Psychiatry Research: Neuroimaging 172 (2009) 55–60

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Psychiatry Research: Neuroimaging

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Pituitary gland volume in currently depressed and remitted depressed patients

Valentina Lorenzettia,b,c,d, Nicholas B. Allenb,c, Alex Fornitoa, Christos Pantelisa,Giovanni De Platod, Anthony Anga, Murat Yücela,b,c,⁎aMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, VIC. AustraliabORYGEN Research Centre, Melbourne, VIC. AustraliacThe University of Melbourne and Melbourne Health, VIC. AustraliadDepartment of Psychology, University of Bologna, Viale Berti Pichat 5, 40126, Bologna, Italy

⁎ Corresponding author.MelbourneNeuropsychiatry CSt, Carlton South, Victoria 3053, Australia. Tel.: +61 3 834

E-mail address: murat@unimelb.edu.au (M. Yücel).

0925-4927/$ – see front matter © 2008 Elsevier Irelanddoi:10.1016/j.pscychresns.2008.06.006

A B S T R A C T

A R T I C L E I N F O

Article history:

Major depressive disorder ( Received 13 January 2008Received in revised form 11 April 2008Accepted 12 June 2008

Keywords:HPA axisStressImagingBrain

MDD) has been associated with increased pituitary gland volume (PGV), which isthought to reflect stress-related dysregulation related to hypothalamic-pituitary-adrenal (HPA) axis activity.However, it is unclear whether PGV alteration reflects a “dynamic” change related to current mood instabilityor if it is a stable marker of illness vulnerability. In this study we investigated PGV in currently depressedpatients (cMDD) (n=31), remitted depressed patients (rMDD) (n=31) and healthy controls (n=33), using 1.5Teslamagnetic resonance imaging (MRI). The groupswerematched for age and gender.We found no significantPGV, intra-cranial volume (ICV) or whole brain volume (WBV) differences between cMDD patients, rMDDpatients and healthy controls. Furthermore, PGV was not correlated with clinical features of depression (e.g.,age of onset; number of episodes; and scores on subscales of the Beck Depression Inventory, the Positive Affectand Negative Affect Scale, and the Mood and Anxiety Symptom Questionnaire). In conclusion, PGV does notappear to be a marker of current or past MDD in adult patients.

© 2008 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

Abnormal hypothalamic–pituitary–adrenal (HPA) axis activity isthought to reflect stress-related dysregulation and is a consistent findingin patients with mental illnesses, particularly those affected by majordepressive disorder (MDD) (Mitchell, 1998; Jiang et al., 2000; Sheline,2000; Pariante and Miller, 2001; Davidson et al., 2002; Pariante, 2003;Parker et al., 2003; Swaab et al., 2005). It has been suggested that chronicHPA activity leads to excessive exposure of certain brain regions toglucocorticoids (Varghese and Brown, 2001), which can have adverse,potentially neurotoxic, effects (Fejò del Mello et al., 2003; Garner, 2004;Lucassen et al., 2006). Specifically, several studies to date indirectlysupport the notion that abnormal HPA activitymayaffect themorphologyof brain regions involved in the stress response in patients with MDD(Nemeroff et al., 1992; Sheline, 2000; Davidson et al., 2002; Fejò delMelloet al., 2003; Pariante, 2003; Garner, 2004; Herman et al., 2005; Swaabet al., 2005; Lucassen et al., 2006).

Thepituitarygland is an integral partof theHPAaxis andmaybeoneofthe regions most affected by stress dysregulation occurring in MDD(Krishnan et al., 1991; Axelson et al., 1992; Schwartz et al., 1997;MacMaster andKusumakar, 2004;MacMaster et al., 2006). Several studieshave found enlarged pituitary gland volume (PGV) in patients with MDD

entre, c/oAGBuilding,161 Barry4 1877; fax: +61 3 9348 0469.

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(Krishnan et al.,1991;MacMaster and Kusumakar, 2004;MacMaster et al.,2006), which has been hypothesized to represent an increased numberand/or size of corticotropin-releasing hormone (CRH) cells in the area.Such conclusions are consistent with evidence that enlarged pituitarygland volume reflects an increase in the size and number of corticotrophs,the cells that produce and secrete adrenocorticotrophic hormone (ACTH)(Krishnan et al., 1991; Axelson et al., 1992; Pariante et al., 2004b; Garneret al., 2005). This alteration in corticotrophic cells has been previouslyhypothesized to be a consequence of either HPA axis hyperactivity (HPAhypothesis of depression) or of a specific dysfunction of a subgroupof CRHneurons that provokes HPA axis hyperactivity (CRH-hypothesis of de-pression) (Pariante andMiller, 2001; Swaab et al., 2005). A clearer under-standing of the volumetric changes in the pituitary is likely to help shedlight on the role of this structure in the neurobiology of theMDD (Axelsonet al., 1992).

Despite the robust evidence implicating theHPA axis inMDD (Axelsonet al., 1992; Christensen and Kessing, 2001; Swaab et al., 2005), only ahandful of studies have examined pituitary gland volume (PGV) in de-pression (Krishnan et al., 1991; Axelson et al., 1992; Sassi et al., 2001;MacMaster and Kusumakar, 2004; MacMaster et al., 2006). Out of fourPGV studies in unipolar depression, three reported increased PGV(Krishnan et al., 1991; MacMaster and Kusumakar, 2004; MacMasteret al., 2006), while one found no significant difference in PGV (Sassi et al.,2001) in currently depressed patients compared to healthy controls. Thestudyof Axelson et al. (1992) examined the relationship between PGV andthe Dexamethasone Suppression Test (DST) in depressed patients, but didnot have a groupof healthy controls, and itwas not informative about PGV

56 V. Lorenzetti et al. / Psychiatry Research: Neuroimaging 172 (2009) 55–60

differences betweenMDDpatients andhealthy subjects. Another study bySchwartz et al. (1997) reported no PGV difference between adult depres-sed patients and healthy controls, but they have examined the SeasonalAffectiveDisorder, a depression subtype, therefore their resultsmaynotbecomparable to ours, as we assessed unipolar depression specifically. Suchknowledge could elucidate whether PGV may be useful as a biologicalmarker of depression vulnerability (a trait feature) or a state marker ofdepressive illness. As a consequence, it is not yet clear from the availablestudies if PGV alteration reflects transient periods of HPA axis dysregula-tion, such as would occur during a depressive episode, or if it is a traitfeature ofMDD, suggesting that it may be stable and chronic in depressedpatients.

In the current study we investigated PGV in currently depressedpatients (cMDD) and individuals with a history of depression but whoare currently in remission (rMDD). This approachenabledus to examineimportant questions regarding whether differences in PGV reflect stateor trait influences. Based on previous findings of increased HPA activityin patients with cMDD (Sheline, 2000; Pariante and Miller, 2001;Davidson et al., 2002; Pariante, 2003; Parker et al., 2003; Swaab et al.,2005), and the potential for reversibility of structural brain changesfollowing recovery fromdepression(e.g., adrenal gland in adult humans(Rubin et al., 1995) and chronic corticosteroid or stress exposure(e.g., hippocampal dentate gyrus in mammals (Lucassen et al., 2006),we hypothesized that enlarged PGV would be a state marker of de-pressive illness. As such, we predicted that the cMDD patients wouldhave an enlarged PGV relative to both rMDD and healthy controls.

2. Methods

2.1. Subjects

Ninety-five subjects were recruited into the study of which 31received a current diagnosis of MDD, 31 were currently medically

Table 1Demographic, neuropsychological and clinical characteristics of the sample

Variable Mean±S.D.

cMDD rMDD

Age 32.52±8.28 35.07±9.96Female (male) 22 (7) 18 (9)Melancholic/atypical 10/3 –

First episode/recurrent 7/22 –

Age of onset 21.07±7.95 26.04±9.43Number of episodes 3.67±3.36 3.09±2.63Medication past 6 monthsyes/no

21/6 12/13

Current anxiety disorderyes/no

18/10 4/23

IQ 104.86±8.74 111.42±9.93WTAR 107.48±11.39 111.74±8.93BDI 36.83±8.93 13.04±11.72MASQ GD Mixed 50.50±7.77 40.44±10.32

GD Dep 47.32±9.19 35.04±11.67GD Anx 32.18±8.73 24.72±7.66A Anx Arousal 42.00±12.15 28.87±7.65High PA 43.57±13.47 65.00±12.35Loss of Int 31.61±6.35 23.48±6.78

PANAS PA 21.61±6.45 28.72±7.95NA 21.21±8.50 14.15±4.70

AUDIT 5.37±6.22 5.69±4.77Pituitary volumes (mm3) 727.22±139.21 740.12±123.42Whole brain volumes (mm3) 1,237,176±124,113.14780 1,232,334±132,95Intra-cranial volumes (mm3) 1,477,353.4±13,810,530,851 1,469,842.6±150,2

cMDD = currently depressed patients; rMDD = remitted depressed patients; H.C. = healthy contro(WASI); BDI=BeckDepression Inventory;MASQ=MoodandAnxietySymptomQuestionnaire;GDMarousal; High PA = high positive affect; Loss of Int = loss of interest; PANAS = Positive and NegatiIdentification Test; PGVs = pituitary gland volumes; WBVs = whole brain volumes; ICV = intra cracontrols; c = current depressedNpast depressedNcontrols; d = current depressedbpast depresseddifference between the degrees of freedom across different measures is due to the fact that we d

and psychiatrically well individuals with a previous history of a diag-nosable MDD, and 33 were healthy comparison subjects who werenot significantly different in age, gender and measures of educationand intelligence. Six participants were excluded from further anal-yses as they were discovered to have brain abnormalities/incidentalfindings (periventricular and frontal hyper-intensities) detected inthe T2 scan. Two of those excluded were currently depressed (bothmales with severe recurrent MDD, mean age±S.D.=35.00±9.89,mean IQ±S.D.=101.00±9.89) and four were remitted patients (allfemales, mean age±S.D.=40.75±6.29, mean IQ±S.D.=111.25±4.64).There were no notable differences between the group excludedbecause of MRI abnormalities and the main sample on any clinicalor demographical measures. The main baseline demographic dataand clinical features of the remaining three groups are presented inTable 1.

The patients were recruited through advertisement in the localmedia from the general community and via outpatient mental healthclinics (specifically ORYGEN Youth Health and the University ofMelbourne Psychology Clinic, Melbourne, Australia). The local internalreview board (Mental Health Research & Ethics Committee, Mel-bourne Health, Melbourne, Australia) approved the study protocol,and the participants gave written informed consent after a completedescription of the study.

Participant's inclusion criteria were: age between 18 and 50 years,English as a preferred language, current IQN70, and colour visionand acuity within normal (or corrected-to normal) limits. Exclusioncriteria were: a history of significant head injury, seizures, impairedthyroid function and steroid use, neurological diseases, electro-convulsive therapy within the past 6 months. All the depressedsubjects with another current Axis I psychiatric disorder (other thananxiety disorders) were excluded, as well as any healthy controlswho had a personal history of psychiatric illness, drug or alcoholdependence.

F(df)1 P

H.C.

34.03±9.91 0.523 (2, 86) 0.59521 (12) 1.159(2)⁎ 0.560– –

– –

– 4.558 (1, 54) 0.037 a

– 0.369 (1, 38) 0.547– 118.602 (4)⁎ b0.0005

– 84.154(2)⁎ b0.0005

111.12±10.86 4.034 (2, 85) 0.021 b

111.64±12.29 1.407 (2, 86) 0.253.55±4.08 120.572 (2, 86) b0.0005 c

27.87±8.28 49.209 (2, 81) b0.0005 c

19.47±7.23 66.848 (2, 82) b0.0005 c

16.41±6.39 37.307 (2, 82) b0.0005 c

21.97±4.44 40.472 (2, 79) b0.0005 c

81.10±14.27 50.194 (2, 80) b0.0005 d

14.72±5.04 58.682 (2, 82) b0.0005 c

32.94±7.26 18.569 (2, 82) b0.0005 b

11.19±1.57 24.977 (2, 83) b0.0005 e

4.61±2.99 0.415 (2, 83) 0.662710.28±103.28 0.452 (2, 86) 0.638

4.97718 1,250,567±141,214.81640 0.154 (2, 86) 0.85856.46469 1,492,735.8±143,141.43347 7.584 (2, 83) 0.117

ls; IQ = Intelligence Quotient as measured by the Wechsler Abbreviated Scale of Intelligenceixed=generaldistress;GDDep=generaldepression;GDAnx=general anxiety;AA=anxious

ve Affect Schedule; PA = positive affect; NA = negative affect; AUDIT= Alcohol Use Disordersnial volumes; a = current depressedbpast depressed; b = current depressedbpast depressed,bcontrols; e = current depressedNpast depressed, controls; ⁎ =χ2 (df)− = not calculable; 1 theid not have information relative to all the patients for the measured variables.

Fig. 1. Pituitary visualized from a coronal slice.

57V. Lorenzetti et al. / Psychiatry Research: Neuroimaging 172 (2009) 55–60

2.2. Clinical measures

All participants underwent a clinical and neuropsychologicalassessment conducted at ORYGEN Youth Health, Melbourne. Traineepsychologists (DM, OS), experienced in recruiting and assessingclinical populations, screened participants with the Structured ClinicalInterview for DSM-IV (SCID-IV-TR) (First et al., 2001), and a seriesof inventories, including the Beck Depression Inventory (BDI) (Beckand Steer, 1987), the Mood and Anxiety Symptom Questionnaire(MASQ) (Watson et al., 1995), Positive Affect and Negative Affect Scale(PANAS) (Watson et al., 1988) and the Alcohol Use Disorders Iden-tification Test (AUDIT) (Babor et al., 1992). We also obtained measuresof premorbid and current intelligence using theWechsler Test of AdultReading (WTAR) (Corporation, 2001) and the Wechsler AbbreviatedScale of Intelligence (WASI) (Corporation, 1999), respectively. The ad-ministration of the tests took approximately 2.5 h, and all the par-ticipants received reimbursement of $20 AUD.

We also assessed the presence/absence of medication during the6 months before the screening in the subgroup with a positive historyof lifetime medication (see Table 1). Thirty-three patients were on astable medication regime for at least 6 months preceding the scan. Ofthese, seventeen patients were on SSRIs (e.g., fluoxetine, fluxovamine,paroxetine, sertraline, citalopram, escitolopram), four on SSNRI(e.g., venlafaxine), three on NaSSAs (e.g., mirtazapine), two on TCAs(e.g., amitriptyline, doxepin), two on MAOIs (e.g., tranylcypromine,moclobemide), one on lithium, one on NRIs (e.g., reboxetine). Threepatients were receiving combination therapy (paroxetine and benzo-diazepine, escitolopram and mirtazapine; lithium and dothiepin, re-spectively), while nine were medication-naïve.

2.3. MRI data acquisition

All the subjects were scanned with a Siemens MAGNETOMAvanto 1.5-Tesla scanner at the St. Vincent's Hospital Melbourne,Victoria. Each scanning session took approximately 1 h, andall the participants received $40 AUD reimbursement. A structuralT1-weighted scan was performed. Image parameters were as follows:time to echo=2.3 ms, time repetition=2.1 ms, flip angle=15°, matrixsize=256×256, voxel dimension=1×1×1 mm. Additionally, MRIabnormalities were assessed using a high-resolution T2-weightedscan. All images were analysed on a LINUX workstation usingANALYZE 7.5 (Mayo) to delineate the regions of interest and generatevolumetric measures.

2.4. Image analysis

2.4.1. Pituitary volumeEach pituitary was traced by the same investigator (VL) who was

blinded to group membership. We utilized a modified version(Pariante et al., 2004b) of the method previously used by ourselvesand others (Sassi et al., 2001; MacMaster and Kusumakar, 2004;MacMaster et al., 2006). Specifically, images were traced in the coronalplane, where the pituitary is best visualized (Garner, 2004; Parianteet al., 2004b; Garner et al., 2005) (see Fig. 1). The mean number ofcoronal slices traced per case was 12.08 (range 8–15), which were1 mm thick. We excluded the infindibular stalk from the tracing, butincluded the hyper-intense region in the posterior pituitary, which isthought to represent high levels of vasopressin concentrations (Sassiet al., 2001; Garner, 2004; Pariante et al., 2004b; Garner et al., 2005).The borders of the pituitary were clearly defined by the diaphragmasellae, superiorly; the sphenoid sinus, inferiorly, and the cavernoussinuses, bilaterally (Garner, 2004; Pariante et al., 2004b; Garner et al.,2005). PGV was calculated by summing the volumes of all the tracedslices. Intraclass correlation coefficients (absolute agreement) for intraand inter-rater reliabilities, assessed on ten randomly selected images,were 0.94 and 0.98, respectively.

2.4.2. Whole brain volumesWhole brain volumes (WBV) were estimated using tools in the FSL

software library (website). Briefly, all images were stripped ofextracerebral tissue using BET (Brain Extraction Tool) (Smith, 2002).Each voxel was classified into grey, white, or CSF (cerebro-spinal fluid)using FAST (Zhang et al., 2001). The number of grey and white mattervoxels was then summed to obtain the WBV estimate.

2.4.3. Intracranial cavityIntracranial volume (ICV) was delineated from a sagittal reformat

of the original 3D-dataset. Themajor anatomical boundarywas the duramater below the inner table and generally visible as awhite line.Wherethe duramaterwas not visible, the cerebral contourwas outlined. Otherlandmarks included the undersurfaces of the frontal lobes, the dorsumsellae, the clivus, and the posterior arch of the craniovertebral junction(Eritaia et al., 2000).

2.5. Statistical analysis

We assessed the distribution of the demographic, neuropsycholo-gical and clinical variables across the groups by running a series of chisquare (χ2) and univariate analysis of variance (ANOVA) (see Table 1).

We used ANCOVA with age as a covariate and diagnosis (cMDD,rMDD, controls) and gender as between-subjects factors to examinegroup differences in PGV. Bivariate correlation analyses were used toexamine the association between PGV and other relevant demo-graphic (age), neuropsychological (current intelligence quotient), andclinical variables (age of onset; number of episodes; scores at the BDI,MASQ and PANAS subscales). Finally, we used Student's t-test toexamine the effects of medication status on PGV by dividing theclinical sample (current and past MDD patients) into those taking ornot taking medication during the 6 months prior to the study. For allcomparisons, α= .05.

3. Results

3.1. Overall group comparisons

There were no group differences on any of the clinical or demo-graphic measures, nor were there any differences in WBV or ICV(see Table 1). ANCOVA assessing group differences in PGV found no

58 V. Lorenzetti et al. / Psychiatry Research: Neuroimaging 172 (2009) 55–60

effect of diagnosis (F(2, 1.901)=0.343, P=0.746), or interaction be-tween diagnosis and gender (F(2, 82) = 0.539, P = 0.585). Therewas however, a significant main effect of gender (F(1, 1.954) = 23.867,P = 0.041), indicating that males had smaller PGV than females ac-ross both groups (males,M =663.79, S.D. = 106.36, females,M = 752.88,S.D. = 118.02).

We found no significant correlation between PGV and age of onset(r = 0.226, P=0.77), number of episodes (r=−0.049, P=0.747), or anyof the other clinical measures (e.g., scores at the BDI, MASQ subscales,PANAS subscales AUDIT). Dividing the patient group into those whowere and thosewhowere not takingmedication 6months prior to thescanning revealed no effect of medication status (t=0.391, df=50,P=0.698).

4. Discussion

Our findings suggest that PGV is not altered in patients with acurrent or past Major Depressive Disorder (MDD). The evidence todate suggests that PGV is not affected in adult MDD patients who areeither currently depressed or in remission, as the studies that havepreviously examined adult depressed patients did not find evidencefor PGV alterations.

Our findings are inconsistent with those of Krishnan et al. (1991)and MacMaster and colleagues (2004, 2006), who have reported in-creased PGV in their samples of currently depressed patients. Com-parison of our findings with those of Krishnan et al. (1991) suggeststheir finding may have been driven by an elderly subgroup of patients,which had an age band (e.g. over 50 years old) not represented in oursample. Indeed, their mid-adult depressed subgroup did not showpituitary alterations. Interestingly, MacMaster and Kusumakar (2004)and MacMaster and colleagues (2006), who also found an associationbetween depression and PGV enlargement, examined adolescentdepressed patients, suggesting that PGV may be a state marker ofdepression in younger and older samples, but not through middleadulthood. Accordingly, Sassi et al. (2001) who studied a euthymicsamplewith age range similar to ours, also failed tofind anydifferencesin PGV. Together, these results suggest that adolescent neurodevelop-mental and ageing neurodegenerative changes may play an importantrole in the interaction between MDD and PGV (Krishnan et al., 1991;MacMaster et al., 2006), and that a relationship between PGV andMDDis not apparent in younger adult patients. Fronto-temporal brainregions undergo neural changes during adolescence (e.g., neurodeve-lopment) and ageing (e.g., neurodegeneration) (Giedd et al., 1999;Sowell et al., 1999, 2001, 2004; Gotgay et al., 2004). These regionsinclude areaswhich are involved in theHPA axis regulation, such as thehippocampus (Gold et al., 1984; Holsboer et al., 1987; Sapolsky et al.,1991; Young et al., 1991; Sheline, 2000; Pariante and Miller, 2001;Davidson et al., 2002; Pariante, 2003; Garner, 2004), the amygdala(Davidson et al., 2002), the anterior cingulate cortex (ACC) (Chao et al.,1989; Diorio et al., 1993; Cerqueira et al., 2005) and the prefrontalcortex (PFC) (Szot et al., 1994; Sheline, 2000; Herman et al., 2003). Assuch, the interaction betweenMDDand age-related brain changesmayfurther impair the HPA axis dysregulation in elderly and adolescentdepressed patients, leading to an alteration of the PGV only in theseclinical subgroups but not in MDD adults.

One possible explanation for the negative finding regarding evi-dence for PGV enlargement in the cMDD group is that 21/27 of thesepatients were on stable medication during the 6 months prior to thescan. Given that the literature to date consistently demonstrates thatantidepressant medication directly suppress HPA axis activity (Mitch-ell, 1998; Pariante, 2003; Barden, 2004; Pariante et al., 2004a; Masonand Pariante, 2006), it may be that medication may have attenuatedany HPA dysregulation occurring during the depressive episode, con-sequently reducing any PGV enlargement. Thus, while we found nodifferences between patients who were and were not medicated inthe 6 months prior to scanning, the medicated group comprised pre-

dominantly cMDD patients, while the unmedicated group comprisedpredominantly rMDD patients. No study has investigated antidepres-sant effects on PGV in adult depressed patients, although reports inadolescent samples provide evidence for alterations in treatment-naïve patients (MacMaster and Kusumakar, 2004; MacMaster et al.,2006). Also, a study by MacMaster et al. (2007) found a relationshipbetween PGV and medication effects (e.g. volumetric increasefollowing antipsychotic treatment), suggesting that the PGV maybe a bio-marker of medication effects. In our study, we used a rela-tively coarsemeasure ofmedication (presence/absence during the past6 months) and further studies are required to investigate the rela-tionship between more detailed parameters of antidepressant treat-ment (e.g. length, dosage), and stage of illness. Moreover, we could notassess the gender by medication interaction due to sample size con-strains. We could only examine medication effects in a separate anal-ysis collapsed across males and females.

A number of studies have consistently found that pituitary sizechanges with gender in healthy adults (Lurie et al., 1990; Takano et al.,1999; Kato et al., 2002; MacMaster et al., 2007). Accordingly, ourfindings support a strong gender effect on pituitary volume, withmales exhibiting smaller pituitary volumes than females in all theanalyses. This may be due to the interaction between the HPA axis andthe hypothalamic–pituitary–gonadal (HPG) axis, which regulates theproduction of sex hormones (Swaab et al., 2005). The relationshipbetween these systems has previously been hypothesized to play arole in the higher prevalence of mood disorders in women comparedto men, and the pituitary gland may be implicated in mechanisms ofgender-related vulnerability to stress (Swaab et al., 2005). However,we did not find any gender×diagnosis interaction effects on the PGV,and it is still unclear whether the pituitary gland may play a specificrole in the gender-related vulnerability to MDD rather than a moregeneral role in the gender-related vulnerability to stress.

The participants were not matched for intelligence quotient (IQ)measures, as cMDD patients had a significantly lower IQ compared tothe other groups. We do not know if it may have affected the PGV, butto our knowledge no study to date has found a relationship betweenIQ and PGV measures. Moreover, a lower IQ is one of the variablesrelated to the presence of a current depressive episode and thisvariable has helped us to characterize the different clinical popula-tions that we have considered.

Our sample was composed of only outpatients, as the recruitmentwas community-based. Therefore, we may not have seen the range ofseverity and chronicity of MDD to observe structural alterations, asthese factors have been hypothesized to be predictive of volumetricchanges in brain structures involved in the stress response andunderlying depression symptomatology. Future studies may be usefulin determining whether pituitary volume may be affected in moreseverely depressed samples.

Whether our PGV findings reflect HPA activity is difficult to deter-mine, as we acquired no direct measures of HPA function in this study,and this may limit the interpretation of our data.

Also, while a previous study by MacMaster et al. (2006) has pro-vided evidence that a positive family history of mental illness mayaffect the PGV of depressed patients, we did not assess family historyin this sample.

The tracing protocol that we used, as well as the ones used by theprevious studies on the PGV (Krishnan et al., 1991; Axelson et al., 1992;Schwartz et al., 1997; Sassi et al., 2001; MacMaster and Kusumakar,2004; Pariante et al., 2004b, 2005; Garner et al., 2005; MacMasteret al., 2006), did not enable us to distinguish the anterior from theposterior lobe of the pituitary gland. The anterior pituitary containscorticotrophs that produce ACTH (Elster, 1993) while the posteriorpituitary secretes oxytocin and vasopressin (Elster, 1993). Thus, theanterior portion is most likely to show dynamic changes with stress,and measuring the whole PGV in this study may have obscured theseeffects. However, the anterior pituitary constitutes more than 80% of

59V. Lorenzetti et al. / Psychiatry Research: Neuroimaging 172 (2009) 55–60

the overall pituitary gland (Pariante et al., 2004b), and the reportedresults are therefore more likely to reflect changes in the anteriorrather than in the posterior pituitary.

Our study has a number of strengths compared with previousstudies on the PGV in adults with unipolar depression. Firstly, we haveprovided a bigger and more homogeneous sample of unipolar adultdepressed patients, compared to the ones of Krishnan et al. (1991)(e.g., 19 depressed patients of which 3 were bipolar, compared to19 healthy controls) and Sassi et al. (2001) (e.g., 13 unipolar patients, ofwhich seven were euthymic, compared with 34 healthy controls).Secondly, we considered a more homogeneous age range of adults(e.g., 18–50 years), compared to the ones considered by Krishnan et al.(1991) (e.g., 23–80 years) and Sassi et al. (2001) (e.g., 24–59 years),which enabled us to rule out the potential confounds of neurodegen-erative and other age-related changes.. Finally, we have selected amore gender-balanced sample than the studies of Krishnan et al.(1991) (e.g., 14 females, 5 males in each group) and Sassi et al. (2001)(e.g., 12 females and one male in the clinical group; 14 females and22 males in the control group). As a consequence, we believe that ourdata may be more “generalizable” than the previous studies.

Consistent with the data of Sassi et al.'s (2001) study, where effectsizes (e.g., negligible effect, d=0.11) are very close to ours (e.g., smalleffect, d=0.19), we did not find evidence that the PGV is a state markerof current depressive illness. Also, our findings suggest that the PGV isnot a trait marker of vulnerability to MDD. Future studies are neededto understand whether and at what extent demographic (e.g., age,gender) and clinical (e.g., severity and length of illness, medication)variables may mediate the relationship between PGV and depression,as this area remains still unexplored.

Acknowledgments

This research was supported by grants from the AustralianResearch Council (I.D. DP0557663). Dr Yücel was supported by aNational Health and Medical Research Council of Australia ClinicalCareer Development Award (509345), a NHMRC Program Grant (I.D.350241) and the Colonial Foundation. Dr Fornito is supported by a J.N.Peters Fellowship. Neuroimaging analysis was facilitated by theNeuropsychiatry Imaging Laboratory managed by Ms Bridget Soulsbyat the Melbourne Neuropsychiatry Centre and supported by Neuros-ciences Victoria. Participant recruitment and assessment have beendone by Ms Orli Schwartz and Ms Diana Maud. Valentina Lorenzetti issupported by an overseas research project scholarship of theDepartment of Psychology, University of Bologna.

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