucleus for auto-nomic arousal and via the basal nucleus for more activeaspects of coping,149 the elevation of amygdala activity maybe a first step that leads to overactivation of systemsinvolved in physiological and behavioral coping.The long-term consequences of this may well be a wear

and tear on the body that results in a number of patho-physiological consequences, since the amygdala regulatesboth autonomic nervous system activity and adrenocor-ticotropic hormone (ACTH) and cortisol productionthrough outputs of its central nucleus.149,150 It is importantto note that there are reports that in recurrent majordepression of long duration the amygdala may undergoshrinkage.131,151 It is thus possible that initial hypertrophygives way to atrophy in this important brain structure.Besides the brain changes in major depression, there areother changes in the body that reflect dysregulated hypo-thalamopituitary axis (HPA) and autonomic activity, andare slow in developing.These constitute allostatic load thatproduces cumulative pathophysiology, which may also bereversible if caught in time. Such cumulative, long-termeffects include bone mineral loss152-154 and abdominal fatdeposition.155-157 Moreover, the combination of long-termallostatic load, together with dysregulation of the auto-nomic nervous system in major depression,158 is associatedwith increased blood platelet reactivity159-161 and increasedrisk for cardiovascular disease.162-165

There are parallels between the story for major depressionand what is known about psychiatric and somatic featuresof Cushing’s disease involving melancholia, depression,abdominal obesity, bone mineral loss, and increased riskfor cardiovascular disease.166-169 In addition, there is evi-dence for hippocampal atrophy in Cushing’s disease alongwith memory impairments.170-172 Interestingly, hippocampalvolume loss in Cushing’s disease is at least partiallyreversible over several years after correction of the hyper-cortisolemia.173-175

Finally, a largely unexplored area concerns the effects ofantidepressant medication on the brain and bodychanges associated with depressive illness. On the onehand, certain antidepressants may contribute to some ofthe associated pathophysiology, such as cardiovascularinstability.176 On the other hand, withdrawal from antid-pressant treatment may cause imbalances in neurotrans-mitter systems, with elevations of excitatory amino acidtone,177 and contribute to the allostatic load that occursas the depressive state continues.178

Conclusion

Translational studies of brain changes in major psychiatricillnesses such as unipolar and bipolar depression and post-traumatic stress disorder are showing that changes in vol-ume of structures such as hippocampus, prefrontal cortex,


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REFERENCES

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and amygdala must be considered as part of the neurobi-ological consequences of these illnesses.17,18,140,179,180

Structural remodeling in these brain regions is importantfor human psychiatric disorders because the altered cir-cuitry is likely to contribute to impaired cognitive functionand affect regulation. Moreover, stress is widely acknowl-edged as a predisposing and precipitating factor in psy-chiatric illness.181,182

Thus, animal models are relevant to human psychiatricdisorders in at least four ways:• First, they have led to—and continue to contribute—

basic knowledge to the ongoing studies of how thehuman brain changes structurally in depression andrelated psychiatric disorders.

• Second, the structural changes that occur with chronicstress appear to be reversible as long as the stress is ter-

minated in time. This suggests the hopeful possibilitythat brain changes in at least some major psychiatricdisorders may be treatable if we can find the rightagents or therapies and intervene in time.

• Third, reversible or not, the effects of chronic stressmay predispose to greater vulnerability to adverse con-sequences from other insults.

• Fourth, the systemic manifestations of the allostaticload generated by chronic psychiatric disorders affectsthe metabolic, immune, and cardiovascular systems,leading to systemic disorders that add to the costs ofhealth care. ❏

Research support has come from the National Institute of Mental HealthGrants MH 41256 and MH58911. The author is also indebted to colleaguesin the John D. and Catherine T. MacArthur Foundation Health Program andits Network on Socioeconomic Status and Health (Nancy Adler, PhD, Chair).


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Plasticidad estructural del cerebro adulto: cómo los modelos animales nos ayudan a comprender los cambios cerebrales en la depresión y los trastornos sistémicos relacionadoscon la depresión

El cerebro interpreta experiencias y las traduce en respuestas conductuales y fisiológicas. Los aconteci-mientos estresantes son aquellas situaciones amenazantes, o al menos, inesperadas y sorpresivas; y las res-puestas fisiológicas y conductuales intentan promover una adaptación a través de un proceso llamado“alostasis.” Los mediadores químicos de la alostasis incluyen el cortisol y la adrenalina de las glándulasadrenales, otras hormonas y neurotransmisores, el sistema nervioso parasimpático y simpático, y citoqui-nas y quimioquinas del sistema inmune. Dos estructuras cerebrales, la amígdala y el hipocampo, jueganpapeles clave en la interpretación de lo que es estresante y en la determinación de respuestas apropiadas.El hipocampo, una estructura clave para las memorias de los acontecimientos y del contexto, expresa recep-tores que lo capacitan para responder a hormonas glucocorticoídeas de la sangre. El hipocampo se atro-fia en numerosos trastornos psiquiátricos y también responde a estresores con cambios en la excitabilidad,disminución de las ramificaciones dendríticas y reducción del número de neuronas del giro dentado. Laamígdala, que es importante para las “memorias emocionales,” aumenta su actividad en el trastorno porestrés postraumático y en la enfermedad depresiva. En modelos animales de estrés existen evidencias delcrecimiento e hipertrofia de células nerviosas en la amígdala. Los cambios en el cerebro después de situa-ciones de estrés agudo y crónico reflejan el patrón observado en los sistemas metabólico, cardiovascular einmune; esto es, una adaptación a corto plazo (alostasis) seguida de un daño a largo plazo (carga alostá-tica), como por ejemplo, ateroesclerosis, obesidad localizada, desmineralización del hueso y deterioro dela función inmune. La carga alostática de este tipo se observa en la depresión mayor y también se puedeexpresar en otros trastornos ansiosos y afectivos crónicos.

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Plasticité structurale du cerveau adulte : comment les modèles animaux nous aident à comprendre les modifications cérébrales dans la dépression et les troubles généraux liés à la dépression

Le cerveau interprète les expériences et les traduit en réponses comportementales et physiologiques. Lesévénements stressants sont ceux qui sont menaçants ou tout au moins inattendus et surprenants et lesréponses physiologiques et comportementales ont pour but de promouvoir l’adaptation via un proces-sus appelé « allostasie ». Les médiateurs chimiques de l’allostasie incluent le cortisol et l’adrénaline sécré-tés par les glandes surrénales, d’autres hormones et des neurotransmetteurs, les systèmes nerveux sym-pathique et parasympathique, et les cytokines et chimiokines produites par le système immunitaire. Deuxstructures cérébrales, l’amygdale et l’hippocampe, jouent un rôle-clé dans l’identification des événementsstressants et l’élaboration de réponses appropriées. L’hippocampe, une structure-clé pour les souvenirs desévénements et contextes, exprime des récepteurs qui lui permettent de répondre aux hormones gluco-corticoïdes du sang. Il subit une atrophie au cours de nombreux troubles psychiatriques et réagit égale-ment aux facteurs de stress par des changements de l’excitabilité, une diminution de la ramification den-dritique et une baisse du nombre de neurones dans le gyrus denté. L’amygdale, qui joue en rôle importantdans les « souvenirs émotionnels », devient hyperactive dans l’état de stress posttraumatique et la dépres-sion. Les modèles animaux de stress montrent l’existence d’une croissance et d’une hypertrophie des cellu-les nerveuses dans l’amygdale. La chronologie des modifications du cerveau à la suite de stress aigus ouchroniques (adaptation à court terme [allostasie] suivie d’une altération à long terme [charge allostati-que]), reflète celle observée au cours des affections touchant, par ex., les systèmes métabolique, cardio-vasculaire et immunitaire, où la phase d’adaptation se complique, respectivement, d’athérosclérose et obé-sité localisée, de déminéralisation osseuse et d’altérations de la fonction immunitaire. Une telle chargeallostatique se rencontre dans la dépression majeure et peut aussi s’exprimer dans l’anxiété chronique etd’autres troubles de l’humeur.

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149. LeDoux JE. The Emotional Brain. New York, NY: Simon and Schuster;1996. 150. Schulkin J, McEwen BS, Gold PW. Allostasis, amygdala, and anticipa-tory angst. Neurosci Biobehav Rev. 1994;18:385-396.151. Sheline YI, Gado MH, Price JL. Amygdala core nuclei volumes aredecreased in recurrent major depression. Neuroreport. 1998;9:2023-2028.152. Michelson D, Stratakis C, Hill L, et al. Bone mineral density in womenwith depression. N Engl J Med. 1996;335:1176-81.153. Cizza G, Ravn P, Chrousos GP, Gold PW. Depression: a major, unrec-ognized risk factor for osteoporosis? Trends Endocrinol Metab. 2001;12:198-203.154. Schweiger U, Weber B, Deuschle M, Heuser I. Lumbar bone mineraldensity in patients with major depression: evidence of increased bone lossat follow-up. Am J Psychiatry. 2000;157:118-120.155. Thakore JH, Richards PJ, Reznek RH, Martin A, Dinan TG. Increasedintra-abdominal fat deposition in patients with major depressive illness asmeasured by computed tomography. Biol Psychiatry. 1997;41:1140-1142.156. Mann JN, Thakore JH. Melancholic depression and abdominal fat dis-tribution: a mini-review. Stress. 1999;3:1-15.157. Weber-Hamann B, Hentschel F, Kniest A, et al. Hypercortisolemicdepression is associated with increased intra-abdominal fat. Psychosom Med.2002;64:274-277.158. Thayer JF, Smith M, Rossy LA, Sollers JJ, Friedman BH. Heart periodvariability and depressive symptoms: gender differences. Biol Psychiatry.1998;44:304-306.159. Musselman DL, Tomer A, Manatunga AK, et al. Exaggerated plateletreactivity in major depression. Am J Psychiatry. 1996;153:1313-1317.160. Lederbogen F, Gilles M, Maras A, et al. Increased platelet aggregabil-ity in major depression. Psychiatry Res. 2001;102:255-261.161. Walsh M-T, Dinan TG, Condren RM, Ryan M, Kenny D. Depression isassociated with an increase in the expression of the platelet adhesionreceptor glycoprotein Ib. Life Sci. 2002;70:3155-3165.162. Musselman DL, Evans DL, Nemeroff CB. The relationship of depressionto cardiovascular disease. Arch Gen Psychiatry. 1998;55:580-592.163. Heuser I. Depression, endocrinologically a syndrome of prematureaging? Maturitas. 2002;41(suppl 1):S19-S23.164. Ballenger JC, Davidson JRT, Lecrubier Y, et al. Consensus statement ondepression, anxiety, and cardiovascular disease. J Clin Psychiatry. 2001;62:24-27.165. Perlmutter JB, Frishman WH, Feinstein RE. Major depression as a riskfactor for cardiovascular disease. Therapeutic implications. Heart Dis.2000;2:75-82.166. Starkman MN, Schteingart DE. Neuropsychiatric manifestations ofpatients with Cushing's syndrome. Arch Intern Med. 1981;141:215-219.167. Starkman MN, Schteingart DE, Schork MA. Depressed mood and otherpsychiatric manifestations of Cushing's syndrome: relationship to hormonelevels. Psychosom Med. 1981;43:3-18.168. Condren RM, Thakore JH. Cushing's disease and melancholia. Stress.2001;4:91-119.169. Gold PW, Loriaux DL, Roy A, et al. Responses to corticotropin-releas-ing hormone in the hypercortisolism of depression and Cushing's disease.N Engl J Med. 1986;314:1329-1335.170. Starkman MN, Gebarski SS, Berent S, Schteingart DE. Hippocampalformation volume, memory dysfunction, and cortisol levels in patients withCushing's syndrome. Biol Psychiatry. 1992;32:756-765.171. Mauri M, Sinforiani E, Bono G, et al. Memory impairment in Cushing'sdisease. Acta Neurol Scand. 1993;87:52-55.172. Forget H, Lacroix A, Somma M, Cohen H. Cognitive decline in patientswith Cushing's syndrome. J Int Neuropsychol Soc. 2000;6:20-29.173. Heinz RE, Martinez J, Haenggeli A. Reversibility of cerebral atrophyin anorexia nervosa and Cushing's syndrome. J Comput Assisted Tomogr.1977;1:415-418.174. Starkman MN, Giordani B, Gebrski SS, Berent S, Schork MA,Schteingart DE. Decrease in cortisol reverses human hippocampal atrophyfollowing treatment of Cushing's disease. Biol Psychiatry. 1999;46:1595-1602.175. Bourdeau I, Bard C, Noel B, et al. Loss of brain volume in endogenousCushing's syndrome and its reversibility after correction of hypercortisolism.J Clin Endocrinol Metab. 2002;87:1949-1954.


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hose of us who study the nervous systembelieve that the brain is the organ that controls ourbehavior. Therefore, what we think and what we do,while obviously influenced by the experience, are resultsof the brain’s processing of information and directingour subsequent actions. Given this basic assumption, itis no wonder that the most common model or analogyof how the brain operates is that of a computer. Whilethis analogy may have some heuristic value, it is likelywrong or at least very limiting.The brain is an organ, likethe liver, heart, and kidney, and is made of chemicals,cells, and tissue.Communication between brain cells is mediated throughneurons with long processes (axons) that connect manycells at once and release small batches of chemical infor-mation (neurotransmitters) to a network of other neu-rons. The neurons receive the signals on their antennae,called dendrites, which protrude, in many cases, quiteelaborately from the cell body. The specific site wherethe chemical signal from one cell makes contact withanother cell is called a synapse, which is made up of sig-naling cells (presynaptic boutons) and receiving cells(postsynaptic spines). The synapse is the structural unitthat transmits the majority of information between neu-rons. Each neuron can have thousands of these synapseson its dendrites and cell body.The real trick for the neu-ron is to calculate (interpret) the temporal and spatiallytransmitted information it receives and to send thatinterpreted message onto the next neurons in a circuit.The aggregation of this information passing and pro-cessing results in thought and behavior.

Adult neural stability

One of the main reasons for viewing the brain as a sta-ble machine or computer is because this analogy helpsexplain how we can remember from one instant to thenext. If the underlying structure was changing all the

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B a s i c r e s e a r c h

T


Structural plasticity of the adult brainFred H. Gage, PhD

The adult brain has long been considered stable andunchanging, except for the inevitable decline thatoccurs with aging. This view is now being challengedwith clear evidence that structural changes occur in thebrain throughout life, including the generation of newneurons and other brain cells, and connections betweenand among neurons. What is as remarkable is that thechanges that occur in the adult brain are influenced bythe behaviors an individual engages in, as well as theenvironment in which an individual lives, works, andplays. Learning how behavior and environment regulatebrain structure and function will lead to strategies tolive more effective lives and perhaps protect from, orrepair, brain damage and brain disease. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:135-141.

Keywords: neurogenesis; adult stem cell; brain structure; neurological disease;depression

Author affiliations: Laboratory of Genetics, The Salk Institute, La Jolla, Calif,USA

Address for correspondence: Fred H. Gage, PhD, Laboratory of Genetics, TheSalk Institute, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA(e-mail: [email protected])

time, how could we do that? For that matter, if the brainis the seat of consciousness, as proposed by FrancisCrick,1 how would we maintain a self identity if the brainwere not stable? Well, the dirty little secret is comingout: the brain is not stable and that is a good thing. Thestructural changes seen in the brain may be required toprovide the extra capacity we need for dealing with com-plexity. It may also provide the underpinning for theadaptability and flexibility, or “plasticity” as neurosci-entists refer to it, that is required for dealing with thevariety of challenges that we face throughout life.In addition, and in some ways even more importantly,structural plasticity provides the mechanism for the brainto repair itself. All organs of the body have some capac-ity to repair themselves following minor injury. Skin, liver,heart, kidney, lung, and blood have some level of repaircapacity, and most have the capacity to generate new cellsto replace damaged ones, at least to a small extent.Until recently, the brain was considered unique in its lackof ability to repair itself once it had matured to adulthood.Researchers were convinced that “Once development wasended, the fonts of growth and regeneration of the axonsand dendrites dried up irrevocably. In the adult center thenerve paths are something fixed and immutable, nothingmay be regenerated” (S. Ramon y Cajal, 1928).2 Thisdogma even influenced clinical research and the acceptedmethods for treating brain damage. In general, the thera-peutic strategy clinicians would suggest could be summedup as “try not to damage your brain, because there is noway to fix it.” The dominant strategy for repairing a bro-ken, injured, or damaged brain was to replace the lostneurotransmitters (for example, providing L-dopa forParkinson’s disease [PD], which works pretty well for awhile) or, more experimentally, to replace the missing ordead neurons (as in neural transplantation for treatingPD, Huntington’s disease [HD],Alzheimer’s disease, amy-otrophic lateral sclerosis, or spinal cord injury). Thereplacement of dead cells by transplantation of externallyderived cells continues both experimentally and clinicallyand, with the new hope provided by the availability (albeitlimited) of the pluripotent human embryonic stem cells,optimism for transplantation therapy has been renewed.The previously accepted dogma of adult neural stabilityis now being called into question. Pioneering studies byRaisman,3 Bjorklund,4 and Aguayo5 and their colleaguesin the 1960s and 1970s revealed that damaged axonscould grow under some extraordinary circumstances.These studies have led to a recent stampede of very

promising work that could lead to the regeneration ofcut or damaged axons due to spinal cord injury.6 Adeeper blow to the dogma of adult neural stability hasbeen the recent acceptance of the ability of certain areasof the adult brain to generate new neurons throughoutlife, known as adult neurogenesis. Early evidence of thisability was generated by Altman and colleagues in the1960s and 1970s,7 and was beautifully extended to birdsby Goldman and Nottebohm in the 1980s,8 and later tononhuman primates and humans in the 1990s.9 Duringthis same period, it was discovered that adult neurogen-esis itself was not stable and predictable, but was, in fact,highly regulated by experience, with stress and agingdecreasing neurogenesis and environmental enrichmentand exercise increasing neurogenesis.

Stem cells in the adult brain

The surprising observation that neurogenesis continuesin the adult nervous system has led to the discovery thatthere are stem cells in the adult brain that generate thenew neurons. A stem cell is an uncommitted cell that,when it divides, can give rise to itself (self-renewal) andcan also give rise to any or all of the three main cell lin-eages of the brain: neurons, astrocytes, and oligoden-drocytes. Using a variety of methods, it is now possibleto isolate these stem cells from the adult brain and usespecific growth factors, like fibroblast growth factor(FGF) and epidermal growth factor (EGF), to inducethem to divide indefinitely in culture dishes in the labo-ratory. Most of the studies that have determined that thecells from the brain are stem cells have done so by study-ing the cells in vitro; the demonstration of “stemness” invivo in the adult brain is difficult. However, the numbersof adult stem cells can be greatly expanded and they canbe genetically marked in culture and then transplantedback to the adult nervous system.10 In these studies, thecells survived well and differentiated or matured intoauthentic neurons in the two areas of the brain whereneurogenesis normally occurs, the hippocampus and theolfactory bulb. However, the adult stem cells did notreadily differentiate into neurons in any other areas.Interestingly, they did differentiate into astrocytes andoligodendrocytes in other areas.This behavior of adult stem cells that were expanded inculture and transplanted back to the adult brain con-trasts with the behavior of fresh tissue derived from thefetal brain that has not been extensively expanded in


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culture. Freshly dissociated cells from the fetal brain, iftaken at the appropriate time and from the appropriatelocation, survive and differentiate quite readily into thetypes of neurons and glial cells from which they wereobtained. In fact, the fetal cells have already maturedsomewhat and have committed themselves to a particu-lar neuronal type; given minimal local environmentalsignals, they proceed toward their predetermined fates.These properties of fetal tissue make it more amenableto therapeutic applications. For example, in experimen-tal treatments for PD, committed dopamine cells arebeing taken from fetal substantia nigra for transplanta-tion; in HD treatment, fetal cells are being taken fromfetal basal ganglia and transplanted into patients.The irony then is that fetal tissue grafts are more maturethan adult stem cells that have been isolated andexpanded in culture.The problem with the adult brain isthat, outside of the limited number of stem cells, theadult cells are too mature and will not withstand the iso-lation and transplantation procedures; they have lost theyouthfulness to survive and integrate into the adultbrain. Part of the problem with fetal tissue is that thereare so few cells available that are at just the right ageand in just the right location, which means that eithermany fetuses must be used for each transplantation orthe cells must be put in culture to expand their number.However, once placed in culture, only the primitive fetalstem cells will divide extensively, and, as was seen withadult stem cells, these fetal stem cells are so immaturethat, unless the adult brain has all the necessary signalsto direct them to a particular neural type, ie, a hip-pocampal neuron, then the cells will either die orbecome glial cells or merely persist as stem cells.The way to make both fetal and adult stem cells moreuseful for therapeutic transplantation applications is todetermine what the signals are in development thatinduce the stem cells to become a particular neuronaltype, and then induce the stem cells toward that lineagein a culture dish just far enough so that, once they aresubsequently transplanted to a particular part of thebrain, they will continue toward that cell type and even-tually integrate and replace the missing function.At this juncture of stem cell biology and adult neurogen-esis, the concept of neural self-repair emerged.The ques-tion was posed: if the adult brain has pockets of stem cellsthat can become neurons, astroglial cells (which play acrucial role in generating and maintaining the health ofneurons), and oligodendrocytes (a third type of cell in the

brain that insulates the neuronal axons so that they cantransmit their information efficiently), then why can’t thebrain repair itself after injury or disease? The answerseemed to be that the brain is capable of repairing itselfand that it already does, to a limited extent. The currentstrategy is, therefore, to try to understand how, and per-haps to what end, adult neurogenesis normally occurs, inorder to find ways whereby we can enhance it, direct it,and more generally harness the residual elements ofneural plasticity that are inherent to neural self-repair asa treatment for brain disorders. Surprisingly, we may notbe too far away from this goal. Let’s first summarize whatwe know about the process of adult neurogenesis.

What is adult neurogenesis/cell genesis?

As it turns out, the birth of new brain cells or neurogene-sis is not an all-or-nothing event.The multipotent stem celldivides periodically in the brain, giving rise to another stemcell (self-renewal) and some progeny that may grow up tobe working cells, but the fate is not guaranteed.The prog-eny must move away from the influence of the motherstem cell into an area that is permissive for maturation. Onaverage, about 50% of these newborn cells never make itand instead die and disappear.Those that do survive maybecome a neuron or glial cell, depending on where theyend up and what type of activity is going on in that brainarea at that time. Even so, it takes over a month from thetime the new cell is born until it is functionally integratedin the brain, receiving and sending information.Thus, neu-rogenesis is a process, not an event, and one that—as I saidearlier and will emphasize repeatedly—is highly regulated.The factors that regulate neurogenesis are being intenselyinvestigated and new factors that modulate different com-ponents of neurogenesis are being discovered on a regularbasis. For example, factors known to be important in devel-opment of the nervous system, like Sonic hedgehog11

(which was first discovered in fly brain and called hedge-hog), have been shown to regulate the proliferation; BMPs(bone morphogenetic proteins) and Notch12 (which werealso first discovered in fly brain) appear to be regulators ofwhether the newborn cells decide to become glia; and mol-ecules associated with the glial cells that surround the stemcells instruct the newborn cells to become neurons. Oncethe cells are committed to becoming a neuron or glial cell,other growth factors like brain-derived neurotrophic fac-tor (BDNF)13 and insulin-like growth factor (IGF)14 playimportant roles in keeping the cells alive and encouraging

Structural plasticity of the adult brain - Gage Dialogues in Clinical Neuroscience - Vol 6 . No. 2 . 2004

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the young cells to mature and become functional. It is theunderstanding of how these growth factors and cellularenvironments control neurogenesis in the normal settingthat will lead to development of therapies aimed atenhancing and directing neurogenesis in disease states.

Where does adult neurogenesis/cell genesis occur?

Neurogenesis, the process of generating new neurons,does not occur spontaneously in every part of the brain.In fact, it only occurs robustly in two areas of the brain,while cell division or cell genesis appears, surprisingly, tooccur everywhere in the brain and spinal cord.15,16 Inmost areas of the brain, cell genesis results in the birthof new glial cells that are likely participating in themicrorepair process. Reports that new neurons are bornoutside of the two well-documented areas of neurogen-esis, eg, the frontal cortex, have not been substantiated.17

It is most likely that the complexity of the methods usedto prove neurogenesis have led to these anomalousobservations, though with new and more sensitive meth-ods, low levels of neurogenesis may be detected in moreregions of the adult brain and spinal cord. Certainly, aswe learn more about the molecular mechanism that con-trols neurogenesis, as well as the environmental stimulithat regulate neurogenesis, we anticipate that we will beable to direct neurogenesis anywhere in the brain.10

The most robust cell proliferation occurs in the ventriclesof the forebrain, where large numbers of cells migrate for-ward to the olfactory bulb, a brain structure involved insmell, where the cells differentiate into a variety of differ-ent kinds of neurons.We are just now learning about howthe olfactory bulb functions normally, and do not have aclear picture as to what role these new cells may play inthe function of this brain structure.18,19 The second brainarea—and the only structure where neurogenesis has beenconfirmed in all adult mammals from mice to man—is thehippocampus, or more precisely the dentate gyrus of thehippocampus.19 The stem cells of the hippocampus residein the interior of the densely packed granule cells. Oncethe stem cells divide and progeny are born, they migrateinto the densely packed area and over the next montheither die or survive and contribute to the function of thecritical brain area.The hippocampus is critical to the for-mation of new memories, and thus any theory for the func-tional significance of neurogenesis will likely interpret thevalue of new neurons in terms of providing flexibility and

adaptability to the processing of new information. Since ittakes a month from the time the new cells are born untilthey are integrated into the functional circuits of the brain,the role that the new neurons play in behavior has likelyless to do with birth of the cells and more to do with theproperties of the newly born functioning neuron.20 Thus,future studies are focusing in part on determining whetherthe spines and synapses of the newly born neurons haveproperties that give them advantages over neurons thathave been in the circuit for the whole life of an animal.One of the most striking aspects of neurogenesis in thehippocampus is the number of events, experiences, and fac-tors that can regulate either the rate of cell division, thesurvival of the newly born neurons, or their integration intothe neural circuitry. First and foremost, there is a cleargenetic underpinning to neurogenesis, with a correlationin mice showing that those strains of mice with higher ratesof neurogenesis learn more quickly.21,22 However, as withmost things, it is not nature or nurture, but more correctlyan interaction or cooperation between the two. For exam-ple, movement of adult and even old mice from a rathersterile simplified cage into a large enriched environmentwith significant complexity and diversity will result in a sig-nificant increase in new neurons by decreasing the num-ber of cells that die.This increase in new neurons correlateswith increased functioning of the hippocampus, as well asa significant improvement in learning and memory. In anattempt in my laboratory to tease out the elements of theenriched environment that are critical for the increasedneurogenesis, van Praag discovered that running on a run-ning wheel alone was sufficient to nearly double the num-ber of dividing cells, resulting in robust increases in newneurons.23,24 In addition to the positive effects of exerciseand environmental enrichment, the process of neurogen-esis is also negatively regulated by events in the environ-ment, such as stress, injury, and disease. Understanding howneurogenesis is normally regulated will be the key to devel-oping strategies to counteract the misregulations of neu-rogenesis.

How does the process of neurogenesisrespond in the damaged, injured,

or diseased brain?

In the last 5 years, a striking number of neurological dis-eases and conditions have been shown to affect neuro-genesis, especially in the hippocampus. For example,most forms of experimental epilepsy25,26 result in a robust


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increase in the proliferation of stem cells within the hip-pocampus. Many of these new cells die, but some surviveand, as a result of the epileptic state, these new cellsmigrate to the wrong place in the hippocampus andappear to differentiate incorrectly.These incorrectly gen-erated new neurons have been speculated to play a rolein the persistence of certain types of abnormal behaviorand pathology that result from the epileptiform activity.By understanding how neurogenesis normally occurs togenerate healthy neurons, it is hoped that this aberrantneurogenesis could be blocked or perhaps the aberrantlygenerated cells could be trained to wire up correctly(even at a later point in time), given the remarkablestructural plasticity of these new brain cells.Cerebral stroke also results in a striking increase in theproliferation of new cells in the hippocampus, but mostof these cells die soon thereafter. In addition, in certaintypes of stroke (like ischemia), there is loss of cells inareas of the brain that do not normally give rise to newneurons, and thus offer little hope for repair.27,28 Quiteremarkably, more recent studies have revealed that, infact, the brain is inducing repair by bringing new cells infrom areas of the brain that do have stems cells anddirecting them to the sites of damage.While with severestrokes, this microrepair is not enough to reverse thedamage, it is likely that this microrepair system is ade-quate to protect, prevent, and repair the brain aftersmall, often-unrecognized strokes. Some of this repair islikely to be behind the often-observed remarkablethough quite variable recovery that occurs after manystrokes. Growth factors like EGF and FGF are nowbeing used to try to enhance the intrinsic repair process,and with encouraging results.29

One of the most striking correlations between diseaseand neurogenesis is in depression.As mentioned above,stress reduces the process of neurogenesis leading tofewer newborn cells in the dentate gyrus, and chronicstress is believed to be the most important causal factorin depression aside from genetic predisposition.30-32

Antidepressants (tricyclic antidepressants, selective sero-tonin reuptake inhibitors, tianeptine, and lithium) aug-ment neurogenesis in the dentate gyrus of experimentalanimals and, interestingly, the time required to observetherapeutic effects of these drugs corresponds to thetime course for neurogenesis. This has led to a hypothe-sis that depression is in part caused by a decrease in neu-rogenesis in the dentate gyrus and thus antidepressanttherapy and physical therapy (ie, running and exercise)

reverse depression by activating neurogenesis in thedentate gyrus. While this is currently only a workinghypothesis, there is converging evidence to support thisview, which is leading to the examination of other fac-tors that affect adult neurogenesis and the determina-tion of their effects on depression.

Harnessing the endogenous capacity for self-repair that exists in the adult brain

We now know that the brain does indeed have a pool ofresidual cells that can divide making new cells that canroam around the brain and spinal cord and, under spe-cial conditions, differentiate into new functioning cells.We are also beginning to understand some of the cellu-lar and molecular factors, as well as environment events,that regulate the process of neurogenesis. Importantly,there is a consistent correlation between improved func-tion and increases in neurogenesis.This is particularly thecase for hippocampus-associated behaviors and func-tions; moreover, several neural diseases have been asso-ciated with changes in neurogenesis. Now the principlestrategy is to learn enough about the factors that regu-late each of the components of neurogenesis in order tocontrol cell proliferation (making more cells), migration(getting the cells to places where they are needed), anddifferentiation (turning the cells into the type of cell thatis needed). For diseases of the brain and spinal cord, thiswill require more knowledge about which cells areaffected in a disease, as well as knowing more about thefactors that regulate the components of neurogenesis:• For depression, epilepsy, and stroke, which are diseases

that involve the hippocampus (a structure where neu-rogenesis does occur), the most straightforward strat-egy would be to induce more neurogenesis or rerouteneurogenesis.

• In diseases like HD and PD, where very specific celltypes die to cause the symptoms, the best strategywould be to induce the local dividing cells to prolifer-ate and then differentiate in small spine neurons, in thecase of HD, and dopamine neurons, in the case of PD.

• In diseases like spinal cord injury or multiple sclerosis,the strategy may not be to make endogenous cellsbecome neurons, but rather to ensheath oligodendro-cytes. Since the endogenous cells already have thecapacity to make these cells at low frequency in theintact spinal cord, the task will be to enhance theendogenous capacity.


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Conclusion

The task ahead—to realize the goals of these strategies—is not an easy one, but it is the knowledge that this is a real-istic and approachable strategy that heralds a remarkablechange in how we even think about brain disease, damage,and repair. I imagine a time when selective drugs will beavailable to stimulate components of neurogenesis, and

this treatment will be combined with very specific physi-cal therapy directed at activating specific brain areas toaccept and integrate the new cells in that brain area.Theimplication of this knowledge is that we will be able toconduct our lives in such a way as to limit brain diseaseand enhance the natural repair process. ❏

I thank Mary Lynn Gage for her valuable assistance with this manuscript.


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Plasticidad estructural del cerebro adulto

Por largo tiempo el cerebro adulto se ha conside-rado estable e inmodificable, excepto por la decli-nación inevitable que ocurre con el envejecimiento.Este punto de vista actualmente se cuestionadebido a claras evidencias acerca de cambios estruc-turales en el cerebro a lo largo de la vida, inclu-yendo la generación de nuevas neuronas y otrascélulas cerebrales, como también de conexionesentre las neuronas. Es destacable el hecho que loscambios que ocurren en el cerebro adulto soninfluenciados por las conductas que el individuoadopta, como también por el ambiente en que vive,trabaja y se desenvuelve. El conocer cómo la con-ducta y el ambiente regulan la estructura y funcióncerebral conducirá a adoptar modos de vida másefectivos y tal vez ayudará a protegerse de, o a tra-tar daños y enfermedades cerebrales.

Plasticité structurale du cerveau adulte

Le cerveau adulte a longtemps été considérécomme stable et immuable, sauf en ce qui concernel’inévitable déclin survenant avec le vieillissement.Ce point de vue est maintenant remis en questionpar des arguments manifestes en faveur de chan-gements structuraux apparaissant dans le cerveauau cours de la vie, dont la création de nouveauxneurones et autres cellules cérébrales et deconnexions parmi les neurones et entre eux. Le faitremarquable est que les changements apparaissantdans le cerveau adulte sont influencés par les com-portements qu’un individu adopte ainsi que parl’environnement dans lequel celui-ci vit, travaille etagit. Apprendre comment le comportement et l’en-vironnement régulent la structure et la fonctioncérébrales conduira à adopter des modes de vieplus efficaces et peut-être se prémunir contre deslésions et pathologies cérébrales, ou les traiter.

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1. Crick F. The Astonishing Hypothesis. New York, NY: Simon & Schuster; 1994.2. Ramon y Cajal S. Degeneration and Regeneration of the Nervous System. MayRM, trans. New York, NY: Hafner; 1928.3. Raisman G. Neuronal plasticity in the septal nuclei of the adult brain. BrainRes. 1969;14:25-48.4. Bjorklund A, Katzman R, Stenevi U, West K. Development and growth ofaxonal sprouts from noradrenaline and 5-hydroxytryptamine neurons inthe rat spinal cord. Brain Res. 1971;31:21-33.5. Richardson PM, McGuiness UM, Aguya AJ. Axons from CNS neuronsregenerate into PNS grafts. Nature. 1980;284:264-265.6. Horner PJ, Gage FH. Regenerating the damaged central nervous system.Nature. 2000;407:963-970.7. Altman J, Das GD. Autoradiographic and histological evidence of post-natal hippocampal neurogenesis in rats. J Comp Neurol. 1965;124:319-335.8. Goldman S, Nottebohm F. Neuronal production, migration and differen-tiation in a vocal nucleus of the adult female canary brain. Proc Natl Acad SciU S A. 1983;80:2390-2394.

9. Eriksson PS, Perfilieva E, Bjork-Eriksson T, et al. Neurogenesis in the adulthuman hippocampus. Nat Med. 1998;4:1313-1317.10. Gage FH. Mammalian neural stem cells. Science. 2000;287:1433-1438.11. Lai K, Kaspar BK, Gage FH, Schaffer DV. Sonic hedgehog regulates adultneural progenitor proliferation in vitro and in vivo. Nat Neurosci. 2003;6:21-27.12. Lim DA, Tramontin AD, Trevejo JM, Herrera DG, Garcia-Verdugo JM,Alverez-Buylla A. Noggin antagonizes BMP signaling to create a niche foradult neurogenesis. Neuron. 1997;28:713-726.13. Pencea V, Bingaman KD, Wiegand SJ, Luskin MB. Infusion of brain-derived neurotrophic factor into the lateral ventricle of the adult rat leadsto new neurons in the parenchyma of the striatum, septum, thalamus, andhypothalamus. J Neurosci. 2001;21:6706-6717.14. Aberg MA, Aberg ND, Hedbacker H, Oscarsson J, Eriksson PS. Peripheralinfusion of IGF-I selectively induces neurogenesis in the adult rat hip-pocampus. J Neurosci. 2000;20:2896-2903.15. Lie DC, Dziewczapolski G, Willhoite AR, Kaspar BK, Shults CW, Gage FH.The adult substantia nigra contains progenitor cells with neurogenic poten-tial. J Neurosci. 2002;22:6639-6649.

16. Horner PJ, Power AE, Kempermann G, et al. Proliferation and differen-tiation of progenitor cells throughout the intact adult rat spinal cord. JNeurosci. 2000;20:2218-2228.17. Kornack DR, Rakic P. Cell proliferation without neurogenesis in adultprimate neocortex. Science. 2001;294:2127-2130.18. Carleton A, Petreanu LT, Lansford R, Alvarez-Buylla A, Lledo PM. Becominga new neuron in the adult olfactory bulb. Nat Neurosci. 2003;6:507-518.19. Carlen M, Cassidy RM, Brismar H, Smith GA, Enquist LW, Frisen J.Functional integration of adult-born neurons. Curr Biol. 2002;12:606-608.20. van Praag H, Schinder AF, Christie BR, Toni N, Palmer TD, Gage FH.Functional neurogenesis in the adult hippocampus. Nature. 2002;415:1030-1034.21. Kempermann G, Kuhn HG, Gage FH. Genetic influence on neurogenesis inthe dentate gyrus of adult mice. Proc Natl Acad Sci U S A. 1997;94:10409-10414.22. Kempermann G, Brandon EP, Gage FH. Environmental stimulation of129/SvJ mice causes increased cell proliferation and neurogenesis in theadult dentate gyrus. Curr Biol. 1998;8:939-942.23. van Praag H, Christie BR, Sejnowski TJ, Gage FH. Running enhances neu-rogenesis, learning and long-term potentiation in mice. Proc Natl Acad Sci U S A. 1999;96:13427-13431.24. van Praag H, Kempermann G, Gage FH. Running increases cell prolif-eration and neurogenesis in the adult mouse dantate gyrus. Nat Neurosci.1999;2:266-270.

25. Liu J, Solway K, Messing RO, Sharp FR. Increased neurogenesis in thedentate gyrus after transient global ischemia in gerbils. J Neurosci.1998;18:7768-7778.26. Parent JM, Yu TW, Leibowitz RT, Geschwind DH, Sloviter RS, LowensteinDH. Dentate granule cell neurogenesis is increased by seizures and con-tributes to aberrant network reorganization in the adult rat hippocampus.J Neurosci. 1997;17:3727-3738.27. Takagi Y, Nozaki K, Takahashi J, Yodoi J, Ishikawa M, Hashimoto N.Proliferation of neuronal precursor cells in the dentate gyrus is acceleratedafter transient forebrain ischemia in mice. Brain Res. 1999;831:283-287.28. Yagita Y, Kitagawa K, Ohtsuki T, et al. Neurogenesis by progenitor cellsin the ischemic adult rat hippocampus. Stroke. 2001;32:1890-1896.29. Nakatomi H, Kuriu, T, Okabe S, et al. Regeneration of hippocampal pyra-midal neurons after ischemic brain injury by recruitment of endogenousneural progenitors. Cell. 2002;110:429-441.30. Jacobs BL, van Praag H, Gage FH. Adult brain neurogenesis and psychi-atry: a novel theory of depression. Mol Psychiatry. 2000;5:262-269.31. D’Sa C, Duman RS. Antidepressants and neuroplasticity. Bipolar Disord.2002;4:183-194.32. Santarelli L, Saxe M, Gross C, et al. Requirement of hippocampal neu-rogenesis for the behavioral effects of antidepressants. Science. 2003;301:805-809.


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espite the devastating impact that mood disor-ders have on the lives of millions worldwide, there is still adearth of knowledge concerning their underlying etiologyand pathophysiology.The brain systems that have hereto-fore received the greatest attention in neurobiologicalstudies of mood disorders have been the monoaminergicneurotransmitter systems, which are extensively distrib-uted throughout the network of limbic, striatal, and pre-frontal cortical neuronal circuits thought to support thebehavioral and visceral manifestations of mood disor-ders.1-3 Thus, clinical studies over the past 40 years haveattempted to uncover the specific defects in these neuro-transmitter systems in mood disorders by utilizing a vari-ety of biochemical and neuroendocrine strategies.

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D


Regulation of cellular plasticity and resilienceby mood stabilizers: the role of AMPA receptor traffickingJing Du, MD, PhD; Jorge A. Quiroz, MD; Neil A. Gray, BS; Steve T. Szabo,PhD; Carlos A. Zarate Jr, MD; Husseini K. Manji, MD, FRCPC

There is increasing evidence from a variety of sources that severe mood disorders are associated with regional reduc-tions in brain volume, as well as reductions in the number, size, and density of glia and neurons in discrete brain areas.Although the precise pathophysiology underlying these morphometric changes remains to be fully elucidated, thedata suggest that severe mood disorders are associated with impairments of structural plasticity and cellular resilience.In this context, it is noteworthy that a growing body of data suggests that the glutamatergic system (which is knownto play a major role in neuronal plasticity and cellular resilience) may be involved in the pathophysiology and treat-ment of mood disorders. Glutamate �-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) GluR1 receptortrafficking plays a critical role in regulating various forms of neural plasticity. It is thus noteworthy that recent studieshave shown that structurally dissimilar mood stabilizers lithium and valproate regulate GluR1 receptor subunit traf-ficking and localization at synapses. These studies suggest that regulation of glutamatergically mediated synaptic plas-ticity may play a role in the treatment of mood disorders, and raises the possibility that agents more directly affect-ing synaptic GluR1 represent novel therapies for these devastating illnesses. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:143-155.

Keywords: lithium; valproate; antidepressant; bipolar disorder; glutamate recep-tor GluR1; phosphorylation

Author affiliations: Laboratory of Molecular Pathophysiology, NationalInstitute of Mental Health, Bethesda, Md, USA

Address for correspondence: Husseini K. Manji, MD, Laboratory of MolecularPathophysiology, Mood and Anxiety Disorders Program, National Institute ofMental Health, 9000 Rockville Pike, Building 10, Unit 3 West, Room 3s250,Bethesda, MD 20892, USA(e-mail: [email protected])

While such investigations have been heuristic over theyears, they have been of limited value in elucidating theunique biology of mood disorders, which must includean understanding of the underlying basis for thepredilection to episodic and often-profound mood dis-turbance, which can become progressive over time.These observations have led to the appreciation that,while dysfunction within the monoaminergic neuro-transmitter systems is likely to play important roles inmediating some components of the pathophysiology ofmood disorders, they do not fully explain all the facetsof these complex neuropsychiatric disorders.4,5

In addition to the acknowledgement that investigationsinto the pathophysiology of complex mood disorders havebeen excessively focused on monoaminergic systems, therehas been a growing appreciation that progress in devel-oping truly novel and improved medications has conse-quently also been limited.A recognition of the clear needfor better treatments and the lack of significant advancesin our ability to develop novel, improved therapeutics forthese devastating illnesses has led to the investigation ofthe putative roles of intracellular signaling cascades andnonaminergic systems in the pathophysiology and treat-ment of mood disorders. Consequently, recent evidencedemonstrating that impairments of neuroplasticity mayunderlie the pathophysiology of mood disorders, and thatantidepressants and mood stabilizers exert major effectson the signaling pathways that regulate cellular plasticityand resilience, have generated considerable excitementamong the clinical neuroscience community, and arereshaping views about the neurobiological underpinningsof these disorders.1,2,6-8

Somewhat surprisingly, the potential role of the gluta-matergic system in the pathophysiology and treatment

of bipolar disorder has only recently begun to be inves-tigated in earnest. Glutamate is the major excitatorysynaptic neurotransmitter regulating numerous physi-ological functions in the mammalian central nervous sys-tem (CNS), such as synaptic plasticity, learning, andmemory, and represents a major neurotransmitter sys-tem in the circuitry thought to subserve many of thesymptoms of severe, recurrent mood disorders.3 In thisperspectives paper, we review the growing body of datathat suggests that severe mood disorders are associatedwith impairments of cellular plasticity and resilience,effects that may arise from perturbations of neu-rotrophic signaling cascades and the glutamatergic sys-tem. We follow with a discussion of the emerging datathat suggests regulating the balance of glutamatergicthroughput via N-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid(AMPA) receptors may play an important role in theactions of our most effective thymoleptic agents, andrepresent very attractive targets for the development ofnovel therapeutics for these devastating disorders.

What is the evidence for impairments of cellular plasticity and resilience in

severe mood disorders?

Structural imaging studies have demonstrated reducedgray matter volumes in areas of the orbital and medialprefrontal cortex (PFC), ventral striatum, and hip-pocampus, and enlargement of third ventricle in mood-disordered patients relative to healthy control sam-ples.3,9,10 Postmortem neuropathological studies haveshown abnormal reductions in glial cell counts/density,neuron size/density, and cortical volume/thickness in thesubgenual PFC, orbital cortex, dorsal anterolateral PFC,amygdala, and in basal ganglia and dorsal raphe nucleiand hippocampus.11-16 Morphometric studies also havereported layer-specific reductions in interneurons in theanterior cingulate cortex (ACC), and reductions in non-pyramidal neurons (~40% lower) in CA2 of the hip-pocampal formation in bipolar disorder subjects com-pared with controls.17 Overall, the layer-specific cellularchanges observed in several distinct brain regions, includ-ing the PFC, ACC, and hippocampus suggest that multi-ple neuronal circuits underlie the neuropathology ofmood disorders.This is not altogether surprising since thebehavioral and physiological manifestations of the ill-nesses are complex and include cognitive, affective,


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Selected abbreviations and acronymsAMPA �-amino-3-hydroxy-5-methyl-4-isoxazole propi-

onic acidCAMKII calcium/calmodulin-dependent protein kinase IIEAAT excitatory amino acid transporterLTD long-term depressionLTP long-term potentiationNMDA N-methyl-D-aspartateMAPK mitogen-activated protein kinasePCP phencyclidinePKA protein kinase APP1 protein phosphatase 1WMH white matter hyperintensities

motoric, and neurovegetative symptomatology, as well asalterations of circadian rhythms and neuroendocrine sys-tems, and are thus undoubtedly mediated by networks ofinterconnected neurotransmitter systems and neural cir-cuits.13 In addition to the accumulating neuroimaging evi-dence, several postmortem brain studies are now pro-viding direct evidence for reductions in regional CNSvolume, cell number, and cell body size. Baumann andassociates18 reported reduced volumes of the left nucleusaccumbens, the right putamen, and bilateral pallidumexternum in postmortem brain samples obtained frompatients with unipolar depression or bipolar depression.The abnormal presence of white matter hyperintensities(WMH) has been reported in multiple magnetic reso-nance imaging (MRI) studies of geriatric patients withaffective disorder, particularly those with late-onsetdepression (ie, elderly depressed patients who experi-ence their first depression after age 60). Elderly adults(>60 years old) with severe WMH are 3 to 5 times morelikely to have depressive symptoms as compared withpersons with only mild or no white matter lesions.19

Tupler and colleagues20 reported that late-onsetdepressed patients had more severe hyperintensity rat-ings in deep white matter than early-onset patients andcontrols, and that late- and early-onset patients hadmore severe subcortical gray matter hyperintensities(particularly in the putamen) compared with controls.Recently, Silverstone and colleagues21 reported thatbipolar patients showed more severe deep WMH onbrain MRI than age-matched unipolar and control sub-jects. WMH severity has been suggested to predictpoorer response to antidepressant therapy.22 In fact,these lesions have been also found to be increased inchildren with psychiatric disorders, but are highestamong bipolar patients, when compared with controls,particularly in the frontal lobes,23 and also early in thecourse of bipolar illness in adolescent subjects.24

Although the cause of WMH in mood disorders isunknown, their presence—particularly in the brains ofyoung bipolar patients—suggests importance in thepathophysiology of the disorder.25,26 Together, theseresults support the contention that WMH indicate dam-age to the structure of brain tissue, and likely disruptionof the neuronal connectivity necessary for normal affec-tive functioning.It is not known whether these structural brain changesseen in patients with severe mood disorders constitutedevelopmental abnormalities that may confer vulnerabil-

ity to abnormal mood episodes, compensatory changes toother pathogenic processes, or the sequelae of recurrentaffective episodes per se. Understanding these issues willpartly depend upon experiments that delineate the onsetof such abnormalities within the illness course and deter-mine whether they antedate depressive episodes in indi-viduals at high familial risk for mood disorders. Neverthe-less, these prominent atrophic changes and impairmentsof plasticity have drawn much attention to the gluta-matergic system, since—as we discuss in detail below—theglutamatergic system is known to play critical roles in reg-ulating various forms of plasticity. Furthermore, as is dis-cussed extensively in this issue and elsewhere,27 alterationsin glutamatergic signaling, mediated by both NMDA andnon-NMDA receptors, are known to play important rolesin stress-induced morphometric brain changes.14,28,29 Sincesome clinicians may be less familiar with the intricacies ofthe regulation of glutamate receptor subtypes, we now pre-sent a brief overview of the functioning and regulation ofNMDA and AMPA glutamatergic receptors. We followwith a discussion of the exciting emerging data suggestingthat glutamatergic signaling represents a very attractivetarget for the development of novel therapeutics forsevere mood disorders.

A primer on glutamatergic signaling: critical roles in cellular plasticity

and resilience

As the principal mediator of excitatory synaptic transmis-sion in the mammalian brain, glutamate participates inwide-ranging aspects of both normal and abnormal CNSfunction. Unlike the monoamines, which require transportof amino acids through the blood–brain barrier, glutamateand aspartate cannot adequately penetrate into the brainfrom the periphery and are produced locally by special-ized brain machinery.30 The metabolic and syntheticenzymes responsible for the formation of these nonessen-tial amino acids are located in glial cells as well as neu-rons.30,31 The major metabolic pathway in the production ofglutamate is derived from glucose and the transaminationof α-ketoglutarate; however, a small proportion of gluta-mate is formed directly from glutamine.The latter is actu-ally synthesized in glia, via an active process (requiringadenosine triphosphate [ATP]), and is then transported toneurons where glutaminase is able to convert this precur-sor to glutamate (Figure 1). Following release, the con-centration of glutamate in the extracellular space is highly

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Figure 1. Glutamatergic system. This figure depicts the various regulatory processes involved in glutamatergic neurotransmission, as described inthe text. In astrocytes, glutamine can undergo oxidation to yield α-ketoglutarate, which can also be transported to neurons and participatein glutamate synthesis. Glutamate is either metabolized or sequestered and stored into secretory vesicles by vesicular glutamate transporters(VGluT). Glutamate can then be released by a calcium-dependent excitotoxic process. Glutamate has its action terminated in the synapseby reuptake mechanisms utilizing distinct GLU transporters (GLUTs), which exist not only on presynaptic nerve terminals, but also on astro-cytes; indeed, current data suggests that astrocytic glutamate uptake may be more important for clearing excess glutamate, raising the pos-sibility that astrocytic loss (as has been documented in mood disorders) may contribute to deleterious GLU signaling, but more so by astro-cytes. It is now known that there are a number of important intracellular proteins, which are able to alter the function of glutamate receptors.Gly, glycine; GTn, glutamate transporter; GTg, glutamate transporter (glial); 5-HT1A, 5-hydroxytryptamine (serotonin) receptor 1A; AMPA,α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid; NMDAR, NMDA receptor; PKA and PKC, protein kinase A and C; PP1, PP2A, andPP2B, protein phosphatase 1, 2A, and 2B; Yotiao, NMDA receptor accessory protein; AKAP, protein A kinase anchoring protein; nNOS, nitricoxide synthetase; Src, a family of protein tyrosine kinases; PTP1D, protein-tyrosine phosphatase 1D; SHP2, src homology 2 domain–contain-ing tyrosine phosphatase; PSD95, postsynaptic density protein 95; CAMKII, calcium/calmodulin-dependent protein kinase II; MyoV, myosin V;SynGAP, Ras guanosine triphosphatase (GTPase)–activating protein; GKAP, guanylate kinase–associated protein; PYK2, proline-rich tyrosinekinase-2; Shank; Shank family of multidomain proteins; Homer, a family of dendritic multidomain proteins; Rap2, a small GTPase; H-ras,Harvey rat sarcoma viral oncogene homologue; Rac1, a Rho family GTPase; ERK, extracellular signal–regulated kinase; Raf, MEK, and Rsk,ribosomal S6 kinases; Hsp70, heat-shock protein 70.Modified and reproduced with permission from reference 30: Szabo ST, Gould TD, Manji HK. Neurotransmitters, receptors, signal transduction, and secondmessengers in psychiatric disorders. In: Schatzberg A, Nemeroff CB, eds. The American Psychiatric Publishing Textbook of Psychopharmacology. Arlington,VA: American Psychiatric Publishing Inc; 2003:3-52. Copyright © 2003; American Psychiatric Publishing Inc.

regulated and controlled, primarily by a sodium-depen-dent reuptake mechanism involving several transporterproteins.The major glutamate transporter proteins foundin the CNS include excitatory amino acid transporters(EAATs): EAAT1 (or GLAST-1), EAAT2 (or GLT-1),and EAAT3 (or EAAC1), with EAAT2 being the most predominantly expressed form in the forebrain.Additionally, these transporters are differentially expressedin specific cell types, with EAAT1 and EAAT2 beingfound primarily in glial cells, EAAT3 localized in neurons,and EAAT4 mainly localized in cerebellum.The physio-logical events regulating the activity of the glutamatetransporters are not well understood, though there is evi-dence that phosphorylation of the transporters by proteinkinases may differentially regulate glutamate transportersand therefore glutamate reuptake (discussed in reference30). Glutamate concentrations have been shown to rise toexcitotoxic levels within minutes following traumatic orischemic injury, and there is evidence that the function ofthe glutamate transporters becomes impaired under theseexcitotoxic conditions.32 Moreover, microdialysis studieshave shown that severe stress increases extracellular lev-els of glutamate in hippocampus, and NMDA glutamatereceptor antagonists attenuate stress-induced atrophy ofCA3 pyramidal neurons.

Glutamate receptor subtypes: a focus on NMDA andAMPA receptors

The many subtypes of glutamatergic receptors in the CNScan be classified into two major subtypes—the ionotropicand metabotropic receptors (Table I).The ionotropic glu-tamate receptor ion channels are assemblies of homo-oligomeric or hetero-oligomeric subunits integrated intothe neuron’s membrane. Every channel is assembled of(most likely) four subunits associated into a dimer ofdimers, as has been observed in crystallographic studies.33,34

Every subunit consists of an extracellular amino terminaland ligand-binding domain, three transmembrane domainsand a re-entrant pore loop (located between the first andsecond transmembrane domains), and an intracellular car-boxyl terminal domain.35 The subunits associate throughinteractions between their amino terminal domains form-ing a dimer that undergoes a second dimerization medi-ated by interactions between the ligand-binding domainsand/or between transmembrane domains.33,34 Three differ-ent subgroups of glutamatergic ion channels have beenidentified utilizing their pharmacological ability to bind dif-

ferent synthetic ligands, each of which is composed of a dif-ferent set of subunits. These are the NMDA receptor(NMDAR), the AMPA receptor (AMPAR), and thekainate receptor (KAR).The latter two groups are oftenreferred to together as the “non-NMDA” receptors, butundoubtedly subserve unique functions (Table I). In theadult mammalian brain, NMDA and AMPA glutamater-gic receptors are colocalized in approximately 70% of thesynapses.36 By contrast, at early stages of development,synapses are more likely to contain only NMDA receptors.Radioligand binding studies have shown that NMDA andAMPA receptors are found in high density in the cerebralcortex, hippocampus, striatum, septum, and amygdala.

NMDA receptors

The NMDA receptor is activated by glutamate andrequires the presence of a coagonist, namely glycine or D-serine, to be activated. However, the binding of bothglutamate and glycine is still not sufficient for the NMDAreceptor channel to open, since, at resting membranepotential, the NMDA ion channel is blocked by Mg2+ ions.Only when the membrane is depolarized (eg, by the acti-vation of AMPA or kainate receptors on the same post-synaptic neuron) is the Mg2+ blockade relieved. Underthese conditions, the NMDA receptor channel will openand permit the entry of both Na+ and Ca2+ (Figure 1).


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Table I. Receptor subtype units. Once released from the presynaptic ter-minal, glutamate is able to bind to numerous excitatory aminoacid (EAA) receptors, including both ionotropic (eg, N-methyl-D-aspartate [NMDA]) and metabotropic receptors. Presynaptic reg-ulation of glutamate release occurs through metabotropic glu-tamate receptors (mGluR2 or mGluR3), which subserve thefunction of autoreceptors. However, these receptors are alsolocated on the postsynaptic element. AMPA, α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid.

Ionotropic receptors

NMDA • NR1

• NR2A, NR2B, NR2C, NR2D

• NR3A, NR3B

AMPA • GluR1, GluR2, GluR3, GluR4

Kainate • GluR5, GluR6, GluR7

• KA1, KA2

Metabotropic receptors

Group I • mGlu1a, mGlu1b, mGlu1c, mGlu1d

Group II • mGlu2, mGlu3

Group III • mGlu4a, mGlu4b, mGlu4c, mGlu4d

• mGlu6

• mGlu7a, mGlu7b, mGlu7c, mGlu7d

• mGlu8a, mGlu8b, mGlu8c, mGlu8d

The NMDA receptor channel is composed of combina-tion of NR1, NR2A, NR2B, NR2C, NR2D, NR3A, andNR3B subunits (Table I).The binding site for glutamatehas been localized to the NR2 subunit and the site forthe coagonist glycine has been localized to the NR1 sub-unit, which is required for receptor function. Two mol-ecules of glutamate and two of glycine are thought to benecessary to activate the ion channel. Within the ionchannel, two other sites have been identified called thesigma (σ) site and the phencyclidine (PCP) site.The hal-lucinogenic drug PCP, ketamine, and the experimentaldrug dizocilpine (MK-801), all bind at the latter site andare considered noncompetitive receptor antagonists thatinhibit NMDA receptor channel function. In preclinicalstudies, drugs of this type have been shown to have neu-roprotective properties against anoxia and hypo-glycemia; these studies await clinical confirmation. Inclinical psychiatric studies, ketamine has been shown totransiently induce psychotic symptoms in schizophrenicpatients, and to produce rapid antidepressant effects indepressed patients.37 These latter observations have ledto the investigation of NMDA antagonists as putativenovel antidepressants.29,37

NMDA receptors play a critical role in regulating synap-tic plasticity.38 The best-studied forms of synaptic plasticityin the CNS are long-term potentiation (LTP) and long-term depression (LTD) of excitatory synaptic transmis-sion. The molecular mechanisms of LTP and LTD havebeen extensively characterized and have been proposedto represent cellular models of learning and memory.38

Induction of LTP and LTD in the CA1 region of the hip-pocampus and in many regions of the brain has nowclearly been demonstrated to be dependent on NMDAreceptor activation. During NMDA-receptor–dependentsynaptic plasticity, Ca2+ influx through NMDA receptorscan activate a wide variety of kinases and/or phosphatasesthat, in turn, modulate synaptic strength. An importantrecent development is the finding that two of the primarymolecules involved––Ca2+/ calmodulin-dependent proteinkinase II (CAMKII) and the NMDA subtype of glutamatereceptor––form a tight complex with each other at thesynapse.39 Interestingly, this binding appears to enhanceboth the autophosphorylation of the kinase and the abil-ity of the entire holoenzyme, which has 12 subunits, tobecome hyperphosphorylated.39 This hyperphosphorylatedstate has been postulated to represent a “memory switch,”which can lead to long-term strengthening of the synapseby multiple mechanisms. One important mechanism

involves direct phosphorylation of the glutamate-activatedAMPA receptors, which increases their conductance.Furthermore, once CAMKII is bound to the NMDAreceptor, it may organize additional anchoring sites forAMPA receptors at the synapse.It is intriguing that activation of synaptic NMDA receptorversus nonsynaptic receptor has an opposite effect on cellsurvival via differential regulation of CREB (cyclic adeno-sine monophosphate [cAMP]–response element bindingprotein) function. Calcium entry through synaptic NMDAreceptors induced CREB activity and brain-derived neu-rotrophic factor (BDNF) gene expression as strongly asdid stimulation of L-type calcium channels. In contrast,calcium entry through nonsynaptic NMDA receptors, trig-gered by glutamate exposure or hypoxic/ischemic condi-tions, activated a general and dominant CREB shut-offpathway that blocked induction of BDNF expression.Synaptic NMDA receptors have antiapoptotic activity,whereas stimulation of extrasynaptic NMDA receptorscaused loss of mitochondrial membrane potential (an earlymarker for glutamate-induced neuronal damage) and celldeath.40

AMPA receptor trafficking plays critical roles in theregulation of various forms of neural plasticity

The AMPA receptor is stimulated by the presence ofglutamate and characteristically produces a fast excita-tory synaptic signal that is responsible for the initialreaction to glutamate in the synapse. In fact, as discussedabove, it is generally believed that it is the activation ofthe AMPA receptor that results in neuronal depolariza-tion sufficient to liberate the Mg2+ cation from theNMDA receptor, thereby permitting its activation. TheAMPA receptor channel is composed of the combina-tion of GluR1, GluR2, GluR3, and GluR4 subunits, andrequires two molecules of glutamate to be activated(Table I).AMPA receptors have a lower affinity for glu-tamate than the NMDA receptor, thereby allowing formore rapid dissociation of glutamate and therefore arapid deactivation of the AMPA receptor (reviewed inreference 41).Emerging data suggest that AMPA receptor trafficking,including receptor insertion and internalization, and deliv-ery to synaptic sites, provides an elegant mechanism foractivity-dependent regulation of synaptic strength.AMPAreceptor subunits undergo constitutive endocytosis andexocytosis; however, the process is highly regulated with a


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variety of signal transduction cascades being capable ofproducing short- or long-term changes in synaptic surfaceexpression of AMPA receptor subunits. Indeed, althoughthe mechanisms of LTP and LTD have not been com-pletely elucidated, it is widely accepted that AMPA recep-tor trafficking is the key player in these phenomena.Most importantly for the present discussion, AMPAreceptor trafficking is highly regulated by the proteinkinase A (PKA), protein kinase C (PKC), CAMKII, andmitogen-activated protein kinase (MAPK) signaling cas-cades; these are the very same signaling cascades thatmood stabilizers and antidepressants exert major effectson.42-45 These observations have led to an extensive seriesof studies, which have clearly demonstrated that AMPAreceptor trafficking is highly regulated by antidepres-sants and mood stabilizers46,47 (see below).

Regulation of AMPA receptor trafficking by signalingcascades

Most vesicle trafficking requires the ordered coating ofa donor membrane, budding and fusion to form trans-port vesicles, transport by passive or active deliveryalong microtubule, and final fusion with the target mem-brane.48 AMPA receptors adopted this mechanism to bedelivered to the neuronal membrane surface. AMPAreceptors are multimeric assemblies of the subunitsGluR1 to GluR4. Each subunit is composed of N-termi-nal extracellular domain, membrane-spanning domain,and C-terminal intracellular domain.49,50 AMPA receptortrafficking is subunit-specific and regulated by phos-phorylation of its C-terminal domain, and subsequentalteration of protein-protein interactions.

PKA pathway

The GluR1 subunit appears to govern the traffickingbehavior of heteromeric GluR1/GluR2 receptors, pre-venting constitutive exchange and conferring inducibledelivery of the heteromer.51 Phosphorylation of GluR1 atthe PKA site p845 facilitates the insertion of GluR1 ontothe membrane and synapses, and is often associated withLTP.52 Dephosphorylation of the GluR1 by protein phos-phatases (eg, calcineurin and protein phosphatase 1[PP1]) target GluR1 to recycling endosomes, whererephosphorylation by PKA may occur and the receptorswill be reinserted onto the membrane.53 Phosphorylationof GluR1 at PKA site can be enhanced by synapse-asso-

ciated protein 97 (SAP97)/protein A kinase anchoringprotein (AKAP79) complex that direct PKA to GluR1via a PDZ (PSD95, disk large, ZO1) domain interaction.54

CAMKII pathway

Numerous studies have demonstrated that CAMKII isrequired for the proper formation of LTP in slice prepa-rations, and in regulating learning and memory inrodents.55 In response to stimulation, CAMKII translo-cates to the postsynaptic site, where it has two majoreffects on AMPA receptor activity at the postsynapticsite during the formation of LTP.55 First, the AMPA sin-gle conductance is directly increased by CAMKII atSer831 of GluR1 subunit.56 Second, CAMKII is requiredfor the delivery of AMPA receptor to the synapse, whichis lacking AMPA receptors.51,57,58 This enhancement ofsynaptic GluR1 level by activation of CAMKII requiresan intact C-terminal domain of GluR1, and is possiblyinvolved in interaction with SAP97.59 PP1, which is alsoknown to be a important modulator for learning andmemory, can dephosphorylate the phosphorylation ofGluR1 at p831 site by CAMKII.60

Extracellular signal–regulated kinase (ERK) MAPKpathway

A recent study reported that the small guananine triphos-phatases (GTPases) Ras and Rap are involved in AMPAreceptor trafficking through a postsynaptic signaling mech-anism. Ras mediates activity-evoked increase inGluR1/GluR4–containing AMPA receptor surface expres-sion at synapses via a pathway that requires p42/44 MAPKactivation. In contrast, Rap mediates NMDA-dependentremoval of synaptic GluR2/GluR3–containing vesicles viaa pathway that involves p38 MAPK. The regulationthrough Ras and Rap, which work as molecular switches,may in turn control the AMPA receptor level at synapses.61

PKC pathways

AMPA GluR2 receptors respond to secondary signalsby constitutive receptor recycling. Phosphorylation ofSer880 on GluR2 provides a switch from receptor reten-tion at the membrane by binding to ABP (AMPA recep-tor–binding protein)/GRIP (glutamate receptor–inter-acting protein), to receptor internalization by binding toPICK 1 (protein interacting with C kinase-1).Therefore,


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phosphorylation of GluR2 at Ser880 by PKC mayrelease the AMPA receptor from the anchoring proteinsand initiate the internalization of receptors.62-65

The mechanism for AMPA receptor trafficking is spe-cific for brain region and neuronal type. For example, theendocytosis of AMPA receptors mediating LTD is trig-gered by very different signaling cascades in differentcell types despite the fact that a conserved cell biologi-cal mechanism (ie, clathrin/dynamine-dependent endo-cytosis) always seems to be involved. Specifically, in CA1pyramidal cells, protein phosphatases seem to beinvolved in triggering LTD through dephosphorylationof GluR1 and phosphorylation of PKA site on GluR1 isassociated with LTP.53 However, in midbrain dopaminecells, activation of PKA appears to trigger LTD andendocytosis of AMPA receptors.66

AMPA receptor trafficking and mood disorders: implication for development

of new medications

In view of the critical role of AMPA receptor traffickingin regulating various forms of plasticity, our laboratoryhas sought to determine if two structurally highly dis-similar antimanic agents, lithium and valproate, exerteffects on AMPA receptor trafficking. Lithium, a mono-valent cation, and valproic acid (VPA), an 8-carbon fattyacid, are the two most commonly used agents in thetreatment of mania. Because lithium and valproate bothrequire several weeks to exert their therapeutic effects,it is widely believed that adaptive changes in intracellu-lar signaling and/or cellular physiology underlie the ben-eficial effects; interestingly, these two agents have beenshown to exert robust effects on the very same signalingpathways known to regulate AMPA receptor trafficking(vide supra). Thus, we investigated whether lithium andvalproate regulate synaptic plasticity and AMPA recep-tor trafficking in the hippocampus, a brain region pre-sumed to be involved in the circuitry of mood disorders.3

We have found that the structurally highly dissimilarantimanic agents lithium and valproate have a commoneffect on downregulating AMPA GluR1 synapticexpression in the hippocampus after prolonged treat-ment with therapeutically relevant concentrations asassessed both in vitro and in vivo. In cultured hip-pocampal neurons, lithium and valproate attenuated sur-face GluR1 expression after long-term treatment.Further supporting the therapeutic relevance of the find-

ing, we found that an agent that provokes mania, namelythe antidepressant imipramine, has an opposite effect as it upregulates AMPA synaptic strength in the hip-pocampus.47,67

Since chronic administration of mood stabilizers bringabout numerous biochemical effects, our laboratory8,68

and others69 have established several criteria that find-ings should meet in order to maximize the likelihood oftheir therapeutic importance:• This effect of mood stabilizers on GluR1 is a common

effect of the structurally dissimilar antimanic agentslithium (a monovalent cation) and valproate (which isan 8-carbon branched fatty acid).

• This attenuation of synaptic GluR1 by lithium and val-proate occurs in the hippocampus, a brain region knownto be involved in critical affective neuronal circuits.

• This effect of lithium and valproate on synaptic GluR1occurs at therapeutic concentrations both in vivo andin vitro.

• Similar to the clinical therapeutic effects, the changesin GluR1 were observed only after chronic (and notacute) administration.

• The effects were specific for antimanic agents, as apromanic antidepressant produced opposite effects.While it is impossible to determine whether synapticGluR1 attenuation occurs in patients being treatedwith lithium or valproate, our experimental conditionsattempt to mimic this situation as closely as possible.

Further supporting our data are recent studies that showthat AMPA receptor antagonists attenuate several“manic-like” behaviors produced by amphetamineadministration. Thus, AMPA antagonists have beendemonstrated to attenuate psychostimulant-induceddevelopment or expression of sensitization and hedonicbehavior without affecting spontaneous locomotion;additionally, some studies have demonstrated thatAMPA receptor antagonists reduce amphetamine- orcocaine-induced hyperactivity.70-75 The need to use cau-tion in the appropriate application of animal models tocomplex neuropsychiatric disorders has been well artic-ulated, and in fact it is unlikely we will ever developrodent models that display the full range of symptoma-tology clinically expressed in man.76,77 However, one cur-rent model of mania, which has been extensively usedand has reasonable heuristic value in the study of mooddisorders, involves the use of psychostimulants in appro-priate paradigms. Thus, psychostimulants like ampheta-mine and cocaine are known to induce manic-like symp-


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toms in healthy volunteers, and trigger frank manicepisodes in individuals with bipolar disorder.78 Thus, thebest-established animal models mania utilize the admin-istration of amphetamine or cocaine to produce hyper-activity, risk-taking behavior, and increased hedonicdrive—all very important facets of the human clinical

condition of mania. Moreover, these psychostimulant-induced behavioral changes are attenuated by theadministration of chronic lithium in a therapeutically rel-evant time frame. Thus, the fact that AMPA receptorantagonists are capable of attenuating psychostimulant-induced sensitization, hyperactivity, and hedonic behav-


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Figure 2. Thymoleptic agents, which exert major effects on the glutamatergic system. The various glutamate receptors and the presumed antiglu-tamatergic drug sites of action are presented. Memantine is a noncompetitive antagonist at the N-methyl-D-aspartate (NMDA) receptor.Felbamate is a noncompetitive NMDA receptor antagonist (glycine NR1 and glutamate NR2B), an α-amino-3-hydroxy-5-methyl-4-isoxa-zole propionic acid (AMPA) receptor antagonist, an mGlu group I receptor (mGluRI) antagonist, and a glutamate release inhibitor. Riluzoleis a glutamate release inhibitor (acting through blockade of Na+ voltage dependent channels), a γ-aminobutyric acid GABAA agonist,and probably an AMPA and kainate (KA) antagonist. The sites for second-generation mGlu group II and III receptor agonists are alsodepicted. NMDAR, NMDA receptor; AMPAR, AMPA receptor; KAR, KA receptor; glu, glutamate; gln, glutamine; mGluRII (or mGluRIII),mGlu group II (or III) receptor; EAAT, excitatory amino acid transporter; TCA, tricarboxylic acid cycle.Modified and reproduced with permission from reference 28: Zarate CA, Quiroz J, Payne J, Manji HK. Modulators of the glutamatergic system: implicationsfor the development of improved therapeutics in mood disorders. Psychopharmacol Bull. 2002;36:35-83. Copyright © 2002. MedWorks Media LLC.

Glia

Glutamine synthetase

gln glu

EAAT1/2/(3)

glu

mGluRI

NMDAR

AMPAR

KAR

mGluRIImGluRI

?

?

??

mGluRI

(-)

mG

luR

III

mG

luR

II

gluglu

gln

glu

gln Memantine

Felbamate

Riluzole

Second-generationmGlu agonist

glu

glutaminase

α-Ketoglutarate

TCA

EAAT3

Postsynaptic neuronPresynaptic neuron

Kainate

REFERENCES

1. Manji HK, Drevets WC, Charney DS. The cellular neurobiology of depres-sion. Nat Med. 2001;7:541-547.2. Nestler EJ, Barrot M, DiLeone RJ, Eisch AJ, Gold SJ, Monteggia LM.Neurobiology of depression. Neuron. 2002;34:13-25.3. Drevets WC. Neuroimaging and neuropathological studies of depression:implications for the cognitive-emotional features of mood disorders. CurrOpin Neurobiol. 2001;11:240-249.4. Manji HK, Lenox RH. Signaling: cellular insights into the pathophysiologyof bipolar disorder. Biol Psychiatry. 2000;48:518-530.

5. Payne JL, Quiroz JA, Zarate CA Jr, Manji HK. Timing is everything: doesthe robust upregulation of noradrenergically regulated plasticity genesunderlie the rapid antidepressant effects of sleep deprivation? Biol Psychiatry.2002;52:921-926.6. D'Sa C, Duman RS. Antidepressants and neuroplasticity. Bipolar Disord.2002;4:183-194.7. Young LT, Bakish D, Beaulieu S. The neurobiology of treatment responseto antidepressants and mood-stabilizing medications. J Psychiatry Neurosci.2002;27:260-265.8. Manji HK, Lenox RH. The nature of bipolar disorder. J Clin Psychiatry.2000;61(suppl 13):42-57.

ior70-75 provides compelling behavioral support for ourcontention that AMPA receptors play important roles inregulating affective behavior.As mentioned already, in striking contrast to the effectsseen with the antimanic agents lithium and valproate, wefound that the chronic administration of the antidepres-sant imipramine—which is capable of triggering manicepisodes in susceptible individuals78—increased hip-pocampal synaptic expression of GluR1.Very recent stud-ies from other laboratories have also demonstrated thatchronic administration of antidepressants enhances mem-brane expression of GluR1 as well as phosphorylation ofGluR1 at the PKA site (p845) and the CAMKII/PKC site(p831).79,80 Furthermore, it is noteworthy that AMPApotentiating agents reportedly have efficacy in preclinicalmodels of depression.81 Additionally, chronic exposure tothe psychostimulants amphetamine and cocaine causedan increase in GluR1 level in the ventral tegmental area(VTA), and these effects have been postulated to repre-sent a trigger for sensitization to drug abuse.82 An elegantseries of studies has recently provided insights into howdopamine receptors, which are activated during psycho-stimulant administration, might influence glutamate-dependent forms of synaptic plasticity, which are beingincreasingly recognized as important to drug addiction.83

They showed that surface GluR1 labeling on processes ofmedium spiny neurons and interneurons was increasedby brief incubation with a dopamine D1 agonist.83

Although these studies were designed to investigate therole of GluR1 in mediating the effects of drugs of abuse,it is noteworthy that many of the symptoms of maniaresemble the effects of psychostimulants (eg, locomotorhyperactivity, racing thoughts, reduced sleep, and psy-chosis). Taken together, the biochemical and behavioralstudies investigating the effects of antimanic (lithium andvalproate) and promanic (antidepressants, cocaine, andamphetamine) agents on GluR1 strongly suggest that

AMPA receptor trafficking is an important target in thepathogenesis and treatment of certain facets of bipolardisorder. The mechanisms by which glutamate receptorsare actively recruited to synapses have long intrigued theneuroscience community; the information reviewed heresuggests that they may also play important roles in thepathophysiology and treatment of complex neuropsychi-atric disorders.

Concluding remarks

Regionally selective impairments of structural plasticityand cellular resiliency, which have been postulated tocontribute to the development of classical neurodegen-erative disorders, may also exist in mood disorders. Itremains unclear whether these impairments correlatewith the magnitude or duration of the biochemical per-turbations in mood disorders, reflect an enhanced vul-nerability to the deleterious effects of these perturba-tions (eg, due to genetic factors and/or early life events),or indeed represent the fundamental etiological processin mood disorders. Nevertheless, it is noteworthy thatthere is growing evidence from preclinical and clinicalresearch that the glutamatergic system is involved in thepathophysiology and treatment of mood disorders.Over the last few years, an impressive amount of infor-mation has been gathered regarding the mechanismsunderlying the regulation of AMPA receptor localiza-tion at synapses. The findings that mood stabilizers—intherapeutically meaningful paradigms—regulate AMPAreceptors at synapses opens new potential avenues fornew drug development in regards to regulating gluta-matergic synaptic strength in critical neuronal circuits(Figure 2). The development of new modulators ofAMPA receptor signaling for the treatment of mood dis-orders may lead to improved therapeutics for these dev-astating disorders. ❏


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9. Strakowski SM, Adler CM, DelBello MP. Volumetric MRI studies of mooddisorders: do they distinguish unipolar and bipolar disorder? Bipolar Disord.2002;4:80-88.10. Beyer JL, Krishnan KR. Volumetric brain imaging findings in mood dis-orders. Bipolar Disord. 2002;4:89-104.11. Rajkowska G. Depression: what we can learn from postmortem studies.Neuroscientist. 2003;9:273-284.12. Rajkowska G, Miguel-Hidalgo JJ, Wei J, et al. Morphometric evidencefor neuronal and glial prefrontal cell pathology in major depression. BiolPsychiatry. 1999;45:1085-1098.13. Rajkowska G. Postmortem studies in mood disorders indicate alterednumbers of neurons and glial cells. Biol Psychiatry. 2000;48:766-777.

14. Manji HK, Quiroz JA, Sporn J, et al. Enhancing neuronal plasticity andcellular resilience to develop novel, improved therapeutics for difficult-to-treat depression. Biol Psychiatry. 2003;53:707-742.15. Manji HK, Duman RS. Impairments of neuroplasticity and cellularresilience in severe mood disorders: implications for the development ofnovel therapeutics. Psychopharmacol Bull. 2001;35:5-49.16. Cotter D, Mackay D, Landau S, Kerwin R, Everall I. Reduced glial cell den-sity and neuronal size in the anterior cingulate cortex in major depressivedisorder. Arch Gen Psychiatry. 2001;58:545-553.17. Benes FM, Kwok EW, Vincent SL, Todtenkopf MS. A reduction of non-pyramidal cells in sector CA2 of schizophrenics and manic depressives. BiolPsychiatry. 1998;44:88-97.


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Regulación de la plasticidad celular y de laresiliencia mediante estabilizadores del ánimo:el papel del tráfico del receptor AMPA

Diversas fuentes aportan una evidencia crecienteacerca de la asociación entre los trastornos delánimo severos y reducciones regionales del volu-men cerebral, como también del número, tamañoy densidad de la glía y de las neuronas en distintasáreas cerebrales. Aunque la fisiopatología especí-fica que está a la base de estos cambios morfomé-tricos no está totalmente aclarada, los datos sugie-ren que los trastornos del ánimo severos estánasociados con un deterioro en la plasticidad estruc-tural y en la resiliencia celular. En este contexto esdestacable que una información creciente sugiereque el sistema glutamatérgico (que se sabe quejuega un papel importante en la plasticidad neuro-nal y en la resiliencia celular) puede estar involu-crado en la fisiopatología y en el tratamiento de lostrastornos del ánimo. El tráfico de la subunidadGluR1 del receptor AMPA (ácido-3-hidroxi-5-metil-4-isoxazol propiónico) juega un papel decisivo en laregulación de varias formas de plasticidad neural.Es de destacar que estudios recientes han mostradoque estabilizadores del ánimo, estructuralmentedisímiles, como el litio y el valproato regulan el trá-fico de la subunidad GluR1 y su localización en lassinapsis. Estos estudios sugieren que la regulaciónde la plasticidad sináptica mediada por el sistemaglutamatérgico puede jugar un papel en el trata-miento de los trastornos del ánimo y se aumenta laposibilidad que agentes que afecten más directa-mente la subunidad GluR1 sináptica se transformenen nuevas terapias para estas devastadoras enfer-medades.

Régulation de la plasticité et de la résiliencecellulaires par les stabilisateurs de l’humeur :le rôle du trafic neuronal du récepteur AMPA

Il existe de plus en plus d’arguments provenant desources différentes en faveur de l’association destroubles de l’humeur sévères avec des réductionsrégionales du volume cérébral, ainsi qu’avec desréductions en nombre, taille et densité de la glie etdes neurones dans des zones cérébrales discrètes.Bien que la physiopathologie exacte sous-tendantces modifications morphométriques soit incomplè-tement élucidée, les données suggèrent que lestroubles de l’humeur sévères sont associés à desdéficits de la plasticité structurale et de la résiliencecellulaire. Dans ce contexte il faut remarquer qu’unnombre croissant de données est en faveur d’uneintervention du système glutamatergique (connupour jouer un rôle majeur dans la plasticité neuro-nale et la résilience cellulaire) dans la physiopatho-logie et le traitement des troubles de l’humeur. Letrafic neuronal de la sous-unité GluR1 du récepteurAMPA (acide glutamate α-amino-3-hydroxy-5-méthyl-4-isoxazole propionique) et les change-ments morphologiques qui en résultent jouent unrôle crucial dans la régulation des différentesformes de plasticité neuronale. À ce titre il fautégalement noter que de récentes études ont mon-tré que des stabilisateurs de l’humeur structurelle-ment différents comme le lithium et le valproaterégulent le trafic neuronal des sous-unités GluR1 etleur localisation dans les synapses. Ces études sug-gèrent que la régulation de la plasticité synaptiquemédiée par le glutamate peut jouer un rôle dans letraitement des troubles de l’humeur et évoquent lapossibilité pour ces maladies invalidantes d’un nou-veau traitement par l’intermédiaire de moléculesaffectant plus directement le GluR1 synaptique.

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eural plasticity is a fundamental process thatallows the brain to receive information and form appro-priate adaptive responses to the same or similar stimuli.The molecular and cellular adaptations underlying learn-ing and memory are the best-characterized and most-studied examples of neural plasticity. However, manydifferent stimuli can activate neural plasticity processesin different brain structures, including environmental,social, behavioral, and pharmacological stimuli. In fact,it could be argued that neural plasticity is one of themost essential and important processes that the brainperforms as it relates to many types of central nervoussystem functions.Thus, disrupted or abnormal plasticity could lead to mal-adaptive neuronal responses and abnormal behavior.This could occur in response to genetic abnormalities ofthe cellular machinery required for plasticity, and abnor-mal or inappropriate stimuli. For example, exposure toinappropriate or prolonged stress has been reported toalter molecular and cellular markers of neural plasticity,and could contribute to stress-related mood disorders.This review will discuss the literature demonstratingaltered neural plasticity in response to stress, and clini-cal evidence indicating that altered plasticity occurs indepressed patients. The second part of the review willpresent evidence that antidepressant treatment blocksthe effects of stress or produces plasticity-like responses.

General mechanisms of neural plasticity

Neural plasticity encompasses many different types ofmolecular and cellular responses that occur when cellsin the brain are induced to respond to inputs from other

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P h a r m a c o l o g i c a l a s p e c t s

N


Neural plasticity: consequences of stress andactions of antidepressant treatmentRonald S. Duman, PhD

Neural plasticity is emerging as a fundamental and crit-ical mechanism of neuronal function, which allows thebrain to receive information and make the appropriateadaptive responses to subsequent related stimuli.Elucidation of the molecular and cellular mechanismsunderlying neural plasticity is a major goal of neuro-science research, and significant advances have beenmade in recent years. These mechanisms include regula-tion of signal transduction and gene expression, and alsostructural alterations of neuronal spines and processes,and even the birth of new neurons in the adult brain.Altered plasticity could thereby contribute to psychiatricand neurological disorders. This article reviews the liter-ature demonstrating altered plasticity in response tostress, and evidence that chronic antidepressant treat-ment can reverse or block the effects, and even induceneural plasticity-like responses. Continued elucidation ofthe mechanisms underlying neural plasticity will lead tonovel drug targets that could prove to be effective andrapidly acting therapeutic interventions. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:157-169.

Keywords: signal transduction; gene expression; neurotrophic factor; neuroge-nesis; neuronal atrophy

Author affiliations: Division of Molecular Psychiatry, Departments ofPsychiatry and Pharmacology, Yale University School of Medicine, NewHaven, CT, USA

Address for correspondence: Ronald S. Duman, PhD, 34 Park Street, New Haven,CT 06508, USA(e-mail: [email protected])

cells or circulating factors. The systems that have beenmost extensively studied are cellular and behavioralmodels of learning and memory, including long-termpotentiation (LTP), in slices of brain and rodent modelsof behavior.The mechanisms identified for learning andmemory most likely also subserve plasticity occurring inother regions and for other adaptive functions of thebrain. This section will briefly discuss some generalmechanisms and concepts of plasticity.

Mechanisms of acute neural plasticity: synaptic trans-mission and protein kinases

The effects underlying the rapid responses to neuronalactivation are mediated by activation of the excitatoryneurotransmitter glutamate and regulation of intracellu-lar signaling cascades (for a review of acute mechanismsunderlying LTP, see reference 1). Glutamate causes neu-ronal depolarization via activation of postsynapticionotropic receptors that increase intracellular Na+.Thisleads to the subsequent activation of N-methyl-D-aspar-tate (NMDA) receptors and the resulting influx of Ca2+.Ca2+ is a major intracellular signaling molecule that acti-vates a signaling cascade, including activation of Ca2+/calmodulin-dependent protein kinase.Within minutes tohours, activation of glutamate and Ca2+-dependent path-ways can result in structural alterations at the level ofdendritic spines. Spines mark the location of glutamatesynapses and have been the subject of intensive investi-gation for understanding synaptic plasticity.2 Changes inthe shape and even number of spines can occur veryrapidly (minutes to hours) after glutamate stimulation.These alterations are made permanent or long-termwhen they are stabilized or consolidated, a process thatrequires gene expression and protein synthesis.

Mechanisms of long-term plasticity: gene expression andprotein synthesis

The Ca2+/cyclic adenosine monophosphate (cAMP)response element (CaRE) binding protein (CREB) isone of the major transcription factors that mediate theactions of Ca2+, as well as cAMP signaling. CREB hasbeen reported to play a role in both cellular and behav-ioral models of learning and memory.3 There are a num-ber of gene targets that are influenced by Ca2+, cAMP,and CREB, and the pattern of gene regulation is depen-dent on the cell type, the length of stimulation, as well asthe magnitude of stimulation. Gene targets that havebeen implicated in learning and memory, and are rele-vant to the effects of stress and antidepressant treat-ment, are the neurotrophic factors. Of particular inter-est is brain-derived neurotrophic factor (BDNF), one ofthe most abundant neurotrophic factors in the brain.

Altered neural plasticity in response to stress

Recent reports have demonstrated altered molecularand cellular responses to stress and have contributed tothe hypothesis that altered neural plasticity contributesto stress-related psychiatric illnesses. Some examples ofstress responses are discussed in this section.

Stress alters learning and memory

Stress is known to significantly influence learning andmemory, and the effects are dependent on the type, dura-tion, and intensity of the stressor. Emotional arousal canenhance learning and memory via synaptic plasticity ofamygdala-dependent pathways, and this is thought to bethe basis for intense, long-term memories of traumaticevents and posttraumatic stress disorder.4,5 However,stress can also impair subsequent learning and memoryand can even lead to amnesia.6 The influence of stress onhippocampal-dependent learning is complex and depen-dent on the type of learning task.In studies of LTP, a consistent suppression of neural plas-ticity is observed after exposure to stress or adrenal glu-cocorticoids.6,7 In one of these studies, the suppression ofLTP was observed after exposure to an uncontrollablestressor and correlated with behavioral performance ina learning and memory task. Giving the animals controlover the stress (ie, the stress could be terminated) didnot lead to reduced LTP or decreased learning and


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Selected abbreviations and acronymsBDNF brain-derived neurotrophic factorcAMP cyclic adenosine monophosphateCaRE cAMP response elementCREB cAMP response element binding proteinFGF-2 fibroblast growth factor–25-HT 5-hydroxytryptamine (serotonin)LTP long-term potentiationNMDA N-methyl-D-aspartatePDE4 phosphodiesterase type IVPKA protein kinaseSSRI selective serotonin reuptake inhibitor

memory.8 A role for BDNF in the actions of stress onLTP has also been suggested.9 For additional referencesand discussion of the effects of stress on learning andmemory, see the reviews in references 4 to 7.

Stress causes atrophy of hippocampal neurons

One of the best-characterized examples of altered struc-tural plasticity in response to stress is the atrophy of hip-

pocampal neurons, which was first described byMcEwen and colleagues (Figure 1).10 They found thatrepeated restraint stress results in atrophy of the den-drites of CA3 pyramidal neurons in the hippocampus,measured as a decrease in the number and length of api-cal dendrites.11 The reduction in dendritic arborizationwas found to be dependent on long-term, repeated expo-sure to restraint stress (3 weeks) and to be reversiblewhen the animals are removed from stress. The atrophy

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Figure 1. Model of hippocampal plasticity showing structural alterations in response to stress: atropy of CA3 pyramidal neurons and decreased neu-rogenesis of dentate gyrus granule cells. Stress results in powerful effects on the hippocampus, partly because of the high levels of gluco-corticoid receptors expressed in this brain region. Stress results in at least two major actions in two different subfields of the hippocampus.Repeated stress causes atrophy or remodeling of CA3 pyramidal neurons, decreasing the number and length of apical dendrites. Administrationof glucocorticoids causes a similar effect, and decreased expression of brain-derived neurotrophic factor (BDNF) could contribute to pyrami-dal cell atrophy. Stress also decreases the proliferation of newborn granule cells in the dentate gyrus, and glucocorticoid administration mim-ics this effect. Chronic antidepressant administration can reverse the atrophy of CA3 neurons and block the downregulation of neurogenesisin the dentate gyrus. The effects of antidepressant treatment occur via acute regulation of serotonin (5-hydroxytryptamine [5-HT]) and nor-epinephrine (NE) and the regulation of intracellular signaling and gene expression. mf, mossy fiber; sc, Schaffer colaterals.

Hippocampalplasticity

Stress

Antidepressant

Antidepressant

Stress

Increased vulnerability resulting fromgenetic and environmental factors

(eg, hypoxia, hypoglycemia, or viral infections)

Glucocorticoid

BDNFNE and 5-HT

BDNF BDNF

BDNF

NE and 5-HT

Glucocorticoid

Increasedsurvival and

growth

Atrophy or death Decreased

neurogenesis

Increasedneurogenesis

Granule cell

Dentategyrus

mf pathway

CA3

CA1

sc

of CA3 pyramidal cells appears to result from the ele-vation of adrenal glucocorticoids that occurs duringstress because chronic administration of corticosterone,the active form in rodent, results in a similar decrease innumber and length of dendrites.12 The actions of stressand glucocorticoids are blocked by administration of anNMDA receptor antagonist, indicating that this gluta-mate receptor is required for atrophy of CA3 neurons.10

Atrophy of CA3 pyramidal neurons occurs after 2 to 3weeks of exposure to restraint stress or more long-termsocial stress, and has been observed in rodents and treeshrews.11-13 In contrast to the atrophy of hippocampus,recent studies demonstrate that chronic stress causeshypertrophy of neurons in the amygdala.14 This studyfound chronic immobilization stress increased the den-dritic arborization of pyramidal neurons in the basolat-eral amygdala, but decreased dendrite length andbranching in the CA3 pyramidal neurons of the hip-pocampus. Hypertrophy of the amygdala could underlieincreased learning and memory as a result of stress-induced emotional arousal, and may be relevant to thepathophysiology of stress-related disorders, includinganxiety, posttraumatic stress, and depression. Increased

arborization of neurons in the amygdala could therebyenhance emotional states or disrupt normal processingof emotional responses.

Stress decreases neurogenesis in the adult hippocampus

In addition to regulation of the morphology of neuronsin the hippocampus, stress influences the number ofnewborn neurons or neurogenesis in the adult hip-pocampus15,16 (Figures 1 and 2). The hippocampus is oneof two brain regions where neurogenesis continues tooccur in adult organism (the other region is in the sub-ventricular zone). In the hippocampus, neural progeni-tor cells are found in the subgranular zone, between thegranule cell layer and the hilus. These cells give rise tonewborn cells that migrate into the granule cell layerand mature into neurons with the morphological andphysiological characteristics of adult granule cells.17

Interestingly, the process of neurogenesis is highly reg-ulated by a variety of stimuli and can be considered aform of neural plasticity. For example, enriched envi-ronment, exercise, and learning increase neurogenesis,while aging and exposure to drugs of abuse decreaseneurogenesis.15,16,18

In addition to these factors, stress also results in a dra-matic downregulation of neurogenesis in the hippocam-pus.10,18 Exposure to just a single stressor is sufficient tosignificantly decrease neurogenesis in the adult hip-pocampus.Adult neurogenesis is decreased by differenttypes of stress, including subordination stress,19 predatorodor,20 maternal separation,21 and footshock.22 In addi-tion, exposure to inescapable stress in the learned help-lessness model of depression decreases adult neurogen-esis and this effect correlates with behavioral despair inthis model.22 Moreover, the reduction in neurogenesisand the behavioral despair is reversed by antidepressanttreatment.

Regulation of CREB and decreased expression ofBDNF in response to stress

Stress results in a wide range of effects that influencemany different neurotransmitter and neuropeptide sys-tems, signal transduction pathways, and altered geneexpression. The hallmark of the stress response is acti-vation of the hypothalamic-pituitary-adrenal (HPA)axis, which includes increased circulating levels of


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Figure 2. Model demonstrating the regulation of adult neurogenesis inthe hippocampus. Neural progenitor cells are restricted to thesubgranular zone (SGZ) that is located between the granule celllayer (GCL) and hilus. These progenitor cells give rise to new-born neurons that migrate into the granule cell layer and matureinto adult neurons. The proliferation and survival of newbornneurons is subject to change and can be considered a form ofneural plasticity. Neurogenesis is influenced by a number of dif-ferent stimuli in either a positive or a negative manner as indi-cated. SSRI, selective serotonin reuptake inhibitor; NE, nora-drenaline; MAOI, monoamine oxidase inhibitor; ECS,electroconvulsive seizures; mfp, mossy fiber pathway.

Neurogenesis in adult hippocampus

GCL

SGZ

mfpDecreased by: • Intruder stress • Predator odor • Footshock stress • Maternal separation • Learned helplessness • Glucocorticoids

Increased by: • SSRI • NE reuptake inhibitor • MAOI • ECS • Exercise

adrenal glucocorticoids.The hippocampus contains veryhigh levels of glucocorticoid receptors and is thereforesignificantly impacted by stress. As mentioned above,studies by McEwen and colleagues have demonstratedthat glucocorticoids contribute to the atrophy anddecreased neurogenesis of hippocampal neurons result-ing from exposure to stress.10

In addition, stress is reported to influence CREB andBDNF in the hippocampus and other brain regions.Thetranscriptional activity of CREB is regulated by phos-phorylation and levels of phospho-CREB are used as anindirect measure of CREB activation and function(Figure 3).The regulation of phospho-CREB is complexand is dependent on the brain region and whether thestress is acute or chronic.23-26 Acute stress increases lev-els of phospho-CREB in many limbic regions associatedwith mood disorders and this may represent a normal orappropriate adaptive responsiveness.24 In contrast,chronic stress leads to decreased levels of phospho-CREB in many limbic brain regions, which could lead todecreased plasticity and function.26

Stress has profound effects on the expression of BDNFin the hippocampus. Levels of BDNF expression in hip-pocampus are dramatically downregulated by both acuteand chronic stress, and this effect could contribute to theatrophy and decreased neurogenesis caused by stress(Figure 1).27-29 The role of other factors that could under-lie the actions of stress on adult neurogenesis is a sub-ject of interest and could lead to novel targets for drugdevelopment.

Atrophy of limbic brain structures indepressed patients

Evidence from basic research studies provide strongsupport for the hypothesis that stress-related illnessessuch as depression could include alterations in brainstructure and neural plasticity. Indeed, direct evidenceto support this hypothesis has been provided by brainimaging and postmortem studies of depressed patients.

Evidence from brain imaging studies

Magnetic resonance imaging studies have demonstratedthat the size of certain brain structures is decreased inmood disorder patients. In particular, these studiesdemonstrate that the volume of the hippocampus isdecreased in patients with depression.30,31 Reduced hip-

pocampal volume is also observed in patients with post-traumatic stress disorder (PTSD).32 The reduction in hip-pocampal volume is directly related to the length of ill-ness.33,34 In addition to hippocampus, atrophy of prefrontalcortex and amygdala—brain regions that control cogni-tion, mood, and anxiety—has also been reported inpatients with depression or bipolar disorder.35

Evidence from postmortem studies

Atrophy of hippocampus or other brain regions couldresult from loss of cells (neurons or glia) or decreasedsize of the cell body or neuronal processes. The mostextensive studies have been conducted on prefrontal andcingulate cortex and demonstrate that the neuronalbody size and number of glia is decreased in depressedpatients.36-38 There is much less known about the hip-pocampus and additional studies will be required todetermine what accounts for the atrophy of hippocam-pus observed in depressed patients.Postmortem analysis of CREB and BDNF has also pro-vided evidence consistent with a loss of neural plasticityin depression. Levels of CREB are decreased in the cere-bral cortex of depressed patients or suicide victims.39,40

Levels of BDNF are also decreased in prefrontal cortexand hippocampus of depressed patients.41 Reduced lev-els of CREB and BDNF, two molecular markers ofneural plasticity, indicate that the ability of limbic brainstructures to mount adaptive responses is compromisedin depressed patients.

Antidepressant treatment increases neuralplasticity

In contrast to the effects of stress, antidepressant treat-ment results in molecular and cellular responses thatdemonstrate an increase in neural plasticity. Moreover,these studies have paved the way for additional studiesthat demonstrate that antidepressant treatment resultsin structural remodeling. In many cases, the effects ofantidepressant treatment oppose or reverse the effectsof stress. Taken together, these findings provide addi-tional support for the hypothesis that neural plasticityplays a significant role in the treatment, as well as thepathophysiology of mood disorders. The evidence forregulation of neural plasticity at the level of neurogen-esis, signal transduction, and gene expression is discussedin the second half of this review.


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Antidepressant treatment increases adult neurogenesis

Neurogenesis is increased by chronic antidepressantadministration

One of the most surprising discoveries of recent times inthe field of depression is that antidepressant treatmentregulates neurogenesis in the adult hippocampus (Figures1 and 2). In contrast to the actions of stress, chronic anti-depressant treatment increases the number of newbornneurons in the adult hippocampus of rodents or treeshrews.42,43 The upregulation of neurogenesis is dependenton chronic antidepressant treatment, consistent with thetime course for the therapeutic action of antidepressants.43

In addition, different classes of antidepressants, includingserotonin (5-hydroxytryptamine [5-HT]) and noradrena-line reuptake inhibitors, and electroconvulsive seizures arereported to increase adult neurogenesis.43-45 Antidepressanttreatment influences two important aspects of neurogen-esis, the rate of cell proliferation (ie, the number of new-born neurons) and the survival of newborn neurons.46 Anincrease in the number of newborn neurons could con-tribute to the reversal of hippocampal atrophy observedin depressed patients.

Antidepressant treatment blocks the downregulationof neurogenesis caused by stress

The influence of antidepressant treatment in the contextof stress has also been examined. These studies demon-strate that chronic antidepressant treatment can blockor reverse the downregulation of neurogenesis thatresults from exposure to stress. Several different typesof stress have been tested, including blockade of intruderstress,42 maternal separation,47 and learned helplessness.22

In addition, different types of antidepressants have beentested, including an atypical antidepressant, tianeptine,42

a selective serotonin reuptake inhibitor (SSRI),22,47 anda neurokinin-1 receptor antagonist.48

The influence of antidepressant treatment on the atrophyof CA3 pyramidal neurons resulting from chronic expo-sure to stress has been examined. These studies demon-strate that chronic administration of tianeptine blocks theatrophy of CA3 apical dendrites that is caused by stress.12

Chronic administration of an SSRI antidepressant did notblock the atrophy of CA3 neurons in this study.Analysisof dendrite branch number and length is tedious and

labor intensive, but additional studies of other antide-pressants are necessary to determine the relevance of thiseffect in the actions of antidepressant treatment.

A functional role for neurogenesis in the action ofantidepressant treatment

A major issue in the field of adult neurogenesis is howto test the function of newborn neurons. A recent studyhas addressed this question by using a combination ofirradiation and mutant mouse approaches.49 This studydemonstrates that focused irradiation of hippocampusin the mouse completely blocks neurogenesis and therewas a corresponding blockade of the behavioral actionsof antidepressant treatment in two behavioral models,novelty suppressed feeding and chronic mild stress. Inaddition, Santarelli et al49 studied the effects of antide-pressants in mice with a null mutation of the 5-HT1Areceptor, a subtype that has been implicated in theactions of antidepressant treatment. They found thatupregulation of neurogenesis by chronic administrationof an SSRI was completely blocked in 5-HT1A nullmutant mice, and that the behavioral effects of SSRItreatment were similarly blocked. These results are thefirst evidence that increased neurogenesis is necessaryfor an antidepressant response in behavioral models.There are a few limitations to this study. First, althoughnovelty-suppressed feeding is responsive to chronic anti-depressant treatment—and this is why it was chosen—this paradigm is a better model of anxiety than depres-sion. Second, although the effects of antidepressanttreatment were blocked, irradiation and 5-HT1A nullmutation alone, in the absence of antidepressant admin-istration, did not produce a depressive phenotype.This isconsistent with another report demonstrating thatdecreased neurogenesis is not correlated with behaviorin the learned helplessness model of depression.50

Together these studies indicate that neurogenesis is notrequired for baseline response. However, it is possiblethat intact neurons are sufficient to sustain baselineresponse and that more long-term inhibition of neuro-genesis would be required to influence activity.

The cAMP-CREB cascade and depression

Neural plasticity upon antidepressant treatment is likelyto involve adaptations of multiple intracellular signalingcascades and even interactions of these pathways. One


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of the pathways that is regulated by antidepressant treat-ment and has been demonstrated to contribute to theactions of chronic antidepressant responses is thecAMP-CREB cascade, the subject of this section.However, it is likely that other signaling pathways arealso regulated by—and play a role in—the actions ofantidepressants. For reviews covering other signal trans-duction pathways, see reference 51 and 52.

Antidepressant treatment upregulates the cAMP-CREB cascade

Several studies have investigated the influence of anti-depressant treatment on the cAMP-CREB pathway(Figure 3).53,54 This work demonstrates that chronic anti-depressant treatment upregulates the cAMP second-mes-senger cascade at several different levels. This includes


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Figure 3. Model demonstrating the upregulation of the cyclic adenosine monophosphate (cAMP)–cAMP response element binding protein (CREB)cascade and expression of brain-derived neurotrophic factor (BDNF) by antidepressant treatment. Chronic, but not acute, antidepressanttreatment upregulates the cAMP-CREB cascade in limbic regions of the brain including the hippocampus and cerebral cortex. This includesincreased coupling of stimulatory G protein (Gs) to adenylyl cyclase, increased levels of cAMP-dependent protein kinase (PKA), and increasedfunction and expression of CREB. CREB can also be phosphorylated and activated by other kinases, including Ca2+/calmodulin-dependentkinase and mitogen-activated protein (MAP) kinase. In this way, CREB could serve as a common target for different types of serotonin (5-hydroxytryptamine [5-HT]) and norepinephrine (NE) receptors, including β-adrenergic (βAR), 5-HT7, α1-adrenergic (α1AR), and 5-HT1A

receptor subtypes. One downstream target of CREB that has been shown to have antidepressant effects is BDNF. The BDNF promoter hasat least one Ca2+/cAMP response element (CaRE) that is regulated by phosphorylation (P) of CREB.

Antidepressant treatment

5-HT/NE 5-HT/NE

βAR5-HT7

Gs Adenylylcyclase

Gi/o α1-AR5-HT1A

PPPP

??? CaRE

RNA

Rolipram cAMP

PKACa2+-dependent orMAP kinase cascades

BDNF/trophic effects:synaptic plasticity,

neurogenesis,neuronal survival

BDNF gene

increased coupling of the stimulatory G protein to adeny-lyl cyclase, increased levels of cAMP-dependent proteinkinase (PKA), and increased levels of CREB as well asphospho-CREB.55-57 Upregulation of these components ofthe cAMP-CREB signaling pathway is dependent onchronic antidepressant treatment, consistent with the timecourse for the therapeutic action of antidepressants. Inaddition, upregulation of the cAMP-CREB cascade isobserved in response to chronic administration of differ-ent classes of antidepressants, indicating that this is a com-mon target of antidepressant treatment.In addition to phosphorylation by PKA, CREB is alsophosphorylated by Ca2+-dependent kinases, such asCa2+/calmodulin-dependent protein kinase, and by mito-gen-activated protein kinase pathways (Figure 3). In thisway, CREB can serve as a target for multiple signaltransduction pathways and neurotransmitter receptorsthat activate these cascades.

Activation of the cAMP-CREB cascade produces anantidepressant response

Direct evidence for cAMP-CREB signaling in the actionof antidepressant treatment has been tested by pharma-cological, viral vector, and mutant mouse approaches.First, drugs that block the breakdown of cAMP producean antidepressant response in behavioral models ofdepression.54 The primary target for inhibition of cAMPbreakdown is cAMP-specific phosphodiesterase type IV(PDE4), and rolipram was one of the first selectivePDE4 inhibitors. In addition, we have found that chronicrolipram administration increases neurogenesis in adulthippocampus.46,58

Second, viral expression of CREB in the hippocampusof rat produces an antidepressant response in the forcedswim and learned helplessness models of depression.59

However, further studies demonstrated that the effectsof CREB are dependent on the brain region where it isexpressed. For example, expression of CREB in thenucleus accumbens produces a prodepressant effect,while expression of a dominant negative mutant ofCREB results in an antidepressant response in theforced swim test.60 Transgenic expression of dominantnegative CREB in the nucleus accumbens is consistentwith this effect.61 The different behavioral effects ofCREB can be explained by different target genes in thehippocampus (ie, BDNF) versus the nucleus accumbens(ie, prodynorphin).

Regulation of neurotrophic factors anddepression

The regulation of CREB by antidepressant treatmentindicates that regulation of gene expression also plays arole in the actions of antidepressants. There have beenmany gene targets identified for antidepressants,51,52 butBDNF is one that has gained attention and is relevantto neural plasticity responses to antidepressant medica-tions. Studies to identify additional gene targets andgene profiles using gene microarray analysis are cur-rently being conducted.

Antidepressant treatment upregulates BDNF

Neurotrophic factors were originally identified and stud-ied for their role in development and neuronal survival.However, it is now clear that these factors are expressedin the adult brain, are dynamically regulated by neuronalactivity, and are critical for the survival and function ofadult neurons. On the basis of these considerations, it isclear why decreased expression of BDNF could haveserious consequences for the function of limbic brainstructures that control mood and cognition. In contrast,antidepressant treatment results in significant upregula-tion of BDNF in the hippocampus and cerebral cortexof rodents.28,53,54 Increased expression of BDNF is depen-dent on chronic treatment, and is observed with differ-ent classes of antidepressants, but not other psychotropicdrugs. The induction of BDNF would be expected toprotect neurons from damage resulting from stress, ele-vated glucocorticoids, or other types of neuronal insult.

BDNF has antidepressant effects in behavioral modelsof depression

The possibility that BDNF contributes to the actions ofantidepressant treatment is supported by behavioralstudies of recombinant BDNF and transgenic mousemodels. Microinfusions of BDNF into the hippocampusproduce an antidepressant-like response in the learnedhelplessness and forced swim models of depression.62

The antidepressant effect of BDNF is observed after asingle infusion, compared with repeated administrationof a chemical antidepressant, and is relatively long-last-ing (up to 10 days after infusion). Transgenic overex-pression of a dominant negative mutant of the BDNFreceptor, trkB, in the hippocampus and other forebrain


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structures is also reported to block the effect of antide-pressant treatment, demonstrating that BDNF signalingis necessary for an antidepressant response.63

Microinfusions of BDNF into the dorsal raphe, a mid-brain region where 5-HT cell bodies are localized, alsoproduces an antidepressant response in the learnedhelplessness model.64 Together, these studies indicatethat BDNF could contribute to antidepressant responsesin both forebrain and brain stem structures by affectingdifferent populations of neurons.Alternatively, it is pos-sible that microinfusions of BDNF into the hippocam-pus influence 5-HT neuronal function by acting at presy-naptic sites, and could therefore enhance 5-HT signalingas observed after brain stem infusions of BDNF.64

A neurotrophic hypothesis of depression

Basic research and clinical studies of BDNF haveresulted in a neurotrophic hypothesis of depression andantidepressant action.53,54 This hypothesis is based in parton studies demonstrating that stress decreases BDNF,reduces neurogenesis, and causes atrophy or CA3 pyra-midal neurons. Brain imaging and postmortem studiesprovide additional support, demonstrating atrophy andcell loss of limbic structures, including the hippocampus,prefrontal cortex, and amygdala. In contrast, antide-pressant treatment opposes these effects of stress anddepression, increasing levels of BDNF, increasing neu-rogenesis, and reversing or blocking the atrophy and cellloss caused by stress and depression. Additional brainimaging and postmortem studies, as well as basicresearch approaches will be required to further test thishypothesis. In any case, the studies to date provide com-pelling evidence that neural plasticity is a critical factorin the pathophysiology and treatment of depression.

Antidepressants influence other neurotrophic factorsystems

Because of the preclinical and clinical evidence impli-cating neurotrophic factors in the pathophysiology andtreatment of depression, studies have been conducted toexamine other neurotrophic factor systems. One of themost robust effects identified to date is that antidepres-sant treatment increases the expression of fibroblastgrowth factor–2 (FGF-2).65 FGF-2 is known to have apotent influence on neurogenesis during developmentand in the adult brain, and could contribute to antide-

pressant regulation of neurogenesis. Studies are underway to examine the role of FGF-2 in antidepressant reg-ulation of neurogenesis and regulation of behavior inmodels of depression. Several other growth factors havebeen identified by microarray analysis and gene expres-sion profiling, including vascular endothelial growth fac-tor, neuritin, and VGF.66 Studies are currently under wayto determine the functional significance of these growthfactors in models of depression.

Clinical evidence of relevance of neural plasticity to antidepressant treatment

Basic research studies clearly demonstrate that antide-pressant treatment regulates signal transduction, geneexpression, and the cellular responses that representneural plasticity.This issue is more difficult to address inclinical studies, but evidence is slowly accumulating.Brain imaging studies have been conducted to examinethe influence of antidepressants on the volume of limbicbrain regions. One study demonstrates that hippocam-pal atrophy is inversely proportional to the length oftime a patient receives antidepressant medication.67 Alongitudinal study of PTSD patients before and afterantidepressant treatment has found that there is a par-tial reversal of hippocampal atrophy in patients receiv-ing medication.68 The latter study demonstrated a corre-sponding increase in verbal declarative memory inresponse to antidepressant treatment.Evidence at the molecular level is also provided by post-mortem studies. Levels of CREB immunoreactivity areincreased in patients receiving antidepressant treatmentat the time of death relative to unmedicated patients.39

In addition, levels of BDNF are increased in patientstaking an antidepressant at the time of death.59 Althoughthese effects must be replicated and extended (for exam-ple, to the regulation of neurogenesis) in additionalbanks of postmortem tissue, the results are consistentwith the hypothesis that neural plasticity is upregulatedin patients receiving antidepressant medication.

Novel targets for the treatment of depression

The hypothesis that antidepressant treatment increasesneural plasticity provides a number of novel targets fordrug development. However, as with any fundamentallyimportant mechanism, care must be taken that the drugsdeveloped for such targets do not interfere with the nor-


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mal function of the brain. Nevertheless, regulation ofneural plasticity is an exciting area of research for designof new drugs for a variety of indications, including learn-ing, memory, cognition, mood, and neurodegenerativedisorders.This section discusses a few of these targets inthe context of the pathways regulated by antidepressantsand stress.

Targets for antidepressant regulation of neurogenesis

Identification of the signal transduction and gene expres-sion pathways that are responsible for the actions of anti-depressant regulation of neurogenesis is a subject ofintense investigation.Activation of the cAMP-CREB sig-naling cascade using either pharmacological or transgenicapproaches is reported to increase both proliferation andsurvival of newborn neurons in the hippocampus,46,58 sup-porting the possibility that antidepressants increase neu-rogenesis via regulation of this intracellular pathway. Genetargets of CREB, as well as other neurotrophic/growth fac-tors that have been shown to regulate adult neurogenesis,include BDNF, FGF-2, and insulin-like growth factor–1, toname but a few.18 Because antidepressant treatmentincreases the expression of both BDNF and FGF-2, thesetwo factors are currently being investigated.This is just apartial listing of the signal transduction cascades and fac-tors that could contribute to antidepressant regulation ofadult neurogenesis.

Targets for regulation of the cAMP-CREB cascade

There are several different sites within the cAMP path-way that could be targeted for drug development. Onethat has already proven to be effective for antidepres-sant treatment is blockade of PDE4 and the breakdownof cAMP. Rolipram is a PDE4-selective inhibitor thathas been demonstrated to have antidepressant efficacyin early clinical trials and behavioral models of depres-sion.69,70 However, the clinical use of rolipram has beenlimited by its side effects, primarily nausea.The identification of four different PDE4 isozymes thatare equally inhibited by rolipram raises the possibilitythat one of the isozymes underlies the antidepressantactions of rolipram, while another mediates its sideeffects. Studies are currently under way to characterizethe regional distribution and function of the three PDE4isozymes expressed in brain (PDE4A, PDE4B, andPDE4D) and the role of these isozymes in the actions of

antidepressant treatment.71 Studies of mutant micedemonstrate that null mutation of PDE4D produces anantidepressant-like phenotype indicating a role for thisisozyme,72 and similar studies are currently under wayfor PDE4A and PDE4B.

BDNF as a target for drug development

The use of BDNF and other neurotrophic factors for thetreatment of neurological disorders has been a subjectof interest for several years, although problems withdelivery, efficacy, and side effects have hampered theseefforts. To more directly replicate the in vivo situation,it may be possible to stimulate the expression of endoge-nous BDNF expression by stimulating signaling path-ways known to regulate this neurotrophic factor. First,activation of the cAMP-CREB cascade by inhibition ofPDE4 increases the expression of BDNF.56

Small molecular agonists for neurotransmitter receptorshave also exhibited some promise. Activation ofionotropic glutamate receptors increases BDNF expres-sion and could be targeted for the treatment of depres-sion.73 One drug that modulates glutamate transmissionand increases BDNF expression is memantine.74

Riluzole, a sodium channel blocker, also increasesBDNF expression, as well as neurogenesis in adult hip-pocampus.75 Specific 5-HT and norepinephrine receptorsubtypes that activate cAMP (eg, β-adrenergic, 5-HT7),Ca2+, or mitogen-activated protein kinase (α1-adrener-gic, 5-HT1A) pathways could also be targets for devel-opment. Characterization of the antidepressant actionsof these compounds will be needed, as well as identifi-cation of additional neurotransmitter and signal trans-duction systems that regulate BDNF.

Conclusions

Studies of the molecular and cellular mechanisms under-lying neural plasticity responses in learning and mem-ory, as well as fear, anxiety, depression, and drug abuseto name but a few, are some of the most exciting andrapidly advancing areas of research in neuroscience.Progress in our understanding of neural plasticity hasprofound implications for the treatment of a number ofpsychiatric and neurodegenerative disorders, and forenhancing performance in what are considered normalsubjects. One of the promising aspects of neural plastic-ity is that it implies that the alterations that occur are


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reversible, even neuronal atrophy and cell loss.Reversibility of structural as well as functional plasticityhas already been demonstrated in response to pharma-cological treatments or even behavioral therapy. As thefundamental mechanisms of neural plasticity are furtherelucidated, new targets and paradigms for enhancing

plasticity will be revealed and will lead to more effectiveand faster-acting therapeutic interventions. ❏

This work is supported by USPHS grants MH45481 and 2 PO1 MH25642, aVeterans Administration National Center Grant for posttraumatic stress dis-order, and by the Connecticut Mental Health Center.


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Plasticidad neuronal: consecuencias del estrésy efectos del tratamiento antidepresivo

La plasticidad neuronal está resultando un meca-nismo fundamental y específico de la función neu-ronal, lo que permite que el cerebro reciba infor-mación y ejecute las respuestas de adaptaciónapropiadas a los estímulos correspondientes. Elesclarecimiento de los mecanismos moleculares ycelulares que subyacen a la plasticidad neuronal esuno de los objetivos principales de la investigaciónen neurociencias y se han realizado avances signifi-cativos en esta área en los últimos años. Estos meca-nismos incluyen la regulación de la transducción deseñales y la expresión génica, como también lasalteraciones estructurales de las espinas neuronalesy sus procesos e incluso el nacimiento de nuevasneuronas en el cerebro adulto. La alteración de laplasticidad podría participar en los trastornos psi-quiátricos y neurológicos. Este artículo revisa la lite-ratura que demuestra que hay una modificación enla respuesta de estrés y hay evidencias que el trata-miento antidepresivo crónico puede revertir o blo-quear estos efectos, e incluso inducir respuestasneurales semejantes a la plasticidad. El esclareci-miento continuo de los mecanismos que subyacena la plasticidad neuronal conducirá a blancos paranuevos fármacos que podrían llegar a constituirseen intervenciones terapéuticas efectivas y de rápidaacción.

Neuroplasticité : conséquence du stress etactions des traitements antidépresseurs

La neuroplasticité se révèle être un mécanisme fon-damental et déterminant de la fonction neuronale,permettant au cerveau de recevoir l’information etd’apporter les réponses adaptatives appropriées auxstimuli ultérieurs qui s’y rattachent. L’élucidation desmécanismes moléculaires et cellulaires sous-jacents àla neuroplasticité est un objectif majeur de larecherche en neurosciences et des progrès significa-tifs ont été réalisés ces dernières années. Ces méca-nismes comprennent la régulation de la transductiondu signal et de l’expression du gène ainsi que lesaltérations structurales des prolongements et desépines dendritiques des neurones et même la nais-sance de nouveaux neurones dans le cerveau adulte.L’altération de ces mécanismes pourrait contribueraux pathologies psychiatriques et neurologiques. Cetarticle passe en revue la littérature pour faire le pointsur les arguments en faveur de l’altération de la plas-ticité en réponse au stress et de la capacité du trai-tement antidépresseur au long cours à en inverserou neutraliser les effets voire même à susciter desréponses semblables à celles de la neuroplasticité. Lapoursuite des efforts pour élucider les mécanismessous-jacents à la neuroplasticité permettra de définirde nouvelles cibles médicamenteuses et de débou-cher ainsi sur des interventions thérapeutiques effi-caces et d’action rapide.

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34. Sheline Y, Sanghavi M, Mintun MA, Gado MH. Depression duration butnot age predicts hippocampal volume loss in medically healthy wormenwith recurrent major depression. J Neurosci. 1999;19:5034-5043.35. Manji H, Duman RS. Impairments of neuroplasticity and cellularresilience in severe mood disorders: implications for the development ofnovel therapeutics. Psychopharmacol Bull. 2001;35:5-49.36. Cotter D, Mackay D, Landau S, Kerwin R, Everall I. Reduced glial cell den-sity and neuronal size in the anterior cingulate cortex in major depressivedisorder. Arch Gen Psychiatry. 2001;58:545-553.37. Ongur D, Drevets WC, Price JL. Glial reduction in the subgenual prefrontalcortex in mood disorders. Proc Natl Acad Sci U S A. 1998;95:13290-13295.38. Rajkowska G, Miguel-Hidalgo JJ, Wei J, et al. Morphometric evidencefor neuronal and glial prefrontal cell pathology in major depression. BiolPsychiatry. 1999;45:1085-1098.39. Dowlatshahi D, MacQueen GM, Wang JF, Young LT. Increased temporalcortex CREB concentrations and antidepressant treatment in major depres-sion. Lancet. 1998;352:1754-1755.40. Dwivedi Y, Rizavi HS, Conley RR, Tamminga CA, Pandey GN. Alteredgene expression of brain-derived neurotrophic factor and receptor tyrosinekinase B in postmortem brain of suicide subjects. Arch Gen Psychiatry.2003;60:804-815.41. Dwivedi Y, Rizavi HS, Roberts RC, Conley RC, Tamminga CA, Pandey GN.Reduced activation and expression of ERK1/2 MAP kinase in the post-mortem brain of depressed suicide subjects. J Neurochem. 2001;77:916-928.42. Czeh B, Michaelis T, Watanabe T, et al. Stress-induced changes in cere-bral metabolites, hippocampal volume, and cell proliferation are preventedby antidepressant treatment with tianeptine. Proc Natl Acad Sci U S A.2001;98:12796-12801.43. Malberg J, Eisch AJ, Nestler EJ, Duman RS. Chronic antidepressant treatmentincreases neurogenesis in adult hippocampus. J Neurosci. 2000;20:9104-9110.44. Madsen T, Treschow A, Bengzon J, Bolwig TG, Lindvall O, Tingström A.Increased neurogenesis in a model of electroconvulsive therapy. BiolPsychiatry. 2000;47:1043-1049.45. Manev H, Uz T, Smalheiser NR, Manev R. Antidepressants alter cell pro-liferation in the adult brain in vivo and in neural cultures in vitro. Eur JPharmacol. 2001;411:67-70.46. Nakagawa S, Kim JE, Lee R, et al. Regulation of neurogenesis in adultmouse hippocampus by cAMP and cAMP response element-binding pro-tein. J Neurosci. 2002;22:9868-9876.47. Lee H, Kim JW, Yim SV, et al. Fluoxetine enhances cell proliferation andprevents apoptosis in dentate gyrus of maternally separated rats. MolPsychiatry. 2001;6:725-728.48. van der Hart M, Czeh B, de Biurrun G, et al. Substance P receptor antag-onist and clomipramine prevent stress-induced alterations in cerebralmetabolites, cytogenesis in the dentate gyrus and hippocampal volume.Mol Psychiatry. 2002;7:933-941.49. Santarelli L, Saxe M, Gross C, et al. Requirement of hippocampal neuroge-nesis for the behavioral effects of antidepressants. Science. 2003;301:805-809.50. Vollmayr B, Simonis C, Weber S, Gass P, Henn F. Reduced cell prolifera-tion in the dentate gyrus is not correlated with the development of learnedhelplessness. Biol Psychiatry. 2003;54:1035-1040.51. Manji H, Drevets WC, Charney DS. The cellular neurobiology of depres-sion. Nat Med. 2001;7:541-547.52. Nestler E, Barrot M, DiLeone RJ, Eisch AJ, Gold SJ. Monteggia LM.Neurobiology of depression. Neuron. 2002;34:13-25.53. Duman R, Heninger GR, Nestler EJ. A molecular and cellular theory ofdepression. Arch Gen Psychiatry. 1997;54:597-606.54. Duman R, J Malberg, S. Nakagawa, C D’Sa. Neuronal plasticity and sur-vival in mood disorders. Biol Psychiatry. 2000;48:732-739.55. Nestler E, Terwilliger RZ, Duman RS. Chronic antidepressant adminis-tration alters the subcellular distribution of cAMP-dependent protein kinasein rat frontal cortex. J Neurochem. 1989;53:1644-1647.56. Nibuya M, Nestler EJ, Duman RS. Chronic antidepressant administrationincreases the expression of cAMP response element binding protein (CREB)in rat hippocampus. J Neurosci. 1996;16:2365-2372.57. Thome J, Sakai N, Shin KH, et al. cAMP response element-mediatedgene transcription is upregulated by chronic antidepressant treatment. JNeurosci. 2000;20:4030-4036.


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58. Nakagawa S, Kim JE, Lee R, Chen J, Fujioka T, Malberg J. Localization ofphosphorylated cAMP response element-binding protein in immature neu-rons of adult hippocampus. J Neurosci. 2002;22:9868-9876.59. Chen A-H, Shirayama Y, Shin KH, Neve RL, Duman RS. Expression of thecAMP response element binding protein (CREB) in hippocampus producesantidepressant effect. Biol Psychiatry. 2001;49:753-762.60. Pliakas A, Carlson RR, Neve RL, Konradi C, Nestler EJ, Carlezon WA.Altered responsiveness to cocaine and increased immobility in the forcedswim test associated with elevated CREB expression in the nucleus accum-bens. J Neurosci. 2001;21:7397-7403.61. Newton S, Thome J, Wallace TL, et al. Inhibition of cAMP response ele-ment-binding protein or dynorphin in the nucleus accumbens produces anantidepressant-like effect. J Neurosci. 2002;24:10883-10890.62. Shirayama Y, Chen AC, Nakagawa S, Russell RS, Duman RS. Brain-derivedneurotrophic factor produces antidepressant effects in behavioral modelsof depression. J Neurosci. 2002;22:3251-3261.63. Saarelainen T, Hendolin P, Lucas G, et al. Activation of the trkB neu-rotrophin receptor is induced by antidepressant drugs and is required forantidepressant-induced behavioral effects. J Neurosci. 2003;23:349-357.64. Siuciak JA, Lewis DR, Wiegand SJ, Lindsay R. Antidepressant-like effectof brain-derived neurotrophic factor (BDNF). Pharmacol Biochem Behav.1997;56:131-137.65. Mallei A, Shi B, Mocchetti I. Antidepressant treatments induce theexpression of basic fibroblast growth factor in cortical and hippocampalneurons. 2002;61:1017-1024.66. Newton S, Collier E, Hunsberger J, Adams D, Salvanayagam E, DumanRS. Gene profile of electroconvulsive seizures: induction of neurogenic andangiogenic factors. J Neurosci. 2003;23:10841-10851.

67. Sheline Y, Gado MH, Kraemer HC. Untreated depression and hip-pocampal volume loss. Am J Psychiatry. 2003;160:1-3.68. Vermetten E, Vythilingam M, Southwick SM, Charney DS, Bremner JD.Long-term treatment with paroxetine increases verbal declarative memoryand hippocampal volume in posttraumatic stress disorder. Biol Psychiatry.2003;54:693-702.69. Horowski R, Sastre-Y-Hernandez M. Clinical effects of the neurotrophicselective cAMP phosphodiesterase inhibitor rolipram in depressed patients:global evaluation of the preliminary reports. Curr Ther Res. 1985;38:23-29.70. Wachtel H. Potential antidepressant activity of rolipram and other selec-tive cyclic adenosine 3’,5’-monophosphate phosphodiesterase inhibitors.Neuropharmacology. 1983;22:267-272.71. Takahashi M, Terwilliger R, Lane S, Mezes PS, Conti M, Duman RS.Chronic antidepressant administration increases the expression of cAMPphosphodiesterase 4A and 4B isoforms. J Neurosci. 1999;19:610-618.72. Zhang H-T, Huang Y, Jin SJC, et al. Antidepressant-like profile andreduced sensitivity to rolipram in mice deficient in the PDE4D phosphodi-esterase enzyme. Neuropsychopharmacology. 2002;27:587-595.73. Li X, Tizzano JP, Griffey K, Clay M, Lindstron T, Skolnick P.Antidepressant-like actions of an AMPA receptor potentiator (LY392098).Neuropharmacology. 2001;40:1028-1033.74. Marvanova M, Lakso M, Pirhonen J, Nawa H, Wong G, Castren E. Theneuroprotective agent memantine induces brain-derived neurotrophic fac-tor and trkB receptor expression in rat brain. Mol Cell Neurosci. 2001;18:247-258.75. Katoh-Semba R, Asano T, Ueda H, et al. Riluzole enhances expression ofbrain-derived neurotrophic factor with consequent proliferation of gran-ule precursor cells in the rat hippocampus. FASEB J. 2001;16:1328-1330.


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tressful life events are among the most potentfactors that trigger or induce depressive episodes inhumans. The brain responds to stress experiences in acomplex manner related to the activation and inhibitionof neurons that are involved in sensory, motor, auto-nomic, cognitive, and emotional processes. Chronicstress, which is known to be accompanied by hyperac-tivity in central nervous neurotransmitter systems,induces cellular changes that can be regarded as a formof plasticity.This causes mood alterations in the affectedindividual and has the potential to reverse the psy-chopathological processes, thus alleviating the symptomsof depression. Since social stress in animals evokessymptoms that resemble those found in depressedpatients, chronic social stress can serve as an experi-mental paradigm to investigate the neuronal processesthat may also occur during depressive disease in humans.Research over past years has led to considerableadvances in the understanding of the neural causes ofdepression and the cellular mechanisms that underlie thebeneficial effects of currently available antidepressants.More importantly, such research forms the basis for thefuture development of more effective antidepressantdrugs.

Stress changes the activity of noradrenergicand adrenergic neurons

Stress is known to activate neurohormonal systems, suchas the hypothalamo-pituitary-adrenal (HPA) axis, torelease the central nervous “stress peptide” corti-cotropin-releasing factor,1 and to secrete glucocorticoidsfrom the adrenal gland.2 These corticosteroids have beenidentified as prominent factors that modify metabolicprocesses in both the body and the brain during stress aswell as depression.3 However, the other group of essen-tial substances in basic and accelerated metabolismincludes the monoamines, noradrenaline, adrenaline,

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S


Cellular consequences of stress and depressionEberhard Fuchs, PhD; Gabriele Flügge, PhD

Stress is known to activate distinct neuronal circuits inthe brain and induce multiple changes on the cellularlevel, including alterations in neuronal structures. On thebasis of clinical observations that stress often precipitatesa depressive disease, chronic psychosocial stress serves asan experimental model to evaluate the cellular and mol-ecular alterations associated with the consequences ofmajor depression. Antidepressants are presently believedto exert their primary biochemical effects by readjustingaberrant intrasynaptic concentrations of neurotransmit-ters, such as serotonin or noradrenaline, suggesting thatimbalances within the monoaminergic systems con-tribute to the disorder (monoaminergic hypothesis ofdepression). Here, we review the results that compriseour understanding of stressful experience on cellularprocesses, with particular focus on the monoaminergicsystems and structural changes within brain target areasof monoaminergic neurons. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:171-183.

Keywords: noradrenaline; adrenaline; serotonin; dopamine; histamine; neuro-nal remodeling; �2-adrenoceptor; 5-HT1A receptor; dopamine transporter; treeshrew

Author affiliations: Clinical Neurobiology Laboratory, German PrimateCenter, Göttingen, Germany

Address for correspondence: Eberhard Fuchs, PhD, Clinical NeurobiologyLaboratory, German Primate Center, Kellnerweg 4, 37077 Göttingen, Germany(e-mail: [email protected])

dopamine, serotonin (5-hydroxytryptamine [5-HT]), andhistamine. The present survey focuses on processesrelated to stress-mediated activation of monoaminergicneurons in the brain.The noradrenergic and adrenergic neurons are located inthe brain stem, where they form groups of cells that pro-ject axons to many parts of the brain. The best-studied group of noradrenergic neurons, located in thepontine locus ceruleus (LC), innervate several brainregions including the neocortex and the limbic system.The limbic system is a collection of regions that appear toregulate emotional processes (Figure 1).The noradrener-gic LC neurons play an important role in the regulationof mood and emotions as well as of attention span.When stimulated through stressful challenge, for exam-ple, noradrenaline is released from the nerve terminalsin the target brain region and is bound to adrenergicreceptors belonging to the group of G protein–coupledreceptors (GPCRs). These membrane-bound proteinsconvey signals from the extracellular to the intracellularcompartment of a cell (Figure 2). GPCR signalingrequires several steps for transmission of the signal, last-ing from milliseconds to many minutes.The binding of anatural agonist such as noradrenaline or adrenaline tothe receptor initiates a cascade of intracellular eventsthat drive the activity of the cell and involve effectorssuch as enzymes (eg, adenylyl cyclase, phospholipase,kinases, and phosphatases), second messengers (eg,cyclic adenosine monophosphate [cAMP], cyclic guano-sine monophosphate [cGMP], calcium ions, and arachi-donic acid), as well as ion channels, which modulate theelectrical activity of the neuron. A long-term effectoccurring minutes after binding GPCR is the regulationof gene transcription and subsequent protein synthesis(Figure 2).5 There are different types of adrenergicreceptors in the brain whose activation either stimulatesor inhibits the respective target neurons. Noradrenalineand adrenaline bind to the same types of adrenergicreceptors, although with slightly different affinities.6

Various experiments have shown that during stress,

noradrenergic and adrenergic neurons release morenoradrenaline and adrenaline, respectively, and that theturnover of these neurotransmitters is accelerated sothat their concentrations and/or amounts of theirmetabolites fluctuate in relation to the intensity andduration of the stressor.7-10 Acute stress induces only atransient rise in noradrenaline levels, but chronic stresswith recurrent environmental challenges can lead torepetitive increases in concentration.As a consequence,adrenoceptors on the surface of the target neurons arebombarded with noradrenaline, leading to a reductionin adrenoceptor numbers (receptor downregulation).11

On the other hand, low concentrations of noradrenalineinduce adrenoceptor upregulation.12

Changes in α2-ARs alter the activity of neurons

The most studied adrenergic receptors, with respect to reg-ulation in chronic stress, are the α2-adrenoceptors (α2-ARs), of which three subtypes are known (A, B, and C).13

Because of their widespread distribution in the brain, α2-ARs are diversely involved in mediating the analgesic andsedative effects of agonists such as dexmedetomidine14 andin modulating the baroreceptor reflex.15 The involvementof α2-ARs in the regulation of attention is suggested by thefinding that methylphenidate (the nonamphetamine stim-ulant used to treat children with attention-deficit hyper-activity disorder) affects neuronal activity in the LC.16

Administration of the antagonist yohimbine (a sympa-tholytic drug that is used to treat impotence) increases fir-ing of the LC neurons, resulting in anxiety-like behaviorin rats and monkeys.17 Brain α2-AR changes have beenobserved in depressed patients (see below).The α2A-AR autoreceptor in LC noradrenergic neurons,regulates noradrenaline release via a negative feedbackloop.14,18 Expression of this autoreceptor is reduced soonafter the onset of stress (see below). In addition, α2A-ARis also expressed in neurons that release the excitatoryneurotransmitter glutamate.19 In general, α2-AR stimu-lation leads to a transient inhibition of neuronal firingthrough hyperpolarization that is related to the modu-lation of calcium and potassium channels.20,21 There isreduced intracellular formation of the second messen-ger, cAMP, which itself regulates many cellular functionsincluding gene transcription.22,23

Different forms of stress, such as immobilization or acold environment, alter α2-AR numbers in distinct brain


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Selected abbreviations and acronyms�2-AR �2-adrenoceptor�-AR �-adrenoceptorDAT dopamine transporterGPCR G protein–coupled receptor5-HT 5-hydroxytryptamine (serotonin)LC locus ceruleus

Cellular consequences of stress and depression - Fuchs and Flügge Dialogues in Clinical Neuroscience - Vol 6 . No. 2 . 2004

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Figure 1. Monoaminergic neurons innervate almost all brain areas. A. Noradrenaline. The noradrenergic neurons of the locus ceruleus project tothe limbic and cortical regions, and to the thalamus, cerebellum, and spinal cord. They play an important role in the regulation of moodand attention. The noradrenergic neurons of cell groups A1, A2, A5, and A7 project to more restricted regions.4 They are important forautonomic function. B. Dopamine. The dopaminergic neurons of the substantia nigra and the adjacent ventral tegmental area (VTA) pro-ject to the striatum and to regions in the neocortex. They are important in the initiation of movements and for emotional processes.Furthermore, there is a dopaminergic cell group in the hypothalamus that regulates neuroendocrine processes. C. Serotonin. The sero-tonergic neurons located in the raphe nuclei project to almost all parts of the brain and are involved in many functions including the reg-ulation of emotional processes. D. Histamine. Histaminergic neurons are located in the tuberomammillary complex of the hypothalamus.They project to all parts of the brain and are important for arousal (the excited brain state). Modulation of neuronal activity by thesemonoamines is an important factor of well-balanced central nervous activity. Stress leads to hyperactivity of the monoamine neurons andthus to a dysregulation of neuronal activity. Currently available antidepressants are thought to adjust the balance between the differentneurotransmitter systems.

B. Dopamine

C. Serotonin D. Histamine

A. Noradrenaline

regions.24,25 We investigated the consequences of chronicpsychosocial stress using a stress paradigm in male treeshrews.26 Our experiments showed that chronic psy-chosocial stress reduces α2-AR expression in brainregions that regulate autonomic functions and emotionalbehavior.27 This receptor downregulation is most proba-

bly related to the stress-mediated rise in noradrenalineconcentrations. Regulation of noradrenaline release isimpaired soon after the onset of the stress period, asrevealed by reduced expression of the α2A-AR in theLC.28 During a stress period lasting several weeks, adren-ergic regulation changes, giving an initially high level and


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Figure 2. Neurotransmission via a G protein–coupled receptor (GPCR): binding of the neurotransmitter to the receptor initiates a cascade of intra-cellular events that drive the activity of the neuron or cell. The G-protein complex, consisting of subunits α, β, and γ, serves as the machin-ery that transduces the extracellular signal to various effectors at the intracellular side of the plasma membrane, to the enzymes adeny-lyl cyclase or phospholipase. These enzymes catalyze the synthesis of second messengers, such as cyclic adenosine monophosphate (cAMP)and diacylglycerol, which regulate gene transcription in the nucleus. Transcripts (mRNA) are later translated into protein. Calcium ionsreleased from intracellular stores and other second messengers activate protein kinases and phosphatases. This leads to phosphorylationand/or dephosphorylation of many intracellular proteins as well as ion channels that are located in the plasma membrane of the cell.Phosphorylation/dephosphorylation induces opening and closing of these channels and this modulates the electrical activity of the neu-ron. These dynamic cellular processes are accelerated during stress when neurotransmitter concentrations are elevated.

Membrane-boundenzyme

ααβ

γ GPCR

G-proteincomplex

Neurotransmitter

cAMP, diacylglycerol, Ca2+

Activation of downstreamenzymes (protein kinases,

phosphatases)

Ion channel

mRNAProtein

synthesis

Genetranscription

DNA

Nu

cleu

s

Neu

ron

al m

emb

ran

e

Cyt

op

lasm

then finally a low level of noradrenaline.This is the casein the prefrontal cortex, a brain area important for theregulation of mood and behavior.29 Following a chronicstress period, noradrenaline concentrations are obvi-ously low throughout the whole brain, probably due toa gradually acquired deficit in transmitter synthesis,transport, and/or release from the noradrenergic neu-rons.30 Interestingly, studies on postmortem materialfrom brains of depressed human patients also revealedthe upregulation of α2-ARs in several brain regions.31-33

These data therefore support the “noradrenaline deficithypothesis,” which assumes there is a reduced nor-adrenaline concentration in the brains of depressedpatients.34 Antidepressants that interact with α2-ARssuch as mirtazapine probably counteract this deficit.35

β-ARs also change during stress

GPCR β-adrenoceptors (β-ARs) increase cAMP syn-thesis.36 They are present in neurons and glial cells.37

When stimulated by agonists (adrenaline or noradrena-line), β-ARs are rapidly internalized into the cells.Therefore, high levels of endogenous agonists quicklyreduce numbers of β-AR molecules in the plasma mem-brane of target cells, inducing desensitization.11,38 β-ARsare first internalized into the cell; they undergo intra-cellular sequestration with subsequent reinsertion intothe plasma membrane, thereby restoring the normalreceptor pattern in the membrane.β-AR dysfunction is thought to play a role in psychiatricdisorders, and β-AR blockers have been used to treatdepression and anxiety.39 The number of β1-ARs in thetemporal and frontal cortex of suicide victims has beenfound to be significantly lower than in matched con-trols.40,41 However, the psychotropic role of β-AR down-regulation is still under discussion since the antidepres-sant desmethylimipramine also downregulates brainβ-ARs.42 On the other hand, the treatment of rats withthe selective serotonin reuptake inhibitors (SSRIs)citalopram and fluoxetine increased β1-AR radioligandbinding in the frontal cortex and striatum.43

Stress downregulates β-ARs in the brain.44 Our data fromthe tree shrew chronic stress model reveal that (i) theeffects are dependent on the duration of a stressful event;(ii) β1- and β2-ARs are differentially regulated; and (iii)the effects differ in different brain regions.45 Some of thestress-induced changes are only transient, since normalreceptor numbers are restored through the reinsertion of

intracellularly sequestered receptor molecules into theplasma membrane. Finally, after 4 weeks of psychosocialstress, the number of β1-ARs was decreased in cells of thehippocampus and parietal cortex.

Stress and 5-HT neurons

It is generally assumed that changes in serotonergic neu-rons underlie depressive diseases because the mostwidely used antidepressants are SSRIs, which raise extra-cellular levels of 5-HT. Several experimental results haveconfirmed the “5-HT deficit hypothesis” of depression.In mammals, the majority of 5-HT-producing neuronsare located in the brain stem, most of them on or nearthe midline, and they innervate almost every area of thebrain.46 The serotonergic neurons of the dorsal raphenucleus that project to the forebrain are autoactive anddischarge in a stereotyped pattern that changes duringthe sleep–wake–arousal cycle.47,48 Due to its wide distri-bution, it has been suggested that the 5-HT system isinvolved in almost every brain function, such as the reg-ulation of neuroendocrine secretion, regulation of car-diovascular and respiratory activity, sleep, nociception,analgesia, and motor output.49-51 5-HT definitely regu-lates mood, since its transporters and receptors are tar-gets for several psychotropic drugs.52 A polymorphism inthe promoter region of the 5-HT transporter (5-HTT)gene is thought to contribute to anxiety in humans,53 andan epidemiological study provides evidence that anallele encoding a short DNA sequence in this promoterregion increases the risk of developing a depressive dis-order.54 Rhesus monkeys with the short-sequence allelehave low concentrations of the 5-HT metabolite 5-hydroxyindoleacetic acid in their cerebrospinal fluid.55

This finding agrees with the view that low brain 5-HTlevels (“decreased serotonergic activity”) have negativeeffects on emotionality. However, 5-HT concentrationper se is probably not the only trigger for mood changes;humans with a genotype conferring high levels ofexpression of monoamine oxidase A (MAOA, theenzyme that degrades 5-HT) are less likely to developantisocial problems than individuals with lower MAOAexpression.54

Stress elevates the concentrations of 5-HT and itsmetabolites in several brain regions, indicating increasedturnover rates of the neurotransmitter,8,56 although theserotonergic neurons of the dorsal raphe nucleus do notchange their discharge rate during stress.46 Nevertheless,


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stress induces alterations in those brain regions that aretargets of the serotonergic neurons, so that repeatedexposure of rats to forced swimming increased 5-HTconcentrations in the striatum, whereas they werereduced in the lateral septum.57 Chronic restraint stressin rats accelerated 5-HT turnover in the hippocampusand produced low amounts of the monoamine.58

Many receptors (>14) are known to mediate the effectsof 5-HT.59 The present survey focuses on the 5-HT1Areceptor, the best characterized 5-HT receptor. ThisGPCR inhibits neuronal activity by reducing cAMP for-mation or phosphoinositide hydrolysis, depending on thetype of neuron where it is expressed, and it modulatespotassium and calcium channels.60,61 The somatodendritic5-HT1A autoreceptors located on the serotonergic neu-rons in the raphe nuclei regulate 5-HT release. Post-synaptic 5-HT1A receptors regulate the activity of neu-rons in cortical, limbic, and other regions. For example,they affect the activity of pyramidal neurons in the hip-pocampus.62-64

The 5-HT1A receptor has been implicated in many func-tions. Like other 5-HT receptors, it is involved in the reg-ulation of mood and emotional behavior,65 and there isevidence that 5-HT1A receptor dysfunction is involvedin depressive disorders. The agonists buspirone andgepirone act as anxiolytics and display antidepressant-like effects in clinical trials.66 Human brain studiesshowed that 5-HT1A receptor binding in depressedpatients is lower than in healthy subjects.67,68 However,there are conflicting data on this issue. Brains of non-violent suicides had increased 5-HT1A receptor bindingin the frontal cortex in one report, whereas anotherreport showed no difference between suicides and con-trols.69,70 Furthermore, other psychiatric diseases—as wellas depression—might cause changes in 5-HT1A recep-tors of the central nervous system. A variant of the 5-HT1A receptor gene was found in Tourette’s patientsand, in schizophrenics, 5-HT1A receptor binding siteswere increased in the ventral prefrontal cortex.71-73

Schizophrenics also displayed some 5-HT1A receptorbinding in the cerebellum, a brain region normallydevoid of these receptors.74

Restraint stress downregulated 5-HT1A receptors in thehippocampus of rats, and this effect was attributed to astress-induced rise in plasma glucocorticoids, the adrenalhormones that regulate the transcription of manygenes.75,76 The stress-induced downregulation of postsy-naptic 5-HT1A receptors in distinct cortical areas and the

hippocampal formation, in tree shrews, could also beattributed to high levels of glucocorticoids.64 However, itis interesting to note in relation to postsynaptic 5-HT1Areceptor downregulation that the effect is not exclusivelydue to high glucocorticoid levels, but also to low testos-terone. Social stress in male animals lowers testosteronelevels, and normal 5-HT1A receptor numbers can berestored by a testosterone substitution (Figure 3).77 It isinteresting that the number of somatodendritic 5-HT1Aautoreceptors in the dorsal raphe nucleus did not changeduring chronic stress in male tree shrews, with only theiraffinity being reduced.64 This agrees with electrophysi-ological data from the rat brain stem, which showed thatstress reduces 5-HT1A autoreceptor functioning.78

Stress affects dopaminergic neurons

Responses of the dopamine system to stress receivedparticular attention because of the potential involve-ment of this catecholamine in human psychopathologiesthat are known to be exacerbated by stress, such as schiz-ophrenia.Groups of dopaminergic neurons are located in the mid-brain, hypothalamus, and other regions.4 The mesocorti-cal and mesolimbic dopamine pathways, which arisefrom the ventral tegmental area, have been implicatedin emotional and memory processes. Dopaminergic cellsof the substantia nigra and the adjacent midbraintegmentum project to the telencephalon including thestriatum, forming the nigrostriatal pathway, which initi-ates motor responses. Dopamine transporter (DAT)knockout mice with high extracellular dopamine levelswere easily aroused by the mild stress of novelty.79

However, in genetically intact animals, the persistentstress-induced activation demonstrated in the nor-adrenergic system has not been demonstrated in thedopaminergic system. Under restraint stress, an initialincrease in mesolimbic dopamine release was later fol-lowed by a decline, suggesting that repeated exposure tothe same stressor results in inhibition rather than acti-vation of dopaminergic neurons.80 Other data suggestthat the effects depend on the severity and controllabil-ity of the stressor, the genetic background of the animals,and their life history.81 The mesocortical dopaminergicsystem is obviously more stress-sensitive than themesolimbic and the nigrostriatal systems.82,83

In the tree shrew model, 4 weeks of psychosocial stressdownregulated DAT in the striatum. We also found a


176

positive correlation between locomotor activity, whichis reduced in stressed animals, and the total number ofDAT binding sites.84 Low levels of DAT may indicatelow extracellular dopamine concentrations. In agree-ment with these findings, social defeat in male rats alsodecreased DAT binding sites in the striatum.85

Dopamine was initially considered to convey its cellularactions via two receptor subtypes, D1 and D2; these exert

opposing effects on the adenylate cyclase system. Fivedistinct dopamine receptors have now been cloned.36

Experiments with various knockouts could not deter-mine where on the neurons these receptor subtypes arelocated (presynaptic versus postsynaptic location).86

However, there are indications that D1 and D5 receptorsare located postsynaptically, whereas D2, D3, and D4receptors are located presynaptically and postsynapti-


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Figure 3. Serotonergic nerve endings (schematic drawing, upper left) in the hippocampal formation release the neurotransmitter serotonin (grayballs), which binds to its receptors, the serotonin-1A (5-HT1A) receptors (orange). The three pseudo-color pictures demonstrate receptorbinding in normal male tree shrews (left), in tree shrews that were submitted to chronic psychosocial stress (middle), and in stressed treeshrews that had received testosterone as a treatment (right). Colors indicate numbers of receptors in the different regions of the hippocampalformation: orange, high receptor numbers; yellow, moderate numbers; green, low numbers; purple, no receptors. Note that after chronicsocial stress receptor numbers are decreased, but that the normal receptor number is restored following testosterone treatment.

Normal

Stress

Stress and testosterone

Terminal ofserotonergicneuron

Releaseof serotonin

Serotoninreceptor

cally, with the presynaptic receptors acting as inhibitoryautoreceptors.87,88 In the tree shrew model, D1 receptorswere slightly increased in the striatum after 4 weeks ofpsychosocial stress (Mijnster et al, unpublished observa-tions), with a reliable increase in the prefrontal cortex,while D2 receptors were upregulated in the hippocam-pus.89 Taken together, these changes in receptors andDAT indicate impaired dopamine release after stress.Such a deficit in dopamine release might also account fora lack of motivation in depression. Antidepressants thatblock the D2 receptor (eg, clomipramine and fluvoxam-ine) might contribute to an improvement in motivation.

Histaminergic neurons under stress

The central nervous histamine system has been lessextensively studied with respect to stress, although it def-initely plays an important role in the stress response. Inmammalian brains, histaminergic neurons are foundexclusively in the posterior ventral hypothalamus, butsend their fibers to all brain regions.36,90 The electrophys-iological properties of these cells are similar to those ofthe other aminergic neurons, with slow spontaneous fir-ing, broad action potentials, and pronounced afterhyper-polarization.91,92 Histamine activates three types of recep-tors whose expression varies between brain regions.36

Histamine modulates glutamatergic neurotransmission.H1 and H2 receptors are mainly postsynaptically locatedwith high densities in limbic brain regions, while H3 is asomatodendritic autoreceptor that regulates release ofthe bioamine.91

The central histamine system is involved in many func-tions.Activity in histaminergic neurons correlates closelywith the sleep–wake cycle, being highest when awake andlowest during rapid-eye movement sleep. Histaminergicneurons are also active in alarm situations and/or duringactivation of the peripheral sympathetic nervous system.91

H1 and H2 receptors modulate release of the “stress hor-mones” corticotropin-releasing factor and vasopressinfrom hypothalamic neurons,93 while various stressors suchas dehydration or hypoglycemia stimulate histaminerelease. Even handling of rats raised histamine release inthe prefrontal cortex of rats.94 Acute restraint stress stim-ulates histamine turnover throughout the diencephalon,whereas during chronic stress histamine turnover in thestriatum and nucleus accumbens is affected.95 A relation-ship between histaminergic neurotransmission and emo-tional processes is suggested by the fact that H1 receptor

antagonists and H3 receptor agonists decrease anxiety, andbecause of the existence of antidepressants that block theH1 receptor (eg, doxepin and amitriptyline).

Stress-induced neuronal remodeling and plasticity

The stress-induced processes described above includechanges in different compartments of cells:• Alterations in membrane-bound proteins that occur

within seconds after the stressful stimulus (eg, confor-mational changes in receptors, enzymes, ion channelsvia stimulation of GPCRs).

• Internalization of receptors and intracellular traffick-ing as described for β-ARs.

• Changes in large enzyme complexes involved in theintracellular signaling cascade.

• Changes in gene transcription, which may lead toeither increased or decreased synthesis of a given pro-tein (Figure 2).

It is possible that these dynamic processes may evenlead to morphological changes in the cells; past researchhas shown that this is indeed the case.The first proof that chronic stress induces a remodelingof brain cells came from morphological studies on den-drites of pyramidal neurons in area CA3 of the hip-pocampus. Dendrites are the major regions of neuronalsynaptic contact with other neurons. Neurons with manyor highly arborized dendrites potentially have largereceptive fields (Figure 4).The retraction of the dendrites of these neurons wasobserved after chronic social stress and this effect wasattributed to the stress-induced rise in glucocorticoids.96,97

Similar phenomena occur in pyramidal neurons in theprefrontal cortex, where glucocorticoids also induce alter-ations in the arborization of dendrites.98 In the CA3 pyra-midal neurons of the hippocampus, dendritic retractioncould be prevented by the antidepressant tianeptine, butnot by the SSRIs fluoxetine and fluvoxamine.99 Also,chronic social defeat in male rats induced a shrinkage ofthe apical dendrites of the CA3 pyramidal neurons, andelectrophysiological measurements revealed that this phe-nomenon was accompanied by a facilitation of actionpotentials, with reduced thresholds and higher ampli-tudes.100 In addition, single experiences of social defeat ontwo consecutive days induced similar changes in the api-cal dendrites, with these changes persisting over 3 weeks.In contrast to chronic daily social defeat, the arborization


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of the dendrites at the basal pole of the pyramidal neu-rons was increased after the double defeat paradigm.100,101

Therefore, two severely stressful experiences had long-lasting consequences on the morphology of neurons thatdiffered from those induced by daily chronic stress. Stresswas also shown to prevent long-term potentiation (LTP, amechanism of synaptic plasticity that is thought to berelated to memory formation) of CA neurons in the hip-pocampus. This inhibition of LTP was observed in malerats after only two exposures to social defeat.101 The anti-depressant tianeptine increases the amplitude of excita-tory postsynaptic potentials and this mechanism appearsto be related to alterations in the phosphorylation of theN-methyl-D-aspartate (NMDA) receptor, one of the mostprominent receptors for the excitatory neurotransmitterglutamate.102

Synapses are often located at the tips of the spine pro-trusions on the dendritic shafts of neurons (Figure 4).The shape of a spine is related to the arrangement of theactin-containing microfilaments, the cytoskeletal fibers.103

Spines may form rapidly under the influence of synap-tic activity.104 Activation of the NMDA receptor initiateschanges in the actin cytoskeleton that stabilize thesynaptic structure.105 Spine formation in the neurons ofthe prefrontal cortex can be induced by even minorstimuli, such as handling the experimental animalsdaily.106 In response to an acute stress, spine density wasenhanced in the hippocampus of male rats, whereas, incontrast, female rats showed reduced spine density.107 Ittherefore appears that spine morphology is modulatedby stress, although other factors such as sex hormonesmay also have an effect on their formation.

Chronic stress and neuronal death?

There have been reports that social stress leads to celldeath in the hippocampal formation.108 However, recentstudies using the optical dissector technique, a reliablemethod for quantification of neurons within an entirebrain region, showed that stress does not affect neuronnumbers in the CA1 and CA3 areas of the hippocam-pus.109 Moreover, experiments using an in situ end-label-ing technique to identify apoptotic (dying) cells showeda significant decrease in the number of apoptotic cellswhen all hippocampal areas were analyzed.110

Although stress-induced death of principal neurons in thehippocampus is questionable, it is clear that stress pro-foundly affects these neurons.Their nuclear ultrastructurechanges as shown in the significant intensification in Nisslstaining.111 An electron microscopic analysis indicated thatthis effect is due to increased heterochromatin formationin the neuronal nuclei.112 The physiological role of thesechanges is unknown, but one may speculate that they areaccompanied by alterations in gene transcription. Recenttree shrew studies showed that chronic psychosocial stressreduced the expression of certain genes that are related tothe shape of neurons and other brain cells.113 In the brainsof adult rats that had been prenatally stressed through thestressful treatment of the pregnant dams, expression ofgenes associated with excitatory neurotransmission andmechanisms of neurotransmitter release were significantlyaltered.114 Furthermore, a large group of genes in the hip-pocampus has been shown to be differentially expressedafter glucocorticoid treatment.76


179

Figure 4. Schematic drawing of a CA3 pyramidal neuron plus its den-drites. Note the small soma in comparison to the highlyarborized apical and basal dendrites. Inset: dendritic shafts canbuild up protrusions (spines) that form synapses with axons ordendrites from other neurons. Synapses are sites of signaltransmission between neurons. Formation and disappearanceof spines are regulated by many factors such as gonadal hor-mones. Chronic psychosocial stress reduces the arborization ofthe apical dendrites, thus reducing the surface area of the neu-ron with the consequence that the neuron receives less infor-mation from other neurons (see text for details).

Apicaldentrites

Basaldentrites

Soma

Axon

Spine

Synapse

Conclusions and further directions

Despite extensive preclinical and clinical investigations,the exact neurobiological processes leading to depres-sion and the mechanisms responsible for the therapeu-tic effects of antidepressant drugs are still not completelyunderstood. Antidepressants are presently believed toexert their primary biochemical effects by readjustingaberrant intrasynaptic concentrations of neuromodula-tors such as 5-HT. However, the limitations of currentantidepressant medications, such as the time delay for afull therapeutic response, the substantial number of non-responders, and bothersome side effects merit a fullexploration of all plausible agents with novel antide-pressant mechanisms of action.

Recent preclinical and clinical studies suggest that majordepressive disorders are associated with cellularresilience and an impairment of synaptic and structuralplasticity, and that antidepressant medications may actby correcting this dysfunction. Although this concept isstill in its infancy, it has increasingly attracted researchefforts that may result in new treatment strategies forthe etiopathophysiology of psychiatric disorders, such asmajor depression. ❏

The contributions of former and current members of the ClinicalNeurobiology Laboratory at the German Primate Center are gratefullyacknowledged. The work summarized here was in part supported by theGerman Science Foundation, the DAAD, and the EC.


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Consecuencias celulares del estrés y la depresión

Se sabe que el estrés activa distintos circuitos neu-ronales en el cerebro e induce múltiples cambios anivel celular incluyendo alteraciones en las estruc-turas neuronales. A partir de las observaciones clí-nicas, que indican que a menudo el estrés precipitauna enfermedad depresiva, el estrés psicosocial cró-nico sirve como modelo experimental para evaluarlas alteraciones celulares y moleculares asociadascon las consecuencias de la depresión mayor.Actualmente se cree que los antidepresivos ejercensus efectos bioquímicos primarios mediante un rea-juste de las concentraciones intrasinápticas abe-rrantes de neurotransmisores, como la serotoninay la noradrenalina, lo que sugiere que el desbalancedentro del sistema monoaminérgico contribuye altrastorno (hipótesis monoaminérgica de la depre-sión). Aquí se revisan los resultados que contribu-yen a nuestra comprensión acerca de las conse-cuencias del estrés sobre los procesos celulares, conparticular atención a los sistemas monoaminérgicosy los cambios estructurales de las áreas cerebralesblanco de las neuronas monoaminérgicas.

Conséquences cellulaires du stress et dépression

Il est reconnu que le stress met en jeu des circuitsneuronaux distincts dans le cerveau et induit denombreux changements au niveau cellulaire, y com-pris des altérations des structures neuronales. Sur labase d’observations cliniques ayant montré que lestress déclenchait souvent une maladie dépressive,le stress psychosocial chronique peut être priscomme modèle expérimental pour évaluer les alté-rations moléculaires et cellulaires associées aux con-séquences d’une dépression majeure. Selon les con-ceptions actuelles, les antidépresseurs exercent leureffet biochimique primaire en réajustant les con-centrations intrasynaptiques aberrantes des neuro-transmetteurs, telles la sérotonine et la noradréna-line. Ceci suggère que les déséquilibres des systèmesmonoaminergiques sont impliqués dans la genèsede la dépression (hypothèse monoaminergique).Cet article passe en revue les résultats qui contri-buent à notre compréhension des conséquences dustress sur les processus cellulaires, avec une atten-tion particulière sur les systèmes monoaminergi-ques et les changements structurels des régionscérébrales cibles des neurones monoaminergiques.


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74. Slater P, Doyle CA, Deakin JF. Abnormal persistence of cerebellar sero-tonin-1A receptors in schizophrenia suggests failure to regress in neonates.J Neural Transm. 1998;105:305-315.75. McKittrick CR, Blanchard DC, Blanchard RJ, McEwen BS, Sakai RR.Serotonin receptor binding in a colony model of chronic social stress. BiolPsychiatry. 1995;37:383-393.76. Datson NA, van der Perk J, de Kloet ER, Vreugdenhil E. Identification ofcorticosteroid-responsive genes in rat hippocampus using serial analysis ofgene expression. Eur J Neurosci. 2001;14:675-689.77. Flügge G, Kramer M, Rensing S, Fuchs E. 5-HT1A-receptors and behav-iour under chronic stress: selective counteraction by testosterone. Eur JNeurosci. 1998;10:2685-2693.78. Laaris N, Le Poul E, Hamon M, Lanfumey L. Stress-induced alterations ofsomatodendritic 5-HT1A autoreceptor sensitivity in the rat dorsal raphenucleus—in vitro electrophysiological evidence. Fund Clin Pharmacol.1997;11:206-214.79. Spielewoy C, Roubert C, Hamon M, Nosten-Bertrand M, Bncur C, GirosB. Behavioural disturbances associated with hyperdopaminergia indopamine-transporter knockout mice. Behav Pharmacol. 2000;11:279-290.80. Imperato A, Cabib S, Puglisi-Allegra S. Repeated stressful experiencesdifferently affect the time-dependent responses of the mesolimbicdopamine system to the stressor. Brain Res. 1993;60:333-336.81. Cabib S, Puglisi-Allegra S. Stress, depression and the mesolimbicdopamine system. Psychopharmacol. 1996;128:331-342.82. Abercrombie ED, Keefe KA, DiFrischia DS, Zigmond MJ. Differentialeffect of stress on in vivo dopamine release in striatum, nucleus accumbensand medial frontal cortex. J Neurochem. 1989;52:1655-1658.83. Tidey JW, Miczek KA. Social defeat stress selectively alters mesocorti-colimbic dopamine release: an in vivo microdialysis study. Brain Res.1996;721:140-114. 84. Isovich E, Mijnster MJ, Flügge G, Fuchs E. Chronic psychosocial stressreduces the density of dopamine transporters. Eur J Neurosci. 2000;12:1071-1078.85. Isovich E, Engelmann M, Landgraf R, Fuchs E. Social isolation after a sin-gle defeat reduces striatal dopamine transporter binding in rats. Eur JNeurosci. 2001;13:1254-1256.86. Picciotto MR. Knock-out mouse models used to study neurobiologicalsystems. Crit Rev Neurobiol. 1999;13:103-149. 87. Meador-Woodruff JH. Update on dopamine receptors. Ann ClinPsychiatry. 1994;6:79-90.88. Vallone D, Picetti R, Borrelli E. Structure and function of dopaminereceptors. Neurosci Biobehav Rev. 2000;24:125-132.89. Mijnster MJ, Isovich E, Fuchs E. Chronic psychosocial stress alters the den-sity of dopamine D2-like binding sites. Soc Neurosci Abstr. 1998;24:277.90. Airaksinen MS, Flügge G, Fuchs E, Panula P. Histaminergic system in thetree shrew brain. J Comp Neurol. 1989;286:289-310.91. Brown RE, Stevens DR, Haas HL. The physiology of brain histamine. ProgNeurobiol. 2001;63:637-672.92. Watanabe T, Yanai K. Studies on functional roles of the histaminergicneuron system by using pharmacological agents, knockout mice andpositron emission tomography. Tohoku J Exp Med. 2001;195:197-217.93. Knigge U, Warberg J. The role of histamine in the neuroendocrine reg-ulation of pituitary hormone secretion. Acta Endocrinol (Copenh).1991;124:609-619.94. Westerink BH, Cremers TI, De Vries JB, Liefers H, Tran N, De Boer P.Evidence for activation of histamine H3 autoreceptors during handling stressin the prefrontal cortex of the rat. Synapse. 2002;43:238-243.95. Ito C. The role of brain histamine in acute and chronic stresses. BiomedPharmacother. 2000;54:263-267.96. Woolley CS, Gould E, Frankfurt M, McEwen BS. Naturally occurring fluc-tuation in dendritic spine density on adult hippocampal pyramidal neurons.J Neurosci. 1990;10:4035-4039.97. Magariños AM, McEwen BS, Flügge G, Fuchs E. Chronic psychosocialstress causes apical dendritic atrophy of hippocampal CA3 pyramidal neu-rons in subordinate tree shrews. J Neurosci. 1996;5:3534-3540.98. Wellman CL. Dendritic reorganization in pyramidal neurons in medialprefrontal cortex after chronic corticosterone administration. J Neurobiol.2001;49:245-253.


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108. Sapolsky RM, Krey LC, McEwen BS. The neuroendocrinology of stressand aging: the glucocorticoid cascade hypothesis. Endocr Rev. 1986;7:284-301.109. Keuker J, Vollmann-Honsdorf GK, Fuchs E. How to use the optical frac-tionator: an example based on the estimation of neurons in the hip-pocampal CA1 and CA3 regions of tree shrews. Brain Res Protocols.2001;7:211-221.110. Lucassen PJ, Vollmann-Honsdorf GK, Gleisberg M, Czeh B, De Kloet ER,Fuchs E. Chronic psychosocial stress differentially affects apoptosis in hip-pocampal subregions and cortex of the adult tree shrew. Eur J Neurosci.2001;14:161-166.111. Fuchs E, Uno H, Flügge G. Chronic psychosocial stress induces mor-phological alterations in hippocampal pyramidal neurons of the tree shrew.Brain Res. 1995;673:275-282.112. Vollmann-Honsdorf GK, Flügge G, Fuchs E. Cortisol treatment and psy-chosocial stress differentially alter the nuclear ultrastructure of hippocam-pal pyramidal neurons. In: Elsner N, Eysel U, eds. Göttingen NeurobiologyReport 1999. Stuttgart, Germany: Georg Thieme Verlag; 1999:524.113. Alfonso J, Pollevick GD, van der Hart MG, Flügge G, Fuchs E, FraschACC. Identification of genes regulated by chronic psychosocial stress andantidepressant treatment in the hippocampus. Eur J Neurosci. 2004;19:659-666.114. Kinnunen AK, Koenig JI, Bilbe G. Repeated variable prenatal stressalters pre- and postsynaptic gene expression in the rat frontal pole. JNeurochem. 2003;86:736-748.


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uring the past two decades, anatomical sub-strates associated with the neuropathology of mood dis-orders have been revealed through both in vivo neuro-imaging studies and morphological and neurochemicalstudies on postmortem brain tissue.While neuroimagingstudies have given significant insight into the gross mor-phological location of dysfunctional brain regions indepression, the neurochemical, cellular, and molecularfeatures of depression are being unlocked by studies inpostmortem brain tissue.Novel studies at the microscopic level are establishingthat the mood disorders are associated with abnormali-ties in cell morphology and distribution, in addition to thelong-recognized neurochemical abnormalities. Majordepressive disorder (MDD) and bipolar disorder (BPD)have been examined in postmortem brain tissue by sev-eral laboratories in the past 6 years. Cell-counting stud-ies report changes in the density and size of both neuronsand glia in a number of frontolimbic brain regions,including dorsolateral prefrontal, orbitofrontal, and ante-rior cingulate cortex, and the amygdala and hippocam-pus. These studies in postmortem brain tissue confirmand extend structural and functional neuroimaging stud-ies that reveal volumetric and metabolic changes in thesame frontolimbic brain regions in the same disorders.Convergence of cellular changes at the microscopic levelwith neuroimaging changes detected in vivo provides acompelling integration of clinical and basic research fordisentangling the pathophysiology of depression.Regionally localized and cell type–specific changes inneuronal and glial cytoarchitecture recently identified in

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C l i n i c a l r e s e a r c h

D


Cellular abnormalities in depression:evidence from postmortem brain tissueCraig A. Stockmeier, PhD; Grazyna Rajkowska, PhD

Keywords: major depression; bipolar disorder; postmortem brain; glia; neuron;neuropathology

Author affiliations: The University of Mississippi Medical Center, Departmentof Psychiatry and Human Behavior, Jackson, Miss, USA

Address for correspondence: Craig Stockmeier, PhD, Department of Psychiatryand Human Behavior, University of Mississippi Medical Center, 2500 N State St,Box 127, Jackson, MS 39216, USA(e-mail: [email protected])

During the past two decades, in vivo neuroimaging stud-ies have permitted significant insights into the generallocation of dysfunctional brain regions in depression. Inparallel and often intersecting ways, neuroanatomical,pharmacological, and biochemical studies of postmortembrain tissue are permitting new insights into the patho-physiology of depression. In addition to long-recognizedneurochemical abnormalities in depression, novel studiesat the microscopic level support the contention that mooddisorders are associated with abnormalities in cell mor-phology and distribution. In the past 6 years, cell-countingstudies have identified changes in the density and size ofboth neurons and glia in a number of frontolimbic brainregions, including dorsolateral prefrontal, orbitofrontal,and anterior cingulate cortex, and the amygdala and hip-pocampus. Convergence of cellular changes at the micro-scopic level with neuroimaging changes detected in vivoprovides a compelling integration of clinical and basicresearch for disentangling the pathophysiology of depres-sion. The ultimate integration of these two researchapproaches will occur with premortem longitudinal clini-cal studies on well-characterized patients linked to post-mortem studies of the same subjects. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:185-197.

mood disorders complement and expand hypotheses ofdysfunction within the monoaminergic, glutamatergic,and γ-aminobutyric acid (GABA) neurotransmitter sys-tems in these disorders.While MDD and BPD are clearly not neurodegenerativedisorders, impaired neuroplasticity is associated withthese mood disorders.The etiology of histopathological changes observed inpostmortem brain tissue is unknown. It is not clear howfactors such as genetic risk factors, neurodevelopmentalabnormalities, the progression of the disease, or exposureto antidepressant or mood-stabilizing medications con-tribute to the abnormal neuronal and glial observations inmood disorders. It remains to be determined whether thechronic administration of clinically effective therapeuticmedications can reverse or even staunch histopathologi-cal changes in the mood disorders.

Alterations in neurons and glia in cerebral cortex

In MDD and BPD, reductions in neuronal density andsize in some populations of cortical neurons have beenindependently reported.1-12 These abnormalities havebeen described in association cortices such as dorsolat-eral prefrontal, orbitofrontal, and anterior cingulate cor-tex, but not in the primary sensory cortical regions suchas somatosensory1 or visual cortex.2 Thus, neuronalabnormalities at the microscopic level in mood disordersappear to be specific to frontolimbic cortical regions—observations in postmortem tissue that are consistentwith in vivo neuroimaging studies of volumetric andmetabolic alterations in the same frontocortical regions.Neuronal abnormalities in mood disorders are not imme-diately evident, inasmuch as there is no significant reduc-tion in the density of Nissl-stained neurons measured

across all cortical laminae.1,3,4 However, when neurons areassessed within individual cortical layers or in subgroupsdetermined by size or immunohistochemistry, markedreductions in neuron density are found in both MDD andBPD. For example, the density of large-sized neuronal cellbodies is reduced in cortical layers II to VI in the dorso-lateral prefrontal and rostral orbitofrontal cortex in MDD.5

These reductions in density of large-sized neuronal cellbodies are accompanied by increases in the density of neu-rons with smaller-sized cell bodies (Figure 1). The con-comitant decrease in the density of large neuronal cellbodies and increase in the density of small neuronal cellbodies suggests that neuronal shrinkage/enlargement orperhaps altered neuronal development, rather than out-right neuronal loss, is responsible for neuronal abnormal-ities in mood disorders.


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Selected abbreviations and acronymsAVP arginine-vasopressinBDNF brain-derived neurotrophic factorBPD bipolar disorderCRH corticotropin-releasing hormoneGABA �-aminobutyric acidGFAP glial fibrillary acidic proteinHPA hypothalamic-pituitary-adrenal (axis)MDD major depressive disorderNAA N-acetylaspartateNMDA N-methyl-D-aspartate

Figure 1. Changes in neuronal size and size-dependent density in layerII of rostral orbitofrontal cortex in a 73-year-old female withMDD as compared to a 71-year-old psychiatrically normalfemale control subject. For both subjects, the postmortemdelay was less than 17 hours and fixation time was less than10 months. Photomicrograph depicting cell composition acrossthe six cortical layers in rostral orbitofrontal cortex (upper left).Expanded printouts of cortical layers with neuronal cell bodiesrepresented by equivalent diameter circles with the area mea-sured for the individual neuron in its equatorial plane (rightpanel). Note that neuronal sizes are smaller in layers II and III inthe depressed subject than in the control subject. Note espe-cially dramatic increases in the density of small neurons in layerII associated with significant reductions in the density of thelargest neurons of this layer. Reprinted from reference 5: Rajkowska G, Miguel-Hidalgo JJ, Wei J, etal. Morphometric evidence for neuronal and glial prefrontal cell pathol-ogy in major depression. Biol Psychiatry. 1999;45:1085-1098. Copyright© 1999, Elsevier.

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In BPD, decreases in laminar neuronal densities have alsobeen reported in the dorsolateral prefrontal cortex4 andanterior cingulate cortex,2,6,7 but not by all studies.1,8

Moreover, in BPD, a decrease in density of pyramidal neu-rons in cortical layers III and V4 and nonpyramidal neu-rons in layer II6 has been observed in the same corticalregions. This last observation coincides with reports onreductions in the density of layer II nonpyramidal neuronsthat are identified with an antibody against the calcium-binding protein, calbindin, in the anterior cingulate cortex7

and dorsolateral prefrontal cortex9 in BPD. Calbindinimmunoreactive neurons are known to colocalize GABA.Our recent measurements of the density and size of cal-bindin-immunoreactive neurons in layer II and the upperpart of layer III of the dorsolateral prefrontal cortexrevealed a 43% reduction in the density of these neuronsin MDD as compared to controls.10 The depression-relateddecrease in calbindin immunoreactive neurons, whichcolocalize GABA, may be closely related to in vivo clini-cal evidence suggesting that MDD is associated withdecreased levels of GABA in cerebral cortex.11

Another manifestation of neuronal pathology in cerebralcortex in mood disorders is the reduced size of neuronalcell bodies. Smaller soma sizes have been reported insubjects with MDD, as compared to normal controls, inthe dorsolateral prefrontal cortex,3,5 orbitofrontal cortex,5

and anterior cingulate cortex.8,12 Two other studies, how-ever, did not report significant changes in neuronal sizein the anterior cingulate cortex.1,2 In a manner more sub-tle than in MDD, reductions in neuronal soma size havebeen observed in BPD by some,4,12 but not by all inves-tigators.1,2,8 In another study, a minor increase in the sizeof small nonpyramidal neurons was noted in the anteriorcingulate cortex in BPD subjects.6

Factors leading to a reduction in the size of neuronalsoma are not known. Smaller soma size may be relatedto smaller dendritic trees and/or abnormal morphologyof synaptic contacts. However, visualization of neuronaldendritic trees in cerebral cortex using the Golgi silverimpregnation method has not yet been conducted in sub-jects with mood disorders. Studies looking at synapticproteins in the anterior prefrontal13 and anterior cingu-late cortex14 describe reductions14 or no changes13 insynaptic proteins in mood disorders. Systematic studiesof dendritic trees and synaptic contacts in prefrontal andcingulate areas are warranted to shed light on the pos-sible etiology of smaller neuronal cell bodies in mood dis-orders.

The most consistent cell abnormality described in mooddisorders has unexpected finding of prominent reductionsin the density and number of glial cells. Glial reductionshave been reported consistently by independent labora-tories in the anterior cingulate cortex, dorsolateral pre-frontal cortex, and orbitofrontal cortex in MDD and/orBPD subjects. For example, a 24% to 41% reduction in thenumber of a general population of Nissl-stained glial cellsis reported in the subgenual region of the anterior cingu-late cortex (ventral part of Brodmann’s area 24) in a smallsubgroup of patients with familial MDD and familial BPD,as compared to control subjects.1 However, when datafrom familial and nonfamilial subgroups of patients werecombined, the reductions are not found.The estimation ofglial cell number in this study is combined across all sixcortical layers, and no information is provided on laminarspecificity of glial loss.Reductions in glial cell density, however, are reported inspecific cortical layers of the anterior cingulate and pre-frontal cortices in four other studies. These glial reduc-tions are observed in layer VI of the supragenual ante-rior cingulate cortex,8 layers III and V of the dorsolateralprefrontal cortex3-5 and in layers III, IV, V, and VI of thecaudal orbitofrontal cortex,5 in mood disorder patients.Glial cell size and shape, in addition to density, appears tobe affected in mood disorders.The size of glial cell bodies(corresponding to glial cell nuclei in Nissl-stained mater-ial) has been estimated in several studies. In three of theseinvestigations, glial size is reported as increased,3-5 whereastwo other studies find glial size to be unchanged in MDDor BPD.1,15 Significant increases in glial size are observedin the dorsolateral prefrontal cortex in BPD4 and to asmaller degree in MDD,5 comparing these cohorts to psy-chiatrically normal control subjects. More recently, similarincreases in glial size are noted in the anterior cingulatecortex in MDD.12 In addition, changes in the shape of glialnuclei to a less rounded conformation are detected in thedorsolateral prefrontal cortex in BPD.4 Reductions in glialdensity, paralleled by an increase in the size of glial nuclei,suggest that some compensatory mechanisms may takeplace in mood disorders. It can be speculated that adecrease in the density of glial cells is indicative of adecrease in the number of normally functioning glial cells.At the same time, glial cells that survive and are not dam-aged might be forced to play a larger role in supporting themetabolic needs of the surrounding neurons.As a conse-quence of increased metabolic demand, the nuclei of theseglial cells might enlarge in size and change in shape.

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Glutamate-induced swelling of astroglia, reported in ani-mal cell cultures,16 may be another factor in the etiology ofenlarged glial cells in depression.Glial cell pathology in mood disorders does not appearto be universally noted throughout the cerebral cortex.Changes in glial cell density or number are not found inthe sensorimotor cortex in either MDD or BPD.1 Recentreports suggest a lack of marked glial pathology in thesupragenual part of the anterior cingulate cortex,12 theentorhinal cortex in BPD and MDD,17 or the most rostralpart of the orbitofrontal cortex in MDD (correspondingto the transitional cortex between Brodmann areas 10and 47).5

Glial pathology in mood disorders has yet to be systemat-ically studied in subcortical structures. Only one reportsuggests that glial pathology extends to limbic subcorticalregions, with a significant reduction in glial number notedin the amygdala in subjects with MDD and unmedicatedsubjects with BPD.17

Alterations in neurons and glia in the hippocampus

Preclinical and neuroimaging studies have implicated thehippocampal formation in the pathophysiology of MDD.In addition, plasticity within the hippocampal formationmay be involved in neurobiological responses to stress andto antidepressant drug action.18 Evidence for an interac-tion between the hippocampus and depression comesfrom magnetic resonance imaging (MRI) studies examin-ing the volume of the hippocampus. Studies using MRIdemonstrate reduced volume of the hippocampus in sub-jects with MDD or a history of MDD,19-26 but not in otherstudies.27-29 It appears that hippocampal atrophy is prefer-entially seen in older, recurrently depressed subjects orsubjects who are refractory to antidepressant medications.Recently, hippocampal volume and function was assessedover the course of illness in younger patients with MDD.26

Recollection memory is diminished in subjects with either a first episode or multiple episodes of depression.However, hippocampal volume is generally considered tobe significantly decreased only in older depressed subjectswith multiple episodes of depression.25,26

Few studies have structurally examined the postmortemhuman hippocampus in depression. Cellular integrity andapoptosis have been examined in the hippocampus in sub-jects with depression, steroid-treated subjects, and normalcontrol subjects.30,31 Using semiquantitative methods, these

studies report no significant cell loss in any hippocampalregion in any of the subject groups. In most of the depres-sives, there was evidence for a slight increase in frag-mented DNA associated with apoptosis and necrotic neu-ron death detected in the dentate gyrus, CA1 and CA4.30

Decreases in astrocytic immunoreactivity for cellularGFAP and the neuron-specific phosphoprotein B50 (orGAP-45) were detected in CA1 and CA2 in depression.31

The authors suggest that apoptosis may only be a minorcontributor to volume changes in the hippocampus indepression, while patterns of reactive astrogliosis andsynaptic reorganization proteins are significantly alteredin only some hippocampal regions in depression. Otherreports of hippocampal changes in mood disorders iden-tify a significant decrease in the density of nonpyramidalneurons in the CA2 region and a reduction in reelin-pos-itive cell density in the hilus in subjects with BPD.32,33 Twoother studies conducted on the postmortem hippocampalformation in a small sample of subjects with BPD reveal adecrease in the density and size of nonpyramidal neuronsin the CA2 region and some disorganization in neuronalclusters in layers II and III of the entorhinal cortex.34,35

Neuronal and glial cell packing density and soma size wereestimated recently in Nissl-stained sections including thehippocampal subfields in 16 subjects with MDD and 16age-matched normal control subjects.36 Representativephotomicrographs are presented in Figure 2. Prominentabnormalities in the CA regions and dentate gyrus arefound in subjects with MDD.There is a significant increasein the mean density of pyramidal neurons in depressedsubjects, as compared with normal control subjects. In thegranule cell layer of the dentate gyrus, cell density is sig-nificantly increased in MDD. In addition, there is a signif-icant decrease in the mean soma size of pyramidal neuronsin depressed subjects, as compared with normal controlsubjects. On the basis of covariate analyses, the main find-ings of increased neuronal density and decreased neuronsoma size in depression are not significantly altered whentaking into consideration such factors as gender, age, post-mortem interval, tissue pH, brain weight, smoking, anti-depressant drug prescription in the last month of life, orsuicide.The substantial increases noted in neuronal pack-ing density and decrease in neuronal soma size detectedin postmortem tissue may be related to the decrease inhippocampal volume noted by some in MDD.Glial pathology in depression appears to extend beyondthe frontal cortex to the hippocampus.A recent study ofthe hippocampus in a large number of subjects with MDD


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and aged-matched normal control subjects reports a sig-nificant increase in the density of glial cells in all hip-pocampal CA subfields and the granule cell layer of thedentate gyrus.36 In MDD, increases in the packing densityof glial cells detected in postmortem tissue suggests apotential reduction in surrounding neuropil (see above),and may be related to decreases in hippocampal volumenoted by neuroimaging studies in MDD (see above).The different pattern of density change noted in depres-sion in the hippocampus in contrast to frontal corticalareas may be related to a unique reduction in neuropil inthe hippocampus in depression. Neuropil consists of thelattice of glial cells and their processes, dendrites, and prox-imal axons surrounding neuron cell bodies.The hypothe-sis of neuropil reduction in the hippocampus in MDD issupported by other postmortem studies revealing adecrease in dendritic spine density on neurons and dimin-ished arborization of apical dendrites in the subiculum ina small group of mixed subjects with bipolar disorder ordepression37 and decreased levels of synaptic proteins

found in CA4 in BPD.38 Thus, the diminished volume ofthe hippocampus noted by some in depression may be crit-ically determined by a loss in neuropil including dendriticbranching, dendritic spine complexity, and glial processes.The expression of brain-derived neurotrophic factor(BDNF) has been measured in the hippocampus of sub-jects with depression, and alterations in these factors mightbe related to changes in cell density and volume in depres-sion. There is preliminary evidence that BDNF in thehuman hippocampus may be regulated by chronic treat-ment with antidepressant medications. In an immunohis-tochemical study of subjects with MDD and others withBPD or schizophrenia, the immunoreactivity of BDNF, asmeasured by optical density, is upregulated in the dentategyrus and hilus only in subjects taking antidepressant med-ications (regardless of psychiatric diagnosis).39 Chen et al39

provide the first evidence beyond rodent studies thatchronic antidepressant drugs upregulate the expression ofBDNF in the human hippocampus. In a recent study,Dwivedi et al40 observed a significant reduction in mRNAand protein levels of BDNF in hippocampus as well asdorsolateral prefrontal cortex in suicide victims with eitherMDD or other psychiatric disorders. In the Dwivedi et al40

study, the decrease in expression of BDNF occurredregardless of antidepressant treatment. It remains to bedetermined whether alterations in BDNF are related toincreases in the packing density of neurons in the hip-pocampal formation or prefrontal cortex.The different pattern of neuronal pathology in the frontalcortex (decrease in density) and hippocampus (increase indensity) suggests unique involvement of these brainregions in the neuropathology of depression. Other evi-dence of dissimilarities between prefrontal cortex and hip-pocampus has been reported in MDD.41-43 Successful clini-cal treatment (or even the use of placebo) in depressionwas associated with an increase in metabolism in prefrontalcortex and a decrease in metabolism in hippocampus.

Alterations in neurons and glia in subcortical structures

The search for morphological abnormalities in subjectswith mood disorders has been less intense in subcorticalstructures than in cerebral cortical regions. Only a fewstudies in postmortem brain tissue on a relatively smallnumber of subjects have attempted to estimate the num-ber of neurons in such subcortical structures as hypo-thalamus, dorsal raphe nucleus, locus ceruleus, and amyg-


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Figure 2. Brightfield photomicrographs of coronal sections of the post-mortem human hippocampal formation. A. Cresyl violet–stainedcoronal section from a 54-year-old male (23-h postmortem inter-val). B. An adjacent coronal section processed by Timm staining.Note the intensely stained granule cell layer of the dentate gyrus(DGgr) in A and B, and the clear demarcation in B between hip-pocampal subfields CA2 and CA3 afforded by the Timm stain-ing. Pyramidal neurons and glial nuclei of CA3 are highlightedin C by the large white arrows and white arrowheads, respec-tively. Neurons and glial nuclei of the granule cell layer of thedentate gyrus are depicted in D by the large black arrows andblack arrowheads, respectively. The scale bars in A and C are 750µm and 25 µm, respectively.

dala.44-52 Results of these subcortical histopathologicalstudies are somewhat inconsistent. Increases, decreases,or no change in the cell number or density are reportedin the hypothalamus and brain stem nuclei in depressedsubjects.Stereological investigation of specific types of hypothal-amic neurons reveals an increase in the numbers of argi-nine-vasopressin (AVP)–immunoreactive neurons, oxy-tocin-expressing neurons, and corticotropin-releasinghormone (CRH) neurons in the paraventricular nucleusin subjects with BPD or MDD, compared to normal con-trols.44,45 Moreover, increases in CRH mRNA, and in thenumber of CRH neurons colocalizing AVP are alsofound in depressed patients.46,47 These findings ofincreases in specific immunoreactive neurons are consis-tent with the evidence of activation of the hypothalamic-pituitary-adrenal (HPA) axis in some subsets ofdepressed patients.48 On the other hand, decreased num-ber and density of nitric oxide synthase–containing neu-rons in the paraventricular hypothalamic nucleus aredescribed in a small group of subjects with either MDDor BPD.49

Subtle structural abnormalities have been reported inmood disorders in the monoaminergic brain stem nuclei,the major sources of serotonin (dorsal raphe nucleus)and norepinephrine (locus ceruleus) projections to thecerebral cortex. An increased number and density oftryptophan hydroxylase immunoreactive neurons isobserved in the dorsal raphe nucleus of suicide victimswith MDD compared with controls.50 In suicide victims,Arango et al51 report fewer pigmented neurons withinthe rostral locus ceruleus.Another study in a larger num-ber of subjects found no differences in the number ofpigmented neurons in the locus ceruleus between sub-jects with MDD (most were suicides) and control sub-jects.52 Although the number of neurons in the locusceruleus does not appear altered in MDD, CRHimmunoreactivity is increased in the locus ceruleus andpontine dorsal and median raphe nuclei.53,54 No changesin neuronal densities were detected in amygdala in sub-jects with either MDD or BPD, as compared to normalcontrols.17

These postmortem findings suggest that some changes inthe morphology of hypothalamic neurons and brain stemneurons may take place in mood disorders. However,future studies employing stereological techniques and alarger number of subjects are required to determine theexact pathology in these regions in depression.

Functional implications of pathologicalchanges in neural circuits

Morphological abnormalities detected postmortem inmood disorders are most likely related to dysfunction ofneural circuits regulating emotional, cognitive, andsomatic symptoms exhibited by subjects with MDD orBPD. In fact, alterations in neuronal density and sizehave been found in the dorsolateral prefrontal,orbitofrontal, and anterior cingulate cortex, the neuronsof which give rise to the frontal circuits critical for highercognitive and limbic functioning.55 Subtle neuronal alter-ations are also reported in the hypothalamus and hip-pocampus, further evidence of dysfunction in limbic cir-cuits in depression.Some of the cellular abnormalities detected postmortemin cortical and subcortical structures in MDD and BPDmay be related to disruption of monoaminergic trans-mission in depression. Studies in postmortem brain tis-sue identify alterations in serotonin and norepinephrinereceptors and transporters in the dorsolateral prefrontalcortex and ventrolateral/orbitofrontal cortex in brainsfrom suicide victims with or without clinical depression.56

These cortical regions also exhibit abnormal cell densityand size in cell-counting studies of postmortem tissue. Forexample, cellular changes found in superficial layers ofthe prefrontal cortex in depressed subjects may berelated to alterations in serotonin-1A receptors in super-ficial layers of cortex in suicide victims.57 In a neu-roimaging study, the authors find that radioligand bind-ing to serotonin-1A receptors is decreased inmedication-free subjects with MDD in several corticalregions, including medial temporal cortex, the temporalpole, orbitofrontal cortex, anterior cingulate cortex, insulaand dorsolateral prefrontal cortex.58 Expression ofanother component of serotonin neurotransmission, theserotonin transporter, is also decreased in the dorsolat-eral prefrontal and ventral/orbitofrontal cortex in post-mortem brains from depressed suicide victims.59,60

Detailed laminar analysis of the density of serotonintransporter–immunoreactive axons reveals that thisdeficit in depression is localized in cortical layer VI of thedorsolateral prefrontal cortex.59 The serotonin-trans-porter deficit may be related to the pathology of layer VIneurons reported in the same cortical layer by post-mortem cell-counting studies in depression.Moreover, subtle neuronal abnormalities reported bysome studies in the monoaminergic brain stem nuclei


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suggest dysfunction of monoaminergic projections orig-inating from the brain stem neurons and terminating infrontolimbic cortical regions. It is likely that the functionsand morphology of cortical neurons are affected by alter-ations in the functional state of noradrenergic, seroton-ergic, and dopaminergic neurons that project axons toprefrontal and anterior cingulate cortex. Postmortemneurochemical studies in MDD report alterations innoradrenergic α2-adrenergic receptors and the norepi-nephrine transporter, as well as levels of tyrosine hydroxyl-ase in the locus ceruleus,52,61,62 serotonin-1A receptors inthe midbrain dorsal raphe nucleus,63 and dopaminergicreceptors and transporters in the amygdala.64

The layer-specific changes in neuronal density and sizeidentified in mood disorders implies that both inhibitorylocal circuit neurons and excitatory projection types ofcortical neurons may be involved in the neuropathologyof mood disorders. Nonpyramidal inhibitory neuronsusing GABA are localized mainly in cortical layer II andestablish local cortico–cortical connections within orbetween adjacent functional columns of cortical cells. Incontrast, pyramidal glutamatergic excitatory neuronsreside predominantly in cortical layers III,V, and VI andgive rise to long projections to other cortical associationalregions (layer III), striatum (layer V), and thalamus(layer VI).Neuronal pathology detected in cortical layers III,V, andVI of the dorsolateral prefrontal cortex and anterior cingulate cortex in MDD may be associated with the pathology of excitatory pyramidal neurons within these laminae that use glutamate as their neurotransmitter.Moreover, the density of pyramidal neurons is selectivelyreduced in the dorsolateral prefrontal cortex in subjectswith BPD,4 further confirming the pathology of gluta-matergic neurons in mood disorders. These findings inpostmortem brain tissue coincide with an in vivo protonmagnetic resonance spectroscopy study in the anteriorcingulate cortex revealing a reduction in glutamate lev-els in depression.65 There is increasing preclinical andclinical evidence that antidepressant drugs directly orindirectly reduce the function of N-methyl-D-aspartate(NMDA) glutamate receptors.66 Depression-relateddecreases in glutamate levels or the density of gluta-matergic pyramidal neurons may alter in cortex and else-where the glutamatergic recognition site and its couplingto the NMDA receptor complex. One study of suicidevictims, some of whom were diagnosed with MDD,reveals changes in the glutamatergic recognition site and

its coupling to the NMDA receptor complex in the ante-rior prefrontal cortex.67 Interestingly, drugs that reduceglutamatergic activity or glutamate receptor–related sig-nal transduction may also have antimanic effects.66

Reductions in size and density of layer II neurons in theorbitofrontal and dorsolateral prefrontal cortex, as wellas reductions in the density of nonpyramidal neurons inlayer II of the anterior cingulate cortex suggest deficientGABAergic neurotransmission. Most nonpyramidal neu-rons in cortical layer II colocalize GABA and recent clin-ical evidence suggests that MDD is associated withdecreased levels of cortical GABA.11

In summary, the localization of morphological abnor-malities in the mood disorders occurs in prefrontolimbiccircuits that are likely to regulate emotional, cognitive,and somatic symptoms in depression.The observation inthe mood disorders of neuronal pathology in specific cor-tical layers gives support to the hypotheses that themonoamine, glutamate, and GABA neurotransmittersystems are involved in the pathophysiology of these dis-orders. It remains to be determined whether the cellularpathology is the reason for, or the consequence of,depression.

Functional implications of glial abnormalities in depression

The glial cells analyzed in the above studies do not rep-resent a homogeneous population of cells. Glial cells arecomposed of distinct populations of oligodendrocytes,microglia, and astrocytes. The crucial role of glial celltypes in brain function is currently being reevaluated. Inaddition to their traditional roles in neuronal migration(radial glia), myelin formation (oligodendrocytes), andinflammatory processes (astrocytes and microglia), glia(predominantly astrocytes) are now thought to providetrophic support to neurons, neuronal metabolism, and theformation of synapses and neurotransmission.15

The three distinct glial cell types cannot be identified inthe previously mentioned studies as those tissues werestained for Nissl substance and such staining does not dis-tinguish reliably between types of glial cells. Nissl stainingonly reveals morphological features of glial cell bodies andnot glial cell processes. On the other hand, recent immuno-histochemical examination of glial fibrillary acidic protein(GFAP), a marker of reactive astroglia, in the dorsolateralprefrontal cortex implicates astrocytes in the overall glialpathology in MDD.68 Although no significant group dif-


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ferences in the packing density of GFAP-reactive astro-cytes are present in this study, there is a significant corre-lation between age and GFAP immunoreactivity amongsubjects with MDD, when the entire group of MDD(young and old) is compared with normal controls.A sig-nificant reduction in the population of reactive astroglia isfound in a small subgroup of young (30 to 45 years old)subjects with MDD, as compared to young control subjectsand older (46 to 86 years old) subjects with MDD (Figures3A and 3B).This subgroup of younger adults with MDDalso had a shorter duration of depression and most ofthese subjects were suicide victims. Recent observationsfrom our laboratory confirm that the levels of GFAP pro-tein are also reduced in these young adults with MDD ascompared to age-matched control subjects (Figures 3Cand 3D), and that GFAP levels are positively correlatedwith age at the time of death and with the age of onset ofdepression.69 Thus, the involvement of GFAP expressionin early- versus late-life depression differs because theunderlying pathophysiology in early-life depression is dif-ferent from that in late-life depression. Clinical evidenceconfirms that late-onset depression (first depressiveepisode when older than 50 years) differs from early-onset

depression by its etiology, phenomenology, and cere-brovascular pathology.70-72

Alterations in GFAP in both BPD and MDD are alsosuggested by a proteomic study in which different formsof GFAP proteins displayed disease-specific abnormali-ties.73 Oligodendrocytes may also be involved in the cel-lular pathology of depression. In both the dorsolateralprefrontal and anterior frontal cortex in subjects withBPD or MDD, there are ultrastructural changes in oligo-dendrocytes and there is a reduction in the density andimmunoreactivity of these cells.74,75 Moreover, key oligo-dendrocyte-related and myelin-related gene expressionis reduced in the dorsolateral prefrontal cortex in BPD.76

While these results are intriguing, further immunohisto-chemical and molecular studies are needed to definitivelydetermine which specific glial cell types are compromisedin BPD and whether the same or different types of glialcells are involved in the pathology reported in MDD.Reductions in glial number and density, in addition tochanges in size and shape, might be related to the dys-function of monoamine and glutamate systems reportedextensively in depression. For example, astrocytes expressvirtually all of the receptor systems, ion channels, and


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Figure 3. An illustration of the pathology of glial cells found in the dorsolateral prefrontal cortex in MDD.5,68 Reductions in the glial fibrillary acidic pro-tein (GFAP) immunoreactive astroglia are found in a subgroup of young adults with major depression as compared to aged-matched controlsubjects and older subjects with major depression (A) and these reductions are correlated with the age of the subjects at the time of death(B). Recent preliminary observations (Si et al, unpublished observation) indicate that the levels of GFAP protein in the same area of the dor-solateral prefrontal cortex are also reduced in these young (D) but not old (C) subjects with major depression as compared to age-matchedcontrol subjects. Note that the level of actin, another protein in brain, is unchanged in a depressed subject as compared to the control. Reproduced (A and B) from reference 68: Miguel-Hidalgo JJ, Baucom C, Dilley G, et al. Glial fibrillary acidic protein immunoreactivity in the prefrontal cortexdistinguishes younger from older adults in major depressive disorder. Biol Psychiatry. 2000;48:861-873. Copyright © 2000, Elsevier.

0

20

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60

80

100

Are

a f

ract

ion

(%)

Controls Depressed Depressed age (years)

GFAP immunohistochemistry

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020 30 40 50 60 70 80 90

20

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Are

a f

ract

ion

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transporters found in neurons.15 Thus, the postsynapticmonoaminergic receptors distributed on glial cell bodiesand processes may play a role in serotonin, norepineph-rine, or dopamine neurotransmission. Moreover, astrogliaare the primary sites of glutamate uptake by glial trans-porters and are important in regulating NMDA receptoractivity.Astroglia regulate the levels of extracellular glu-tamate and thereby protect neurons in vitro from celldeath and provide energy for neurons.Astrocytic pathol-ogy in MDD may indirectly promote glutamate-mediatedneuronal excitotoxicity, with consequences that may bedetected by functional neuroimaging.A mounting body of data suggests that treatment withantidepressant or mood-stabilizing medications regulatesneuronal survival and also influences neurogenesis.Pharmacologically induced increases in neurogenesis inadult rodent brain have been reported in two indepen-dent studies.77,78 Moreover, there is evidence that treat-ment with lithium induces an increase in the astrocyticprotein GFAP in rodent hippocampus79,80 and the neurallobe of the pituitary.81 However, whether these increasesrepresent a protective or compensatory effect of thesemedications, and the mechanisms underlying the regula-tion of neurogenesis and glial proliferation have to befurther investigated. Furthermore, a precise link betweencell loss and atrophy, observed in the postmortem humanbrain, and medication-induced production of new cells,observed in the animal brain, has yet to be established.

Limitations in postmortem pathology studies in mood disorders

Postmortem studies cannot yet clearly define whether atrue loss of cells underlies prominent reductions in celldensity and size detected in mood disorders. For the esti-mation of a total number of neurons or glia in a particularbrain region, it is essential that the total volume of a stud-ied area be calculated.To measure the entire volume, theexact borders of the studied region have to be estab-lished,82,83 so that sampling is confined to the region withinthese borders. Unfortunately, in most studies of mood dis-orders in postmortem tissue, limited availability of thecomplete tissue region, as well as limitations in reliably dis-tinguishing cytoarchitectonic borders of a studied region,have prevented the estimation of a total tissue volumeand, consequently, total cell number. In one study wherethe total cell number was estimated in the subgenual cor-tex, a loss of glial but not neuronal cells has been demon-

strated in familial mood disorders.1 Glial reductionsreported in this study may in fact reflect a true loss of glialcells since the neuroimaging studies in the same corticalregion show a reduction in the volume of gray matter.84

There are unquestionable limitations to the use of post-mortem brain tissue in studying the mood disorders.56

Some of the critical issues to be considered when inter-preting the studies of postmortem brain tissue include thepsychiatric status of the subject at the time of death andthe underlying psychiatric disorder, whether “control” sub-jects were psychiatrically normal, the cause of death of thesubjects (suicide or by other means), evolving criteria usedto establish psychiatric diagnoses, the possible inclusion ofsubjects with concurrent psychoactive substance use dis-orders, the regional and hemispheric localization of thebrain regions being studied, and the presence and durationof treatment with a psychotropic medication. Other fre-quent drawbacks to studies of postmortem brain tissueinclude low numbers of subjects per cohort, or inadequateexpertise in cytoarchitectonic delineation of individualbrain regions. Ideally, longitudinal clinical studies on well-characterized patients should be linked to subsequent post-mortem studies of the same subjects. It is important to seekto control for the potential effects of suicide on post-mortem biological observations in depression. In two ofour studies,5,36 enough depressed nonsuicide subjects wereavailable to tentatively determine that the main findingsof these studies appear to persist regardless of whether thedepressed subjects died by suicide or natural causes.Whilesuicide makes tissues available for most postmortem stud-ies of depression, the results obtained with this cohort mustbe cautiously interpreted since the majority of living indi-viduals with depression do not attempt or commit suicide.In the mood disorders, the alterations in cell density andsize are likely to be related to the disorder itself and notto the age of subjects at the time of death, postmortemdelay, or the time of fixation of the tissue. Statisticalanalyses conducted in all of the above morphometricstudies yielded no significant correlation between celldensity or size and any of these confounding variables. Itcannot be ruled out, however, that some of the cellularalterations in mood disorders are related to prior treat-ment with antidepressants and lithium (for further dis-cussion see reference 85).The question of whether cell abnormalities can be attrib-uted to the effect of therapeutic medications is open todebate. There have been no systematic studies on theeffect of antidepressant and mood-stabilizing medications


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on cell number and morphology in the postmortemhuman brain, most likely due to an insufficient number oftreated versus untreated subjects.

Conclusion

Cellular abnormalities in mood disorders are observedin the dorsolateral prefrontal cortex, anterior cingulatecortex, orbitofrontal cortex, hippocampus, and amygdala.In these same brain regions, neuroimaging studies revealvolumetric, metabolic, and neurochemical alterations insubjects with mood disorders.Structural neuroimaging studies in mood disorders pro-vide evidence of modest but intriguing volumetric changesthat suggest cell loss and/or atrophy.86 Some studies, butnot all, report enlargement of the lateral and third ventri-cles in mood disorders87 that may be indicative of atrophyof surrounding cortical and subcortical regions.Functional neuroimaging studies in MDD and BPD lendfurther support to physiological abnormalities in corticaland subcortical frontolimbic regions. Abnormal regula-tion of glucose metabolism, regional cerebral blood flow,and high-energy phosphate metabolism are observed inthe prefrontal and temporal cortex, basal ganglia, andamygdala in mood disorders.88 Neuroimaging studies thatexamine neurochemical changes in the living brain pro-vide further support for the hypothesis that mood disor-ders are associated with changes in cell viability and func-tion. For example, high-resolution magnetic resonancespectroscopy in unmedicated subjects with BPD reportdecreased N-acetylaspartate (NAA) levels bilaterally inthe hippocampus89 and in the dorsolateral prefrontal cor-tex,90 as compared to healthy controls. In contrast, thera-peutic doses of lithium increase levels of NAA in thebrain of subjects with BPD.91 Such increases in NAA arefound in a number of regions including frontal cortex, andare localized almost exclusively in the gray matter. NAAis regarded as a measure of neuronal viability and func-tion, and therefore the changes in NAA levels seen inBPD strongly implicate alterations in neuronal viability,which may be related to alterations in cell number, celldensity, and size, and related volumetric changes.Interestingly, recent magnetic resonance spectroscopicstudies of nonhuman primates exposed to early life stres-sors or repeated stressors also reveal a significantdecrease in NAA. The NAA decrease in the animalsexposed to repeated stressors was normalized by chronictreatment with the antidepressant tianeptine.92 Increases

in glutamate-glutamine-GABA metabolites in the adultanterior cingulate cortex of these animals were alsoobserved 10 years after the stressors.These NAA mea-sures reflect neuronal integrity and metabolism whereaschanges in glutamate-glutamine-GABA metabolites mayreflect changes in membrane structure, glial functions, andglutamate content.Together, the above data suggest thatstructural and metabolic alterations observed in vivo maybe related to alterations in cell viability, which, itself, maybe related to alterations in cell number, density, and sizeobserved in postmortem tissues at the microscopic level.The studies reviewed above undeniably prove the use-fulness of postmortem tissue in unraveling the micro-scopic anatomical substrate of depression. For the firsttime, postmortem cell-counting studies in mood disordershave established that MDD and BPD are brain diseaseswith unique pathological alterations in neuronal and glialcells.The precise region- and layer-specific alterations inneuronal and glial architecture observed in mood disor-ders are consistent with the hypotheses of specific dys-function in monoamine, glutamate, and GABA neuro-transmitter systems in these disorders. Moreover,colocalization of cellular changes detected in postmortemtissues with in vivo neuroimaging findings proves thatpostmortem studies provide an important interfacebetween clinical and basic research in unraveling theneuroanatomical substrates of depression.Postmortem studies in depression also indicate that whileMDD and BPD are clearly not neurodegenerative disor-ders, these disorders are associated with impaired cellularneuroplasticity and resilience. It remains to be fully eluci-dated to what extent these findings represent neurodevel-opmental abnormalities, progression of the disorder, bio-chemical changes (in glucocorticoid or trophic factorslevels) accompanying repeated disease episodes, or theresults of treatment with therapeutic medications. It isunknown whether the cellular changes observed post-mortem in mood disorders can be reversed by antidepres-sant and mood-stabilizing medications.Although molecu-lar and genetic mechanisms associated with depression areyet to be unraveled, preliminary microarray studies of geneexpression in postmortem brain tissues from subjects withmood disorders confirm that the dorsolateral prefrontaland anterior cingulate cortex are sites of pathology inmood disorders.93,94 ❏

The authors acknowledge the support of the National Alliance for Researchon Schizophrenia and Depression, and Public Health Service GrantsMH60451, MH61578, MH63187, MH67996, and P20 RR17701.


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Alteraciones celulares en la depresión: evidencia proveniente de tejido cerebral postmortem

Durante los últimos veinte años, los estudios deneuroimágenes in vivo han permitido un impor-tante conocimiento acerca de la ubicación generalde regiones cerebrales disfuncionales en la depre-sión. En paralelo y a menudo intersectándose, losestudios neuroanatómicos, farmacológicos y bio-químicos del tejido cerebral postmortem están faci-litando nuevos conocimientos sobre la fisiopatolo-gía de la depresión. Además de las alteracionesneuroquímicas reconocidas desde hace bastantetiempo, nuevos estudios a nivel microscópico per-miten sostener que los trastornos afectivos estánasociados con alteraciones en la morfología y en ladistribución celular. En los últimos séis años, estu-dios de recuento celular han identificado cambiosen la densidad y el tamaño tanto de las neuronascomo de la glía en varias regiones cerebrales fron-tolímbicas que incluyen las cortezas dorso-lateralprefrontal, órbito-frontal y cingulada anterior, y laamígdala y el hipocampo. La convergencia de cam-bios celulares a nivel microscópico con cambios enlas neuroimágenes detectados in vivo provee unaintegración forzada de la investigación clínica ybásica para desentrañar la fisiopatología de ladepresión. La integración definitiva de estas dosaproximaciones de investigación ocurrirá cuando sepuedan relacionar los estudios clínicos longitudi-nales premortem en pacientes bien caracterizadoscon los estudios postmortem de los mismos sujetos.

Anomalies cellulaires observées dans le tissu cérébral post mortem au cours de ladépression

Ces 20 dernières années, les études de neuro-imagerie invivo ont permis des avancées significatives dans la com-préhension de la localisation générale des régionscérébrales dysfonctionnelles au cours de la dépression. Lesétudes neuroanatomiques, pharmacologiques et bio-chimiques des tissus cérébraux post mortem offrent ainsi,en parallèle et souvent de façon croisée, un aperçu renou-velé de la physiopathologie de la dépression. De nouvellesétudes au niveau microscopique ont conforté l’hypothèseselon laquelle les troubles de l’humeur sont associés à desanomalies de la distribution et de la morphologie cellu-laires, tout en confirmant l’existence d’anomalies neu-rochimiques connues depuis longtemps dans la dépression.Ces 6 dernières années, des études de comptage cellulaireont identifié des modifications dans la densité et la tailledes neurones et de la névroglie au niveau d’un certainnombre de régions cérébrales frontolimbiques comprenantles cortex dorsolateral préfrontal, orbitofrontal et cingu-laire antérieur, ainsi qu’au niveau de l’amygdale et de l’hippocampe. La convergence entre les modifications cel-lulaires observées au niveau microscopique et les change-ments in vivo détectés par neuro-imagerie témoigne avecéloquence de l’utilité d’associer la recherche clinique etfondamentale en vue d’élucider la physiopathologie de la dépression. L’ultime intégration de ces deux approchesde recherche consistera à rapprocher les données issuesd’études cliniques longitudinales pre mortem sur despatients bien définis avec celles provenant d’études postmortem sur ces mêmes patients.

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73. Johnston-Wilson NL, Sims CD, Hofmann JP, et al. Disease-specific alterationsin frontal cortex brain proteins in schizophrenia, bipolar disorder, and majordepressive disorder. The Stanley Neuropathology Consortium. Mol Psychiatry.2000;5:142-149.74. Orlovskaya DD, Vostrikov VM, Rachmanova NA, et al. Decreased numer-ical density of oligodendroglial cells in postmortem prefrontal cortex inschizophrenia, bipolar affective disorder and major depression. SchizophrRes. 2000;41:105.75. Uranova N, Orlovskaya D, Vikhreva O, et al. Electron microscopy ofoligodendroglia in severe mental illness. Brain Res Bull. 2001;55:597-610.76. Tkachev D, Mimmack ML, Ryan MM, et al. Oligodendrocyte dysfunctionin schizophrenia and bipolar disorder. Lancet. 2003;362:798-805.77. Chen G, Rajkowska G, Du F, et al. Enhancement of hippocampal neuro-genesis by lithium. J Neurochem. 2000;75:1729-1734.78. Malberg JE, Eisch AJ, Nestler EJ, et al. Chronic antidepressant treatmentincreases neurogenesis in adult rat hippocampus. J Neurosci. 2000;20:9104-9110.79. Rocha E, Achaval M, Santos P, et al. Lithium treatment causes gliosis andmodifies the morphology of hippocampal astrocytes in rats. Neuroreport.1998;9:3971-3974.80. Rocha E, Rodnight R. Chronic administration of lithium chlorideincreases immunodetectable glial fibrillary acidic protein in the rat hip-pocampus. J Neurochem. 1994;63:1582-1584.81. Levine S, Saltzman A, Klein AW. Proliferation of glial cells in vivo inducedin the neural lobe of the rat pituitary by lithium. Cell Prolif. 2000;33:203-207.82. Rajkowska G, Goldman-Rakic PS. Cytoarchitectonic definition of pre-frontal areas in the normal human cortex: II. Variability in locations of areas9 and 46. Cereb Cortex. 1995;4:323-327.83. Uylings HB, Sanz-Arigita E, de Vos K, et al. The importance of a human3D database and atlas for studies of prefrontal and thalamic functions. ProgBrain Res. 2000;126:357-368.84. Drevets W, Price J, Simpson JR Jr, et al. Subgenual prefrontal cortexabnormalities in mood disorders. Nature. 1997;386:824-827.85. Miguel-Hidalgo JJ, Rajkowska G. Morphological brain changes in depres-sion: can antidepressants reverse them? CNS Drugs. 2002;16:361-372.86. Soares J, Mann J. The anatomy of mood disorders—review of structuralneuroimaging studies. Biol Psychiatry. 1997;41:86-106.87. Elkis H, Friedman L, Wise A, et al. Meta-analyses of studies of ventricu-lar enlargement and cortical sulcal prominence in mood disorders. Arch GenPsychiatry. 1995;52:735-746.88. Drevets WC. Neuroimaging studies of mood disorders. Biol Psychiatry.2000;48:813-829.89. Bertolino A, Frye M, Callicott JH, et al. Neuronal pathology in the hip-pocampal area of patients with bipolar disorder. Biol Psychiatry. 1999;45:135S.90. Winsberg ME, Sachs N, Tate DL, et al. Decreased dorsolateral prefrontalN-acetyl aspartate in bipolar disorder. Biol Psychiatry. 2000;47:475-481.91. Moore GJ, Bebchuk JM, Hasanat K, et al. Lithium increases N-acetyl-aspartate in the human brain: in vivo evidence in support of bcl-2’s neu-rotrophic effects? Biol Psychiatry. 2000;48:1-8.92. Czeh B, Michaelis T, Watanabe T, et al. Stress-induced changes in cere-bral metabolites, hippocampal volume, and cell proliferation are preventedby antidepressant treatment with tianeptine. Proc Natl Acad Sci U S A.2001;98:12796-12801. 93. Evans S, Akil H, Choudary P, et al. Microarray studies in mood disorders:distinct patterns seen between major depression and bipolar disorder intwo frontal cortical regions. ACNP 41st Annual Meeting. December 8-12,2002. San Juan, Puerto. Scientific Abstract 36; 2002.94. Tomita H, Vawter M, Evans S, et al. Gene expression profiles in postmortembrains of mood disorder patients. Society for Neuroscience, 33rd AnnualMeeting, New Orleans, La. Program No. 640.19.2003 Abstract Viewer/ItineraryPlanner. Washington, DC: Society for Neuroscience, 2003. Online, 2003.Available at http://sfn.scholarone.com/itin2003/. Accessed 5 May 2004.


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he recent development of neuroimaging tech-nologies that permit in vivo characterization of theanatomical, physiological, and receptor pharmacologicalcorrelates of mood disorders have enabled significantadvances toward delineating the neurobiological corre-lates of mood disorders. Because these conditions werenot associated with gross brain pathology or with clearanimal models for spontaneous, recurrent mood episodes,the availability of tools allowing noninvasive assessmentof the human brain proved critical to illuminating thepathophysiology of major depressive disorder (MDD)and bipolar disorder (BD). The results of studies apply-ing imaging technologies and postmortem studies have

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Neuroplasticity in mood disordersWayne C. Drevets, MD

Keywords: major depressive disorder; bipolar disorder; neuroplasticity; neuro-imaging abnormalities; postmortem studies

Author affiliations: Wayne C. Drevets, MD, Mood and Anxiety DisordersProgram, NIH NIMH/MIB, 15K North Dr, Bethesda, Md, USA

Address for correspondence: Wayne C. Drevets, MD, Mood and AnxietyDisorders Program, NIH NIMH/MIB, 15K North Dr, MSC 2670, Bethesda, MD20892-2670, USA(e-mail: [email protected])

Neuroimaging and neuropathological studies of major depressive disorder (MDD) and bipolar disorder (BD) have iden-tified abnormalities of brain structure in areas of the prefrontal cortex, amygdala, striatum, hippocampus, parahip-pocampal gyrus, and raphe nucleus. These structural imaging abnormalities persist across illness episodes, and prelimi-nary evidence suggests they may in some cases arise prior to the onset of depressive episodes in subjects at high familialrisk for MDD. In other cases, the magnitude of abnormality is reportedly correlated with time spent depressed.Postmortem histopathological studies of these regions have shown abnormal reductions of synaptic markers and glialcells, and, in rare cases, reductions in neurons in MDD and BD. Many of the regions affected by these structural abnor-malities show increased glucose metabolism during depressive episodes. Because the glucose metabolic signal is domi-nated by glutamatergic transmission, these data support other evidence that excitatory amino acid transmission is ele-vated in limbic-cortical-striatal-pallidal-thalamic circuits during depression. Some of the subject samples in which thesemetabolic abnormalities have been demonstrated were also shown to manifest abnormally elevated stressed plasma cor-tisol levels. The co-occurrence of increased glutamatergic transmission and cortisol hypersecretion raises the possibilitythat the gray matter volumetric reductions in these depressed subjects are partly accounted for by processes homologousto the dendritic atrophy induced by chronic stress in adult rodents, which depends upon interactions between elevatedglucocorticoid secretion and N-methyl-D-aspartate (NMDA)–glutamate receptor stimulation. Some mood-stabilizing andantidepressant drugs that exert neurotrophic effects in rodents appear to reverse or attenuate the gray matter volumeabnormalities in humans with mood disorders. These neurotrophic effects may be integrally related to the therapeuticeffects of such agents, because the regions affected by structural abnormalities in mood disorders are known to playmajor roles in modulating the endocrine, autonomic, behavioral, and emotional experiential responses to stressors. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:199-216.

guided clinical neuroscience toward models in whichboth functional and structural brain pathology play rolesin the pathogenesis of mood disorders.Longitudinal positron emission tomography (PET) imag-ing studies of MDD and BD identified abnormalities ofregional cerebral glucose metabolism and cerebral bloodflow (CBF), which, in some cases, persisted beyond symp-tom remission, and in other cases appeared mood state-dependent (reviewed in reference 1; Figure 1). Thesereversible abnormalities presumably reflect areas wheremetabolic activity increases or decreases to mediate orrespond to emotional and cognitive manifestations of thedepressive syndrome, because local glucose metabolismand CBF (which is tightly coupled to glucose metabo-lism) reflect summations of the energy utilization associ-ated with terminal field synaptic transmission duringneural activity.2-4 In contrast, abnormalities that persistindependently of the mood state may instead reflect neu-ropathological sequelae of recurrent illness or neurode-velopmental abnormalities that may confer vulnerabilityto MDD (eg, in cases where they are evident in otherwisehealthy individuals at high familial risk for developingmood disorders). Such abnormalities in CBF and metab-olism may reflect pathological changes in synaptic trans-mission associated with altered neurotransmitter recep-tor function, cerebrovascular disease, changes in neuronalarborization or synapse formation, or abnormalities incellular viability or proliferation.5 For example, areaswhere CBF and metabolism appeared irreversiblydecreased in depressives relative to controls in PET stud-ies of MDD and BD were subsequently associated withfocal tissue reductions in magnetic resonance imaging(MRI)–based morphometric and postmortem histopatho-logical studies of MDD and BD.6-10

Abnormalities of gray matter volume and histology havenow been identified in several brain structures using vol-umetric MRI and postmortem neuropathological assess-ments, which in many cases were guided by initial appli-

cation of functional imaging approaches. The regionsaffected by these abnormalities have been shown to playmajor roles in modulating emotional behavior by elec-trophysiological, lesion analysis, and functional neuro-imaging studies in experimental animals and healthyhumans. Thus, the structural abnormalities in theseregions may prove relevant to the emotional dysregula-tion that is clinically manifest in mood disorders.

Sensitivity for detecting neuroimaging abnormalities in depression

The neuroimaging abnormalities discovered to date havenot had effect sizes sufficient to permit sensitive or spe-cific classification of individual cases. Moreover, the psy-chiatric imaging literature is in disagreement regardingthe specific location and direction of some abnormalities.Many limitations in the sensitivity in reproducing find-ings across studies appear to be accounted for simply bytechnical issues of image acquisition and/or analysis.1 Inother cases, however, disagreements within the literatureappear to reflect differences in subject selection criteriaapplied across studies, because the conditions encom-passed by the diagnostic criteria for MDD appear to beheterogeneous with respect to pathophysiology and eti-ology.It is noteworthy that neuroimaging laboratories selectingdepressed subjects according to MDD criteria alone haverarely been able to replicate their own previous findingsin independent subject samples. Instead, neuroimagingabnormalities appear to be specific to subsets of MDDsubjects.1 For example, requiring that subjects have famil-ial aggregation of illness and an early age at illness onsetimproved sensitivity for identifying subject samples withreproducible neuroimaging abnormalities. Clinical dif-ferences related to the capacity for developing mania orpsychosis or having a late age at illness onset have alsobeen shown to influence neuroimaging data. For exam-ple, elderly MDD subjects with a late age at depressiononset have an elevated prevalence of MRI signal hyper-intensities (in T2-weighted MRI scans, as putative corre-lates of cerebrovascular disease) in the deep and periven-tricular white matter, which is not the case for elderlydepressives with an early age at depression onset.Similarly, elderly MDD cases with a late-life onset anddelusional MDD cases have been shown to have lateralventricular enlargement—a feature which is generallynot present in MDD cases who are elderly but have an


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Selected abbreviations and acronymsACC anterior cingulate cortexBD bipolar disorderFPDD familial pure depressive disease5-HT 5-hydroxytryptamine (serotonin)MDD major depressive disorderNMDA N-methyl-D-aspartatePFC prefrontal cortexVTA ventral tegmental area

early age of MDD onset, or in midlife depressives whoare not delusional. In addition, enlargement of the thirdventricle has been consistently reported in BD, but notin MDD.A major technical issue that influences the sensitivity fordetecting neuroimaging abnormalities across studies isthe low spatial resolution of imaging technology relativeto the size of brain structures of primary interest. Withrespect to morphometric assessments of gray matter vol-ume, the volumetric resolution of state-of-the-art imagedata has recently been about 1 mm3, compared with thecortex thickness of only 3 to 4 mm. MRI studies involv-ing images of this resolution have been able to repro-ducibly show regionally specific reductions in mean graymatter volume across groups of clinically similar depres-

sives versus controls. However, they have lacked sensi-tivity to detect the relatively subtle tissue reductionsextant in mood disorders in individual subjects.Moreover, studies attempting to replicate such findingsusing data acquired at lower spatial resolutions (ie, voxelsizes ≥1.5 mm3) have commonly been negative becauseof the substantial partial volume effects that arise whenattempting to segment regions of only 3- to 4-mm cortexthickness in such low-resolution MRI images.

Volumetric MRI imaging abnormalities inmood disorders

Frontal lobe structures

Volumes of the whole brain and entire frontal lobe gen-erally have not differed between depressed and healthycontrol samples. In contrast, volumetric abnormalitieshave been identified in specific prefrontal cortical (PFC),mesiotemporal, and basal ganglia structures in mood dis-orders.The most prominent reductions in the cortex havebeen identified in the anterior cingulate gyrus ventral tothe genu of the corpus callosum, where gray matter vol-ume has been abnormally decreased 20% to 40% indepressed subjects with familial pure depressive disease(FPDD), familial BD, and psychotic depression6,11-13 rela-tive to healthy controls or mood-disordered subjects withno first-degree relatives with mood disorders.These find-ings were confirmed by postmortem studies of clinicallysimilar samples (see below). Effective treatment withselective serotonin (5-hydroxytryptamine [5-HT]) reup-take inhibitors did not alter the subgenual PFC volumein MDD,6 although the PFC appeared significantly largerin BD subjects chronically medicated with lithium ordivalproex than BD subjects who were either unmed-icated or medicated with other agents,1 compatible withevidence that chronic administration of these mood sta-bilizers increases expression of the neurotrophic factorsin rodents.14

In the posterior orbital, cortex, and ventrolateral PFC,volume has also been shown to be reduced in in vivo vol-umetric MRI studies15,16 and in postmortem neuropatho-logical studies of MDD.17,18 Reductions in gray matter volume were also found in the dorsomedial/dorsalanterolateral PFC in MDD subjects versus controls,19 andpostmortem studies of MDD and BD reported abnormalreductions in the size of neurons and/or the density ofglia.18,20,21

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Figure 1. Summary of neuroimaging abnormalities in early-onset, pri-mary, major depressive disorder (MDD). The regions whereneurophysiological imaging abnormalities have been consis-tently reported in unmedicated MDD samples are listed andapproximately shown on this midsagittal brain diagram inwhich subcortical structures are highlighted onto the medialsurface. Because only the medial wall of the cortex is shown,the location of the lateral orbital/ventrolateral prefrontal cor-tex (PFC)/anterior insular region is better illustrated in Figure2B. The “ventral anterior cingulate” region refers to both pre-genual and subgenual portions (see text and Figure 2). Thearrows in front of each region name indicate the direction ofresting state abnormalities in glucose metabolism in unmed-icated, depressed MDD samples relative to healthy control sam-ples. In some cases, abnormalities in both directions have beenreported which may depend either on the specific regioninvolved or on the clinical state (eg, treatment responsive vsnonresponsive; see text). The red arrows have indicatehistopathological and/or gray matter volumetric abnormalitiesin postmortem studies of primary mood disorders.

Dorsal anterior cingulate

Ventralanteriorcingulate

Ventral striatum

Areas where neuropathological/morphometric changes reportedIndicates direction of metabolic abnormality relative to control

Amygdala

or

Hippocampus

Medialcerebellum

Posteriorcingulate

Medial thalamus

Ventrolateral PFC Anterior insulaLateral orbital cortex

Dorsal caudate

Dorsal medial/anterolateral PFC

Temporal lobe structures

Morphometric MRI studies of specific temporal lobestructures reported significant reductions in the hip-pocampal volume in MDD, with magnitudes of differ-ence ranging from 8% to 19% with respect to healthycontrols.22-28 Sheline et al23 and MacQueen et al28 reportedthat the hippocampal volume was negatively correlatedwith the total time spent depressed or with the numberof depressive episodes in MDD. Other groups found nosignificant differences between MDD and control sam-ples.29-35 The inconsistency in the results of MDD studiesmay reflect pathophysiological heterogeneity within theMDD samples studied. For example, Vythilingam et al36

reported that the hippocampal volume was abnormallydecreased in depressed women who also had sufferedearly-life trauma, but not in women who had depressionwithout early-life trauma.In BD, reductions in hippocampal volume were identi-fied by Noga et al37 and Swayze et al38 relative to healthycontrols, although Pearlson et al39 and Nugent et al27

found no differences between BD and control samples.In postmortem studies of BD, abnormal reductions in themRNA concentrations of synaptic proteins40 and in api-cal dendritic spines of pyramidal cells41 were specificallyobserved in the subicular and ventral CA1 subregions ofthe hippocampus. A recent study using high-resolutionMRI scans found that the volume of the subiculum, butnot the remainder of the hippocampus, was decreased inBD relative to control samples.27

Two studies reported abnormalities of the hippocampalT1 MRI signal in MDD. Krishnan et al42 observed that theT1 relaxation time was reduced in the hippocampus, butnot in the entire temporal lobe, in unipolar depressivesrelative to healthy controls, and Sheline et al23 observedthat elderly subjects with MDD have a higher number ofareas with a low MRI signal than age-matched controlsin T1-weighted images. The significance of such abnor-malities remains unclear.In the amygdala, the literature is in disagreement. Studiesof MDD have reported that amygdala volume isdecreased,43,44 increased,45 or not different26 in depressivesrelative to healthy controls. Similarly, in BD, amygdalavolume was reported to be increased,46-48 decreased,39,49,50

or not different38 relative to healthy controls. Althoughthe extent to which disagreements in the results acrossstudies are accounted for by confounding factors (suchas medication effects) remains unclear, it appears more

likely that MRI images acquired at ≤1.5 tesla lack thespatial and tissue contrast resolution needed to measureamygdala volumes with sufficient validity and reliability.The amygdala’s small size and proximity to other graymatter structures seriously limits the specificity (accu-racy) for delimiting amygdala boundaries in imagesacquired using MRI scanners of ≤1.5-tesla field strength.High-resolution MRI images acquired at 3-tesla mag-netic field strength, in contrast, permit valid and reliablevolumetric measures of the human amygdala. A recentstudy employing this technique established that meanamygdala volumes are decreased bilaterally (P<0.001) inMDD relative to healthy control samples.51 Amygdalavolumes were decreased both in currently depressed andcurrently remitted MDD subsamples. Although meanamygdala volumes did not differ between BD and con-trol samples, they were smaller in BD subjects who hadnot been recently medicated with mood stabilizers thanin BD subjects who had been taking such agents, consis-tent with evidence that some mood stabilizers exert neu-rotrophic effects.14

Basal ganglia

Volumes of some basal ganglia structures have also beenreported to be abnormally decreased in mood disorders.Husain et al52 reported that the putamen was smaller indepressives (mean age 55) than controls, and Krishnan etal53 found a smaller caudate nucleus volume in depressives(mean age 48) than controls. In a sample limited to elderlydepressives, Krishnan et al54 also reported smaller putamenand caudate volumes relative to controls.These findingswere consistent with the postmortem study of Baumannet al,55 which found that caudate and accumbens area vol-umes were markedly decreased in both MDD and BDsamples relative to control samples. Nevertheless, Dupontet al56 and Lenze et al57 failed to find significant differencesin caudate or lentiform nucleus (putamen plus globus pal-lidus) volumes between younger MDD subjects and con-trols. The factors accounting for the discrepant resultsacross studies remain unclear.

Abnormalities of corpus callosal volume in mood disorders

The genual subsection of the corpus callosum wasreduced in volume in both depressed women with MDDand their high-risk, female offspring (insufficient num-


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bers of males were studied to determine whether theabnormality extends to males).58,59 These white matterregions contain the transcallosal fibers connecting theorbital cortex, anterior cingulate cortex (ACC), andmedial PFC with their homologous cortices in the con-tralateral hemisphere. The volumes of the splenial sub-region of the corpus callosum was also decreased inmood-disordered versus control samples, which containstranscallosal fibers from the posterior cingulate cortex.

Other cerebral structures

Morphometric studies of other brain structures indepression have produced less consistent results. Of MRIstudies of the thalamus, Dupont et al56 reported that thethalamic volume was decreased in unipolar depressivesrelative to controls, but Krishnan et al42,54 found no dif-ferences between depressives and controls. Two studiesof thalamic volume in BD also have reported conflictingresults. Of MRI studies of the cerebellum, two reportedthat the vermal volume is reduced in depressives relativeto controls,60,61 while a third did not.62

Consistent with evidence that the hypothalamic-pitu-itary-adrenal (HPA) axis function is elevated in somemood-disordered subgroups, enlargement of the pituitaryand adrenal glands has been reported in MDD. Krishnanet al63 showed that MRI-based measures of cross-sec-tional area and volume of the pituitary were increased(by 34% and 41%, respectively) in depressives (n=19)versus controls (n=19).This observation is consistent withevidence that the adrenal gland is also abnormallyenlarged in MDD,1 which would putatively result fromchronically elevated stimulation of the adrenal cortex byadrenocorticotropic hormone (ACTH).

Postmortem neuropathological assessmentsof mood disorders

Most of the regions where MRI studies demonstratedvolumetric abnormalities in mood disorders were alsoshown to contain histopathological changes or gray mat-ter volumetric reductions in postmortem studies of MDDand BD. Reductions in gray matter volume, thickness, orwet weight have been reported in the subgenual ACC,posterolateral orbital cortex, and ventral striatum inMDD and/or BD subjects relative to controls.7,9,18,55 Thehistopathological correlates of these abnormalitiesincluded reductions in glial cells with no equivalent loss

of neurons, reductions in synapses or synaptic proteins,elevations in neuronal density, and reductions in neu-ronal size.9,17,18,20,40,64,65 Abnormal reductions in glial cellcounts and density, and/or glia-to-neuron ratios have alsobeen found in MDD in Brodmann area (BA) 24 cortexof the pregenual ACC,20 the dorsal anterolateral PFC(BA9),21,66 and the amygdala.1,67 Finally, the mean size ofneurons was reduced in the dorsal anterolateral PFC(BA9) in MDD subjects relative to controls,18 and thedensity of neurons was decreased in the ACC in BD.68 Inseveral of these studies, the decreases were largelyaccounted for by differences in the left hemisphere.1,7,9,17,67

In the amygdala and the dorsal anterolateral PFC (BA9),the glial type that specifically differed between MDD andcontrol samples was the oligodendrocytes. In contrast,astrocyte and microglial cell counts did not differ signifi-cantly between MDD or BD samples and healthy controlsamples in the amygdala.1 Oligodendroglia are best char-acterized for their role in myelination, and the reductionin oligodendrocytes may conceivably arise secondary toan effect on myelin, either through demyelination, abnor-mal development, or atrophy in the number of myelinatedaxons. Notably, the myelin basic protein concentration wasfound to be decreased in the frontal polar cortex (BA10)in MDD subjects.69 Compatible with these data, the con-centration of white matter within the vicinity of the amyg-dala27 and the white matter volume of the genual and sple-nial portions of the corpus callosum are abnormallyreduced in MDD and BD.58,59 These regions of the corpuscallosum were also smaller in child and adolescent off-spring of women with MDD who had not yet experienceda major depressive episode, in comparison to age-matchedcontrols, suggesting that the reduction in white matter inMDD reflects a developmental defect that exists prior tothe onset of depressive episodes.58 All of these observa-tions support the hypothesis that the glial cell loss in mooddisorders is accounted for by a reduction in myelinatingoligodendrocytes.Further evidence supporting this hypothesis comes fromseveral reports that deficits in glia in the cerebral cortexdepend upon laminar analysis, with the greatest effects inlayers III, V, and VI.18,20,70,71 The intracortical plexuses ofmyelinated fibers known as “bands of Baillarger” are gen-erally concentrated in layers III and V. The size of theseplexuses varies across cortical areas, so if the oligoden-drocytes related to these plexuses were affected, differentareas would be expected to show greater or lesser deficits.Layer VI in particular has a relatively large component of


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myelinated fibers running between the gray and whitematter.Finally, a population of satellite oligodendrocytes existsnext to neuronal cell bodies that have largely unknownfunctions, but do not appear to have a role in myelinationunder normal conditions.72 An electron microscopic studyof the PFC in BD revealed decreased nuclear size, clump-ing of chromatin, and other types of damage to satelliteoligodendrocytes, including indications of both apoptoticand necrotic degeneration.73 Fewer signs of degenerationwere seen in myelin-related oligodendrocytes in whitematter. Satellite oligodendrocytes may play a role in main-taining the extracellular environment for the surroundingneurons, which resembles the functions mediated by astro-cytes.These oligodendrocytes are immunohistochemicallyreactive for glutamine synthetase, suggesting that theyfunction like astrocytes and take up synaptically releasedglutamate for conversion to glutamine and cycling backinto neurons.74 Many studies of glial function have not dis-tinguished astrocytes from oligodendrocytes, and the twoglial types may share several functions.In other brain regions, reductions in astroglia have beenreported by postmortem studies of mood disorders. In thefrontal cortex, Johnston-Wilson et al75 found that fourforms of the astrocytic product glial fibrillary acidic pro-tein (GFAP) were decreased in mood-disordered subjectsrelative to controls, although it remained unclear whetherthis decrement reflected a reduction in the astrocyte den-sity or in GFAP expression. Using immunohistochemicalstaining for GFAP,Webster et al76 did not find significantdifferences in cortical astrocytes between controls, andMDD or BD cases. Other studies also did not find differ-ences in GFAP between mood disorder cases and con-trols.66

Factors that may conceivably contribute to a loss of oligo-dendroglia in mood disorders include the elevated gluco-corticoid secretion and glutamatergic transmission evidentduring depression and mania. Glucocorticoids affect gliaas well as neurons,77 and elevated glucocorticoid levelsdecrease the proliferation of oligodendrocyte precursors.78

Moreover, oligodendrocytes express α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA) and kainate-type glutamate receptors, and are sensitive to excitotoxicdamage from excess glutamate as well as to oxidativestress.1 These vulnerabilities putatively contribute to oligo-dendrocyte degeneration in ischemic brain injury anddemyelinating diseases,79,80 although no data exist to estab-lish a similar role in mood disorders.The targeted nature

of the reductions in gray matter volume and glial cells tospecific areas of the limbic-cortical circuits that showincreased glucose metabolism during depressive episodesis noteworthy given the evidence reviewed below that theglucose metabolic signal is dominated by glutamatergictransmission.The hypothesis that glutamate transmissionis elevated in these areas in depression was also supportedby a postmortem study in depressed suicide victims.81

Elevations of glutamate transmission and cortisol secre-tion in mood disorders may also contribute to reductionsin gray matter volume and synaptic markers by inducingdendritic atrophy in some brain structures. In the medialPFC and parts of the hippocampus and amygdala of adultrodents, the dendritic arbors undergo atrophy ordebranching in response to specific types of repeated orchronic stress.82 The effects of stress on dendritic arboriza-tion depend both upon the type of stress applied andanatomical location. For example, chronic unpredictablestress produces dendritic atrophy in the basolateral amyg-dala, whereas chronic immobilization stress increased den-dritic branching in pyramidal and stellate neurons withinthe basolateral amygdala, but did not affect dendriticarborization in the central nucleus of the amygdala.83,84

These dendritic reshaping processes depend upon inter-actions between N-methyl-D-aspartate (NMDA) gluta-matergic receptor stimulation and glucocorticoid secretionassociated with repeated stress.82

The depressives with BD and FPDD who show regionalreductions in gray matter volume also show evidence ofhaving increased cortisol secretion and glutamate trans-mission. Specifically, depressives with FPDD or BD aremore likely to show abnormal suppression of cortisolsecretion by dexamethasone and blunted hypoglycemicresponse to insulin8 and to release excessive amounts ofcortisol during stress.8,85 Subjects with FPDD or familialBD also show elevations of glucose metabolism, whichlargely reflects glutamate transmission, in the medial andorbital PFC, amygdala, ventral striatum, and cingulate cor-tex regions that show reductions in gray matter volumeand cellular elements.

Association between structural and metabolic abnormalities

The glucose metabolic signal is dominated by changes inglutamate transmission, and so the findings that gray mat-ter reductions appear to occur specifically in regions thatshow hypermetabolism during depression raise the possi-


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bility that excitatory amino acid transmission plays a rolein the neuropathology of mood disorders.At least 85% to90% of the glucose metabolic measure is accounted for byglutamate transmission from afferent projections origi-nating within the same structure or from distal struc-tures.4,86-89 In the depressed phase of familial MDD and BD,regional cerebral metabolism and CBF are abnormallyincreased in the amygdala, lateral orbital/ventrolateralPFC, ACC anterior to the genu of the corpus callosum(“pregenual” ACC), posterior cingulate cortex, ventralstriatum, medial thalamus, and medial cerebellum.1 Duringeffective antidepressant drug or electroconvulsive therapy,metabolic activity decreases in all of these regions,1,8 com-patible with evidence that these treatments result in desen-sitization of NMDA glutamatergic receptors in the frontalcortex.90 In addition to these areas of increased metabolicactivity, areas of reduced CBF and metabolism in depres-sives relative to controls were found in the ACC ventral tothe genu of the corpus callosum (ie, “subgenual” ACC7)and the dorsomedial/ dorsal anterolateral PFC.19,91,92 Yeteven in these regions, metabolic activity increases duringthe depressive relapse induced by tryptophan depletion (adietary challenge that depletes central 5-HT transmis-sion),93 and metabolism is increased in the subgenual ACCin the unmedicated-depressed phase relative to theunmedicated-remitted phase. In all of these regions whereglucose metabolism is increased in the depressed phaserelative to the remitted phase, reductions in cortex volumeand/or histopathological changes have been found in in vivo MRI studies and/or postmortem studies of MDDand/or BD.The hypothesis that the elevations in glucose metabolismseen in these circuits reflect elevations in glutamatergictransmission is supported by evidence that the anatomicalprojections between affected areas are excitatory innature.The abnormally increased CBF and metabolism inthe ventrolateral and orbital PFC, ventral ACC, amygdala,ventral striatum, and medial thalamus evident in depres-sion (Figure 2) implicate a limbic-thalamo-cortical circuitinvolving the amygdala, the mediodorsal nucleus of thethalamus and the orbital and medial PFC, and a limbic-striatal-pallidal-thalamic circuit involving related parts ofthe striatum and the ventral pallidum along with the com-ponents of the other circuit.95 The first of these circuits canbe conceptualized as an excitatory triangular circuit,whereby the basolateral nucleus of the amygdala and theorbital and medial prefrontal regions are interconnectedby excitatory (especially glutamatergic) projections with

each other and with the mediodorsal nucleus.96-100 Thismeans that increased metabolic activity in these structureswould presumably reflect increased synaptic transmissionthrough the limbic-thalamo-cortical circuit.The limbic-stri-atal-pallidal-thalamic circuit constitutes a disinhibitory sideloop between the amygdala or PFC and the mediodorsalnucleus.The amygdala and the PFC send excitatory pro-jections to overlapping parts of the ventromedial stria-tum.101 This part of the striatum sends an inhibitory pro-jection to the ventral pallidum,102 which in turn sendsGABAergic (GABA, γ-aminobutyric acid), inhibitoryfibers to the mediodorsal nucleus.99

Implications for the pathogenesis of emotion dysregulation

The circuits described above have also been implicatedin the depressive syndromes arising secondary to lesionsor degenerative illnesses. Lesions involving the PFC (eg,tumors or infarctions) and the diseases of the basal gan-glia (eg, Parkinson’s disease or Huntington’s disease) areassociated with higher rates of depression than other sim-ilarly debilitating conditions and result in dysfunction atdistinct points within these circuits and affect synaptictransmission in diverse ways.103 Consistent with thishypothesis, imaging studies of depressive syndromes aris-ing secondary to neurological disorders have generallyshown results that differ from those reported for primarymood disorders. For example, in contrast to the findingsof increased CBF or metabolism in parts of the orbitalcortex in primary depressives, orbital cortex flow isreportedly decreased or not significantly different in sub-jects with depressive syndromes arising secondary toParkinson’s disease, Huntington’s disease, or basal gan-glia infarction relative to nondepressed subjects with thesame illnesses.104-107 Primary and secondary depressivesyndromes may thus involve the same neural network,although the direction of the physiological abnormalitieswithin individual structures may differ across conditions.A common substrate in these cases may be dysfunctionof the PFC-striatal modulation of limbic and visceralfunctions, because the idiopathic neuropathologicalchanges evident in the orbital and medial PFC and ven-tral striatum in primary mood disorders (see above) andthose found in neurodegenerative conditions all appearto be capable of inducing depressive syndromes (eg,Parkinson’s disease, Huntington’s disease, and cere-brovascular disease).


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PFC-amygdalar projections may also play a role in thepathogenesis of depressive and anxiety symptoms inmood disorders.Although the reciprocal PFC-amygdalarprojections are excitatory in nature, these connectionsultimately appear to activate inhibitory interneurons,which, in turn, lead to functional inhibition in the pro-jected field of the amygdala (for PFC-amygdalar projec-tions) or the medial PFC and ventrolateral PFC.96,108-110

The function of the PFC in modulating the amygdalaappears to be impaired in mood disorders, according tofunctional MRI data showing that abnormally sustainedamygdala activity in response to aversive words or sadfaces in MDD is associated with blunted activation ofPFC areas.108,111 Thus, the volumetric and/or histopatho-logical changes evident in the subgenual and pregenualACC, lateral orbital cortex, dorsomedial/dorsal antero-lateral PFC, hippocampal subiculum, amygdala, and ven-tral striatum may interfere with the modulation of emo-tional behavior, as discussed below.

Ventral ACC

The ACC ventral and anterior to the genu of the corpuscallosum (“subgenual” and “pregenual,” respectively;Figure 2) shows complex relationships between CBF,metabolism, and illness state, which appear to beaccounted for by a left-lateralized reduction in the corre-sponding cortex, initially demonstrated by MRI-basedmorphometric measures6,12-16,112 and later by postmortemneuropathological studies of familial BD and MDD.9 Thus,computer simulations that correct the PET data acquiredfrom this region for the partial volume effect of the reduc-tion in gray matter volume measured in MRI scans of thesame subject conclude the “actual” metabolic activity inthe remaining subgenual PFC tissue is increased in depres-sives relative to controls, and decreases to normative lev-els during effective treatment.113 This hypothesis appearsto be compatible with the observations that effective anti-depressant pharmacotherapy results in a decrease in meta-


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Figure 2. Altered metabolism in the prefrontal cortex (PFC) ventral to thegenu of the corpus callosum (c.c.) (ie, subgenual PFC) in mooddisorders. A. Negative voxel t values where glucose metabolismis decreased in depressives relative to controls in coronal (31 mm anterior to the anterior commissure, or y=31) and sagit-tal (3 mm left of midline, or x=-3) planes of a statistical para-metric image comparing depressives relative to controls.7 Thisimage localized an abnormality in the subgenual portion of theanterior cingulate cortex (subgenual ACC7), which was subse-quently shown to be accounted for by a corresponding reduc-tion in cortex volume on the left side (see text). Anterior (or left)is to the left of the image. B. Mean, normalized, glucose meta-bolic values for the left subgenual ACC measured using mag-netic resonance imaging (MRI)–based region-of-interest analy-sis. Metabolism is decreased in depressed subjects with eitherbipolar disorder (BD) or major depressive disorder (MDD) relativeto healthy controls. In contrast, subjects scanned in the manicphase of BD (“bipolar manic”) have higher metabolism thaneither depressed or control subjects in this region. *P<0.025 con-trols versus depressed; †P<0.01 depressed versus manic;‡P<0.05 controls versus manic. Although none of these subjectswere involved in the study that generated the images shown inFigure 3, the mean glucose metabolism in this independent sam-ple of depressives and controls also confirmed the areas ofabnormally increased activity in the depressives in the amygdala,lateral orbital cortex, ventrolateral PFC, and medial thalamus (notshown in A, which only illustrates negative t values corre-sponding to hypometabolic areas in the depressives). Figure 2A reproduced with permission from reference 6: Drevets WC,Price JL, Simpson JR, et al. Subgenual prefrontal cortex abnormalities inmood disorders. Nature. 1997;386:824-827. Copyright © 1997, NaturePublishing Group.Figure 2B reproduced with permission from reference 94: Drevets WC.Neuroimaging and neuropathological studies of depression: Implicationsfor the cognitive emotional manifestations of mood disorders. Curr OpinNeurobiol. 2001;11:240-249. Copyright © 2001, Elsevier.

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bolic activity in this region in MDD,8,10,114 that duringdepressive episodes metabolism shows a positive rela-tionship with depression severity,8,115,116 and that flowincreases in this region in healthy, nondepressed humansduring sadness induced via contemplation of sad thoughtsor memories.114,117,118

The reduction in volume in this region exists early in theillness in familial MDD11 and BD.12 The gray matter deficitmay nevertheless worsen or initially become apparent fol-lowing illness onset based upon preliminary evidence intwins discordant for MDD that the affected twin has asmaller volume than their unaffected cotwin.119 Kimbrellet al120 reported that the subgenual ACC metabolism cor-related inversely with the number of lifetime depressiveepisodes, compatible with the possibility that the reductionin metabolism in this region measured via PET reflects apartial volume effect of a gray matter reduction that wors-ens with repeated illness.In the pregenual ACC, Drevets et al95 initially foundincreased CBF in MDD, and subsequent studies extendedthis observation by demonstrating complex relationshipsbetween pregenual ACC activity and subsequent antide-pressant treatment outcome. Wu et al121 reported thatdepressed subjects whose mood improved during sleepdeprivation showed elevated metabolism in the pregenualACC and amygdala in their pretreatment scans. Mayberget al122 reported that, while metabolism in the pregenualACC was abnormally increased in depressives who sub-sequently responded to antidepressant drugs, metabolismwas decreased in depressives who later had poor treat-ment response. Finally, in a tomographic electroen-cephalographic (EEG) analysis, Pizzagalli et al123 reportedthat depressives who ultimately showed the best responseto nortriptyline showed hyperactivity (higher theta activ-ity) in the pregenual ACC at baseline, compared with sub-jects showing the poorer response. During effective anti-depressant treatment, most PET studies have shown thatpregenual ACC flow and metabolism decrease in post-treatment scans relative to pretreatment scans.1 The find-ing that this region contains histopathological changes inMDD and BD20,64,68 suggests the hypothesis that the abnor-mal reduction in metabolism in treatment-nonresponsivecases reflects more severe reductions in cortex.In rodents and nonhuman primates, the regions thatappear homologous to human subgenual and pregenualACC, namely the infralimbic, prelimbic, and ventral ACCs,have extensive reciprocal connections with areas impli-cated in the expression of behavioral, autonomic, and

endocrine responses to threat, stress, or reward/nonreward,such as the orbital cortex, lateral hypothalamus, amygdala,accumbens, subiculum, ventral tegmental area (VTA),raphe, locus ceruleus, periaqueductal grey (PAG), andnucleus tractus solitarius.7,124 Humans with lesions thatinclude these ventromedial PFC structures show abnor-mal autonomic responses to emotionally provocative stim-uli and an inability to experience emotion related to con-cepts that ordinarily evoke emotion.125 Electricalstimulation of the ACC elicits fear, panic, or a sense offoreboding in humans, and vocalization in experimentalanimals.126 Similarly, rats with experimental lesions of pre-limbic cortex demonstrate altered autonomic, behavioral,and neuroendocrine responses to stress and fear-condi-tioned stimuli.The prelimbic and infralimbic cortices con-tain abundant concentrations of glucocorticoid receptors,which, when stimulated by corticosterone (CORT), reducestress-related HPA activity.127 Lesions of these cortices con-sequently result in exaggerated plasma ACTH and CORTresponses to restraint stress.127 In rats, bilateral or right-lat-eralized lesions of the ACC and prelimbic and infralimbiccortex attenuate sympathetic autonomic responses, stress-induced CORT secretion, and gastric stress pathology dur-ing restraint stress or exposure to fear-conditioned stim-uli.128-130 In contrast, left-sided lesions of this area increasesympathetic autonomic arousal and CORT responses torestraint stress.130 These data suggest that the right sub-genual PFC facilitates expression of visceral responsesduring emotional processing, while the left subgenual PFCinhibits or modulates such responses.130 It is thus notewor-thy that the gray matter reduction in this region in MDDand BD was lateralized to the left side, suggesting that itmay contribute to disinhibition of neuroendocrine andautonomic function in depression.127,131,132

The ventral ACC also appears to participate in processingof behavioral incentive and motivated behavior. Theseareas send efferent projections to the VTA and substantianigra, and receive dense dopaminergic innervation fromVTA.124 In rats, electrical or glutamatergic stimulation ofmedial PFC areas that include prelimbic cortex elicitsburst firing patterns from dopamine (DA) cells in the VTAand increases DA release in the accumbens.113 These pha-sic, burst firing patterns of DA neurons appear to encodeinformation regarding stimuli that predict reward anddeviations between such predictions and actual occurrenceof reward.133 Ventral ACC dysfunction may thus conceiv-ably contribute to disturbances of motivated behavior andhedonic perception in mood disorders.


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Dorsomedial/dorsal anterolateral PFC

Metabolism and CBF are abnormally decreased in thedorsolateral and dorsomedial PFC in MDD.1 The dor-somedial PFC region includes the dorsal ACC92 and anarea rostral to the dorsal ACC involving cortex on themedial and lateral surface of the superior frontal gyrus(approximately corresponding to BA9 and BA32).8,19,91

Postmortem studies of MDD and BD found abnormalreductions in the size of neurons and/or the density ofglia in this portion of BA9,18,20,134 which may account forthe reduction in metabolism in this region in MDD, andfor the failure of antidepressant drug treatment to cor-rect metabolism in these areas.8,19 Nevertheless, currentlyremitted MDD subjects who experience depressiverelapse during tryptophan depletion show increasedmetabolic activity within these areas in the depressedversus the remitted conditions,93 similar to other struc-tures where histopathological and gray matter volumechanges exist in MDD.Flow normally increases in the vicinity of this dorso-medial/dorsal anterolateral PFC in healthy humans asthey perform tasks that elicit emotional responses orrequire emotional evaluations.1 In healthy humans, CBFincreases in this region during anxious anticipation of anelectrical shock to an extent that correlates inversely withchanges in anxiety ratings and heart rate, suggesting thatthis region functions to attenuate emotional expression.In rats, lesions of the dorsomedial PFC result in exag-gerated heart rate responses to fear-conditioned stimuli,and stimulation of these sites attenuate defensive behav-ior and cardiovascular responses evoked by amygdalastimulation,128 although the homologue to these areas inprimates has not been clearly established. In primates,the BA9 cortex sends efferent projections to the lateralPAG and the dorsal hypothalamus through which it maymodulate cardiovascular responses associated with emo-tional behavior.124 It is thus conceivable that dysfunctionof the dorsomedial/dorsal anterolateral PFC may con-tribute to impairments in the ability to modulate emo-tional responses in mood disorders.

Lateral orbital/ventrolateral PFC

In the lateral orbital cortex, ventrolateral PFC, and ante-rior insula, the resting CBF and metabolism have beenabnormally increased in unmedicated subjects with pri-mary MDD (Figure 3).1 The elevated activity in these

areas in MDD appears to be mood-state dependent,95

and, during treatment with somatic antidepressant ther-apies, flow and metabolism decreases in these regions.1

The relationship between depression severity and phys-iological activity in the lateral orbital cortex/ventrolat-eral PFC is complex. While CBF and metabolismincrease in these areas in the depressed phase relative tothe remitted phase of MDD, the magnitude of these mea-sures is inversely correlated with ratings of depressiveideation and severity.95,116,135 Moreover, while metabolicactivity is abnormally increased in these areas in treat-ment-responsive unipolar and bipolar depressives, moreseverely ill or treatment-refractory samples show CBFand metabolic values lower than or not different fromthose of controls.81,139 This inverse relationship betweenorbital cortex/ventrolateral PFC activity and ratings ofdepression severity extends to some other emotionalstates as well. Posterior orbital cortex flow also increasesin subjects with obsessive-compulsive disorder or simpleanimal phobias during exposure to phobic stimuli and inhealthy subjects during induced sadness,140-142 with thechange in posterior orbital CBF correlating inverselywith changes in obsessive thinking, anxiety, and sadness,respectively.These data appear to be consistent with electrophysio-logical and lesion analysis data showing that parts of theorbital cortex participate in modulating behavioral andvisceral responses associated with defensive, emotional,and reward-directed behavior as reinforcement contin-gencies change.124,143,144 The orbital cortex and amygdalasend overlapping projections to each of these structuresand to each other through which they appear to modu-late each other’s neural transmission.124,143,145

Activation of the orbital cortex during depression maythus reflect compensatory attempts to attenuate emo-tional expression or interrupt unreinforced aversivethought and emotion. Consistent with this hypothesis,cerebrovascular lesions of the orbital cortex are associ-ated with an increased risk for depression.146 These obser-vations also suggest that the reduction of CBF andmetabolism in the orbital cortex and ventrolateral PFCduring antidepressant drug treatment may not be a pri-mary mechanism through which such agents amelioratedepressive symptoms. Instead, direct inhibition of patho-logical limbic activity in areas such as the amygdala andventral ACC may attenuate the mediation of depressivesymptoms.8 The orbital cortex neurons may thus “relax,”as reflected by the return of metabolism to normal lev-


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els, as antidepressant drug therapy attenuates the patho-logical limbic activity to which these neurons putativelyrespond.145

The amygdala

In the amygdala, neurophysiological activity is alteredboth at rest and during exposure to emotionally valencedstimuli in some depressive subgroups.The basal CBF andmetabolism are elevated in mood-disordered subgroupswho meet criteria for FPDD (Figure 3),8,95,135,136 for MDDmelancholic subtype,148 type II or nonpsychotic type IBD,136,149 or for those who are responsive to sleep depri-vation.121 In contrast, metabolism has not been abnormalin unipolar depressives meeting criteria for depressionspectrum disease,136,137 or in MDD samples meetingDiagnostic and Statistical Manual of Mental HealthDisorders (DSM) criteria,150-152 although the interpreta-tion of the latter results was confounded by technicalproblems that reduced sensitivity for measuring amyg-dala activity.136 During antidepressant treatment that bothattenuates depressive symptoms and prevents relapse,amygdala metabolism decreases toward normative lev-els.8

Functional imaging data acquired as subjects view emo-tionally valenced stimuli that normally activate the amyg-dala also demonstrate altered physiological responses inMDD. In the left amygdala, the hemodynamic responseto viewing fearful faces was blunted in depressed chil-dren153 and depressed adults,94 consistent with the eleva-tion of basal CBF and metabolism in the left amygdalain such cases (physiologically activated tissue is expectedto show an attenuation of further rises in the hemody-namic/metabolic signal in response to tasks that normallyengage the same tissue). The duration of the amygdalaresponse to emotionally valenced stimuli is also abnor-mally prolonged in response to sad stimuli in depression.Drevets et al94 observed that, although the initial amyg-dala CBF response to sad faces was similar in depressivesand controls, this response habituated during repeated


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Figure 3. Areas of abnormally increased blood flow in subjects withmajor depressive disorder (MDD). The image sections shownare from an image of t values, produced by a voxel-by-voxelcomputation of the unpaired t statistic to compare regionalCBF between a depressed sample selected according to crite-ria for familial pure depressive disease (FPDD) (n=13) and ahealthy control sample (n=33).95 The positive t values showncorrespond to areas where flow is increased in the depressivesrelative to the controls. The abnormal activity in these regionswas replicated using glucose metabolism imaging in indepen-dent subject samples.8,135,136 A. Sagittal section at 17 mm left ofmidline illustrating areas of increased CBF in depression in theamygdala and orbital cortex. B. Area of increased flowextended through the lateral orbital cortex to the ventrolateralprefrontal cortex (VLPFC) and anterior insula.8,95 The x coordi-nate locates sagittal sections in millimeters to the left of mid-line. The PET images in A and B from which the t image wasgenerated have been stereotaxically transformed to the coor-dinate system of Talairach and Tournoux,137 from which the cor-responding atlas outline is shown. Anterior is left. Figure 3A reproduced with permission from reference 126: Price JL,Carmichael ST, Drevets WC. Networks related to the orbital and medialprefrontal cortex: a substrate for emotional behavior? Prog Brain Res.1996;107:523-536. Copyright © 1996, Elsevier.Figure 3B reproduced with permission from reference 138: Drevets WC,Videen TO, Snyder AZ, MacLeod AK, Raichle ME. Regional cerebralblood flow changes during anticipatory anxiety. Soc Neurosci Abstr.1994;20:368. Copyright © 1994, Society for Neuroscience.

Amygdala

Orbital cortex

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x=-17

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exposure to the same stimuli in the controls, but not inthe depressives over the imaging period. Similarly, Siegleet al44 reported that hemodynamic activity increased inthe amygdala during exposure to negatively valencedwords to a similar extent in depressives and controls, but,while the hemodynamic response rapidly fell to baselinein the controls, it remained elevated in the depressives.The amygdala plays major roles in organizing otherbehavioral, neuroendocrine, and autonomic aspects ofemotional and stress responses to experiential stimuli.For example, the amygdala facilitates stress-related cor-ticotropin-releasing hormone (CRH) release154 and elec-trical stimulation of the amygdala in humans increasescortisol secretion,155 suggesting a mechanism via whichexcessive amygdala activity may participate in inducingthe CRH and cortisol hypersecretion that is evident inMDD. In PET studies of MDD and BD, CBF and metab-olism in the left amygdala correlates positively withstressed plasma cortisol secretion, which may reflect theeffect of either amygdala activity on CRH secretion orcortisol or CRH on amygdala function.136

If the reduction in amygdala volume is associated withreductions in synaptic contacts formed by afferent projec-tions from regions known to modulate amygdala function,then amygdala neuronal activity may become disinhibited.The above reports that amygdala blood flow and metabo-lism are abnormally elevated and hemodynamic responsesto emotional stimuli are abnormally persistent in MDDsupport this hypothesis. Notably, Siegle et al44 reported thatthe abnormally prolonged hemodynamic responses of theamygdala to sad words occurred particularly in the MDDsubjects who had reduced amygdala volumes. If the neu-rotrophic effects of mood-stabilizing drugs restore andprotect modulatory connections formed between theamygdala and cortex,1 then the volumetric changesobserved during treatment may contribute to their ther-apeutic effects in mood disorders.

Abnormalities in anatomically related limbic and subcortical structures

In the medial thalamus and ventral striatum, CBF andmetabolism are abnormally elevated in the depressedphase of MDD and BD, and decrease during antide-pressant pharmacotherapy.8,95,134,136,154,156,157 Several groupsalso reported abnormally increased CBF in the posteriorcingulate cortex in the unmedicated, depressed phase ofMDD.8,112,158 Bench et al158 specifically reported that the

elevation of posterior cingulate flow in depressives rela-tive to controls correlated positively with anxiety ratings.Exposure to aversive stimuli of various types results inincreased physiological activity in the posterior cingulatecortex.159 The posterior cingulate cortex sends majoranatomical projections to the pregenual ACC.160

Neuroreceptor imaging abnormalitiesin mood disorders

Neuroreceptor imaging studies of mood disorders havedemonstrated reductions in 5-HT1A receptor binding inmood disorders, which would appear to hold major impli-cations for alterations in neuroplasticity in these condi-tions. Both presynaptic (in the raphe) and postsynaptic(insula, anterior, and posterior cingulate cortices, parieto-occipital cortex, orbital/ventrolateral PFC) 5-HT1A bind-ing is abnormally decreased in MDD and panic disorder(irrespective of the current presence of comorbid depres-sion), and postsynaptic 5-HT1A receptor binding is alsodecreased in BD.85,116,161-165 The magnitudes of these differ-ences have been similar to those found by postmortemstudies of primary mood-disordered samples17,165 anddepressed suicide victims.166 These data are also compat-ible with results of studies showing that MDD and panicdisorder subjects show blunted thermic and adrenocor-ticotropin/cortisol responses to 5-HT1A receptor agonistchallenge.85,162

The 5-HT1A receptor plays major roles in the neuroplas-ticity involving serotonergic and other neurons.167,168 Inaddition, during fetal development and subsequently dur-ing 5-HT neuronal injury, stimulation of astrocyte andradial glial cell-based 5-HT1A receptors results in releaseof the trophic factor S100β, which promotes 5-HT neu-ronal arborization.168,169 If glial function is reduced during5-HT system development in BD and MDD, it is con-ceivable that arborization of the 5-HT neurons may beattenuated, potentially reflected by the widespreadreductions of 5-HT transporter and postsynaptic 5-HT1A

receptor expression seen in MDD.17,85,116,163,166,170 Such ahypoplastic process may also underlie the finding that thearea expressing 5-HT1A receptors in the dorsal raphenucleus is abnormally decreased in depressed suicides.166

It is conceivable that the persistently increased anxietybehaviors and the exaggerated fear and behavioraldespair responses shown by 5-HT1A receptor knockoutmice at least partly reflect effects of deficient 5-HT1A

receptor function on neuroplasticity during neurodevel-


210

opment.162 It remains unclear, however, whether thereduction in 5-HT1A receptor function and expressionconstitutes a neurodevelopmental or an acquired abnor-mality in mood disorders.165

Concluding remarks

The convergent results from studies of mood disordersconducted using neuroimaging, lesion analysis, and post-

mortem techniques support models in which the signsand symptoms of major depression can emanate fromdysfunction within PFC, striatal, and brain stem systemsthat modulate emotional behavior.Antidepressant ther-apies may compensate for this dysfunction by attenuat-ing the pathological limbic activity that mediates suchsymptoms,9 and by increasing genetic transmission ofneurotrophic factors that exert neuroplastic effects withinthe pathways modulating emotional expression.14 ❏


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Neuroplasticidad en los trastornos afectivos

Los estudios neuropatológicos y de neuroimágenes en la depresión mayor (DM) y en el trastorno bipolar (TB)han identificado anormalidades de la estructura cerebral en áreas de la corteza prefrontal, la amígdala, elcuerpo estriado, el hipocampo, el giro parahipocámpico y el núcleo del rafe. Estas anormalidades estructu-rales en las neuroimágenes se mantienen más allá de los episodios de la enfermedad y las evidencias preli-minares sugieren que en algunos casos ellas pueden aparecer antes del inicio de los episodios depresivos ensujetos con alto riesgo familiar de DM. En otros casos, la magnitud de la anormalidad se correlaciona con eltiempo que lleva la depresión. Estudios histopatológicos postmortem de estas regiones han mostrado dis-minuciones anormales de marcadores sinápticos y de células gliales y, en raros casos, disminución de neuro-nas en la DM y el TB. Muchas de las regiones afectadas por estas alteraciones estructurales muestran unaumento del metabolismo de la glucosa durante los episodios depresivos. Dado que la señal metabólica deglucosa está comandada por la transmisión glutamatérgica, estos datos sustentan el argumento a favor delincremento de la transmisión del aminoácido excitatorio en los circuitos límbico-córtico-estriato-pálido-talá-micos durante la depresión. Algunos de los sujetos de las muestras en que se encontraron estas anormalida-des metabólicas también tuvieron cifras elevadas de cortisol plasmático en respuesta al estrés. La apariciónconcomitante del aumento de la transmisión glutamatérgica y de la hipersecreción de cortisol incrementa laprobabilidad que las disminuciones del volumen de sustancia gris en los sujetos con depresión se deba enparte a los procesos equivalentes a los de la atrofia dendrítica inducida por el estrés crónico en roedores adul-tos, lo que depende de las interacciones entre el aumento de la secreción de glucocorticoides y la estimula-ción del receptor de glutamato N-metil-D-aspártico (NMDA). Algunos antidepresivos y estabilizadores delánimo que ejercen efectos neurotróficos en roedores parece que revierten o disminuyen las anormalidadesdel volumen de sustancia gris en humanos con trastornos afectivos. Estos efectos neurotróficos pueden estarrelacionados íntegramente con los efectos terapéuticos de dichos fármacos, ya que se sabe que las regionesafectadas por alteraciones estructurales en los trastornos afectivos tienen un papel importante en la modu-lación de las respuestas endocrina, autonómica, conductual y emocional que se experimenta frente al estrés.

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65. Eastwood SL, Harrison PJ. Synaptic pathology in the anterior cingulatecortex in schizophrenia and mood disorders. A review and a Western blotstudy of synaptophysin, GAP 43, and the complexins. Brain Res Bull.2001;55:569-578.66. Cotter DR, Pariante CM, Everall IP. Glial cell abnormalities in major psy-chiatric disorders: the evidence and implications. Brain Res Bull. 2001;55:585-595.67. Bowley MP, Drevets WC, Öngür D, Price JL. Low glial numbers in theamygdala in mood disorders. Biol Psychiatry. 2002;52:404-412.68. Benes FM, Vincent SL, Todtenkopf M. The density of pyramidal and non-pyramidal neurons in ACC of schizophrenic and bipolar subjects. BiolPsychiatry. 2001;50:395-406.69. Honer WG, Falkai P, Chen C, Arango V, Mann JJ, Dworks AJ. Synapticand plasticity-associated proteins in anterior frontal cortex in severe men-tal illness. Neuroscience. 1999;91:1247-1255.70. Rajkowska G, Halaris A, Selemon LD. Reductions in neuronal and glialdensity characterize the dorsolateral prefrontal cortex in bipolar disorder.Biol Psychiatry. 2001;49:741-752.71. Cotter D, Mackay D, Chana G, Beasley C, Landau S, Everall I. Reducedneuronal size and glial cell density in area 9 of the dorsolateral prefrontalcortex in subjects with major depressive disorder. Cereb Cortex. 2002;12:386-394. 72. Ludwin SK. The function of perineuronal satellite oligodendrocytes: animmunohistochemical study. Neuropathol Appl Neurobiol. 1984;10:143-149.73. Uranova N, Orlovskaya D, Vikhreva O, et al. Electron microscopy ofoligodendroglia in severe mental illness. Brain Res Bull. 2001;55:597-610.74. D’Amelio F, Eng LF, Gibbs MA. Glutamine synthetase immunoreactivitypresent in oligodendroglia of various regions of the central nervous system.Glia. 1990;3:335-341.


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Neuroplasticité dans les troubles de l’humeur

Les études de neuropathologie et de neuro-imagerie des troubles dépressifs majeurs (TDM) et des troubles bipolaires (TB)ont identifié des anomalies de la structure cérébrale dans les aires du cortex préfrontal, de l’amygdale, du striatum, del’hippocampe, du gyrus parahippocampique et du noyau du raphé. Ces anomalies structurelles à l’image persistent à tra-vers les épisodes de la maladie et des arguments antérieurs suggèrent qu’elles peuvent se produire avant l’apparition desépisodes dépressifs chez les sujets à haut risque familial de TDM. Dans d’autres cas, l’importance des anomalies serait liéeà la durée de la dépression. Des études histopathologiques post mortem de ces régions ont montré des réductions anor-males des marqueurs synaptiques et des cellules gliales et, dans quelques rares cas, des diminutions du nombre des neu-rones dans les TDM et les TB. De nombreuses régions atteintes par ces anomalies structurelles présentent un métabolismedu glucose augmenté pendant ces épisodes dépressifs. La transmission glutamatergique dominant le signal métaboliquedu glucose, ces données confortent un autre argument à savoir que la transmission de l’acide aminé excitateur est élevéedans les circuits limbiques-corticaux-striataux-pallidaux-thalamiques pendant la dépression. Certains échantillons, chezdes sujets chez qui on a trouvé des anomalies métaboliques, ont également montré des concentrations anormalementélevées de cortisol plasmatique en réponse au stress. L’apparition concomitante de l’augmentation de la transmission glu-tamatergique et de l’hypersécrétion de cortisol accroît la possibilité que les réductions de volume de la substance grisechez ces personnes dépressives soient en partie justifiées par des processus identiques à ceux de l’atrophie dendritiqueinduite par le stress chronique chez les rongeurs adultes, qui dépend des interactions entre la sécrétion élevée des glu-cocorticoïdes et la stimulation du récepteur glutamatergique N-méthyl-D-aspartate (NMDA). Certains médicaments anti-dépresseurs et thymorégulateurs exerçant des effets neurotrophiques chez les rongeurs semblent inverser ou atténuerles anomalies du volume de la substance grise chez les humains atteints de troubles de l’humeur. Ces effets neu-rotrophiques peuvent être intégralement liés aux effets thérapeutiques de tels médicaments, parce qu’il est reconnu queles régions affectées par des anomalies structurelles au cours des troubles de l’humeur jouent un rôle majeur dans la mo-dulation des réponses aux agents stressants au niveau de l’expérience endocrine, autonome, comportementale et émo-tionnelle.

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epression is a common, chronic, and often dis-abling psychiatric illness, which is estimated to affect 5%to 10% of the population. It frequently appears in earlylife, has a chronic course, and is considered a risk factorfor other medical illnesses, such as coronary vascular dis-ease, diabetes, and osteoporosis.This is not altogether sur-prising given the extensive bidirectional “mind-body”interactions mediated via the autonomic nervous system,immune system, and a host of neuroendocrine factors.According to the World Health Organization (WHO),depression is the leading global cause of years of life livedwith disability and the fourth leading cause of disability-adjusted life-years. Disability-adjusted life-years isdefined as the reduction in an individual’s productive life,and takes into account premature mortality.1,2

Considering the high morbidity and mortality associatedwith depression, it is unfortunate that the psychologicaland neurobiological underpinnings of depression havenot been specifically defined.Although major depressionis currently diagnosed by means of a diagnostic system(Diagnostic and Statistical Manual of Mental HealthDisorders, Fourth Edition [DSM-IV]) based upon phe-nomenology, this disorder most likely embodies a het-erogeneous set of disorders with multiple causes.Therefore, one of the major goals of current and futureresearch on depression is the development of a diagnos-tic system based on etiology.3

This goal is becoming increasingly closer to reality due torecent progress in the identification of neural circuits,neurochemicals, and signal transduction mechanisms

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Cellular plasticity and resilience and thepathophysiology of severe mood disordersDennis S. Charney, MD; Georgette DeJesus, MD; Husseini K. Manji, MD

Keywords: mood disorder; depression; neuroplasticity; stress; resilience; brainmorphology

Author affiliations: National Institute of Mental Health, Bethesda, Md, USA

Address for correspondence: Prof Dennis S. Charney, National Institute ofMental Health, 15K North Drive, Room 101, Bethesda, MD 20892-2670, USA (e-mail: [email protected])

Recent advances in the identification of the neural cir-cuits, neurochemicals, and signal transduction mecha-nisms involved in the pathophysiology and treatment ofmood disorders have led to much progress towardunderstanding the roles of genetic factors and psy-chosocial stressors. The monoaminergic neurotransmit-ter systems have received the most attention, partlybecause of the observation that effective antidepressantdrugs exert their primary biochemical effects by regu-lating intrasynaptic concentrations of serotonin and nor-epinephrine. Furthermore, the monoaminergic systemsare extensively distributed throughout the network oflimbic, striatal, and prefrontal cortical neuronal circuitsthought to support the behavioral and visceral manifes-tations of mood disorders. Increasing numbers of neu-roimaging, neuropathological, and biochemical studiesindicate impairments in cellular plasticity and resiliencein patients who suffer from severe, recurrent mood dis-orders. In this paper, we describe studies identifying pos-sible structural, functional, and cellular abnormalitiesassociated with depressive disorders, which are poten-tially the cellular underpinnings of these diseases. Wesuggest that drugs designed to enhance cellular plastic-ity and resilience, and attenuate the activity of mal-adaptive stress-responsive systems, may be useful for thetreatment of severe mood disorders. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:217-225.

underlying the pathophysiology and treatment of depres-sive illness.4,5 Advances toward specifying the contribu-tion of genetic factors,6 psychosocial stressors,7,8 andgene–environment interactions to susceptibility todepression are also taking place.9,10 It is anticipated that,in the next few years, combined use of genomic and pro-teomic strategies to refine complex psychiatric diseasesinto mechanism-based subcategories may ultimatelyfacilitate the matching of specific target-based therapiesto particular markers in certain patient subgroups.11

Of all brain systems, the monoaminergic neurotransmit-ter systems have received the greatest attention in neu-robiological studies of depressive disorders.The implica-tion of these systems in depression is based on severalobservations: (i) effective antidepressant drugs exert theirprimary biochemical effects by regulating intrasynapticconcentrations of serotonin and norepinephrine; and (ii)antihypertensives that deplete these monoamines some-times precipitate depressive episodes in susceptible indi-viduals.Furthermore, the monoaminergic systems are extensivelydistributed throughout the network of limbic, striatal, andprefrontal cortical (PFC) neuronal circuits implicated inthe behavioral and visceral manifestations of mood disor-ders.12 Over the past 40 years, clinical studies have aimedto uncover specific flaws in these neurotransmitter systemsin mood disorders by using various biochemical and neu-roendocrine approaches. In fact, assessment of cere-brospinal fluid (CSF) chemistry, neuroendocrine responsesto pharmacological challenge, and neuroreceptor andtransporter binding have demonstrated a number ofabnormalities of the serotonergic, noradrenergic, and otherneurotransmitter and neuropeptide systems in mood dis-orders.Although such studies have been useful in the past, theyhave proved to be of limited value in clarifying the par-ticular pathophysiology of depressive disorders. In orderto clarify the biological underpinnings of these disorders,

there should be an appreciation of the episodic and oftenintense mood disturbance, which can become progressiveover the time. Furthermore, the phenotypic expression ofthe disease involves not only episodic and often profoundmood disturbances, but also a constellation of cognitive,motor, autonomic, endocrine, and sleep/wake abnormal-ities.Additionally, while most antidepressants exert theirinitial effects by increasing the levels of serotonin and/ornorepinephrine in the synapse, clinical antidepressanteffects exclusively result after chronic administration(days to weeks). This suggests that a cascade of down-stream effects is ultimately responsible for the clinicalantidepressant effects of these medications.These obser-vations have led to the recognition that, althoughmonoaminergic neurotransmitter system dysfunctionundoubtedly plays an important role in mediating somefacets of the pathophysiology of depressive disorders,additional fundamental alterations in cellular plasticitycascades are most likely involved.13-15

The functional impairments during mood episodes havelong been recognized; however, there is increasing evi-dence of significant interepisode impairment as well.Thedevastation of these disorders is further complicated bythe fact that the medications currently used for their treat-ment are associated with variable rates of efficacy and notintolerable side effects.An appreciation for both the needfor more efficacious treatment for mood disorders and theabsence of significant advances in the development oftruly innovative therapeutics has led to the investigationof intracellular signaling cascades and their role in thepathophysiology and treatment of mood disorders.Thus,while traditionally viewed exclusively as neurochemicaldisorders, recent evidence suggests the presence of impair-ments of cellular plasticity cascades, which produce notonly functional, but also morphological impairments; thisevidence has generated considerable excitement amongthe clinical neuroscience community and is reshapingviews about the neurobiological underpinnings of thesedisorders. Thus, as we discuss in detail below, increasingneuroimaging, neuropathological, and biochemical stud-ies suggest impairments in cellular plasticity and resiliencein patients who suffer from severe, recurrent mood disor-ders. The term “neuroplasticity” encompasses diverseessential processes by which the brain perceives, adapts to,and responds to a variety of internal and external stimuli.Manifestations of neuroplasticity in the adult central ner-vous system (CNS) include alterations of dendritic func-tion, synaptic remodeling, long-term potentiation (LTP),


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Selected abbreviations and acronymsBDNF brain-derived neurotrophic factorCREB cyclic adenosine monophosphate (cAMP) response

element binding proteinERK extracellular response kinaseHPA hypothalamo-pituitary-adrenal (axis)LTP long-term potentiationMAP mitogen-activated proteinPFC prefrontal cortex

axonal sprouting, neurite extension, synaptogenesis, andneurogenesis. In this perspective paper, we describe stud-ies identifying possible structural, functional, and cellularabnormalities associated with depressive disorders—thepotential cellular underpinnings of these micro- andmacromorphological brain changes.We suggest that ther-apeutics designed to enhance cellular plasticity andresilience, and to attenuate the activity of maladaptivestress-responsive systems may have considerable utility forthe treatment of severe mood disorders.

Brain imaging studies in depressed patients

Positron emission tomography (PET) imaging studieshave unveiled various abnormalities of glucose metabo-lism and regional cerebral blood flow (CBF) in limbic andPFC structures in patients with mood disorders.Althoughsome disagreement exists regarding the specific locationsand the direction of some of these abnormalities, unmed-icated subjects with familial major depression show a con-sistent increase in regional CBF and metabolism in theamygdala, orbital cortex, and medial thalamus, anddecreased metabolism and CBF in the dorsomedial/dor-sal anterolateral PFC and anterior cingulate cortex ven-tral to the genu of the corpus callosum (ie, subgenual PFC)relative to healthy controls.16,17 These abnormalities suggestthat limbic-thalamic-cortical and limbic-cortical-striatal-pallidal-thalamic circuits, involving the amygdala, orbitaland medial PFC, and anatomically related parts of thestriatum and thalamus are involved in pathophysiology ofdepression. Additionally, these circuits have been impli-cated more generally in emotional behavior by electro-physiological, lesion analysis and brain mapping studies ofhumans and experimental animals.12,15

Some of these abnormalities reverse during symptomremission, suggesting that there are areas where neuro-physiological activity may increase or decrease in order tomediate or respond to the emotional and cognitive man-ifestations of depression. However, CBF and metabolismdo not completely normalize during effective antidepres-sant treatment in many of these areas.Morphometric magnetic resonance imaging (MRI) andpostmortem investigations have also demonstrated alter-ations in cerebral structure that persist regardless ofmood state and may contribute to the correspondingabnormalities of metabolic activity.16,17 Structural imagingstudies have shown reduction in gray matter volumes inareas of the orbital and medial PFC, ventral striatum and

hippocampus, and enlargement of third ventricles inmood-disordered patients when compared to healthycontrols. Complementary postmortem studies havedemonstrated abnormal decreases in cortex volume, glialcell counts, and/or neuron size in the subgenual PFC,orbital cortex, dorsal anterolateral PFC, and amyg-dala.12,14,15-17

It remains unclear whether these alterations in brain struc-ture represent developmental abnormalities that mayincrease an individual’s susceptibility to abnormal moodepisodes, compensatory changes to other pathogenicprocesses, or the sequelae of recurrent affective episodesper se.The clarification of these issues will in part dependon investigations that outline the onset of such abnormal-ities within the illness course, as well as determine whetherthey precede depressive episodes in individuals with a highfamilial risk for mood disorders. The lack of completereproducibility among neuroimaging or postmortem stud-ies is not altogether surprising, and the disparities likelyrepresent variations in experimental design and in patientpopulations. Further research is needed in order to under-stand whether more specifically defined subtypes ofdepression or mood disorders are associated with any spe-cific abnormality.18 The marked reduction in glial cells inthese regions has been especially interesting, given thetremendous recent progress in our understanding of thecritical roles of glial cells in regulating neuronal function.Thus, there is now compelling evidence that radial glialcells have the potential not only to guide newly born neu-rons, but also to self-renew and to generate both neuronsand astrocytes. Furthermore, recent data have shown thatastrocytes increase the number of mature, functionalsynapses on CNS neurons sevenfold, demonstrating thatCNS synapse number can be profoundly regulated by glia.Glial cells are also known to play critical roles in regulat-ing synaptic glutamate levels, CNS energy homeostasis,and the liberation of trophic factors, which in turn partic-ipate in the development and maintenance of synaptic net-works formed by neuronal and glial processes.16,17,19-22 Glialfunction abnormalities could thus prove essential to struc-tural plasticity impairments and overall pathophysiologyof mood disorders.

Stress and brain morphology

The majority of studies of adaptive neuronal plasticity inresponse to stress, as well as hypothalamo-pituitary-adrenal (HPA) axis hormones, have focused on the hip-

Cellular plasticity and resilience and severe mood disorders - Charney Dialogues in Clinical Neuroscience - Vol 6 . No. 2 . 2004

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pocampus.This is partly due to early studies on neuronalpopulations of the limbic brain regions, including the den-tate gyrus granule cell layer, and the CA1 and CA3 pyra-midal cell layers. These cell layers and their connections(mossy fiber pathway and Schaffer collateral) have longbeen used as cellular models of learning and memory (ie,LTP). However, it is clear that stress and glucocorticoidsalso influence the survival and plasticity of neurons inother brain regions (such as PFC, vide infra) that have notyet been studied in the same detail as the hippocampus.Dendritic remodeling of hippocampal neurons is one ofthe best-characterized effects of stress on cellular mor-phology.23,24 Dendritic remodeling is deeply observed in theCA3 pyramidal neurons as atrophy-decreased number andlength of the apical dendritic branches.This stress-inducedatrophy of CA3 neurons results after 2 to 3 weeks of expo-sure to restraint stress or more long-term social stress, andhas been observed in rodents and tree shrews.23,24 Althoughthe effects of chronic stress in the CA3 layer tend to bemost pronounced, slight structural changes are also foundin the CA1 and dentate gyrus following a 1-month multi-ple stress paradigm.25 Profound alterations in mossy fiberterminal morphology and significant synapse loss have alsobeen described.The hippocampus has a very high concen-tration of glutamate and expresses both glucocorticoid(GR) and mineralcorticoid (MR) corticosteroid receptors,though these may be relatively scarce in the hippocampusof primates,26,27 and more prevalent in cortical regions. MRactivation in the hippocampus (CA1) is associated withreduced calcium currents, while GR activation leads to increased N-methyl-D-asparate (NMDA) receptorthroughput and increased calcium currents that could pre-dispose to neurotoxicity. In fact, increasing evidence impli-cates glutamatergic neurotransmission in stress-inducedhippocampal atrophy and death.Histopathological changes in rat PFC after corticos-terone administration have recently been describedalthough this area has not been as comprehensively stud-ied as the hippocampus. Using a Golgi-Cox procedure,Wellman28 examined pyramidal neurons in layers II andIII of the medial PFC, quantifying dendritic morphologyin three dimensions. In this study, he demonstrated a sig-nificant rearrangement of apical dendrites in corticos-terone-treated animals, with an increase in the dendriticmaterial proximal to the soma and a decrease in distaldendritic material.This suggests that stress may result ina significant reorganization of the apical dendritic arborin medial PFC in rats.

It is noteworthy that glucocorticoids may exert delete-rious effects on neural plasticity and morphology, since asignificant percentage of mood disorder patients showsome form of HPA axis activation. It has been hypothe-sized that the depressive subtypes most frequently asso-ciated with HPA activation are also the most likely to beassociated with reductions in hippocampal volume.23

Many patients with Cushing’s disease, in which pituitarygland adenomas cause cortisol hypersecretion, also showmarked depressive symptoms, as well as hippocampalatrophy. Moreover, some patients with Cushing’s diseasealso show reduced hippocampal volumes, correlatinginversely with plasma cortisol concentrations. Correctivesurgical treatment results in an enlargement of hip-pocampal volume in proportion to the treatment-associ-ated decrease in urinary free cortisol concentrations.29,30

HPA axis hyperactivity in mood disorder patients hasbeen demonstrated by a variety of techniques/measures,including increased cortisol levels in plasma (especiallyat the circadian nadir), urine, and CSF, increased cortisolresponse to adrenocorticotropic hormone (ACTH),blunted ACTH response to corticotropin-releasing hor-mone (CRH) challenge, enlarged pituitary and adrenalglands, and reduced CRH receptor density in the brain(presumably reflecting a compensatory downregulationto sustained CRH elevations) at postmortem examina-tion. In both unipolar and bipolar patients, reduced cor-ticosteroid receptor feedback has been implicated in thisprocess by challenge studies with dexamethasone anddexamethasone plus CRF.31,32

The results of recent longitudinal studies investigatingthe effects of early life stress and inherited variation inmonkey hippocampal volumes underscore the need forcaution when interpreting the clinical neuroimaging stud-ies described above. These longitudinal studies in mon-keys randomized paternal half-siblings (monkeys raisedapart from one another by different mothers in theabsence of fathers) to one of three postnatal conditionsthat interfered with various facets of early maternal care.Paternal half-siblings with small adult hippocampal vol-umes showed an initial larger relative increase in cortisollevel following removal of all mothers after weaning.33

However, plasma cortisol levels 3 and 7 days later didcorrelate with hippocampal size. These studies suggestthat small hippocampal volume also reflects an inheritedtrait, and emphasize the need for caution in the simpleattribution of causality in the cross-sectional morpho-metric studies of the hippocampus in humans.


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Stress effects on cellular plasticity and resilience

In addition to the cellular mechanisms described above,it is now clear that stressors may exert major effects on cellular plasticity and resilience by regulating theexpression and function of growth factor cascades.33,34

Neurotrophic factors (eg, nerve growth factor [NGF]and brain-derived neurotrophic factor [BDNF]), as wellas cytokines, insulin-like growth factor–1 (IGF-1), andglial-derived neurotrophic factor (GDNF), increase cellsurvival.35,36 These factors promote cell survival throughthe suppression of intrinsic, cellular apoptotic machin-ery, rather than by inducing cell survival pathways. Thisoccurs via binding of these factors to membrane recep-tors and regulation of intracellular signal transductionpathways that can control apoptosis, including regula-tion of Bcl-2 family members. Mitogen-activated protein(MAP) kinase cascade, the phosphatidylinositol-3 kinase(PI-3K)/Akt pathway, and the PI-3K cascade are cur-rently thought to be responsible for mediating many ofthe effects of neurotrophic factors.37

The family of receptors known as Trks, which contain anintrinsic tyrosine kinase domain, mediates neurotrophicfactor signaling. Nerve growth factor binds to the TrkAreceptor, while BDNF binds to TrkB. The resultingreceptor activation results in phosphorylation and acti-vation of effectors, including PI-3K, as well as proteincoupling leading to of the MAP kinase cascade activa-tion. Recent studies have shown that MAP kinase cas-cade activation can inhibit apoptosis by inducing thephosphorylation of Bad (a major proapoptotic protein)and increasing the expression of Bcl-2 (a major anti-apoptotic protein).This increased Bcl-2 expression likelyinvolves a protein known as the cyclic adenosinemonophosphate (cAMP) response element binding pro-tein (CREB).38,39 Phosphorylation of Bad takes place viaactivation of a downstream target of the MAP kinasecascade, ribosomal S-6 kinase (Rsk). This phosphoryla-tion by Rsk promotes the inactivation of Bad.Additionally, Rsk activation mediates the actions of theMAP kinase cascade and neurotrophic factors on theexpression of Bc1-2. Rsk can phosphorylate CREB,leading to induction of Bcl-2 gene expression. A grow-ing body of evidence indicates that not only is Bcl-2 neu-roprotective, but also that it exerts neurotrophic effectsand promotes neurite sprouting, neurite outgrowth, andaxonal regeneration.40-43

Recently, it has been demonstrated that chronic stress (21days’ foot-shock) induces a marked and persistent hyper-phosphorylation of an extracellular response kinase(ERK) in higher PFC layer dendrites, while phospho-CREB was reduced in the frontal cortex and other cor-tical regions.44 Since CREB is phosphorylated and acti-vated by phospho-ERK1/2 directly, this reductionindicates that chronic stress could downregulate CREBphosphorylation indirectly, and subsequently downregu-late the transcription of some genes such as Bcl-2 andBDNF. In this context, it is worth mentioning that arecent study revealed that severe stress exacerbatesstroke outcome by suppressing Bcl-2 expression.45 In thisstudy, stressed mice expressed approximately 70% lessBcl-2 mRNA than unstressed mice following stroke. Inaddition, stress greatly exacerbated stroke in control mice,but not in transgenic mice that express increased neuronalBcl-2. High corticosterone concentrations were signifi-cantly correlated with a greater stroke size in wild-typemice, but not in transgenic mice overexpressing Bcl-2.Therefore, enhanced Bcl-2 expression seems to offset thepotentially harmful consequences of stress-induced neu-ronal endangerment, and suggests that pharmacologicallyinduced upregulation of Bcl-2 may be useful in the treat-ment of a various disorders that have been linked toendogenous or acquired impairments of cellularresilience. It is now clear that the neurotrophic fac-tor–ERK1/2–MAPK–Bcl-2 signaling cascade has a criti-cal role in cell survival in the CNS and that a fine balanceexists between the levels and activities of cell survival andcell death factors. BDNF–ERK1/2–CREB–Bcl-2 cascadedysregulation may be a key mechanism via which pro-longed stress induces atrophy of select vulnerable neu-ronal subpopulations, distal dendrites, or both.Althoughdysregulation of this cascade most likely results indecreased neuronal survival, the differential survival islikely modulated not only by region-specific expressionof protective factors, but also by the network propertiesof vulnerable structures. Therefore, it is likely that thedynamics of the impairments of cellular plasticity andresilience are determined by intrinsic properties of theaffected regions.There is emerging evidence—mainly from postmortemstudies—supporting a role for abnormalities in neu-rotrophic signaling pathways in depression. Decreased lev-els of CREB, BDNF, and the TrkB receptor have beendescribed in suicide victims.46-48 Depressed individuals mayalso have genetic abnormalities in CREB and BDNF.


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Sequence variations in the CREB1 gene have beenobserved in depressed women.6 A coding variant of BDNFmay be associated with the personality trait of neuroticism,which is a risk factor for depression.49 Furthermore, tworecent studies50,51 suggest that a polymorphism in the pro-BDNF molecule is associated with bipolar disorder (a con-dition in which depressive episodes are accompanied bymanic episodes). This polymorphism is associated withalterations in BDNF trafficking and secretion in vitro, aswell as with alterations in hippocampal working memoryin humans.52 Therefore, an opportunity exists to study theinteraction of life stress, signal transduction–related genes,neuroimaging abnormalities consistent with deficientstructural plasticity, and susceptibility to depression.15

Antidepressant mechanisms and neurotrophic signaling cascades

An increasing amount of evidence suggests that antide-pressants regulate neurotrophic signaling cascades.Antidepressant treatment increases CREB phosphoryla-tion and CREB-mediated gene expression in mice limbicbrain regions.53 Various classes of chronic antidepressanttreatments, as well as electroconvulsive treatment (ECT),upregulate CREB and BDNF expression, suggesting thatthe CREB cascade and BDNF are common post-receptortargets of antidepressants.54,55 This increase is exclusivelyseen after chronic use, thus corresponding to the onsetclinical antidepressant effects with these therapies.Additional evidence that relates upregulation of thesepathways and antidepressant treatment comes from anti-depressant-like performance in behavioral models.56 Inrats, CREB overexpression in the dentate gyrus or BDNFinjection leads to an antidepressant-like effect in thelearned-helplessness paradigm and the forced swim testmodel of antidepressant efficacy.57-59

Chronic antidepressant treatment also increases the neu-rogenesis of dentate gyrus granule cells.60-62 This effect hasnot been observed with acute antidepressant treatment.These studies show that chronic administration of differ-ent classes of antidepressants and ECT lead to an increasein the proliferation and survival of new neurons. Lithium,an effective antidepressant potentiating agent, alsoincreases neurogenesis in the dentate gyrus.63 It is note-worthy that in contrast to the findings seen with chronicantidepressant use, increases in neurogenesis do not occurwith chronic administration of nonantidepressant psy-chotropic medications. Increases in neurogenesis have

been reported to occur with conditions that stimulate neu-ronal activity (eg, enriched environment, learning, exer-cise). This suggests that neurogenesis is positively regu-lated by, and might be reliant on, neuronal plasticity.The enhancement of hippocampal neurogenesis followingchronic antidepressant use highlights the level to whichthese efficacious treatments can regulate long-term neu-roplastic processes in the brain. Since stress and antide-pressants have opposite effects on hippocampal neuroge-nesis, it is likely that the clinical symptoms of depressionare related to changes in hippocampal neurogenesis. Inorder to assess whether antidepressant-induced hip-pocampal neurogenesis is functionally relevant, Santarelliand associates64 utilized both genetic and radiologicalmethods to show that disruption of antidepressant-induced neurogenesis blocked behavioral responses toantidepressants. In this study, serotonin 1A receptor nullmice were insensitive to the neurogenic and behavioraleffects of fluoxetine, a serotonin selective reuptakeinhibitor. In mice, X-irradiation of the hippocampus pre-vented the neurogenic and behavioral effects of twoclasses of antidepressants. Together, the above findingssuggest that some of the behavioral effects observed withchronic antidepressant use may be mediated by the stim-ulation of neurogenesis in the hippocampus. However, asKempermann65 clearly articulated, much more research isrequired in order to adequately link changes in adult hip-pocampal neurogenesis to the pathophysiology and treat-ment of depression.Agents capable of reversing the hypothesized impairmentsof cellular resilience, reductions in brain volume, and celldeath or atrophy in depression have the potential ofbecoming new therapeutic classes of antidepressant drugs.New molecular targets might include phosphodiesteraseinhibitors that increase CREB phosphorylation, MAPkinase phosphatase inhibitors that increase expression ofthe antiapoptotic protein bcl-2, presynaptic glutamatereceptor subtypes that attenuate glutamate release, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA)potentiators that increase BDNF expression, and NMDAantagonists that enhance plasticity and cell survival.14

Concluding comments

A substantial body of evidence suggests that impairmentsin neuroplasticity and cellular resilience play a central rolein the underlying biology of mood disorders.Additionally,there is a growing appreciation that new medications that


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simply imitate “traditional” drugs, those aiming to directlyor indirectly alter monoaminergic throughput, may be oflimited benefit to those patients with refractory depres-sion. Those strategies assume that the target circuits arefunctionally intact and that changes in synaptic activitywill alter the postsynaptic throughput of the system. Theevidence discussed here indicates that, in addition to neu-rochemical changes, many patients suffering from mooddisorders also have marked structural alterations in cru-cial neuronal circuits. Therefore, in order to obtain anoptimal treatment response, it will most likely be crucialto provide both trophic and neurochemical support. Theaim of the trophic support would be to enhance andmaintain normal synaptic connectivity, therefore permit-ting the chemical signal to restore maximum functioningof vital circuits essential for normal affective functioning.In fact, preliminary studies suggest that regional structuralchanges in the brains of patients with mood disorders maybe related with not only severity and duration of the ill-ness, but also with altered treatment response to phar-macotherapy and ECT.The evidence also suggests that, somewhat similar to thetreatment of other chronic medical conditions, such ashypertension and diabetes, prompt and sustained treat-ment may be necessary to prevent many of the injuriouslong-term sequelae associated with mood disorders.Although the evidence hints at an association betweenhippocampal atrophy and illness duration in depressedpatients, it remains unclear whether the volumetric andcellular changes observed in other brain areas are related

to affective episodes. In fact, some studies have describedreduced gray matter volumes and increased ventricle sizein patients with mood disorders at the time of their firstepisode and in early onset of the disease.12,15

In conclusion, relevant genotypes for mood disorders arebeing identified, and clinical research techniques are nowcapable of defining neurobiological phenotypes. Similarly,results from transcriptomic and proteomic studies whichidentified neurotrophic signaling as targets for the long-term actions of antidepressants and mood stabilizers haveplayed a role (along with neuroimaging and postmortembrain studies) in a reconceptualization about the patho-physiology, course, and optimal long-term treatment ofsevere mood disorders. These data suggest that, whilemood disorders are clearly not classical neurodegenera-tive diseases, they are in fact associated with impairmentsof cellular plasticity and resilience.As a consequence, thereis a growing appreciation that optimal long-term treatmentwill most likely be achieved by attempting to prevent theunderlying disease progression and its attendant cellulardysfunction, rather than exclusively focusing on the treat-ment of signs and symptoms.We are optimistic that a newgeneration of research will clarify the relation among envi-ronmental and genetic risk factors to quantify the risk forthe development of depression more precisely. Theseadvances will result in a dramatically different diagnosticsystem based upon etiology, and ultimately in the discov-ery of new approaches to the prevention and treatment ofsome of mankind’s most devastating and least understoodillnesses. ❏


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7. Kendler KS, Karkowski LM, Prescott CA. Causal relationship betweenstressful life events and the onset of major depression. Am J Psychiatry.1999;156:837-841.8. Kendler KS, Thornton LM, Prescott, Prescott CA. Gender differences inthe rates of exposure to stressful life events and sensitivity to their depres-sogenic effects. Am J Psychiatry 2001;158:587-593.9. Caspi A, Sugden K, Moffitt TE, et al. Influence of life stress on depression:moderation by a polymorphism in the 5-HTT gene. Science. 2003;301:386-389.10. Kendler KS, Karkowski-Shuman L. Stressful life events and genetic lia-bility to major depression: genetic control of exposure to the environment.Psychol Med. 1997;7:539-547. 11. Gould TG, Manji HK. The molecular medicine revolution and psychiatry:bridging the gap between basic neuroscience research and clinical psychi-atry. J Clin Psychiatry. 2004. In press.12. Drevets WC. Neuroimaging and neuropathological studies of depres-sion: implications for the cognitive-emotional features of mood disordersCurr Opin Neurobiol. 2001;11:240-249.


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Plasticidad celular, resiliencia y fisiopatologíade los trastornos afectivos graves

Los avances recientes en la identificación de los cir-cuitos neurales, los mecanismos neuroquímicos y detransducción de señales que participan en la fisiopa-tología y el tratamiento de los trastornos afectivoshan conducido hacia un significativo progreso en lacomprensión de los papeles de los factores genéticosy de los estresores psicosociales. Los sistemas de neu-rotransmisión monoaminérgica han concitado lamayor atención, en parte, debido a la observaciónque los antidepresivos eficaces ejercen sus efectosbioquímicos primarios a través de la regulación delas concentraciones intrasinápticas de serotonina ynoradrenalina. Además, los sistemas monoaminér-gicos se distribuyen extensamente a través de la redde circuitos neuronales límbicos, estriatales y corti-cales prefrontales, que se piensa son los responablesde las manifestaciones conductuales y viscerales delos trastornos afectivos. Un número creciente deestudios de neuroimágenes, neuropatológicos y bio-químicos revelan deterioros en la plasticidad celulary la resiliencia en pacientes que padecen trastornosafectivos graves y recurrentes. En este artículo se des-criben estudios que identifican posibles anormalida-des estructurales, funcionales y celulares que se asocian con los trastornos depresivos, los que consti-tuyen potencialmente los fundamentos celulares deestas enfermedades. Se sugiere que los fármacosdiseñados para incrementar la plasticidad celular y laresiliencia, y atenuar la actividad de los sistemas quedeterminan una mala adaptación al estrés, puedenser útiles para el tratamiento de los trastornos afec-tivos graves.

Plasticité cellulaire, résilience et physiopathologie des troubles de l’humeursévères

Les progrès récents concernant l’identification des sub-stances chimiques et circuits neuronaux et des méca-nismes de transduction du signal impliqués dans la phy-siopathologie et le traitement des troubles de l’humeuront amélioré la compréhension des rôles des facteursgénétiques et des facteurs psychosociaux de stress. Lessystèmes de neurotransmetteurs monoaminergiques ontretenu le plus d’attention, en partie parce que l’on aremarqué que les principaux effets biochimiques des anti-dépresseurs efficaces s’exercent en régulant les concen-trations intrasynaptiques de sérotonine et noradrénaline.De plus, les systèmes monoaminergiques sont largementdistribués dans le réseau des circuits neuronaux lim-biques, striataux et du cortex préfrontal supposés déter-miner les manifestations comportementales et orga-niques des troubles de l’humeur. De plus en plus d’étudesde neuro-imagerie, neuropathologie et biochimie souli-gnent l’altération de la plasticité cellulaire et de la rési-lience chez les patients souffrant de troubles de l’humeursévères et récurrents. Dans cet article, nous décrivons desétudes identifiant de possibles anomalies structurelles,fonctionnelles et cellulaires associées aux troubles dépres-sifs, qui constituent les bases cellulaires potentielles deces pathologies. Nous suggérons que les médicamentsconçus pour augmenter la plasticité cellulaire et la rési-lience et atténuer l’activité des systèmes inadaptés deréponse aux stress pourraient être utiles au traitementdes troubles de l’humeur sévères.

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46. Dwivedi Y, Rao JS, Rizavi HS, et al. Abnormal expression and functionalcharacteristics of cyclic adenosine monophosphate response element bind-ing protein in postmortem brain of suicide subjects. Arch Gen Psychiatry.2003;60:273-282.47. Dwivedi Y, Rizavi HS, Conley RR, Roberts RC, Tamminga CA, Pandey GN.Altered gene expression of brain-derived neurotrophic factor and receptortyrosine kinase B in postmortem brain of suicide subjects. Arch GenPsychiatry. 2003;60:804-814.48. Yamada Y, Yamamoto M, Ozawa H, Riederer P, Sito T. Reduced phos-phorylation of cyclic AMP-responsive element binding protein in the post-mortem orbitofrontal cortex of patients with major depressive disorder. JNeural Transm. 2003;110:671-680. 49. Sen S, Neese RM, Stoltenberg SF, et al. A BNDF coding variant is associ-ated with the NEO personality inventory domain neuroticism, a risk factorfor depression. Neuropsychopharmacology. 2003;28:397-401. 50. Sklar P, Gabriel SB, McInnis MG, et al. Family-based association study of76 candidate genes in bipolar disorder; BDNF is a potential risk locus. MolPsychiatry. 2002;7:579-593.51. Neves-Pereira M, Mundo E, Muglia P, King N, Macciardi F, Kennedy JL. Thebrain-derived neurotrophic gene confers susceptibility to bipolar disorder: evi-dence from a family-based association study. Am J Hum Genet. 2002;71:651-655.52. Egan MF, Kojima M, Callicott JH, et al. The BDNF val66met polymor-phism affects activity-dependent secretion of BDNF and human memoryand hippocampal function. Cell. 2003;112:257-269.53. Thome J, Sakai N, Shin K, et al. cAMP response element-mediated genetranscription is upregulated by chronic antidepressant treatment. J Neurosci.2000;20:4030-4036.54. Nibuya M, Morinobu S, Duman RS. Regulation of BDNF and TrkB mRNAin rat brain by chronic electroconvulsive seizure and antidepressant drugtreatments. J Neurosci. 1995;15:7539-7547.55. Nibuya M, Nestler EJ, Duman RS. Chronic antidepressant administrationincreased the expression of cAMP response element binding protein (CREB)in rat hippocampus. J Neurosci. 1996;16:2365-2372.56. Duman RS, Malberg J, Thome J. Neural plasticity to stress and antide-pressant treatment. Biol Psychiatry. 1999;46:1181-1191. 57. Siuciak JA, Lewis DR, Wiegand SJ, Lindsay RM. Antidepressant-like effectof brain-derived neurotrophic factor (BDNF). Pharmacol Biochem Behav.1997;56:131-137.58. Shirayama Y, Chen AC, Nakagawa S, Russell DS, Duman RS. Brain-derivedneurotrophic factor produces antidepressant effects in behavioral modelsof depression. J Neurosci. 2002;22:3251-3261.59. Chen AC, Shirayama Y, Shin KH, Neve RL, Duman RS. Expression of thecAMP response element binding protein (CREB) in hippocampus producesan antidepressant effect. Biol Psychiatry. 2001;49:753-762. 60. Jacobs BL, Praag H, Gage FH. Adult brain neurogenesis and psychiatry:a novel theory of depression. Mol Psychiatry. 2000;5:262-269. 61. D’Sa C, Duman R. Antidepressants and neuroplasticity. Bipolar Disord.2002;4:183.62. Manev H, Uz T, Smalheiser NR, Manev R. Antidepressants alter cell pro-liferation in the adult brain I vivo and in neural cultures in vitro. Eur JPharmacol. 2001;411:67-70. 63. Chen G, Rajkowska G, Du F, Seraji-Bozorgzaed N, Manji HK. Enhancementof hippocampal neurogenesis by lithium. J Neurochem. 2002;75:1729-1734. 64. Santarelli L, Saxe M, Gross C, et al. Requirement of hippocampal neu-rogenesis for the behavioral effects of antidepressants. Science.2003;301:805-809.65. Kempermann G. Regulation of adult hippocampal neurogenesis—impli-cations for novel theories of major depression. Bipolar Disord. 2002;4:17-33.


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Animal model

he epilepsy induced in the rat by lithium pilo-carpine (Li-Pilo) constitutes an animal model of humanmesial temporal lobe epilepsy.1 Neuronal damage ismainly detected in hippocampus, thalamus, piriform cor-tex, entorhinal cortex, and neocortex. At present, mag-netic resonance imaging (MRI) is the most sensitiveimaging method for the study of mesial temporal lobeepilepsy, but the examination is often restricted to thedetection of hyperintensities. In previous studies, we usedMRI to explore the morphological changes resultingfrom an injection of Li-Pilo that leads to epilepsy.2,3 Inorder to improve the predictive value of MRI images, weperformed a texture analysis4 of MRI images combinedwith a discriminant analysis. The results presented hereindicate that this procedure can detect defects that can-not be visualized by classic examination and permits amore correct classification of the images.

Materials and methods

MRI protocol

MRI images were recording using an MRI scanner oper-ating at 4.7 tesla (SMIS, UK).The rats were anaesthetizedfor MRI by an intramuscular injection of 37 mg/kg keta-

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Author affiliations: FORENAP, Rouffach, France (Jean-François J. Nedelec,PhD; Jean-Paul Macher, MD); Institut de Physique Biologique, Faculté deMédecine, Strasbourg, France (Olivier Yu; Jacques Chambron)

Address for correspondence: Jean-François J. Nedelec, PhD, FORENAP, Institutefor Research in Neuroscience and Neuropsychiatry, BP29, 68250 Rouffach, France(e-mail: [email protected])

This article is published following the 14th Biological Interface Conferenceheld in Rouffach, France, between October 1 and 5, 2002, on the theme of“Drug Development.” Other articles from this meeting can be found inDialogues in Clinical Neuroscience (2002, Vol 4, No 4).

T


Texture analysis of the brain: from animalmodels to human applicationsJean-François J. Nedelec, PhD; Olivier Yu, MD, PhD;Jacques Chambron, MD, PhD; Jean-Paul Macher, MD

Magnetic resonance imaging (MRI) is widely used toimage brain in vivo both in studies in animal models andfor human diagnosis. A large part of the value of MRIis due to the fact that soft tissue contrast is enhanced bythe substantial variation in the T1 and T2 relaxation timesbetween tissues. It may be possible to use an alternativeapproach, which does not rely on the absolute mea-surement of relaxation times. Generally speaking, tex-tures are complex visual patterns composed of entities,or subpatterns, that have characteristic brightness, color,slope, size, etc. Thus, texture can be regarded as a simi-larity grouping in an image. The properties of the localsubpattern give rise to the perceived lightness, unifor-mity, density, roughness, regularity, linearity, frequency,phase, directionality, coarseness, randomness, fineness,smoothness, and granulation. The purpose here is toillustrate how texture analysis can be used in animalmodels and in human clinical applications, as well as inthe search for further pharmacological applications inhumans. Thus, this article summarzes three different MRIstudies in (i) rats, using the lipocarpine epileptic ratmodel as an animal model; (ii) patients with Alzheimer’sdisease; and (iii) patients with schizophrenia.© 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:227-233.

Keywords: texture analysis; brain; rat; epilepsy; MRI; gray-level dependencehistogram; Alzheimer’s disease; schizophrenia

mine and 5.5 mg/kg xylazine.A T2-weighted spin-echo fast-imaging method sequence (repetition time [TR]=3800 msand echo time [TE]=80 ms) was used with 4-cm field ofview, a 256×256 pixel matrix, 1-mm thickness, upon coro-nal slices of the whole brain.

Animals and Li-Pilo protocol

Eleven 21-day-old, male, Sprague-Dawley rats were usedfor the experiments. The images of the 11 rats obtainedbefore the injection of Li-Pilo served as control. All therats first received lithium chloride (3 mEq/kg) intraperi-toneally.After 18 h, the rats received a subcutaneous injec-tion of pilocarpine (30 mg/kg) and 30 min later 1 mg/kgmethylscopolamine intraperitoneally, in order to reducethe peripheral consequences of pilocarpine administration.Two hours after onset of status epilepticus (SE), the ratsreceived 2 mg/kg diazepam by deep intramuscular injec-tion in order to improve their survival. Images of all therats were performed 24 h after onset of SE.

Texture analysis

Conventional texture analysis was performed using sta-tistical methods, mostly based on first-order and second-order histograms derived from the co-occurrence matrix,which describes the spatial gray level dependencies.Another possibility is the run-length matrix, which is thematrix of the run-length frequency occurring in the imagefor a certain angle of sight (lines of the same pixel level).This method has been fully described by Haralick.4 Theco-occurrence matrix is based on the probability that pairsof pixels with a given level will appear. For each orienta-tion (0°, 45°, 90°, and 135°) and for each distance betweentwo pixels forming a pair, a number of co-occurrencematrix parameters may be calculated: contrast (an uneventexture provides large/high contrast values); correlation(relationship between two pixels); homogeneity (unifor-mity of the gray levels); and entropy (coarse-grained qual-

ity of the texture).The software MaZda was used to ana-lyze the texture of the digitized images within all regionsof interest (ROI) and yielded 300 parameters.5

Statistical analysis

The statistical analysis was carried out using softwarefrom Statistica, Statsoft Inc. Discriminant analysis wasused for multigroup classification. Using stepwise analy-sis, we checked the ability of each texture parameter todiscriminate between two groups of ROIs, ie, presence orabsence of lesions in piriform or entorhinal cortices.As a preliminary step, we determined the most importantparameters that best discriminated the “lesion” ROIsfrom the “safe” ROIs observed before the Li-Pilo proto-col.The question to be answered here is whether the twogroups are well distinguished on the basis of the set oftexture parameters. If the discrimination is successful onthe basis of the set of selected parameters, it makes senseto classify particular piriform or entorhinal cortices interms of group membership, ie, in terms of into whichgroup they are most likely to be classified.The search for hidden defects could then be undertaken inthe nonmodified images, obtained after the Li-Pilo proto-col, in order to discriminate between lesion and safe ROIs.

Results

In all 21-day-old rats (n=20), pilocarpine injections led toSE within about 50 min. However, only 80% of rats werestill epileptic after a mean delay of 70.2±24.6 days(mean±SD). MRI images obtained before Li-Pilo treat-ment were considered as control group images (Figure 1)(64 ROIs were used for the texture analysis).Among the 20 rats followed for 4 months, 16 exhibitedseizures, whereas 4 did not. Retrospectively, three groupsof rats could be characterized according to type of imagesand the possibility of late epilepsy:• Group A: 6 rats with obvious lesions characterized by

a hypersignal on T2-weighted images in the piriform orentorhinal cortices 24 h after the SE (Figure 1; 44 ROIswere used for texture analysis); all these rats exhibitedlate epileptic seizures.

• Group B: 4 rats with control-like images (without anyhypersignals), as shown in Figure 1, which did not presentlate epilepsy (34 ROIs were used for texture analysis).

• Group C: 10 rats with control-like images (without anyhypersignals), as shown Figure 1, but which subse-

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Selected abbreviations and acronymsAD Alzheimer’s diseaseGLDH gray-level dependence histogramLi-Pilo lithium-pilocarpine MMSE Mini-Mental State ExaminationROI region of interestSE status epilepticus

quently became spontaneously epileptic (80 ROIs wereused for texture analysis).

Therefore, the conventional MRI study could not predictthe fate of the 10 rats in group C, which did not displayvisible lesions in their brain images 24 h after SE, butsubsequently became epileptic.The results of the texture analysis yielded 200 textureparameters in each ROI. Preliminary discriminant analy-sis yielded a classification function corresponding to thecontrol group or group A. Each function was a linearcombination of the features (or texture parameters) thatyielded the best discrimination. For a given ROI,described by the texture parameters, a classification scorewas calculated from the classification functions. Each

ROI was then classified into one group or the other,according to the highest classification score.The above classification process was then used as a basisfor prediction for the 114 apparently normal ROIsfrom the 57 brain slices of the rats in groups B and C.The resulting classification gave 84 control ROIs and30 lesion ROIs. Indeed, only 2 rats had control ROIs andwere safe (group B). About 50% of the lesion ROIs ofthe other 12 rats were distributed bilaterally (10 rats ingroup C and 2 rats in group B). During the 4 months’clinical follow-up, 10 rats became epileptic and 4 ratsremained nonepileptic, among which 2 had been incor-rectly classified as epileptic (Table I).

Discussion and conclusion

The MRI study that was based only on the presence ofhyperintensity signals in the piriform and entorhinal cor-tices predicted 6 late chronic epilepsy and 5 safe rats.Thismissed latent disease in 3 rats.The combined texture anddiscriminant analyses that were based on pixel patternabnormalities selected 3 texture parameters that charac-terized structural abnormalities relevant to the hypersig-nal, both in the modified images of 6 rats and in the imagesof 4 rats with apparently nonmodified images, predictingthe late chronic epilepsy in 10 rats.The classification basedon early texture abnormalities in the piriform and entorhi-nal cortices improved the results of the regular MRI study.6

Human applications in AD

The method of gray-level dependence histograms(GLDH) as defined by Chetverikov for 2D7 and gener-alized to 3D by Kovalev and Petrou8-10 leads to derivedfeatures of texture anisotropy from MRI data. The aim

Texture analysis of the brain - Nedelec et al Dialogues in Clinical Neuroscience - Vol 6 . No. 2 . 2004

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Figure 1. Magnetic resonance imaging (MRI) scans in rats before treat-ment (Control) and after treatments with lithium pilocarpine (Li-pilo). Group A exhibited late epileptic seizures. Group B pre-sented no late epilepsy. Group C had control-like images, butsubsequently became epileptic.

Control

Group B

Group A

Group C

Table I. Groups and classification of rats. Group A exhibited late epileptic seizures. Group B presented no late epilepsy. Group C had control-like images,but subsequently became epileptic. ROI, region of interest.

Group A B C

Rats (n) 6 4 10

Number of ROIs used in texture analysis 44 34 80

Hyperintensity of piriform and entorhinal cortices Yes No No

Texture and discriminant analysis classification of ROIs 42 lesions 8 lesions 22 lesions

2 control 26 control 58 control

Prediction of disease 6 epilepsy 2 safe 10 epilepsy

2 epilepsy

Effective chronic epilepsy Yes No Yes

was to evaluate Alzheimer’s disease (AD) patients fora correlation between the anisotropic features and theirscore on the Mini-Mental State Examination (MMSE),which is routinely used to help diagnose AD.11

Methods

Two groups of subjects were investigated and analyzed inthis study: 12 control volunteers and 13 AD patients.Thecontrol group was matched with the AD group in termsof age and gender.The mean age (range) at time of inves-tigation was 56.77 (39-72) years for the AD patients and58.33 (47-72) years for the control volunteers.MRI T1-weighted images with coronal orientation wererecorded for each subject. Each data set had 180×180×124pixels and the voxel size was 0.9375×0.9375×1.5 mm.Thescans were segmented to isolate the brain from externalstructures (eyes, ventricles, bones, etc).12 The brains werefurther segmented to isolate the white and gray matter, aswell as the border between the two types of tissues.Because the texture analysis technique effectively countsthe number of pairs of voxels that appear in the same rel-ative position and have certain fixed gray values, the rela-tive gray values of the voxels are extremely important.Thus, a normalization set is used in order to have the samerelative gray level values for different scans: the smallestgray-level value is assigned to 1 and the highest to 255 forthe segmented scan; 0 is assigned to the voxels that do notbelong to the ROI.

3D texture representation: isotropy or anisotropy

A coordinate system is defined as a cube of data cube inwhich the x and y axes form the plane of each slice, andthe z axis is perpendicular to each slice. The azimuthalangle φ is measured on the x,y plane away from the direc-

tion of the x axis. The pair of values φ,z defines a uniqueorientation in 3D space.We can then calculate the quan-tity h (φ,z;d). One component of h is the number of pairsof voxels that are at distance d from each other, along thedirection φ ,z with one member of the pair having a grayvalue k and the other l. If the data are isotropic, the func-tion h must be independent of direction and therefore a3D representation is a sphere. Any deviation from thisshape indicates anisotropy in the data.The 3D represen-tation for a fixed distance is a closed digital surface, whichis called an indicatrix. Projections of the orientation his-tograms can be obtained as illustrated in Figure 2 for acontrol subject and an AD patient.

Feature extraction

Three features are used to analyze the shape of the 3Dindicatrix9: the anisotropy coefficient, the integralanisotropy measure or standard deviation, and the localmean curvature.Another set of features can be extractedby expanding the indicatrix in terms of spherical har-monics. The coefficients of such an expansion can char-acterize any 3D closed surface: coefficient A0,0 is the meanradius of the indicatrix; any other nonzero coefficient rep-resents different types of anisotropy.Anisotropic featureswere extracted from four brain regions: the whole brain,white matter, gray matter, and the border between grayand white matter. In every single region, five different dis-tances d were used: 0.9375, 1.5, 2, 2.5, and 3 mm.

MMSE score and correlation with the isotropy coefficient

The MMSE score is used to detect dementia. The maxi-mum score is 30 (typically above 29 for healthy volunteers).Scores between 10 and 24 are considered to indicate mild-to-moderate dementia cases, and scores below 10 indicatesevere dementia.The scores obtained in the AD patients(named AD1 to AD13) and the control volunteers (namedCO1 to CO12) are displayed in Table II.Two of the scoresdo not match the clinical diagnosis:AD3 and CO2.While many features correlate well with the MMSEscores, Figure 3 illustrates the best correlation (-0.876)with the MMSE score, which was obtained for the fea-ture A1,1 in gray matter for a distance of 0.9375 mm.Subject AD3 is interesting because this patient wasimaged before the onset of the first clinical symptoms, ata time when there may have been ongoing structuralbrain changes.

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Figure 2. Projections of the orientation histogram on the z=0 planeobtained from MRI T1 images: from an Alzheimer’s diseasepatient (left) and a healthy volunteer (right). The isotropic fea-tures of the histogram are related to brain pathology.

Discussion and conclusion

The GLDH method can be used to produce many fea-tures that strongly correlated with the MMSE scores whenapplied to the gray matter components of the MRI T1scans.The features computed reflected the microtexturalproperties of the brain, ie, the texture anisotropy at scalesof the order of 1 mm, rather than the shape of each ROI.Moreover, they correlate better with the condition of thesubject rather than with age. In general, the AD brains pre-sented greater anisotropy in their gray matter texture thanthe control brains. Both 2D features and 3D features cor-relate with the MMSE score, indicating that the informa-tion is already available in each individual layer.13

Human applications in schizophrenia

Texture analysis can also provide feature parameters fordifferent classes, which can then be used for classification.Generalized 4D co-occurrence matrices have been used toanalyze the 3D MRI T2-weighted brain images from con-trols and patients with schizophrenia.The ROI approachhas a number of potential problems: inter- and intraoper-ator reproducibility; difficulties detecting neuroanatomicboundaries; and the requirements that are of interest haveto be specified from the outset. This approach does notneed prior hypotheses and, because it is automated, repro-ducibility and comparability are ensured.

Methods

Two groups of subjects were investigated and analyzedin this study: 19 control subjects and 21 patients withschizophrenia. The controls were matched for age, gen-der, and social class.3D MRI T2-weighted images were collected for each sub-ject. Each data set consisted of slices with a 0.856-mmspatial resolution with interslice distance of 3 mm. Theimages were segmented such that only the brain compo-nent was extracted for further analysis. The anisotropicsampling of the data along the z axis (interslice direction)is handled by an appropriate scaling factor of 3.5(3/0.856) for all data sets. For the texture analysis, we usegeneralized co-occurrence matrices.14

3D texture analysis

The co-occurrence matrix is a generalized histogram,which records the frequency with which a certain com-bination of characteristics appear in the relevant posi-tion. Usually the main characteristic used is the grayvalue of the image, but other features can be used, suchas gradient magnitude or relative orientation of the gra-dient vectors. Because they are independent of rotationand translation of the data, co-occurrence matrices offer


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Table II. Mini-Mental State Examination (MMSE) score for subjects withAlzheimer’s disease (AD1, AD2, AD3, etc) and controls (CO1,CO2, CO3, etc).

Alzheimer’s disease Controls

Patient MMSE score Subject MMSE score

AD1 25 CO1 30

AD2 8 CO2 28

AD3 30 CO3 30

AD4 25 CO4 29

AD5 23 CO5 30

AD6 25 CO6 30

AD7 28 CO7 29

AD8 22 CO8 30

AD9 19 CO9 30

AD10 - CO10 30

AD11 14 CO11 30

AD12 24 CO12 30

AD13 12

Figure 3. Feature |A1, 1| in gray matter for d=0.9375 mm versus the scoreon the Mini-Mental State Examination (MMSE). � Alzheimer’sdisease patient (AD1, AD2, AD3, etc); � control volunteers(CO1, CO2, CO3, etc).Reproduced from reference 13: Segovia-Martinez M, Petrou M, Crum W.Texture features that correlate with the Mini Mental State Examination(MMSE) Score. EMBS Proc. 2001:910-913. Copyright © 2001 IEEE.

CO5CO4CO10CO6CO11CO1CO12CO3CO6AD7

AD6

AD1

AD4AD8AD9

AD11

AD2

AD13

AD12

AD5

MMSE

A 1,

1

AD3

CO9CO2

3025201510

0

2

4

6

8

10

CO7

descriptors that include these properties.The calculated co-occurrence matrix was w[g(i), g(j),a(i,j), d(i,j)]], where w is the frequency of the occurrenceof a voxel pair i,j, with gradient magnitude g(i) and g(j)respectively, an angle a(i,j) between their gradient vec-tors, and a distance d(i,j) from each other.

Results

Three series of experiments were conducted: one consid-ering the whole brain, one considering the bottom half ofthe brain, and one considering the bottom quarter of thebrain.The division of the brain was performed by identify-ing the slices that are anatomically most similar to slices 12and 24 of the anatomical atlas of Talairach and Tournoux.15

In each experiment, each element of the co-occurrencematrix was tested as a class discriminator according tothe t test. The feature were selected by thresholding thet values using various limits. The classification was thenperformed using Statistica software.

For the whole brain, the best results were obtained byretaining the features with t>4.5, and 14 subjects was mis-classified. Using the bottom half of the brain, there weretoo many features with t>4.5 and so only the featureswith t>5.5 were used, and only 7 subjects were misclassi-fied.When the bottom quarter of the brain was used, thefeatures were so good that only features with t>7.5 wereretained and only 2 subjects were misclassified, as illus-trated in Figure 4.

Conclusions

The most significant conclusion is that the brains ofpatients with schizophrenia show structural differencesfrom the brains of the control subject. Moreover, fromthe three series of analysis performed, it appears thatthese differences are located in the bottom quarter of thebrain. Finally, it was demonstrated that the co-occurrencematrices could characterize the two classes of subjectswith 90% accuracy using 3D T2-weighted MRI.

Perspectives

Texture analysis is a new approach for image analysis.Once the pharmacological aspect in the rat model isclearly demonstrated, extension to potential applicationsfor humans can be considered. In fact, brain plasticitycould be assessed with such a technique in brain diseasessuch as epilepsy, dementia, and schizophrenia. Drugeffects could also be investigated in order to evaluatewhether brain anisotropy or asymmetry varies duringdrug therapy. Finally, such an analysis could be correlatedwith neurocognitive tests to measure improvements insubjects’ performance. ❏

The authors thank Dr I. J. Namer, Dr M. Petrou, and V. A. Kovalev, who allowedtheir original data to be included in this overview. This was handled inside theEEC COST-B 11 Action entitled “Quantitative magnetic resonance imaging tex-ture,” led by Dr R. Lerki.

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Figure 4. 2D scattergram produced by projecting the high-dimensionalfeature space onto a 2D feature space and preserving theeuclidean distances between the points.

-20

-6

-4

-2

0

2

4

6

8

-15 -10 -5 0 5 10 15 20

Control subject

Schizophrenic patient

REFERENCES

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3. Roch C, Leroy C, Nehlig A, Namer IJ. Predictive value of cortical injury onthe development temporal lobe epilepsy in immature rats: a magnetic res-onance imaging (MRI) approach using the lithium-pilocarpine model.Epilepsia. 2001;42(suppl 7):233.4. Haralick RM. Statistical and structural approaches to texture. IEEE Proc.1979;67:786-804.5. Szczypiński P, Kociołek M, Materka A, Strzelecki M. Computer programfor image texture analysis. Proceedings of the International Conference onSignals and Electronic Systems. 18-21 September 2001. Lodz, Poland.2001:255-261.


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6. Yu O, Mauss Y, Roch C, Namer IJ, Chambron J. Detection of late epilepsyby texture analysis of MR brain images in the lithium pilocarpine model.Magn Reson Imaging. 2002;20:771-775.7. Chetverikov D. GLDH-based analysis of texture anisotoropy and symme-try: an experimental study. In: Proceedings 12th IPCR, Jerusalem, Israel.1994:1071-1073.8. Kovalev VA, Kruggel F, Gertz HJ, von Cramon DY. Three-dimensional tex-ture analysis of MRI brain datasets. IEEE Trans Med Imaging. 2001;20:424-433.9. Kovalev VA, Petrou M. Texture anisotropy in 3D images. IEEE Trans ImageProcessing. 1999;8:346-360.10. Kovalev V, Petrou M. Multidimensional co-occurrence matrices for objectrecognition and matching. Graph Models Image Processing. 1996;58:187-197.

11. Fosltein M, Folstein S, McHugh P. Mini-Mental State: a practical methodfor grading the cognitive state of patients for the clinician. J Psychiatr Res.1975;12:189-198.12. Freeborough P, Fox N. MR image texture analysis applied to the diag-nosis and tracking of Alzheimer’s disease. IEEE Trans Med Imaging.1998;17:475-479.13. Segovia-Martinez M, Petrou M, Crum W. Texture features that correlate withthe Mini-Mental state Examination (MMSE) Score. EMBS Proc. 2001:910-913.14. Kovalev VA, Petrou M, Bondar YS. Texture anisotropy in 3D images. IEEETrans Image Processing. 1999;l8:346-360.15. Talairach J, Tournoux P. Coplanar Stereotaxic Atlas of the Human Brain. NewYork, NY: Thieme; 1988.

Análisis de la textura del cerebro: desde losmodelos animales a las aplicaciones en elhombre

Las imágenes de resonancia magnética (IRM) se uti-lizan ampliamente para estudiar cerebros in vivo,tanto a nivel experimental en modelos animalescomo para orientaciones diagnósticas en el hombre.Gran parte del valor de las IRM se relaciona con elhecho que el contraste del tejido blando se acentúapor la variación importante de los tiempos de rela-jación T1 y T2 entre los tejidos. Puede ser posible elempleo de una aproximación alternativa que no sebasa en la medición absoluta de los tiempos de rela-jación. En general, las texturas son patrones visualescomplejos compuestos de entidades o subconfigu-raciones que tienen brillo, color, ángulo, tamaño, etc.característicos. De esta forma, una textura puedeconsiderarse una agrupación de unidades semejan-tes en una imagen. Las propiedades de la subconfi-guración local determinan la percepción de lumino-sidad, uniformidad, densidad, aspereza, regularidad,alineación, frecuencia, fase, orientación, grosor,aspecto aleatorio, fineza, tersura y granulaciones. Elpresente artículo se propone ilustrar cómo se puedeaplicar el análisis de la textura en modelos animalesy en clínica humana, al igual que en la investigaciónde futuras aplicaciones farmacológicas en el hombre.Se presentan tres estudios diferentes de IRM: (1) enanimales, utilizando el modelo de rata epiléptica porlipocarpina, (2) en pacientes con enfermedad deAlzheimer y (3) en pacientes con esquizofrenia.

Analyse de la texture du cerveau : desmodèles animaux aux applications humaines

L’imagerie par résonance magnétique (IRM) est lar-gement utilisée dans les études du cerveau in vivo,tant sur le plan expérimental dans les modèles ani-maux que dans une visée diagnostique chezl’homme. Une grande partie de la valeur de l’IRM estliée au fait que le contraste du tissu mou est accen-tué par la variation importante des temps de relaxa-tion T1 et T2 dans les divers tissus. Il est peut-être pos-sible d’utiliser une autre approche, qui ne s’appuiepas sur la mesure absolue des temps de relaxation.De façon générale, les textures résultent de motifsvisuels complexes composés d’entités et de sous-motifs ayant une brillance, une couleur, un angle,une taille, etc., caractéristiques. Une texture donnéepeut donc être considérée comme un groupementde similitudes au sein d’une image. Les propriétésd’un sous-motif local sont à l’origine de ce qui estperçu en termes de luminosité, d’uniformité, de den-sité, d’inégalité, de régularité, de linéarité, de fré-quence, de phase, de direction, de grossièreté, d’as-pect aléatoire, de finesse, d’aspect lisse et degranulation. Le présent article se propose d’illustrercomment l’analyse de la texture peut être utiliséedans des modèles animaux et dans des applicationscliniques humaines, ainsi que dans la recherche d’ap-plications pharmacologiques futures chez l’homme.Trois études IRM y sont présentées, l’une menée dansun modèle animal de rat rendu épileptique par lapilocarpine, la seconde chez des patients atteints demaladie d’Alzheimer et la troisième chez des schi-zophrènes.

agnetic resonance imaging (MRI) is one of themost exciting imaging technologies for texture analysis: itoffers the best soft tissue contrast, which can be dramati-cally varied during imaging. Careful study of the depen-dence of texture parameters on MRI data collection strat-egy is essential for texture analysis in order to avoidartificial texture from the scanner.This is critical, since dif-ferent centers may vary their measuring sequences andacquisition protocols for their clinical investigations.The basic problem in quantitative MRI texture analysis isthe large number of different measuring techniques andimaging parameters, which can be easily changed duringa clinical examination. Thus, different techniques andimaging parameters produce totally different patterns inthe texture parameters of the same tissues in clinicalexaminations with different sensitivity to artificial textureoverlaid by the scanner. The main problem in textureanalysis with MRI is to avoid this artificial texture andminimize its influence. The presented work was per-formed in the framework of a European research projectCOST (Cooperation in the Field of Scientific andTechnical Research) B11 between 1998 and 2002 by insti-tutions from 13 European countries, aimed at the devel-opment of quantitative methods for MRI texture analy-sis.1 For further detail of texture analysis, parameters, andsoftware, see the article by Materka in this volume2 or ref-erences 3 to 7.

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Keywords: texture analysis; magnetic resonance imaging; brain; trabecular bone

Author affiliations: Department of Biophysics and Medical Radiation Physics,German Cancer Research Centre, Heidelberg, Germany

Address for correspondence: Department of Biophysics and Medical RadiationPhysics, German Cancer Research Centre, Im Neuenheimer Feld 280, D-69009Heidelberg, Germany(e-mail: [email protected])


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Problems in texture analysis with magneticresonance imagingLothar R. Schad, PhD

Since its introduction in the 1980s, magnetic resonanceimaging (MRI) has become recognized as a powerful invivo diagnostic tool. The objective of this article is to dis-cuss developments in quantitative MRI—and particularlytexture analysis—that maximize diagnostic information.A fundamental part of the work involves careful study ofthe optimal MRI data collection strategies for textureanalysis. This is critical, because different centers may varytheir measuring sequences and acquisition protocols forclinical reasons, and may be reluctant to vary these for tex-ture investigation. Different measuring techniques, suchas spin echo, gradient echo, and echo planar, and differ-ent measuring parameters produce totally different pat-terns in texture. Careful investigation of the dependenceof all these variables using texture phantoms (test objects)will help understand how MRI image texture is formedfrom tissue structures. Therefore, it is essential to designand test reliable and accurate test objects for a detailedassessment of texture analysis methods in MRI. The mainfeature of these test objects is their ability to simulate tis-sue-like textures with tissue-like MR relaxation properties.Long-term stability is also vital, as is uniformity of theoverall texture. Another aspect is to examine the testobjects under a whole range of MRI measuring sequencesand imaging conditions using different scanners to deter-mine their stability and utility. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:235-242.

Material and methods

The complexity of this problem can be demonstrated byconsidering a typical measuring spin echo sequence asmeasured by a commercial whole-body imager. Variousparameters can be easily changed during clinical investi-gation: image contrast is mainly defined by repetition time(TR) and spin echo time (TE); image resolution is definedby slice thickness (TH), field of view (FOV), and matrixsize (MA), which also influence texture analysis.The para-meters of k-space acquisition and reconstruction are veryimportant: k-space is the artificial space in which the rawMRI data are collected, and the image contrast and tex-ture is very sensitive to k-space strategies. Other parame-ters like coil setup and number of active coil segments arealso responsible for signal and flip angle (α) variations inthe image. Careful investigation of the dependence of allthese variables will help understand how MRI image tex-ture is formed in tissue structures. In our studies, MRIacquisition was performed in the standard head coil of a1.5-T scanner (Siemens Vision, Erlangen, Germany).

Spin echo technique

One of the most important measuring techniques in clin-ical diagnosis is the spin echo sequence, in which 90° and180° radio frequency (RF) pulses produce the spin echosignal. In addition, gradients are used in x, y, and z direc-tions to localize the signal.8 The advantages of this tech-nique are reduced artifacts, clearly defined contrast, andcommon availability.The disadvantages are the contrastdependency on RF pulse quality, and slice cross-talking,which is typical of a two-dimensional (2D) technique.This imaging technique allows measurement of the threerelevant MRI tissue parameters: spin density (ρ), spin-lat-tice relaxation time (T1), and spin-spin relaxation time

(T2), which are most responsible for tissue contrast andtexture.According to the theoretical equation for the spinecho signal9:

S ≈ ρ ⋅ (1−e-TR/T1) ⋅ e-TE/T2 [1]

in which S is the spin echo signal, the contrast ρ can be cre-ated by a long TR and short TE, resulting in a flat imagecontrast and texture at high signal intensity (Figure 1a).T1contrast can be created by short TR and short TE in spinecho imaging (Figure 1b). On the other hand,T2 contrastis created by long TR and long TE, mainly reflecting thewater content of the tissue (Figure 1c).These three phys-ical tissue parameters are described in reference 1.The realphysical properties of tissues may be obscured by artificialcontrast and texture from the scanner.

Slice profile

Slice profile is defined by the slice gradient and theshape of the RF pulse. Ideally, we would like to measurea rectangular slice, but due to technical reasons the realslice profile is Gaussian shaped.The consequence is thatwe have signal contributions from neighboring slicesthat influence the tissue texture.To minimize this effect,an interleaved slicing scheme is used in multislice 2Dimaging.

k-space

Another aspect of artificial texture is connected to the k-space, which describes the strategy for raw data collec-tion. The k-space contains the measured signal frequen-cies kx and ky, the so-called hologram from which the realMRI image can be calculated by a Fourier transform

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Selected abbreviations and acronymsEPI echo planar imagingFLASH fast low angle shotFOV field of viewMA matrix sizeRF radio frequencyROI region of interestSNR signal-to-noise ratioTE spin echo timeTH slice thicknessTR repetition time

Figure 1. Spin echo images of a patient with meningioma. A. ρ-image(TR/TE = 2000 ms/10 ms). B. T1 image (TR/TE = 600 ms/10 ms).C. T2 image (TR/TE 2000 ms/100 ms). TR, repetition time; TE,spin echo time.

A B C

(Figure 2). Some imaging techniques measure only everysecond line in the k-space to speed up the imagingsequence, which results in a reduction in the signal-to-noise ratio (SNR) by 1/√2 and aliasing artifacts, with con-sequences for image texture. Restriction to the center ofthe k-space with zero filling of the outer part results inthe same SNR effect without aliasing.

RF excitation

Another important variable is the RF characteristic andsensitivity of the transmitting and/or receiving coil, whichcan produce a lot of artificial texture from the scanner.This is demonstrated in Figure 3 using hard image scaling,which shows a clear signal inhomogeneity due to nonidealRF pulses at the outer range of the phantom (ie, coil).Another coil effect on image texture is produced withcoil arrays, where the summarized image is a result of thecombination of single coils, each of which contributes itsown coil characteristics (eg, SNR, sensitivity, and RF exci-tation profile) to the summarized image.This means thatimage texture could slightly differ between the objectcenter and the object boundary, where protons are closeor far away from the center of the coil.

Gradient echo techniques

Significant effects on image contrast and texture are intro-duced by the imaging sequence itself, since the imaging sig-nal can have a very complex dependence on the physicalproperties of the underlying tissue. One example is the so-

called gradient echo technique like FLASH (fast low angleshot),10 where the 90° and 180° RF pulses are replaced bya low-angle RF pulse with a bipolar gradient schemeresulting in a gradient echo signal. This measuring tech-nique can be used as fast imaging 2D technique or as a real3D imaging technique because of the compact timing ofthe sequence. On the other hand, the FLASH signal has acomplex dependence on T1, the local spin-spin relaxationtime (T2*), and the flip angle α, according to:

S ≈ A ⋅ e-TE/T2*

with A = ρ ⋅ sin� ⋅ 1− e-TR/T1

[2]1− cos� ⋅ e-TR/T1

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Figure 2. The effect of k-space filling on image contrast and texture. Theexample demonstrates the strong dependence of image tex-ture on the k-space filling factor as used in so-called “keyhole”techniques.

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Thus, the different flip angle distributions produced bythe coil characteristics result in different signals, and as aconsequence in different image texture patterns asdemonstrated in Figure 4.A very complex signal and texture situation is present inso-called single shot imaging techniques like echo planarimaging (EPI),11 where k-space is filled in one shot withmultiple gradient echoes. This is achieved by a gradientscheme in which the upper corner of the k-space isreached by a single gradient pulse followed by a series ofblips resulting in a rectangular movement through the k-space. This technique is very sensitive to local suscepti-bility artifacts, resulting in image distortions and strongT2* contrast dependence.Some special imaging techniques like spiral imaging canproduce a very complex pattern in the image texture,since this single shot technique moves on a spiral throughthe k-space, which can be achieved by oscillating gradi-ents with a phase shift of 90° in the x and y directions.This technique requires data interpolation in k-space tobring the measured data onto a Cartesian coordinate sys-tem before Fourier transform.This interpolation can pro-duce spurious artifacts with the consequence that theimage texture is dependent on k-space interpolation andimage reconstruction. In addition, the problem of texturedependence on measuring technique is more complicated

due to the large number of imaging sequences availableon modern scanners, as illustrated in Figure 5.

Results and discussion

SNR dependence

Figures 6a and 6b show the results of a FLASH experi-ment in a normal volunteer for SNR dependence mea-surement of texture parameters.The measuring parame-ters of the FLASH experiment were: TR/TE/α = 2 ms/9 ms/30°; bandwidth (BW) = 195 Hz/pixel; MA =512×512; FOV = 280 mm; TH = 2 mm; and acquisitions(AQ) = 1 to 324 resulting in an SNR = 1 to 18. Textureparameters (SNR, entropy 5×5, correlation 5×5) of whitematter, gray matter, and noise are shown as a function ofthe number of acquisitions (=SNR2). Figure 6c demon-strates that no texture can be measured in white matterusing standard image resolution (0.5×0.5×2 mm3) asdescribed above, since the SNR of white matter has thesame characteristics as noise. In contrast, the SNR of graymatter reaches a nearly constant value at about 16 acqui-sitions and no further improvement can be reached dueto the true underlying texture of the tissue. The sameobservation holds for a typical parameter of microtex-ture, like entropy 5×5 (Figure 6d), while no dependence

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Figure 4. Example of a 3D FLASH (fast low angle shot) dataset of a normal volunteer measured by the conventional head coil. A. Excellent T1 con-trast in the original transversal images. B. A strong signal inhomogeneity is obvious in the reconstructed sagittal images as seen in thehomogeneous phantom and corresponding anatomical structures, like upper part of the brain and cerebellum. This artifact is due to theradio frequency excitation profile of the head coil, which produces different T1 weightings and signal amplitudes as a function of theflip angle according to the FLASH formula (Equation 2).

A B

on SNR can be detected for a typical parameter ofmacrotexture, like correlation 5×5 (Figure 6e). Based onthis observation, a sufficient SNR>4 is necessary to mea-sure the real textural behavior of the human brain.12,13

Normalization

A texture test object (PSAG) was developed on the basisof polystyrene (PS) and agar solution (AG) to mimic tex-ture properties artificially. PS spheres are available fromthe technological process of PS production.Two types ofspheres were used for the phantom construction: ran-domly distributed spheres of diameter 0.2 to 3.15 mm; ormechanically separated spheres of diameter 0.8 to 1.25mm, 1.25 to 2 mm, or 2 to 3.15 mm. Polyethylene tubes ofdiameter 1.5 and 2.8 cm were filled with spheres and by

a hot solution of 4% agar (free and doped with DyCl3).One milliliter of 0.1% NaN3 was added per liter of agarfor microbiological stability.14

A second texture test object containing foam at differentdensities in Gd-DTPA solution was used to describemicrotexture properties. Phantom tubes containing foamswith coarse, middle, and fine density were constructed andfilled with a Magnevist® (Schering, Berlin) solution at aconcentration of 1:4000. Problems with the foam phantomsare air bubbles, which create susceptibility artifacts in theimages, and so a careful preparation of the foam phantomsis necessary. Both types of phantoms were placed next tothe head of a volunteer and a position for the imaging slabwas chosen such that all vials and part of the volunteer’sbrain were contained in the 3D slab.With this setup sev-eral 3D data sets with different imaging parameters were


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Figure 5. Sketch of the family of imaging techniques available on modern scanners. There are many strategies of mixing spin echoes with gradientechoes to speed up imaging time with the consequence of very complex image contrast and texture.CISS, constructive interference in thesteady state; CSE, conventional SE; DESS, dual echo steady state; EPI, echo planar imaging; FAST, Fourier-acquired steady state; FISP, fast imag-ing with steady precession; FLASH, fast low angle shot; FSE, fast spin echo; FSPGR, fast spoiled gradient recalled acquisition into steady state;GRASE, gradient and spin echo; GRASS, gradient recalled acquisition into steady state; GSE, gradient SE; HASTE, half-Fourier acquisitionsingle-shot turbo SE; IR, inversion recovery; MPRAGE, magnetization prepared gradient echo imaging; RARE, rapid acquisition with relaxationenhancement; SE, spin echo; SPGR, spoiled gradient recalled acquisition into steady state; T1-FFE, T1-weighted fast field echo; T2-FFE, T2-weighted fast field echo; TFLAIR, turbo fluid attenuated IR; TGSE, turbo GSE; TIR, turbo IR; TIRM, turbo IR magnitidue; TSE, turbo SE. Copyright © Siemens.

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acquired to demonstrate the influence of resolution andSNR, as well as the dependence of the texture parameterson different imaging parameters (eg, α,TR,TE). In a pilotstudy, texture parameters such as mean gradient show thesame behavior in phantoms as in white matter for differ-ent patients, indicating that a normalization of textureparameters using test objects is possible (Figure 7).However, texture normalization is necessary, but it is notpossible to mimic all texture features by phantoms.15

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Figure 6. FLASH (fast low angle shot) images of a normal volunteer formeasuring signal-to-noise (SNR) dependence of texture para-meters at (A) SNR =1 (1 acquisition) and (B) SNR =18 (324 acqui-sitions). C to E. Texture parameters (SNR, entropy 5×5, correla-tion 5×5) of white matter, gray matter, and noise are shown asa function of the number of acquisitions (=SNR2). SD, standarddeviation.

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Clinical application

The aim of this pilot study was to assess the possibility ofquantitative description of texture directivity in trabecu-lar bone with an attempt to quantitative description oftrabecular bone structural anisotropy using textureanalysis of 3D FLASH MRI. A series of 3D FLASHimages, all of 256×256 pixels, with the voxel size of0.4×0.4×0.4 mm3, were measured on a standard 1.5-Tscanner (Siemens Vision, Erlangen, Germany) using asmall flex coil.The images in Figure 8 represent trabecu-lar bone cross-sections in the sagittal and reconstructedtransversal direction. For bone image texture analysis, cir-cular regions of interest (ROI) were marked on corre-sponding bone cross-sections and effort has been made

to maintain a large-size ROI for better statistical signif-icance of texture parameters. The texture of the boneimage shows apparent directivity, which reflectsanisotropy of its physical structure according to the direc-tion of gravity (Figure 8c). Quantitative analysis of thisdirectivity is important to medical diagnosis, eg, in earlydetection of osteoporosis, as the directivity may varyaccording to the development of the disease. ❏

The author like to thank Michael Bock (DKFZ Heidelberg, Germany), MilanHajek (University Prague, Czech Republic), Richard Lerski (University Dundee,Scotland), Arvid Lundervold (University Bergen, Norway), Andrzej Materka(University Lodz, Poland), Lubomir Pousek (Technical University Prague, CzechRepublic), Yan Rolland (University Rennes, France), and Ivan Zuna (DKFZHeidelberg, Germany) for their help during the COST B11 action in manyaspects of texture measurements and analysis.

Figure 8. Example of a trabecular bone 3D FLASH (fast low angle shot)measurement of a normal volunteer in sagittal (A) and recon-structed transversal direction (B) for testing texture directivity.(C) Texture parameters like run length nonuniformity show aclear dependence on direction of gravity. Quantitative analy-sis of this directivity is important to medical diagnosis, eg, inearly detection of osteoporosis, as the directivity may varyaccording to the development of the disease.

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REFERENCES

1. COST B11. Quantitation of Magnetic Resonance Image Texture.Department of Physiology, University of Bergen, Norway. Available athttp://www.uib.no/costb11/. Accessed 3 December 2003.2. Materka A. Texture analysis methodologies for magnetic resonance imag-ing. Dialogues Clin Neurosci. 2004;6:243-250.3. Haralick R, Shanmugam K, Dinstein I. Textural features for image classifi-cation. IEEE Trans Syst Man Cybern. 1973;3:610-621.4. Haralick R. Statistical and structural approaches to texture. Proc IEEE.1979;67:786-804.5. Lerski RA, Straughan K, Schad LR, Boyce D, Blüml S, Zuna I. MR image tex-ture analysis—an approach to tissue characterization. Magn Reson Imaging.1993;11:873-887.6. Materka A. MaZda User’s Manual. Available at http://www.eletel.p.lodz.pl/cost/ Accessed 3 December 2003.7. Materka A, Strzelecki M, Lerski R, Schad L. Feature evaluation of texturetest objects for magnetic resonance imaging. In: Pietikainen M, ed. TextureAnalysis in Machine Vision, Series in Machine Perception and Artificial Intelligence.Vol 40. Singapore: World Scientific; 2000:197-206.8. Hahn EL. Spin echoes. Phys Rev. 1950;80:580-594.9. Schad LR, Brix G, Zuna I, Härle W, Lorenz WJ, Semmler W. Multiexponentialproton spin-spin relaxation in MR imaging of human brain tumors. J ComputAssist Tomogr. 1989;13:577-587.10. Haase A, Frahm J, Matthaei D, Hänicke W, Merboldt KD. FLASH imag-ing. Rapid NMR imaging using low flip-angle pulses. J Magn Reson.1986;67:258-266.11. Mansfield P. Multi-planar image formation using NMR spin echoes. J PhysChem Solid State Phys. 1977;10:L55-L58.12. Lysaker M, Lundervold A, Tai XC, Bock M, Schad LR. Noise removal withtissue boundary preservation using fourth-order partial differential equations.In: Proceedings of the International Society for Magnetic Resonance inMedicine (ISMRM). Glasgow, UK, 2000. Abstracts 2001 (ISSN 1524-6965).2001;9:126.13. Lysaker M, Lundervold A, Tai XC, Bock M, Schad L. Noise removal with tis-sue boundary preservation. Collection of Abstracts First SIAM-EMSConference. Applied Mathematics in our Changing World. Berlin, Germany,2 to 6 September 2001.14. Jirák D, Hájek M, Herynek V. New type of phantom for texture analysisbased on polystyrene and agar gel. European Society for Magnetic Resonancein Medicine and Biology, 17th Annual Meeting, Paris, France, 14 to 17September 2000. MAGMA. 2000;11:343.15. Schad LR, Blüml S, Zuna I. MR tissue characterization of intracranial tumorsby means of texture analysis. Mag Reson Imaging. 1993;11:889-896.

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Problemas en el análisis de la textura con imá-genes de resonancia magnética

Las imágenes de resonancia magnética (IRM) se hanreconocido como una poderosa herramienta parael diagnóstico in vivo desde su introducción en ladécada de 1980. El objetivo de este artículo es dis-cutir el desarrollo de las IRM de tipo cuantitativo yen particular el análisis de la textura, lo que permitemaximizar la información para el diagnóstico. Unaparte fundamental de este trabajo incluye un cui-dadoso estudio de las mejores estrategias de reco-lección de datos de las IRM para el análisis de la tex-tura. Este aspecto es central ya que diferentescentros, por razones clínicas, pueden variar lasecuencia de medición y los protocolos de adquisi-ción de datos y por lo tanto pueden ser reacios amodificar éstos para la investigación de la textura.Diversas técnicas de medición como el eco de spin,el eco de gradiente y el eco planar y distintos pará-metros de medición producen patrones de texturatotalmente diferentes. Una investigación cuidadosade la influencia de todas estas variables medianteel empleo de texturas fantasma (objetos de prueba)ayudará a comprender cómo se forman las texturasde las IRM a partir de las estructuras de los tejidos.Por lo tanto, resulta esencial diseñar y ensayar obje-tos de prueba exactos y confiables que permitanuna evaluación detallada de los métodos de análi-sis de la textura de las IRM. La característica princi-pal de estos objetos de prueba es su capacidad parasimular texturas parecidas a los tejidos y con pro-piedades semejantes a estos últimos. La estabilidada largo plazo de estos modelos es de gran impor-tancia, como también la uniformidad global de latextura. Otro aspecto consiste en examinar la esta-bilidad y utilidad de los objetos de pruebamediante diversas secuencias de medición y condi-ciones en que se realicen las IRM empleando dis-tintos resonadores.

Problèmes posés par les analyses de textureau cours de l’imagerie par résonance magnétique

Depuis son introduction au milieu des annéesquatre-vingt, l’imagerie par résonance magnétique(IRM) est devenue un puissant outil de diagnostic invivo. L’objectif de cet article est d’évaluer les avan-cées de l’IRM quantitative, notamment en ce quiconcerne les analyses de texture, qui optimisent l’in-formation diagnostique. Cet article sera en grandepartie consacré à l’étude minutieuse des meilleuresstratégies de recueil de données d’IRM pour les ana-lyses de texture. Ceci est essentiel, dans la mesure oùles séquences de mesures et les protocoles de saisiecliniques peuvent varier selon les centres et que cesderniers peuvent être réticents à les modifier pourles analyses de texture. Des différences aussi biendans les techniques de mesure utilisées, qu’il s’agissede l’écho de spin, l’écho de gradient, l’écho planaire,etc., que dans les paramètres mesurés retentissentsur la texture en entraînant l’apparition de motifstotalement différents. L’évaluation précise de l’in-fluence de ces variables en utilisant des images éta-lon (« fantômes ») de texture (ou objets de test) faci-litera une meilleure compréhension du mode deconstitution des textures des structures tissulaires enIRM. Par conséquent, il est essentiel de développeret d’expérimenter des objets de test exacts etfiables pour une évaluation détaillée des méthodesutilisées en IRM pour les analyses de texture. La prin-cipale caractéristique de ces modèles réside dans leurcapacité à imiter des textures semblables à celles pro-duites par les tissus et ayant des propriétés de relaxa-tion RM identiques. La stabilité à long terme de cesmodèles revêt également une grande importance,tout comme l’uniformité globale des textures qui enrésultent. Un autre objectif est d’examiner la stabi-lité et l’utilité de ces objets de test en les soumettantà toute une gamme de séquences de mesures et deconditions d’imagerie par IRM et de les étudier avecdivers types d’appareils d’IRM.

agnetic resonance imaging (MRI) is a uniqueand powerful tool for medical diagnosis, in that it is anoninvasive technique that allows visualization of soft tis-sues. There is an increasingly growing interest in usingMRI for early detection of many diseases, such as braintumors, multiple sclerosis, and others. The diagnosticinformation is often included in the image texture.1,2 Insuch cases, it is not sufficient to analyze image propertieson the basis of point-wise brightness only; higher-orderstatistics of the image must be taken into account.Texture quantitation, ie, its description by preciselydefined parameters (features) is then needed to extractinformation about tissue properties. Numerical values oftexture parameters can be used for classification of dif-ferent regions in the image, eg, representing either tissuesof different origin or normal and abnormal tissues of agiven kind. Changes of properly selected texture para-meters in time can quantitatively reflect changes in tissuephysical structure, eg, to monitor progress in healing.A European research project COST (Cooperation in theField of Scientific and Technical Research) B11 was per-formed between 1998 and 2002 by institutions from 13European countries, aimed at development of quantitativemethods of MRI texture analysis.3 It gathered experts ofcomplementary fields (physics, medicine, and computer sci-ence) to seek MRI acquisition and processing techniquesthat would make medical diagnoses more precise andrepetitive. One of the unique outcomes of this project isMaZda,4 a package of computer programs that allowsinteractive definition of regions of interest (ROIs) inimages, computation of a variety of texture parameters foreach ROI, selection of most informative parameters,exploratory analysis of the texture data obtained, and auto-matic classification of ROIs on the basis of their texture.The MaZda software has been designed and implementedas a package of two MS Windows®, PC applications:MaZda.exe and B11.exe.4 Its functionality extends beyondthe needs of analysis of MRI, and applies to the investiga-

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Texture analysis methodologies for magneticresonance imagingAndrzej Materka, MSEE, PhD, DSc

Methods for the analysis of digital-image texture arereviewed. The functions of MaZda, a computer programfor quantitative texture analysis developed within theframework of the European COST (Cooperation in theField of Scientific and Technical Research) B11 program,are introduced. Examples of texture analysis in magneticresonance images are discussed. © 2004, LLS SAS Dialogues Clin Neurosci. 2004;6:243-250.

Keywords: quantitative texture analysis; magnetic resonance imaging

Author affiliations: Institute of Electronics, Technical University of Lodz,Lodz, Poland

Address for correspondence: Technical University of Lodz, Institute ofElectronics, ul Wolczanska 223, 90-924 Lodz, Poland(e-mail: [email protected])


tion of digital images of any kind, where information is car-ried in texture. The essential properties of the MaZdapackage is described in this report, illustrated by examplesof its application to selected MRI texture analysis.

Texture analysis methods

Although there is no strict definition of the image tex-ture, it is easily perceived by humans and is believed tobe a rich source of visual information about the internalstructure and three-dimensional (3D) shape of physicalobjects. Generally speaking, textures are complex visualpatterns composed of entities or subpatterns that havecharacteristic brightness, color, slope, size, etc. Thus, tex-ture can be regarded as a similarity grouping in animage.5 The local subpattern properties give rise to theperceived lightness, uniformity, density, roughness, reg-ularity, linearity, frequency, phase, directionality, coarse-ness, randomness, fineness, smoothness, granulation, etc,of the texture as a whole.6 A large collection of examplesof natural textures is contained in the album by Brodatz.7

There are four major issues in texture analysis:• Feature extraction:To compute a characteristic of a dig-

ital image that can numerically describe its textureproperties.

• Texture discrimination: To partition a textured imageinto regions, each corresponding to a perceptuallyhomogeneous texture (leading to image segmentation).

• Texture classification:To determine to which of a finitenumber of physically defined classes a homogeneoustexture region belongs (eg, normal or abnormal tissue).

• Shape from texture: To reconstruct 3D surface geome-try from texture information.

Feature extraction is the first stage of image textureanalysis.The results obtained from this stage are used fortexture discrimination, texture classification, or objectshape determination.Consider an MRI cross-section of human skull (Figure1a) in which an elongated, kidney-shape bright object inthe upper-right quadrant of the image is needed to benumerically characterized.This object is our ROI in thisexample, and is marked in red in Figure 1b. For a popu-lation of images, the subimage covered by ROI is a ran-dom variable. Assuming that texture is homogeneouswithin the ROI and that the area of the ROI is suffi-ciently large, one can compute a number, say N, of statis-tical parameters based on image points contained in theROI. Depending on definition of these statistics, differ-ent properties of the ROI texture can be highlighted;these parameters are called texture features. In the exam-ple illustrated, the calculated parameters can be arrangedto form a feature vector [p1, p2, …, pN]. Such a vector is acompact description of the image texture. Comparison ofvectors computed for images measured for differentpatients indicates whether the texture covered by ROIrepresents normal or abnormal tissue.Feature vectors can be applied to the input of a devicecalled a classifier. On the basis of its input, the classifiertakes the decision as to which predefined texture classesits input represents. Consider a population of K images,each showing a different instance of texture A.A featurevector is computed for each image, and applied to theinput of the classifier. In an ideal case, “seeing” a vectordrawn from texture of class A, the classifier respondswith the information “class A” at its output. Similarly, fora population of K images, K feature vectors can be com-puted. Any of these could be applied to the input of theclassifier. In an ideal case, the response of the classifier toa feature vector computed for texture class B is “class B.”(Sometimes a classifier cannot make a correct decision;in such cases, it wrongly recognizes a texture class differ-ent from the one represented at the input, or it is unableto make a choice between assumed texture classes.) The concept of textured image segmentation is illustratedin Figure 2.The left and right halves of the image in Figure2a have different textures. In the process of image seg-

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Selected abbreviations and acronymsANN artificial neural networkLDA linear discriminant analysisNDA nonlinear discriminant analysisPCA principal component analysisROI region of interest

Figure 1. A cross-section of human skull (A), with the region of interest(ROI) marked in red (B).

A B

mentation, the two regions are automatically identifiedand marked in different colors, eg, orange and blue inFigure 2b. (Some parts of the image are wrongly recog-nized as regions of yet other texture types, though.) Thereare two main techniques of image segmentation: super-vised, where texture classes are known in advance; andunsupervised, where they are unknown, and so the seg-menting device has to identify not only the texture classes,but also their number. There exist a variety of differenttexture segmentation methods, such as region growing,maximum likelihood, split-and-merge algorithms, Bayesianclassification, probabilistic relaxation, clustering, andneural networks.2 All of these are based on feature extrac-tion, which is the initial step and is necessary to describe(measure and analyze) the texture properties.This paper is confined mainly to feature extraction andtexture classification techniques, which are typically thebasic steps performed to support medical diagnosis.Approaches to texture analysis are usually categorizedinto structural, statistical, model-based, and transformmethods.

Structural approaches

Structural approaches6,8 represent texture by well-definedprimitives (microtexture) and a hierarchy of spatial

arrangements (macrotexture) of those primitives. Todescribe the texture, one must define the primitives andthe placement rules.The choice of a primitive (from a setof primitives) and the probability of the chosen primitiveto be placed at a particular location can be a function oflocation or the primitives near the location. The advan-tage of the structural approach is that it provides a goodsymbolic description of the image; however, this propertyis more useful for texture synthesis than analysis tasks.The abstract descriptions can be ill defined for naturaltextures because of the variability of both micro- andmacrostructure and no clear distinction between them.A powerful tool for structural texture analysis is providedby mathematical morphology.9,10 This may prove to beuseful for bone image analysis, eg, for the detection ofchanges in bone microstructure.

Statistical approaches

In contrast to structural methods, statistical approaches donot attempt to explicitly understand the hierarchical struc-ture of the texture. Instead, they represent the texture indi-rectly by the nondeterministic properties that govern thedistributions and relationships between the gray levels ofan image. Methods based on second-order statistics (ie, sta-tistics given by pairs of pixels) have been shown to achievehigher discrimination rates than the power spectrum(transform-based) and structural methods.11 Human tex-ture discrimination in terms of the statistical properties oftexture is investigated in reference 12.Accordingly, the tex-tures in gray-level images are discriminated spontaneouslyonly if they differ in second-order moments. Equal second-order moments, but different third-order moments, requiredeliberate cognitive effort.This may be an indication that,for automatic processing, statistics up to the second ordermay be the most important.13 The most popular second-order statistical features for texture analysis are derivedfrom the so-called co-occurrence matrix.8 These have beendemonstrated to feature a potential for effective texturediscrimination in biomedical images.1,14

Model-based approaches

Model-based texture analysis15-20 using fractal and sto-chastic models attempts to interpret an image texture byuse of a generative image model and a stochastic model,respectively.The parameters of the model are estimatedand then used for image analysis. In practice, the com-

Texture analysis methodologies for MRI - Materka Dialogues in Clinical Neuroscience - Vol 6 . No. 2 . 2004

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Figure 2. Textured image segmentation.

A

B

putational complexity arising in the estimation of sto-chastic model parameters is the primary problem. Thefractal model has been shown to be useful for modelingsome natural textures. It can be used also for textureanalysis and discrimination16,21-23; however, it lacks orien-tation selectivity and is not suitable for describing localimage structures.

Transform methods

Transform methods of texture analysis, such as Fourier24-26

and wavelet27-29 transforms, produce an image in a spacewhose coordinate system has an interpretation that isclosely related to the characteristics of a texture (such asfrequency or size). Methods based on the Fourier trans-form perform poorly in practice, due to lack of spatiallocalization. Gabor filters provide means for better spatiallocalization; however, their usefulness is limited in practicebecause there is usually no single filter resolution at whichone can localize a spatial structure in natural textures.Compared with the Gabor transform, the wavelet trans-form have several advantages:• Varying the spatial resolution allows it to represent tex-

tures at the most appropriate scale.• There is a wide range of choices for the wavelet func-

tion, and so the best-suited wavelets for texture analy-sis can be chosen a specific application.

Wavelet transform is thus attractive for texture segmen-tation. The problem with wavelet transform is that it isnot translation-invariant.30,31

Regardless of their definition and underlying approachto texture analysis, texture features should allow gooddiscrimination between texture classes, show weakmutual correlation, preferably allow linear class separa-bility, and demonstrate good correlation with physicalstructure properties. For a more detailed review of basictechniques of quantitative texture analysis, the reader isreferred to reference 2. In this paper, we will discuss prac-tical implementation of these techniques, in the form ofMaZda computer program.

MaZda: a software package for quantitative texture analysis

The main steps of the intended image texture analysis areillustrated in Figure 3. First, the image is acquired bymeans of a suitable scanner.The ROIs are defined usingthe interactive graphics user interface of the MaZda pro-

gram. (The name “MaZda” is an acronym derived from“Macierz Zdarzen,” which is Polish for “co-occurrencematrix.”Thus, MaZda has no connection with the Japanesecar manufacturer.) Up to 16 ROIs can be defined for animage; they may overlap each other. Once ROIs are estab-lished, MaZda allows calculation of texture parametersavailable from a list of about 300 different definitions thatcover most of the features proposed in the known litera-ture.The parameters can be stored in text files.One can demonstrate using properly designed test imagesthat some of the higher-order texture parameters, espe-cially those derived from the co-occurrence matrix, showcorrelation to first-order parameters, such as the imagemean or variance.To avoid this unwanted phenomenon,prior to feature extraction, image normalization is prefer-ably performed.Typically, the features computed by MaZda are mutuallycorrelated. Morover, not all of them are equally useful forclassification of given texture classes or to measure prop-erties of the underlying physical object structure. Thus,

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Figure 3. Main steps of digital image texture analysis.

MR scanner

MaZda program

B11 program

Textureclassification

Datapreprocessing

Featureselection

Featureextraction

Imagenormalization

ROI selection

Imageacquisition

there is a need for feature selection.To do that, a subset ofthe computed parameters can be selected manually.MaZda also offers the possibility of selecting the best 10features automatically following two different selectioncriteria. One criterion maximizes the ratio of between-classto within-class variance, which resembles the Fisher coef-ficient.32,33 The second minimizes a combined measure ofprobability of classification error and correlation betweenfeatures.33

The selected best 10 parameters can be used for textureclassification. Still, it is too difficult for a human operatorto imagine and understand relationships between para-meter vectors in the 10-dimensional data space.The B11program of the MaZda package allows further data pro-cessing to transform them to a new, lower-dimension dataspace.The data preprocessing employs linear transforms,such as principal component analysis (PCA)34 and lineardiscriminant analysis (LDA),32 as well as nonlinear oper-ations leading to artificial neural network (ANN)–basednonlinear discriminant analysis (NDA).35 The B11 pro-gram displays both input and transformed data in a formof a scatter-plot graph.B11 allows also raw and transformed data vectors classi-fication, and evaluation of the usefulness of texture fea-tures calculated using MaZda to classification of differ-ent texture classes present in image regions. For dataclassification, the nearest-neighbor classifier (k-NN)36 andANN classifiers33,37,38 are implemented. Neural networksof the architecture defined during training can be testedusing data sets composed of feature vectors calculatedfor images not used for the training.At the time of writing this paper, MaZda version 3.20was available. It implements procedures which allow4:• Loading image files in most popular MRI scanner stan-

dards (such as Siemens, Picker, Brucker, and others).MaZda can also load images in the form of WindowsBitmap files, DICOM files, and unformatted gray-scaleimage files (raw images) with pixel intensity encodedwith 8 or 16 bits.Additionally, details of image acquisi-tion protocol can be extracted from the image infor-mation header.

• Image normalization. There are three options: default(analysis is made for original image); ±3σ (image meanm value and standard deviation σ is computed, thenanalysis is performed for gray scale range between m-3σ and m+3σ); or 1%-99% (gray-scale rangebetween 1% and 99% of cumulated image histogramis taken into consideration during analysis).

• Definition of ROIs. The analysis is performed withinthese regions. Up to 16 regions of any shape can bedefined; they can be also edited, loaded, and saved asdisk files. A histogram of defined ROIs may be visual-ized and stored.

• Image analysis, which is computation of texture featurevalues within defined ROIs.The feature set (almost 300parameters) is divided into following groups: his-togram, co-occurrence matrix, run-length matrix, gra-dient, autoregressive model, and Haar wavelet–derivedfeatures. Detailed description of feature definitions canbe found in reference 33.

• Computation of feature maps that represent distributionof a given feature over an analyzed image. It is possibleto save or load feature maps into a special floating-pointfile format or into Windows Bitmap file.

• Display of image analysis reports, saving, and loadingreports into disk files.

• Feature reduction and selection, in order to find a smallsubset of features that allows minimum error classifi-cation of analyzed image textures.This is performed bymeans of two criteria, as explained above. Selected fea-tures can be transferred to B11 program for furtherprocessing and/or classification.

• Image analysis automation by means of text scripts con-taining MaZda language commands. Scripts allow load-ing analyzed images and their ROI files, running theanalysis, and saving report files on disk.

The number of features computed by MaZda is still increas-ing. New feature groups are added according to suggestionsof the project members and other MaZda users.Also, newprocedures for data processing are being appended.

MaZda module

MaZda generates windows and selected window ele-ments when the program main functions are invoked.The report window contains list of numerical values ofall parameters computed by MaZda for defined ROIs.The selection of texture parameters that are actuallycomputed is made using appropriate options window.Once computed, the reports can be stored as text files.There is a possibility of manual and automatic featureselection that gives lists of 10 parameters maximizingselection criteria. These lists can be stored as text files.Texture parameters can also be computed by MaZda ina rectangular ROI moved automatically around the ana-lyzed image. Parameter values calculated for each ROI


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position can be arranged to form a new image, a so-calledfeature map. Options of feature maps calculations (ROIsize and step size of ROI movement) can be selectedusing appropriate window tools.

B11 module

This program can either be called from the MaZda win-dows or run as a separate MS Windows application. Inputto this program is a file containing data vectors corre-sponding to selected texture parameters. The content ofthis file is displayed in the left panel of B11 window.Theresults of data transformation and classification areshown in the right panel of the window. The input datapreprocessing and classification options can be selectedin the options window.Nonlinear transforms and classifiers implemented in B11employ feedforward ANNs.Training techniques of thesenetworks are described in the User’s Manual of MaZda.33

The clusters formed in the transform data spaces can bevisualized in the form of 2D or 3D scatter plots, whichare generated by B11 program and discussed in the nextsection.

Application example 1

Figure 4a shows an MRI image that contains regions ofdifferent texture, and its respective ROIs are illustrated inFigure 4b. Each circular region in Figure 4a represents across-section through a tube filled with polystyrenespheres of different diameters, which were test objectsmanufactured in the Institute of Clinical and ExperimentalMedicine in Prague. The example experiment describedhere was made to verify whether texture classes repre-sented in the image in Figure 4a could be classified based

on some selected texture parameters computed using theMaZda software.There were 22 images showing different cross-sections ofthe test objects, leading to 22 examples of texture of eachclass. Numerical values of about 300 texture statisticalparameters were computed using MaZda module. Thisstep produced eighty-eight 300-dimensional data vectors.A list of 10 best features was then automatically generatedbased on Fisher coefficient criterion (maximization of theratio F of between-class to within-class variance).The bestparameters were then passed to the B11 module.Thus, theinput to B11 was made of eighty-eight 10-dimensional datavectors, with 22 vectors for each texture class.A scatter plot of the input data in the 3D data space wasmade of first three best texture features. The raw datawere transformed to lower-dimensional spaces, using thePCA, LDA, and NDA projections. In each case, theFisher coefficient F was calculated for the obtained datavectors.They were also classified using a 1-NN classifier,and tested using a leave-one-out technique.36

The PCA projection to a lower-dimensionality data spacedoes not improve the classification accuracy.This can beexplained by the fact that PCA is optimized for repre-sentation of data variability, which is not the same as datasuitability for class discrimination (which is the case ofLDA).Although the LDA gives lower value of the Fishercoefficient F, it eliminates the classification errors. Thus,

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Figure 4. A. Magnetic resonance image cross-section of four test objectsof different texture. B. Four regions of interest (four textureclasses) defined for the image in A.

A B

Figure 5. Magnetic resonance image with eight regions of interest (ROIs)marked with different colors. ps1, ps2, ps3, foam 1, foam 2,and foam 3 are test objects.

noise

foam 1ps3

ps2

ps1

foam 2

foam 3

the lower F coefficient does not necessarily indicateworse classification. Extremely large F can be obtainedusing NDA; however, one should verify (using a separatetest dataset) whether the ANN does not suffer from theovertraining problem.38 An overtrained network does notgeneralize the training data well and, consequently, itmay wrongly classify unseen data points.

Application example 2

Figure 5 shows an MRI image that contains cross-sectionof human scull, along with cross-section of six artificial testobjects (phantoms designed and manufactured to gener-ate standard texture patterns), three on each side of thescull. There are altogether eight ROIs defined for thisimage, each marked with a different color.The numericalexperiment carried out on seven images of the describedcategory aimed to verify whether one can find featuresable to separate mixed, artificial, and natural textures.The experiment was performed in three parts. First, higher-order features were considered only.Those were co-occur-rence matrix, run-length matrix, gradient, and autoregres-sive model–derived parameters. The best of these wereautomatically selected by MaZda. Using the B11 program,the two sets of best features were transformed (PCA andLDA) and the transform data were used as new featuresfor classification (by means of a 1-NN classifier testedusing the “leave-one-out” technique). The results are

shown in Table I, which indicates, that lowest error figure(3/56) was obtained for the LDA data, with no possibilityof perfect classification.In the second part of the experiment, histogram-basedfeatures were added to the higher-order ones used in thefirst part. Table I shows significance of these parametersin region discrimination. Perfect classification wasachieved for LDA-transformed data. One can notice thateven if histogram data do not represent texture, they aresignificant to ROI classification.In the third part, wavelet-based features only were used.Table I shows that perfect ROI discrimination is possibleeven in the raw data space.This family of features seems todescribe texture for classification purposes extremely well.The results collected (Table I) indicate that one cannotspecify in advance which particular texture features willbe useful for discrimination of texture classes, and thatraw-data texture features usually do not allow perfectdiscrimination—some pre-processing is necessary, eg, bymeans of linear or nonlinear discriminant transforms.

Summary

Texture analysis applied to MRI (and other modalities) isone of the methods that provide quantitative informationabout internal structure of physical objects (eg, humanbody tissue) visualized in images.This information can beused to enhance medical diagnosis by making it moreaccurate and objective. Within the framework of aEuropean COST B11 action, a unique package of com-puter programs has been developed for texture quantita-tive analysis in digital images.The package consists of twomodules: MaZda.exe and B11.exe.The modules are seam-lessly integrated, and each of the modules can be run asa separate application. Using the package, one can com-pute a large variety of different texture features and usethem for classification of regions in the image. Moreover,MaZda allows generation of feature map images that canbe used for visual analysis of image content in a new fea-ture space, highlighting some image properties.The pack-age has already been used for quantitative analysis of MRIimages of different kinds,39 such as images of human liver40

and computed tomography images for early detection ofosteoporosis.23 The program appears to be a usefulresearch tool in a PhD student laboratory.41

The MaZda package is available on the Internet.33 So farmore than 300 researchers from all over the world havedownloaded it onto their computers. ❏


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Table I. Number of classification errors (out of 56 samples) for best higher-order features (histogram and wavelet-based features excluded;wavelet-based features excluded; and wavelet-based featuresonly). POE, probability of classification error; ACC, average cor-relation coefficient; PCA, principal component analysis; LDA, lin-ear discriminant analysis.

“As computed” data Standardized data

Raw PCA LDA Raw PCA LDA

Best higher-order features

(histogram and wavelet-based features excluded)

Fisher 22 31 19 22 31 19

POE+ACC 26 28 3 6 4 4


(wavelet-based features excluded)

Fisher 1 1 1 9 9 1

POE+ACC 24 24 0 4 3 2


(wavelet-based features only)

Fisher 3 4 0 0 0 0

POE+ACC 3 6 0 0 0 0

Delivery of MRI images by Dr Richard Lerski of Dundee University andHospital (Figure 2a), Prof Milan Hajek of the Institute of Clinical andExperimental Medicine in Prague (Figure 4), Prof Lothar Schad of German

Cancer Research Centre in Heidelberg (Figures 1 and 5), and Dr MichalStrzelecki (Figure 2) is very much appreciated.

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Metodologías de análisis de la textura paraimágenes de resonancia magnética

Se revisan los métodos de análisis de la textura deimágenes digitales. Se presentan las funciones delMaZda, un programa computacional para el análi-sis cuantitativo de la textura, desarrollado dentrodel marco del Programa Europeo de Cooperaciónen el Campo de la Investigación Científica y Técnica(COST) B11. Se discuten ejemplos de análisis de latextura en imágenes de resonancia magnética.

Méthodologies d’analyse de la texture pourl’IRM

Les méthodes d’analyse de la texture de l’imagenumérisée sont ici passées en revue. Les fonctionsde MaZda, un logiciel développé dans le cadre duprogramme européen COST (Cooperation in theField of Scientific and Technical Research) B11 pourl’analyse quantitative de la texture, sont présentées.Des exemples d’analyse de la texture sur des imagespar résonance magnétique sont discutés.

REFERENCES

1. Lerski R, Straughan K, Shad L, et al. MR image texture analysis—anapproach to tissue characterisation. Magn Reson Imaging. 1993;11:873-887.2. Materka A, Strzelecki M. Texture analysis methods—A review. COST B11Technical Report, Lodz-Brussels: Technical University of Lodz; 1998. Available athttp://www.eletel.p.lodz.pl/cost/publications.html. Accessed 3 December 2003.3. COST B11. Quantitation of Magnetic Resonance Image Texture.Department of Physiology, University of Bergen, Norway. Available athttp://www.uib.no/costb11/. Accessed 3 December 2003.4. MaZda Software. Medical Electronic Division, Technical University of Lodz,Lodz, Poland. Available at http://www.eletel.p.lodz.pl/cost/software.html.Accessed 3 December 2003.5. Rosenfeld A, Kak A. Digital Picture Processing. New York, NY: Academic Press;1982.6. Levine M. Vision in Man and Machine. New York, NY: McGraw-Hill; 1985.7. Brodatz P. Textures—A Photographic Album for Artists and Designers. NewYork, NY: Dover; 1966.8. Haralick R. Statistical and structural approaches to texture. Proc IEEE.1979;67:786-804.9. Serra J. Image Analysis and Mathematical Morphology. London, UK: AcademicPress; 1982.10. Chen Y, Dougherty E. Grey-scale morphological granulometric textureclassification. Opt Eng. 1994;33:2713-2722.11. Weszka J, Deya C, Rosenfeld A. A comparative study of texture measuresfor terrain classification. IEEE Trans Syst Man Cybern. 1976;6:269-285. 12. Julesz B. Experiments in the visual perception of texture. Sci Am.1975;232:34-43.13. Niemann H. Pattern Analysis. Berlin, Germany: Springer-Verlag; 1981.14. Strzelecki M. Segmentation of Textured Biomedical Images Using NeuralNetworks [in Polish]. PhD Thesis, Technical University of Lodz, Poland; 1995.15. Cross G, Jain A. Markov random field texture models. IEEE Trans Patt AnalMach Int. 1983;5:25-39.16. Pentland A. Fractal-based description of natural scenes. IEEE Trans PattAnal Mach Int. 1984;6:661-674.17. Chellappa R, Chatterjee S. Classification of textures using GaussianMarkov random fields. IEEE Trans Acous Speech Sig Proc. 1985;33:959-963.18. Derin H, Elliot H. Modeling and segmentation of noisy and texturedimages using Gibbs random fields. IEEE Trans Patt Anal Mach Int. 1987;9:39-55.19. Manjunath B, Chellappa R. Unsupervised texture segmentation usingMarkov random fields. IEEE Trans Patt Anal Mach Int. 1991;13:478-482.20. Strzelecki M, Materka A. Markov random fields as models of texturedbiomedical images. Proceedings of the 20th National Conference Circuit TheoryElectronic Networks 1997. Kolobrzeg, Poland; 1997:493-498.21. Chaudhuri B, Sarkar N. Texture segmentation using fractal dimension.IEEE Trans Patt Anal Mach Int. 1995;17:72-77.

22. Kaplan L Kuo C. Texture roughness analysis and synthesis via extendedself-similar (ESS) model. IEEE Trans Patt Anal Mach Int. 1995;17:1043-1056.23. Materka A, Cichy P, Tuliszkiewicz J. Texture analysis of X-ray images fordetection of changes in bone mass and structure. In: Pietikainen M, ed.Texture Analysis in Machine Vision, Series in Machine Perception and ArtificialIntelligence. Vol 40. Singapore: World Scientific; 2000:189-195.24. Rosenfeld A, Weszka J. Picture recognition. In: Fu K, ed. Digital PatternRecognition. Berlin, Germany: Springer-Verlag; 1980:135-166.25. Daugman J. Uncertainty relation for resolution in space, spatial fre-quency and orientation optimised by two-dimensional visual cortical filters.J Opt Soc Am. 1985;2:1160-1169.26. Bovik A, Clark M, Giesler W. Multichannel texture analysis using localisedspatial filters. IEEE Trans Patt Anal Mach Int. 1990;12:55-73.27. Mallat S. Multifrequency channel decomposition of images and waveletmodels. IEEE Trans Acous Speech Sig Proc. 1989;37:2091-2110.28. Laine A, Fan J. Texture classification by wavelet packet signatures. IEEETrans Patt Anal Mach Int. 1993;15:1186-1191.29. Lu C, Chung P, Chen C. Unsupervised texture segmentation via wavelettransform. Patt Rec. 1997;30:729-742.30. Brady M, Xie Z. Feature selection for texture segmentation. In: BowyerK, Ahuja N, eds. Advances in Image Understanding. Los Alamitos, Calif: IEEEComputer Society Press; 1996.31. Lu C, Chung P, Chen C. Unsupervised texture segmentation via wavelettransform. Patt Recog. 1997;30:729-742.32. Fukunaga K. Introduction to Statistical Pattern Recognition. San Diego, Calif:Academic Press; 1991.33. Materka A. MaZda User’s Manual. Available at http://www.eletel.p.lodz.pl/cost/ Accessed 3 December 2003.34. Krzanowski W. Principles of Multivariable Data Analysis, Oxford, UK: OxfordUniversity Press; 1988.35. Mao J, Jain A. Artificial neural networks for feature extraction and mul-tivariate data projection. IEEE Trans Neural Networks. 1995;6:296-316.36. Duda R, Hart P. Pattern Classification and Scene Analysis. New York, NY:Wiley; 1973.37. Freeman J, Skapura D. Neural Networks—Algorithms, Applications andProgramming Techniques. Redwood City, Calif: Addison-Wesley; 1991.38. Hecht-Nielsen R. Neurocomputing. Reading, Mass: Addison-Wesley; 1989.39. Materka A, Strzelecki, Lerski R, et al. Feature evaluation of texture testobjects for magnetic resonance imaging. In: Pietikainen M, ed. TextureAnalysis in Machine Vision, Series in Machine Perception and Artificial Intelligence.Vol 40. Singapore: World Scientific; 2000:197-206.40. Jirak D, Dezortova M, Taimy P, et al. Texture analysis of human liver. JMagn Reson Imaging. 2002;15:68-74.41. Szczypinski P, Kociolek M, Materka A, et al. Computer program forimage texture analysis in PhD student laboratory. ICSES 2001. Lodz.2001:255-261.

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2004 · Volume 6 · No. 1

Predictors of Response to Treatment inNeuropsychiatry

EditorialJean-Paul Macher, Marc-Antoine Crocq ________________________ 1

State of the artGenetic variation and pharmacogenomics:concepts, facts, and challengesMargret R. Hoehe, Thomas Kroslak ____________________________ 5

Basic researchThe future of genetic testing for drug response Deborah J. Morris-Rosendahl, Bernd L. Fiebich______________ 27

Pharmacological aspectsSex-dependent modulation of treatment responseDavid R. Rubinow, Molly Moore ______________________________ 39

Current perspectives in the management of treatment-resistant depressionRajesh M. Parikh, Barry D. Lebowitz __________________________ 53

Treatment-refractory schizophreniaAsaf Caspi, Michael Davidson, Carol A. Tamminga __________ 61

Differing response to antipsychotic therapy in schizophrenia: pharmacogenomic aspectsManfred Ackenheil, Klaus Weber ______________________________ 71

PosterSpectral EEG sleep profiles as a tool for prediction of clinical response to antidepressant treatmentJean-Paul Macher, Rémy Luthringer, Luc Staner ______________ 78

Clinical researchTreatment goals: response and nonresponseJean-Paul Macher, Marc-Antoine Crocq ______________________ 83

Poor response to treatment: beyond medicationCésar Carvajal __________________________________________________ 93

Clinicians’ predictions of patient response to psychotropic medicationsPierre Schulz, Patricia Berney ________________________________ 105

2004 · Volume 6 · No. 2

Neuroplasticity

EditorialJean-Paul Macher, Marc-Antoine Crocq______________________ 113

State of the artStructural plasticity of the adult brain: how animal models help us understand brain changes in depression and systemic disorders related to depressionBruce S. McEwen ______________________________________________ 119

Basic researchStructural plasticity of the adult brain Fred H. Gage __________________________________________________ 135

Regulation of cellular plasticity and resilience by mood stabilizers: the role of AMPA receptor trafficking Jing Du, Jorge A. Quiroz, Neil A. Gray, Steve T. Szabo,Carlos A. Zarate Jr, Husseini K. Manji ______________________ 143

Pharmacological aspectsNeural plasticity: consequences of stress and actions of antidepressant treatment Ronald S. Duman ______________________________________________ 157

Cellular consequences of stress and depressionEberhard Fuchs, Gabriele Flügge ____________________________ 171

Clinical researchCellular abnormalities in depression: evidence from postmortem brain tissueCraig A. Stockmeier, Grazyna Rajkowska ____________________ 185

Neuroplasticity in mood disordersWayne C. Drevets ______________________________________________ 199

Cellular plasticity and resilience and the pathophysiology of severe mood disordersDennis S. Charney, George DeJesus, Husseini K. Manji ____ 217

Free papersTexture analysis of the brain: from animal models to human applicationsJean-François J. Nedelec, Olivier Yu,Jacques Chambron, Jean-Paul Macher________________________ 227

Problems in texture analysis with magnetic resonance imagingLothar R. Schad ______________________________________________ 235

Texture analysis methodologies for magnetic resonance imagingAndrzej Materka ______________________________________________ 243

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