Neuroimaging Biomarkers in Mild Traumatic Brain Injury (mTBI) · matic brain injury (TBI)—is that...

REVIEW

Neuroimaging Biomarkers in Mild Traumatic Brain Injury(mTBI)

Erin D. Bigler

Received: 18 February 2013 /Accepted: 7 August 2013 /Published online: 24 August 2013# Springer Science+Business Media New York 2013

Abstract Reviewed herein are contemporary neuroimagingmethods that detect abnormalities associated with mild trau-matic brain injury (mTBI). Despite advances in demonstratingunderlying neuropathology in a subset of individuals whosustain mTBI, considerable disagreement persists in neuro-psychology about mTBI outcome and metrics for evaluation.This review outlines a thesis for the select use of sensitiveneuroimaging methods as potential biomarkers of brain injuryrecognizing that the majority of individuals who sustain anmTBI recover without neuroimaging signs or neuropsycho-logical sequelae detected with methods currently applied.Magnetic resonance imaging (MRI) provides several mea-sures that could serve as mTBI biomarkers including thedetection of hemosiderin and white matter abnormalities, as-sessment of white matter integrity derived from diffusiontensor imaging (DTI), and quantitative measures that directlyassess neuroanatomy. Improved prediction of neuropsycho-logical outcomes in mTBI may be achieved with the use oftargeted neuroimaging markers.

Keywords Mild Traumatic Brain Injury (mTBI) .

Concussion . Neuropsychology . Neuroimaging .

Biomarkers . Neuropathology . Brain damage .

Cognitive and neurobehavioral sequelae

Abbreviation

ACR Anterior corona radiataANAM Automated neurological assessment metricsCC Corpus callosumCT Computed tomographyCTE Chronic traumatic encephalopathyCVLT California verbal learning testDAI Diffuse axonal injuryDKI Diffusion kurtosis imagingDMN Default mode networkDOI Day-of-injuryDSS Disability status scaleDTI Diffusion tensor imagingEF Executive functionEN Executive networkFE Finite elementFLAIR Fluid attenuated inversion recoveryfMRI Functional magnetic resonance imagingGCS Glasgow coma scaleGM Gray matterGRE Gradient recalled echoGWI Gulf war illnessLDFR Long delay free recallLOC Loss of consciousnessM MeanMCI Mild cognitive impairmentMR Magnetic resonanceMRI Magnetic resonance imagingMRS Magnetic resonance spectroscopyMS Multiple sclerosismTBI Mild traumatic brain injury

E. D. Bigler (*)Department of Psychology, Brigham Young University, 1001SWKT, Provo, UT 84602, USAe-mail: [email protected]

E. D. BiglerNeuroscience Center, Brigham Young University, Provo, UT, USA

E. D. BiglerMagnetic Resonance Imaging Research Facility, Brigham YoungUniversity, Provo, UT, USA

E. D. BiglerDepartment of Psychiatry, University of Utah,Salt Lake City, UT, USA

E. D. BiglerThe Brain Institute of Utah, University of Utah,Salt Lake City, UT, USA

Neuropsychol Rev (2013) 23:169–209DOI 10.1007/s11065-013-9237-2

mcTBI Mild complicated traumatic brain injuryOI Orthopedically injuredPCS Post-concussion(al) syndromePTA Post-traumatic amnesiaRAS Reticular activating systemRAVLT Rey auditory verbal learning testRT Reaction timeSD or s.d. Standard deviationSDMT Symbol digit modality testSWI Susceptibility weighted imagingTBI Traumatic brain injuryTBSS Tract-based spatial statisticsUF Uncinate fasciculusWHO World Health OrganizationWM White matterWMHs White matter hyperintensities

Introduction

A simplified view of concussion—the mildest form of trau-matic brain injury (TBI)—is that although it is an acute braininjury any residuals from a concussion are short-lived physi-ological aberrations without lasting neurological sequelae.Indeed, the majority of concussions run a benign course withspontaneous return to baseline level of function without anysystematic treatment. In the absence of an enduring neurolog-ical deficit, the neuropsychological argument has been madethat any post-concussion cognitive or behavioral change infunction does not reflect permanent neuropathology. Reasonsbehind these assumptions are that indisputably the majority ofthose who experience a mild TBI (mTBI)1 return to pre-injurybaseline and resume typical function, at least based on tradi-tional neuropsychological measures (Rohling et al. 2011).Transient perturbation of neuronal physiology seems a likelyexplanation and fits well with the majority of positive out-comes documented in mTBI research.

Throughout the 1980s and early 1990s, apparent confirma-tion of no identifiable gross neuropathology was the conclu-sion of the majority of mTBI cases who underwent computedtomography (CT) ormagnetic resonance imaging (MRI) (seeBigler and Snyder 1995). However, given the delicate natureof neurons—especially axons with diameters of just a few

microns—neuropathologists have still considered the likeli-hood of neuronal damage in mTBI (see Blennow et al. 2012).The mechanical deformation from stretching, twisting andshearing actions brought on by head impact andacceleration/deceleration of the brain during the event thatcaused the concussion at least transiently alters cellular mor-phology and function. For example, Peerless and Rewcastlespeculated in 1967 that “….concussion depends upon varyingdegrees of damage to the axon as well as the neuron. Thecurrent definition of concussion—immediate loss of con-sciousness with rapid and complete recovery of cerebral func-tion—should not exclude the fact that a small number ofneurons may have been permanently disconnected or haveperished.” (Peerless and Rewcastle 1967, p. 577).

It is now the 21st century and although advanced neuroim-aging methods in the living individual are not capable ofmatching histological precision, they do permit detection ofneuropathological findings at the millimeter to sub-millimeterlevel and technologically are far advanced over techniques ofjust a decade ago. Contemporary neuroimaging permitsin vivo studies of the injured brain, acutely as well as chron-ically and prospectively, with techniques that more directlyexamine cerebral microstructure. Moreover, precise biome-chanical finite element (FE) studies empirically show wherethe greatest strains occur in the brain when subjected to head-impact or acceleration/deceleration movement, as depicted inFig. 1 from Chatelin et al. (2011). As shown in Fig. 1, theseregions of greatest axonal elongation, stress and strain occur inthe very regions where acute magnetic resonance (MR) diffu-sion tensor imaging (DTI) changes are well documented inmTBI during both the acute and chronic phase (see also Chuet al. 2010; Metting et al. 2013).

Current neuroimaging methods now demonstrate that asubgroup of mTBI patients have more than a transient phys-iological disruption in neural function showing identifiableunderlying neuropathology (Bigler and Maxwell 2012;Gonzalez and Walker 2011; Kasahara et al. 2012; Kim et al.2013; Lewine et al. 2007; Lipton et al. 2012; Matthews et al.2012; Messe et al. 2011; 2012; Niogi and Mukherjee 2010;Wada et al. 2012).

This review examines the current status of advanced neu-roimaging findings in neuropsychological outcome researchon mTBI. Neuroimaging improvements have resulted in anumber of techniques that appear to be sensitive in detectingsubtle pathology associated with mTBI (Benson et al. 2012;Kou et al. 2010; Niogi and Mukherjee 2010). Because of theobjectivity that accompanies neuroimaging and image analy-sis techniques, neuroimaging findings may serve a biomarkerrole for the investigation of cognitive and neurobehavioraloutcome from mTBI (Kou et al. 2010). Candidate neuroim-aging biomarkers of mTBI are listed in Table 1 and introducedbelow. This review will only address structural imaging tech-niques and not functional neuroimaging metrics that also may

1 Definition of concussion and mTBI remains controversial and defini-tional issues have been discussed by Bigler (2008), but for this review theterms concussion and mTBI are used interchangeably. Based on theInternational and Interagency Initiative toward Common Data Elementsfor Research on Traumatic Brain Injury and Psychological Health “TBI isdefined as an alteration in brain function, or other evidence of brainpathology, caused by an external force” (p. 1637, Menon et al. 2010).

170 Neuropsychol Rev (2013) 23:169–209

have implications for biomarker roles in mTBI (seeBryer et al. 2013; Zhou and Lui 2013); nor will thisreview deal with any treatment outcome factors that maycome from improved neuroimaging biomarker identifica-tion of mTBI.

To highlight the various points made in this review, indi-vidual cases with mTBI with several types of neuroimagingabnormalities will be presented. From a clinical neuropsycho-logical perspective, clinical decision making must occur on anindividual basis all-the-while understanding what group dataanalyses may show. Where individual cases are used in thisreview, they were carefully selected to reflect findings basedon larger studies and not just case studies.

Candidate Neuroimaging Biomarkers

Numerous magnetic resonance (MR) techniques currentlyidentify trauma-related neuropathology (Duhaime et al.2012; Hunter et al. 2012). The MR imaging (MRI) methodknown as diffusion tensor imaging (DTI; Fox et al. 2013) hasbecome the most sensitive and predictiveMRImetric in mTBIresearch (Niogi and Mukherjee 2010). DTI is an establishedneuroimaging procedure used diagnostically and in researchacross a variety of neurological diseases and disorders, espe-cially those that predominantly influence white matter (WM)integrity (Alexander et al. 2007; Chanraud et al. 2010;Chapman et al. 2012; Sundgren et al. 2004; Travers et al.2012; Wycoco et al. 2013; Zappala et al. 2012). As will bediscussed in this review, mTBI may be viewed as a disruptionin WM neural networks (Mayer et al. 2012a; Pandit et al.2013; Shumskaya et al. 2012; Stevens et al. 2012; L. Tanget al. 2011; Voelbel et al. 2012), where damage or disruptionof myelin integrity and oligodendrocytes may characterizemuch of the pathology that comes from TBI when chronicproblems persist (Maxwell 2013). The key element in net-works is pathways (Catani and Thiebaut de Schotten 2012),and fundamental to all pathways is axon integrity. In regard tocontemporary neuroimaging, DTI provides the best

From Chu et al. 2010

From Chatelinet al. 2011

From Metting et al.2013

Fig. 1 (Top) Pictorial description of the neuroimaging steps in develop-ing a finite element (FE) model to map diffusion information demonstrat-ing where greatest axonal deformation occurs in the brain as a conse-quence of trauma. The final image on the right shows the most vulnerableareas to be corpus callosum, deepWM tracts of both cerebral hemispheresand the brainstem. From Chatelin et al. (2011) and used with permissionfrom the Journal of theMechanical Behavior of BiomedicalMaterials andElsevier Publishing. (Bottom Left). Red depicts regions of abnormaldiffusion tensor imaging (DTI) findings within 6 days of sustaining an

mTBI. Note that where these changes occur match the prediction basedon where the greatest axonal elongation strain occurs. From Chu et al.(2010) used with permission from American Society of Neuroradiology.(Bottom Right). During the chronic phase of mTBI the colorized regionsdepict where group DTI differences are found in mTBI compared withnon-injured controls used with permission from Metting et al. (2013).Note how the chronic findings are also predicted by the loci of greateststrain as shown in the FE modeling by Chatelin et al. (2011)

Table 1 Potential MRI biomarkers of mTBI

Imaging modality Measures

SWI Hypointensities reflective of blood by-products(i.e. hemosiderin)

FLAIR WMHs indicating WM signal abnormality and/orincreased perivascular space

Neuropsychol Rev (2013) 23:169–209 171

visualization and MR metrics of water diffusion that directlyassess axon integrity (Mori et al. 2012). Indeed, the researchand clinical applications of DTI are well established, includingits use in providing in vivo visualization and analysis of WMintegrity in mTBI (see Huston and Field 2013).

DTI and mTBI In a review specific to DTI and TBI, Hulkoweret al. (2013) conclude that, “…DTI effectively differentiatespatients with TBI and controls, regardless of the severity andtimeframe following injury….” (in press, e-pub page1). TheHulkower et al. review was based on the first 100 publishedDTI studies that examined the ability of DTI to detect differ-ences between controls and TBI and included over 30 studiesthat specifically assessed mTBI. As an index of WM integrityDTI metrics may serve as biomarkers of the health of WMconnections in mTBI (Bigler and Bazarian 2010; Ling et al.2012; Niogi and Mukherjee 2010; Kou et al. 2010; Bigler2013). For neuroimaging findings to serve as biomarkers inTBI, including mTBI, there must be neuropathological con-firmation of the relationship between what is observed fromneuroimaging with that viewed histologically (Bigler andMaxwell 2011). Animal studies of TBI with in vivo DTImetrics, compared to histological confirmation, provide thenecessary neuropathological foundation to infer in the livinghuman what a particular DTI finding may mean at the histo-logical level (see Bennett et al. 2012; Hylin et al. 2013; Buddeet al. 2011). Likewise, in cases of epilepsy and cerebralneoplasm, there is pre-surgical and post-surgical confirmationof how DTI changes relate to damaged neural tissue(Abdullah et al. 2013; Liu et al. 2013). Given the status ofDTI research and clinical findings, DTI is one of severalneuroimaging methods that meet criteria for use as a biomark-er of WM integrity.

Specific to mTBI, in a study that examined only corpuscallosum (CC) DTI findings, Aoki et al. (2012) demonstratedin a meta-analysis of 15 mTBI studies, that DTI consistentlydemonstrated differences between mTBI groups and controls.The consistency of these findings across studies allowed Aokiet al. (2012) to conclude that DTI metrics were sufficient “…to detect white matter damage in the CC ofmTBI patients.” (p.870). Aoki et al. focused on the CC because of its vulnerabilityin mTBI to stretch and strain (Bayly et al. 2012; McAllisteret al. 2012), but also other studies have shown the vulnerabil-ity of other long coursing tracts in the brain like the superiorlongitudinal fasciculus and tracts within frontal and temporallobe regions (Shenton et al. 2012). Biomechanics of whatproduces an mTBI likely relate to a variety of outcome differ-ences (Breedlove et al. 2012), and sports concussion is cer-tainly in a different class from auto-pedestrian head injuriesthat produce mTBI. Nonetheless, even in sports concussion,which may produce the mildest of injuries, DTI is capable ofdistinguishing those with significant parenchymal injury andthose without, at least in the acute and early sub-acute stages

(Bazarian et al. 2012; Cubon et al. 2011; Gardner et al. 2012;Koerte et al. 2012a; Maugans et al. 2012; Slobounov et al.2012; Virji-Babul et al. 2013).

Other Neuroimaging with Biomarker Potential: Hemosiderin,White Matter Hyperintensities (WMH), and Regional andWhole Brain Atrophy Several other candidate measures asneuroimaging biomarkers of mTBI, as listed in Table 1, in-clude detection of hemosiderin (a by-product of blood degra-dation with paramagnetic properties detectable by MRI) as anindication of shear-force injury, currently best detected usingsusceptibility weighted imaging (SWI; Benson et al. 2012).As will be discussed below, cerebral microvasculature is justas small and delicate as neural tissue and therefore susceptibleto deformation injury where petechial hemorrhage may attendmTBI (see Bigler 2004). In individuals with no risk factors forcerebrovascular disease and under 50 years of age, MRIdetection of hemosiderin is unlikely unless there is injury ordisease (Hunter et al. 2012; Kubal 2012; Sharp and Ham2011). In 52 children with orthopedic injury only, no childhad hemosiderin deposition detected whereas in the 41 chil-dren with mTBI, Bigler et al. (2013a) found that 12 childrenhad hemosiderin. As with the detection of hemosiderin, thepresence of WM signal abnormalities—referred to as WMhyperintensities (WMHs)—are less common in individualsunder 50 years as well (Hopkins et al. 2006), but have beennoted to occur with increased frequency in TBI (Bigler et al.2013a; Marquez de la Plata et al. 2007). Another imagingtechnique that involves WM is diffusion kurtosis imaging(DKI; Zhuo et al. 2012); this provides another metric shownto be affected in mTBI (Grossman et al. 2012a). In thistechnique, because of biological constraints in normal tissue,water diffusion metrics (e.g., kurtosis imaging) should have arather uniform distribution but the shape of the distributiondeviates when damage occurs. Small focal contusions occur inmTBI and may result in focal areas of atrophy (Bigler et al.2013a). Regions of focal atrophy may be quantified as maywhole brain volumetric changes. Indeed, longitudinal volu-metric studies that provide quantitative metrics show wholebrain volume loss over time in mTBI subjects (MacKenzieet al. 2002; Ross et al. 2012; Zhou et al. 2013).

Neuroimaging biomarkers in mTBI neuropsychologicaloutcome research could be applied in numerous ways. Forexample, Sorg et al. (2013) examined 30 war veterans with ahistory of mTBI (on average more than 2 years post injury)with a subgroup of 13 showing impaired neuropsychologicalperformance—defined as performance at least one standarddeviation below the mean—on at least one executive function(EF) measure. Figure 2 plots out DTI detected WM differ-ences that related to reduced EF performance in the mTBIgroup, showing that these regions of reduced EF performancecorresponded with reduced WM integrity in the ventral pre-frontal WM, posterior cingulum bundle, genu, and splenium


Fig. 2 Atlas-based region of interest (ROI) placement and group com-parisons of fractional anisotropy (FA) values depicting where significantdifferences occurred in mTBI patients with reduced EF. Placement of thetract-based special statistics (TBSS)-derived white matter skeleton re-gions of interest in standard space on a T1 image. Note in each case ofreduced EF, FA is also reduced although not always significant. Also,most importantly, note that in all comparisons no significant FA differ-ences were observed between controls and those with intact EF. Colorsreflect different ROIs of white matter (WM) tracts. Note: AIC=anteriorinternal capsule; Ant. Cing.=anterior cingulum bundle; DPFWM=dorsalprefrontal white matter; EF=executive functions; FA=fractional

anisotropy; HC=healthy controls; PIC=posterior internal capsule; Post.Cing. = posterior cingulum bundle; ROI=region of interest; TBSS=tract-based spatial statistics; VPFWM=ventral prefrontal white matter. Errorbars represent standard error of the mean. aP corrected<.10, bP corrected<.05. Used with permission from Sorg et al. (2013) and Wolters Kluwer/Lippincott Publishers. Note once again the overlap of where significantDTI differences occur in relation to what was shown in Fig. 1, but now byexamining neuropsychological outcome specific to white matter (WM)tracts, a clearer picture emerges as to where pathology affects cognitiveperformance


of the CC. These are all well-known regions that participate inEF networks and likewise, vulnerable to mechanical deforma-tion during head injury (Chatelin et al. 2011). Relevantly, theSorg et al. study basically replicates similar observations inmTBI research involving these brain regions and EF, as re-ported by others (Jorge et al. 2012; Wada et al. 2012). Takingthis approach the neuroimaging DTI biomarker findings pro-vide novel information about brain-behavior relations thatcould never be gleaned from just the neuropsychological data,since group averaging neuropsychological test findings mayobscure those with impairment.

As another example, Hellyer et al. (2012) took a differentapproach using DTI and other MR metrics assessed throughmachine learning to first segregate TBI patients, the majorityof whom had mTBI and no visible abnormality on the scanfrom controls. The MRI-based machine learning classifierextracted just from the CC achieved 86 % correct classifica-tion of those with TBI, the majority of whom had mTBI. Inturn, these classifiers positively related to impairments in EFand speed of processing. A different MR biomarker approachfrom that of Sorg et al. (2013) but with convergence, demon-strating the added information that neuroimaging provides forunderstanding the neural basis of mTBI effects on cognition.

The presence of DTI findings in cases of mTBI has alsobeen used to predict outcome. For example, Rao et al. (2012)obtained DTI at 1 month post-injury where frontotemporalDTI findings related to clinically significant depressionassessed at 1 year post injury. Messe et al. (2011; 2012) usedDTI findings in the subacute (8–21 days) timeframe com-pared to chronic phase (~6 months) where persistence inabnormal DTI findings was associated with persistence ofpost-concussion syndrome (PCS).

These examples demonstrate ways in which neuroimagingstudies may serve as biomarkers of brain injury to identifyindividuals with mTBI who have demonstrable neuroimagingfindings. The traditional criterion variable of mTBI, the injuryitself, constitutes an unreliable marker of any behavioral orcognitive sequelae (Bigler et al. 2013c). Further, transientpathophysiological effects dominate mTBI but are co-mingled with structural and enduring pathology in a minorityof those injured. Therefore, the fact of having sustained anmTBI in no way can distinguish the two, and therefore, the“injury” itself when traditionally classified as only an eventthat has occurred cannot identify who does or does not havepersistent pathology.

One could argue that there is abundant neuropsychologicalliterature that supports the transient nature of mTBI with nolasting effect (Carroll et al. 2004b; Larrabee et al. 2013;Rohling et al. 2011); but all of this prior literature is basedalmost entirely on the assumption that the event—the concus-sive injury itself—is a sufficient independent variable thatcharacterizes the injury. Likewise, all of the post-mTBI symp-toms that constitute what has been referred to as the post-

concussive syndrome (PCS) overlap with myriad other neu-rological and neuropsychiatric signs and symptoms and there-fore, their lack of any specificity renders PCS criteria incapa-ble as effectively serving as a bio-behavioral marker of mTBI.If, as is now being shown, neuroimaging methods demon-strate residual neural findings in some who have sustainedmTBI then the mere classification of mTBI by the event thatproduced it will lead to erroneous conclusions.

This review examines the potential role that neuroimagingbiomarkers of brain pathology can play in the next decade ofmTBI outcome research (Kan et al. 2012; Walker and Tesco2013). Much of the confusion over mTBI sequelae is attrib-utable to the absence of reliable biomarkers of brain injury.Only a brief historical perspective of mTBI will be offeredhere, as numerous other reviews have covered much of thatmaterial in great detail (see Prigatano and Gale 2011; Bigler2008; Ruff 2011; Iverson 2005). Likewise, the history ofneuroimaging in mTBI and contemporary methods, includingunderlying MR physics, has been reviewed elsewhere (seeShenton et al. (2012). Most of this review will focus onmethods of structural neuroimaging, but there is also a largebody of mTBI research on functional neuroimaging tech-niques, especially functional MRI (fMRI) and MR spectros-copy (MRS), which will not be reviewed here but has been bySlobounov et al. (2012) and Bryer et al. (2013). At the outsetof this review, it is categorically stated that the majority ofthose who sustain an mTBI display and/or experience symp-toms that are brief and run a benign course, including whatoccurred to this author.2 This review is concerned only withthe minority of mTBI subjects who have sustained an injurywhich may no longer constitute a simple transient event(Bigler et al. 2013b).

Opposing Views of mTBI: Neuroimaging BiomarkersWill Help Resolve Controversies

To put the Peerless and Rewcastle’s speculation of axonaldamage in mTBI into context, their 1967 publication becamethe classic paper that established the concept of the WM shearlesion in TBI; that, in turn, became the foundation for the

2 I sustained a sports-related concussion playing high school football in1966. I must have displayed significant PTA and confusion on thesideline because I was taken to the emergency room for evaluation andsubsequently hospitalized overnight for observation. I am amnestic tothose events, but it was recorded on an 8 mm tape. However, I recoveredrapidly, as the injury was on a Friday and I practiced Monday and playedin the next game on the following Friday. This, of course, was long beforereturn-to-play guidelines but for me, post-concussion symptoms wereminimal and short lived.


concept of diffuse axonal injury (DAI, see Adams et al. 1982).The term shear lesion that is now commonplace nomenclaturein the neuropsychological literature of TBI began with thisPeerless and Rewcastle (1967) publication. Specific to mTBI,and coincidentally in 1967, Taylor also reviewed the topic ofpost-concussional sequelae outlining core clinical features ofthe post-concussional syndrome (PCS), which nearly threedecades later would become labeled post-concussional disor-der by DSM-IV standards (DSM-5 has dropped mention ofPCS, instead using the major or mild neurocognitive disorderdue to TBI as the qualifier). Taylor’s opening statement ac-knowledged a 100 year strident debate as to whether PCSwasa residual of brain injury, manifestation of psychogenic prob-lems, or something malingered.

Almost 50 years since that publication the debate continuesjust as strongly, especially in the field of neuropsychology.The debate is not about acute concussive effects, as there isuniform agreement that the acute effects alter brain function.Indeed, especially within the realm of sports concussion, well-designed neuropsychological studies have unequivocallydemonstrated consistent acute neurocognitive and neurobe-havioral effects of a concussion (McCrea 2007), as well asindisputable evidence of acute electrophysiological aberra-tions associated with concussion (Arciniegas 2011; Shaw2002), alongwith development of reproducible animal modelsof concussion and its acute neuropathological effects (Chenet al. 2012b; Hylin et al. 2013). Likewise, there is minimaldebate about the fact that the majority who sustain mTBI dorecover (or, at least return to baseline; see Mott et al. 2012;Vasterling et al. 2012; Rona 2012). A principal debate centerson whether mTBI results in residual neuropathologicalchanges in some as manifested by persistence ofneurocognitive and neurobehavioral deficits 3 months or lon-ger after mTBI.

The camp that views mTBI as a transient physiologicalevent with no lasting sequelae is captured by Geiffenstein’sstatement that mTBI “…is a self-contained condition thatresolves quickly without special treatment, a generally accept-ed conclusion by fair-minded neuropsychologists” (seeCarone and Bush 2013, p. xiii). As another example of thisperspective, Boone (2013) states, “The field [referring toneuropsychology] as a whole is taking the position that thereis no long-term cognitive consequence from mTBI” (p. 275).The other camp contends that most who experience mTBIrecover and return to pre-injury baseline as measured bytraditional neuropsychological assessment methods.However, as Ponsford et al. (2011) observed in a longitudinalstudy involving adults who sustained mTBI, some did exhibitongoing impairment in memory function after 3 months and“…that at least a proportion of these mTBI participants didhave subtle residual cognitive sequelae 3 months post-injury”(p. 945). With some studies estimating 6 % to 35 % occur-rences on the incidence of PCS in children after TBI, Barlow

et al. (2010) found in a prospective longitudinal cohort of 670children (aged 0–18 years) who presented to the emergencyroom and assessed to have sustained mTBI, that 13.7 % hadpersisting symptoms at 3 months or longer post-injury whencompared to a consecutive case-controlled cohort of 197children who sustained an extra-cranial injury but were notdiagnosed with mTBI. Similarly, in a prospective cohort studyof concussion (all types) and resolution of symptoms thatenrolled 280 patients (11–22 years old) over a 12 monthperiod, Eisenberg et al. (2013) showed that 15 % remainedsymptomatic at 3 months.

In an adult sample, followed up to 14 years post TBI, thatcontained a substantial number of patients with mTBI,McMillan et al. (2012) found persistence of disability basedon the Glasgow Outcome Scale-Extended. In another study,Levin et al. (2012) identified 102 mTBI patients at baselinewithin 4 days of injury and tracked them for 3 months com-pared to similarly tracked orthopedically injured (OI) con-trols. At 3 months using a conservative cut-point for symptomendorsement of PCS, about 10 % of the mTBI sample hadPCS compared to under 2 % of the OI subjects. At 3 monthsmTBI patients in the Levin et al. study did differ from OIcontrols on a computer-basedmeasure of processing speed butnot on traditional neuropsychological measures.

The studies above, and others (see also Dean and Sterr2013; Kumar et al. 2013; Pontifex et al. 2012; Tallus et al.2013), show that some with mTBI endorse and displaypersisting cognitive and neurobehavioral problems beyond3 months. However, are the problems specific to brain injuryor related to some other factor? There is abundant literaturethat discusses a host of pre-morbid personality and emotionalfactors that may predispose the individual who sustains anmTBI to misattribute residual symptoms to the injury ratherthan a pre-existing condition (Silver 2012). A fundamentalproblem with this is that misattribution assumes absence ofpathology that could explain the symptom. Without someindependent biomarker as to potential bona fide neural dam-age, a true distinction between accurate or misattributed per-ception in mTBI is impossible.

Some criticize the mTBI neuropsychological literature be-cause of not controlling for depression, litigation and effort,but Heitger et al. (2009) controlled for all of these factors andobserved subtle cognitive deficits associated with mTBI after6 months. Hanten et al. (2012) in a well-designed, withinsubjects longitudinal study of mTBI compared to OI andnon-injured controls that tracked 59 mTBI patients (cognitivetesting at <1 week, 1, and 3 months post-injury), all with noCT abnormalities on the day of injury (DOI) scan, foundpersisting memory problems to 3 months in some of thosewith mTBI. Konrad et al. (2011) examined 33 mTBI patientson average 6 years post-injury, all of whom passed symptomvalidity testing but nonetheless demonstrated persisting,chronic cognitive and emotional dysfunction. Dean and Sterr


(2013) have also controlled for these factors finding residualsubtle cognitive impairments associated with mTBI beyond3 months post injury.

The Hanten et al. (2012) and Hellyer et al. (2012) studiesare particularly relevant for this review because they under-score the significance of advanced neuroimaging findings inmTBI for understanding neuropsychological outcome after3 months post injury. The Hanten et al. investigation waslongitudinal, obtained baseline imaging within 96 h, andfollowed up at 1 and 3 months post-injury. The study alsoincluded OI subjects as well as non-injured controls that wereall imaged with high-field 3-Tesla MRI. Of the 59 (32 %)mTBI subjects, 19 had identifiable trauma-related pathologyon follow-upMRI, even though none had identifiable DOI CTabnormalities. Even though MRI findings were based only onqualitative ratings by a neuroradiologist, clinically identifiablepathology involving the frontal lobes was associated withpersisting deficit in an EF working memory task in themTBI patients (see also the study by Raz et al. 2011 thatshows MRI correlates with impaired EF performance inmTBI as well as Fig. 2 from Sorg et al. 2013). In the Hellyeret al. (2013) investigation the MRI “classifier” not only dis-tinguished mTBI subjects, as well as those with more severeinjury, from controls but also related to performance on EFmeasures in TBI patients on average almost 3 years postinjury. These studies support that underlying pathology occursin some who sustain an mTBI.

Unlike what is emergingwith contemporary neuroimaging,past measures that have been used clinically and in researchwith mTBI are too coarse to be effective biomarkers. Day-of-injury (DOI) CT and even traditional markers of injury sever-ity such as loss of consciousness (LOC) or post-traumaticamnesia (PTA), are poor predictors of outcome whenGlasgow Coma Scale (GCS) scores are in the mild 13–15range (Smits et al. 2007). Further, even identifiable abnormal-ities on a DOI CT scan have limited predictive ability formTBI, likely because they under detect the number and typeof abnormalities (Smits et al. 2007). Past restrictions in mTBIresearch design (see full discussion of design issues asreviewed by Bigler et al. 2013c), plus the problems of howmTBI groups should be properly composed (Luoto et al.2012), combined with the absence of a reliable mTBI bio-marker, have resulted in major limitations in fully understand-ing mTBI outcomes.

As summarized nearly two decades ago by Cipolotti andWarrington (1995), if neuropsychological assessment is toprovide unique information about a condition and its relation-ship to underlying neurological impairment, there must be amethod to independently define the pathophysiology or “braindamage” of that condition or objectively rule it out. In otherwords, to use neuropsychological measures as dependentvariables to characterize a disorder, the independent variablereflecting neurological impairment must be specific to the

condition being examined. Once established, hypothesesabout how brain impairment or damage that may selectivelydisrupt some components of a cognitive or behavioral systemcan then be examined. This is how neuropsychology hasdemonstrated neurocognitive and neurobehavioral correlateswith other major neurological and neuropsychiatric disorders.To date this approach has not been applied to mTBI becausethere has been no independent marker of brain pathology,other than the event of having sustained a “mild head injury.”Abundant mTBI studies exists on the lack of neuropsycho-logical findings of mTBI, but almost all of those studies haveused the injury itself as the only independent factor to classifythe mTBI condition. If the fact of sustaining an mTBI isinsufficient to identify a neural condition with potential lastingsequelae, then the event by itself becomes an inadequatecriterion related to outcome—what is needed is a biomarkerthat identifies those with neural changes from the mTBI event.

Therefore, this review turns to the neuropathology of mTBIand first asks a basic question: Are there discernible abnor-malities that can be demonstrated in mTBI using contempo-rary neuroimaging? If so how should they be characterized asindependent variables in studying neuropsychological out-come? The current review assumes basic understanding andbackground in neuroimaging and neuropathology of TBI,which has been covered in other reviews (Chanraud et al.2010; Travers et al. 2012; Wilde et al. 2012; Hunter et al.2012). It must be emphasized again that this is not a discussionof all who have sustained an mTBI because the assumption isthat likely the majority have only experienced a transient eventand advanced neuroimaging techniques would be negative insuch individuals. Thus, the assumption that guides this reviewis that these pathological neuroimaging markers of mTBI willonly be found in a subset of individuals with mTBI.

Neuropathology of mTBI

From a neuropathological standpoint, TBI can be viewed on acontinuum and as summarized by Graham and Lantos (2002)shear and tensile strains at the axonal level are the “mostimportant single factors contributing to the severity of braindamage in any patient who sustains a blunt head injury be-cause it occurs at the moment of injury” (p.867). Chatelin et al.(2011), integrating various MRI methods including DTI, de-veloped FE biomechanical models to experimentally examineshear and tensile strains at different injury severity levels.Their results have particular relevance to understandingmTBI because they illustrate, as previously shown in Fig. 1,where the greatest axonal shear/strain effects occur in TBI—corona radiata, corpus callosum, and brainstem. Given thatinformation, and turning to acute imaging, as shown by Chuet al. (2010), knowing where the shear-strain effects aregreatest predicts where DTI findings are most likely to acutely


occur in mTBI (see Fig. 1, lower left panel), as well as whereabnormalities will be chronically identified, as shown in Fig. 1(lower right panel and Metting et al. 2013).

The study by Tang et al. (2012) demonstrates the continu-um or range of MR diffusion tensor findings in TBI. Theseinvestigators examined TBI patients frommild to severe com-pared to a control sample. As shown in Fig. 3, when the TBIsubjects were compared to controls, there were significant andquite uniformly reduced fractional anisotropy (FA) valuesthroughout brain WM, but particularly the corpus callosumand all areas as identified by Chatelin et al. (2011). However,basically within these same regions when severity was gradedfrom mild to severe, the degree of FA changes related specif-ically to severity of injury with general conformation to wherethe changes occurred in comparison to the control sample. Ifone accepts that WM damage can be viewed on a continuum,studyingmoderate to severe forms of TBI may provide insightand understanding of milder forms of TBI.

Other biomechanical studies show that the shear-strainrelationship on WM is directly proportional to the severityof injury (Bayly et al. 2012). From this perspective, tensilestrain may distort the axon and if this occurs within appropri-ate levels of tolerance, only physiological disruption results.This physical effect may be transient or may have subclinicalrelevance for understanding mTBI, since in the majority of

those who sustain an injury full return to baseline is the norm,lending credence that reversibility of acute mTBI effects oc-curs in the majority (Parkinson 1992). Nonetheless, such aphysical effect has the potential of initiating complex molec-ular and metabolic pathologies (Choe et al. 2012), along withcellular inflammatory reactions (Loov et al. 2013).Furthermore, there are a host of non-traumatic reversibleencephalopathies that present with acute disturbances in phys-iologic functioning only to exhibit complete restoration offunction as homeostasis returns resulting in no apparent se-quelae including neuropsychological (Bavikatte et al. 2010;Pula and Eggenberger 2008). The fact that “recovery”—meaning return to presumed baseline—occurs in disorderslike reversible encephalopathies lends support to transientand mutable physiological changes associated with mTBI thatmay not have a permanent effect. Experimentally, in vitro andin vivo mTBI models show how physiological disruption mayoccur on a continuum followed by recovery and restoration(Greer et al. 2012; Johnson et al. 2012b). In this sense resto-ration implies a rebuilding where neural repair restoresunimpaired functioning. Regardless of how function isreturned to its normal state, much of the injury in mTBI mayresult in no lasting neuropathological effect.

Traumatic axonal injury or TAI can be modeled fromsubtle physiological perturbation on the mildest end of injury,which may have no lasting effect, to distinct axonal damagethat does result in histologically identified anatomicalchanges, which if to a sufficient degree will result in structuraldamage making macroscopic detection possible with ad-vanced neuroimaging methods. Both animal and human stud-ies also demonstrate that neuropathological changes are di-rectly related to injury severity (Colgan et al. 2010; Mao et al.2010; Maxwell et al. 2010; Rostami et al. 2012; Turner et al.2013). Animal studies of blast injury can manipulate damageparameters that go from no discernible parenchymal effectsverified by histological analysis to presence of edema,microhemorrhages, and neuronal changes (Risling et al.2011; Saljo et al. 2011)—in particular WM pathology (Parket al. 2012). Human blast-related TBI studies document WMpathology in military personnel exposed to such forces (MacDonald et al. 2013, 2011). Quantitative neuroimaging studiesdemonstrate parenchymal volume loss linearly related to in-jury severity from mild to severe (Levine et al. 2008; Wildeet al. 2006; Ghosh et al. 2009). All of this supports the viewthat TBI neuropathology occurs on a continuum. From thisperspective, understanding more severe TBI provides a frame-work for understanding the mildest of injuries and how pa-thology may be expressed in mTBI.

Turning to the seminal neuropathological contributions ofStrich (1956) and Peerless and Rewcastle (1967), as neuropa-thologists who studied more severe traumatic brain injuries,they viewed the brain’s microscopic environment and recog-nized the delicate and vulnerable nature of an axon when

Fig. 3 a Fractional anisotropy (FA) comparisons between control andmild to moderate TBI subjects using voxel by voxel TBSS analysis (p <0.05). Yellow: Control>mild to moderate TBI. Lower FA (yellow) re-flects extensive WM abnormalities. b Correlation analysis of FA andseverity scores using voxel by voxel TBSS results comparing mild tosevere TBI subjects (p <0.05). Blue: significant negative correlationsbetween FA and severity. These findings demonstrate that as severityincreases from mild to severe TBI so do DTI differences. Used withpermission from Tang et al. (2012) and Springer-Verlag. Note ; FA=fractional anisotropy; TBSS=tract-based spatial statistics


presented with traumatic forces including blunt trauma andacceleration/deceleration. Although Strich (1956) examinedonly severe cases of brain injury and individuals who diedwithin 6 weeks post-injury, she reviewed various anecdotalneuropathological studies and made observations implicatingneuronal damage in concussion, reporting “… the possibilitythat it may play a part [referring to neuronal injury], should beborne in mind” (p. 184). Following this, Peerless andRewcastle (1967) speculated that concussion could result in“…damage to the axon as well as the neuron” and thenindicated that such damage may “disconnect” the neuron (p.577). These speculations happened to be confirmed a year laterby Oppenheimer (1968) and his case study identified as J.M.

Oppenheimer’s findings occurred before the introductionof the GCS rating method (see Teasdale and Jennett 1974),and therefore the definition of concussion or mTBI at that timewas dependent upon the clinical description by Oppenheimer.The case study “J.M” was described as having been struck bya “motor scooter” and “stunned” yet apparently did not loseconsciousness, but did have 10 to 15 min of retrograde amne-sia and anterograde amnesia of about 20 min (Oppenheimer1968). He had a parietal scalp “bruise, but no skull fractureand no neurological signs” (p. 301). Unfortunately however,he did sustain a chest injury in the accident, including multiplerib fractures that ultimately proved fatal. He also had alongstanding history of being “…bronchitic, and died of chestcomplications 13 days after the injury” (p. 301).

When J.M.’s brain was microscopically examined, thefollowing was observed at autopsy:

The brain looked entirely normal except for a tinysoftening in the lateral sulcus on one side of the mid-brain. There was no vascular disease, and no sign ofbrain swelling. Histologically, there was some myelindestruction and numerous axonal retraction bulbs in themidbrain lesion. Nine blocks, from various parts of thebrain, were stained for microglia. In every block, at leastone microglial cluster was found (Oppenheimer 1968,p.301)

While Oppenheimer was the first to provide this amount ofneuropathological detail in an mTBI patient, others haveconfirmed such post-mortem observations as well (seeBigler 2004; Bigler and Maxwell 2012; Blumbergs et al.1994; McKee et al. 2010, 2012). Figure 4 shows classicneuropathology of axonal damage, beading and axon bulbformation; indeed the types of pathologies that Peerless andRewcastle (1967) observed in more severe TBI was found atpost-mortem in an mTBI patient who succumbed not to TBIbut to chest-pulmonary injury from the impact (see Bigler andMaxwell 2012). However, the classic pathology of shearinjury and axon beading and degradation, when it occurs inmTBI, is only one facet of the potential mTBI pathologies thatmay occur. For example, in an mTBI patient who died from a

heart attack several months post-injury, Bigler (2004) demon-strated both macrophages and hemosiderin were present inWM—micro-bleeds likely from trauma with macrophagesindicative of neuroinflammatory processes (Bigler 2013).Presence of hemosiderin is a blood by-product from degradedblood, in TBI considered a residual from shearing of themicrovasculature or breakdown of the vascular wall fromtrauma (Benson et al. 2012), and a distinct marker of TAIdetected by MRI (Scheid et al. 2006).

Two MR methods are sensitive in detecting edema andmicrohemorrhages: 1) DTI, for neuroinflammation, and 2)gradient recalled echo (GRE) sequences, in particular sus-ceptibility weighted imaging (SWI), for microhemorrhages.Certain DTI metrics, like fractional anisotropy (FA), aresensitive to inflammation, because with inflammation a re-striction in water dispersion occurs. FA is a DTI metric

Fig. 4 A medium power light micrograph of part of a central whitematter tract from a patient who suffered mTBI with complications anddied from respiratory failure as a result of thoracic injuries 18 h after entryto hospital. The field is part of a paraffin section labeled for β-amyloidprecursor protein, a marker for axonal injury. The irregular, orangeprofiles represent axons within which focal loss of fast axonal transporthas resulted in abnormal accumulation of the amyloid protein. The purplecircles are the nuclei of glial supporting cells and are probably mostlyoligodendrocytes.Within this field are a range of types of abnormal axonswhich represent different stages in the pathological cascade culminatingin secondary axotomy. Injured axons form “axonal swellings” (*) oneither side of the focus of loss of axonal transport. Axonal swellingscontinue to increase in diameter as a result of continued anterograde andretrograde axonal transport and a constriction occurs (black arrows) atsome point within the axonal swelling. The axon undergoes secondaryaxotomy thereat and separates into fragments. The regions of increasedaxonal caliber are then at the ends of the fragments and are referred to as“axonal degeneration bulbs” (white arrows). The axonal fragment nowseparated from the neuronal cell body then degenerates. From Bigler andMaxwell (2012) used with permission from Springer. The inset in thelower left shows the classic line drawings from Peerless and Rewcastle(1967) used with permission from the Canadian Medical Association.The line drawing shows different stages of axonal beading from none in(A) to extensive beading and axonal fragmentation in (D). Note how the“beading” reflective of axonal damage is distinctly observed in thephotomicrograph from the mTBI subject (black arrows)


reflective of water diffusion, on a scale of 0.0 to 1.0, withnormative values typically within a midrange. FA that is toolow may reflect WM degradation (intracellular water mixeswith extracellar because axon membranes do not effectivelyconstrain water or are absent) and FA too high, may reflectedema because water diffusion is constrained (Chanraud et al.2010; Shenton et al. 2012). Elevated FA may occur whenwater dispersion is restricted because of regional or moregeneralized swelling. As already shown in Fig. 1 (lower leftpanel), acute inflammation in mTBI occurs in characteristicregions vulnerable to damage (see Chu et al. 2010). Withregards to detecting microhemorrhages and trauma-relatedvascular pathology, as mentioned, the SWI sequence is par-ticularly sensitive with microhemorrhages and indicators ofhemosiderin deposition occurring within the same regions ofvulnerability (Turtzo et al. 2012; Benson et al. 2012). A mostimportant factor with traumatically induced micro-bleeds isthat they may evolve over hours to 1–2 days post-injury(Oehmichen et al. 2003). Given CT limitations in detectingblood and the fact that in mTBI a CT scan, if done at all, istypically the first scan performed, usually within an hour ortwo of coming into the emergency department (ED), andtherefore blood or blood byproducts may not be observedin the initial imaging as demonstrated in Fig. 5 (also seeBigler 2008).

Graham and Lantos (2002) provide a rationale for viewingTBI on a continuum, emphasizing the vulnerability of axons,

the delicate nature of blood vessels in the brain, and the micro-vascular damage that can result from trauma (see also Fujitaet al. 2012; Sangiorgi et al. 2013). Damage from TBI affectsboth brain parenchyma and blood vessels, which whensheared or damaged in ways where blood may leak into theparenchyma, leave degraded blood by-products in the form ofhemosiderin potentially detectable by MRI. Figure 6 showsthe detection of hemosiderin in the region of the forceps minorin a child with mTBI associated with a high velocity impactsports concussion that had “normal” conventional neuroim-aging in the ED. As with the case presented in Fig. 5, a healthyindividual without cardiovascular or cerebral vascular riskfactors under the age of 30 at the time of injury would notbe expected to have any hemosiderin deposition identified onimaging. Figure 6 depicts pathology (hemosiderin deposition)detected after a sports accident where the significance of theimpact dynamics can be less substantial than in motor vehicleaccidents. Here, DTI demonstrates reduced connectivity in thefrontal region, likely a consequence of the mTBI although nopre-injury baseline imaging was available for compariosn.

In addition to the shear/strain effects on blood vessels, TBIat the moderate-to-severe level of injury severity is a well-known cause of deficits in vascular autoregulation (Yokoboriet al. 2011). Using transcranial Doppler testing of dynamiccerebral autoregulation, Junger et al. (1997) demonstrated inmTBI patients 48 h post-injury that eight out of 29 (28 %) “…demonstrated poorly functioning or absent cerebralautoregulation versus none of the controls” (p. 425). In pro-fessional boxers with suspected chronic brain injury frommultiple concussive as well as sub-concussive blows to thehead, impaired cerebral hemodynamics has been demonstrat-ed (Bailey et al. 2013). Cerebral autoregulation is key to bloodflow dynamics that subserve neural function that generatescognition and behavior (Logothetis 2008). Therefore, even ifneural tissue is not damaged, if precisely regulated vascularflow does not occur normally, because of vascular injurydeficits may arise. Interestingly, Pomschar et al. (2013) haveshown in mTBI that some develop abnormal intracranialvenous drainage patterns, speculating that microvascular dam-age in mTBI alters vascular compliance adversely influencingvenous drainage. Likewise, Metting et al. (2013) used perfu-sion CT on the DOI scan to identify blood flow and inferedema in cases of mTBI, which in turn, also related to DTIoutcome months post-injury (see also Metting et al. 2010,2009). One of their interpretations on the effects of mTBI isthat it disrupts the vascular autoregulation by damaging theperivascular nerve network (see Ueda et al. 2006). Avariety ofabnormal fMRI findings have been reported in mTBI(Johnson et al. 2012a; Slobounov et al. 2011b; Chen et al.2012a), and some form of subtle vascular pathology aloneand/or in combination with neuronal pathology may be re-sponsible for such findings. Regardless, from a structuralimaging perspective, presence of hemosiderin in the mTBI

Fig. 5 Negative day of injury (DOI) computed tomography (CT) imag-ing is shown in the two axial images on the top left. Approximately6 weeks post-injury, follow-up MRI demonstrated hemosiderin deposi-tion (dark splotches ) within the deep right frontal lobe (bottom leftcoronal image ). These abnormalities persisted 5 years post-injuryreflected as white matter hyperintensities (WMHs) on the FLAIR se-quence in the right frontal region as well as the hemosiderin deposition.All axial scans are in radiological orientation where right is on theviewer’s left


patient may not only infer TAI but disrupted vascular integrity(Scheid et al. 2006).

As already indicated traumatic axonal injury or TAImay bethe better term to use with regards to understanding all path-ological effects that relate to axonal damage from trauma (seediscussion of this point by Bigler and Maxwell 2012), butregardless of whether the DAI or TAI term is used, in mTBIwhen focal WM abnormalities are visibly present, they arebest detected in the fluid attenuated inversion recovery(FLAIR) sequence (Marquez de la Plata et al. 2007; an exam-ple of a trauma-related FLAIR signal abnormality is shown inFig. 5). The distribution of hyperintense foci on FLAIR im-aging or the hypointense hemosiderin signal on GRE/SWI inTBI reflecting DAI/TAI pathology is shown in Fig. 7 fromChatelin et al. (2011). While the Fig. 7 illustration is based on

all levels of severity, the distribution of these types of lesionsas observed in mTBI is identical—lesions at the gray/whitemargins, deep white matter, and corpus callosum.

In the Bigler et al. (2013a) study that included 41 childrenwith mild complicated TBI (mcTBI; defined as some type ofpositive neuroimaging typically on initial CT imaging) all ofwhom had GCS of 13–15 but on CT had evidence of skullfracture, small contusion or some form of hemorrhage oredema, 12 (29.3 %) had identifiable hemosiderin depositionon follow-up MRI at least 6 months post-injury. Given thatthese observations were based on standard GRE sequence andnot SWI, where sensitivity in detecting hemosiderin is 2–3times greater, indicates that this is an under estimate in thenumber of TAI-type findings in mcTBI (see Benson et al.2012). Nonetheless, even with these limitations almost one-

Fig. 6 Hemosiderin deposition (black arrow) detected with susceptibil-ity weighted imaging (SWI) in an older child who sustained an mTBI in asporting accident. Day-of-injury (DOI) computed tomography (CT) scanwas negative. Diffuse tensor imaging (DTI) tractography (right image)shows reduced frontal projection of aggregate tracts within the superior

anterior frontal region (arrow). Color reflects DTI convention where bluereflects vertically oriented tracts, green indicates anterior-posterior orien-tated tracts and warm colors (orange-red) reflect tracts that are laterallyoriented. Axial MR images are not in radiological perspective, because ofthe 3-D image presentation. Right is on the viewer’s right

Fig. 7 Chatelin et al. (2011) summarized the location and distribution ofdiffuse axonal injury (DAI) pathology based on several neuroimagingand postmortem studies to show white matter (WM) pathology in deeptracts within each hemisphere, corpus callosum, and upper brainstem asindicated by black stars. Bar graphs represent a simple frequency count of

visually identifiable abnormalities. Note the frequent occurrence of DAIwithin the frontal and temporal lobes. Red stars signify abnormalities indeep WM regions and black indicates cortical and corpus callosum loci.Used with permission from the Journal of the Mechanical Behavior ofBiomedical Materials and Elsevier publishing


third of mcTBI patients have a marker of TAI when MRI isperformed yet there are no systematic large-scale neuropsy-chological outcome studies in the literature that specificallyhave studied this type of lesion mapping and mTBI.

Animal models of mTBI can provide a direct comparisonbetween neuroimaging and histological confirmation of neu-roimaging findings and confirm presence of neuropathologyeven at the mild end of TBI severity (Dewitt et al. 2013). Theshape of the animal brain, especially the rodent brain and skullare very different from the human and therefore the precisebiomechanics of human brain injury cannot exactly be dupli-cated in animal models (Spain et al. 2010). Porcine modelsthat more directly mimic the human brain have been devel-oped (Sullivan et al. 2013) and probably hold the greatestpromise for improved understanding of mTBI neuropathologybased on neuroimaging findings integrated with histologicalconfirmation and its effect on cognition and behavior (seeBrowne et al. 2011). These animal models provide histopath-ological confirmation of what may be observed with in vivoneuroimaging in the human with TBI, including mTBI (seeBudde et al. 2011; Turtzo et al. 2012).

Although involving multiple concussive blows to the head,ante-mortem imaging in those individuals with suspect chron-ic traumatic encephalopathy (CTE) is providing the neuro-imaging background for improved understanding of the post-mortem histopathology of CTE (Baugh et al. 2012; Handrattaet al. 2010). Hart et al. (2013) and Strain et al. (2013) havefound cognitive deficits and depression in older retiredNational Football League (NFL) players compared to healthycontrols, associated with WM-defined DTI findings.Likewise, Lehman et al. (2012) found three times higher ratesof neurodegenerative mortality in retired NFL players andCTE has been established in NFL players (McKee et al.2010, 2012). There are more unknowns than what is knownabout sports-related CTE, but the possibilities of this being alatent phenomenon of mTBI raises sobering questions andmeans the very best in research design should be applied tostudying mTBI and potential relations with CTE (seeVictoroff 2013) and other forms of neurodegeneration (Leeet al. 2013). Nonetheless, abnormal WM findings have beenreported in physician-observed concussion from hockey-related injury as well as soccer players without symptomaticconcussion, but with history of head and soccer ball impact(Koerte et al. 2012b, a; Lipton et al. 2013). The presumedrelationships with such findings are the history of concussionand sub-concussive injuries. While CTE may require multipleconcussive and sub-concussive blows to the head, each blowoccurs within the realm of mTBI. Taken together, the animaland human literature on mTBI provides support for the use ofadvanced neuroimaging methods, that effectively detect vari-ous common brain pathologies in some who have sustainedmTBI, and these should become biomarkers for future mTBIinvestigations related to neuropsychological outcome.

Absence of Biomarkers of mTBI Lead to PsychogenicExplanations

As pointed out by Taylor in 1967, during the chronic phaseafter concussion, on conventional neurological testing, “thereare no abnormal signs on physical examination” (p. 67).Without any objective marker of neurological impairment,psychological explanations naturally came to the forefront.Indeed, returning to Taylor (1967), he continues:

I have yet to read an article on the subject of postconcussional sequelae in the British literature whichdoes not contain the words “litigation” or “psychogen-ic” in the first dozen lines. Even a critical leading articleabout post-traumatic headache, written in April 1966,which concluded that treatment was not easy but waswell worthwhile, contained the statement that in at leasttwo-thirds of the patients there is a psychogenic ele-ment. This is certainly an advance on a similar leaderof 1961, which said: “Above all, the general physician . .. should find it advisable to refer a patient to the psychi-atrist as soon as possible . . .” and concluded: “In theultimate analysis it [accident neurosis] is a social diseaseand a function of industrial morale.” (p. 67)

Taylor was referring to Miller’s classic papers on “accidentneurosis” (Miller 1961a, b, 1962; Miller and Cartlidge 1972),where at best residual effects from concussion were portrayedby Miller as a functional disorder characterized by ‘neurosis’in the title. At worst, residual effects that persisted fromconcussion were considered by Miller as feigned, malingeredimpairments associated with secondary gain, especially whenlitigation was involved. For Miller, “attribution to cellulardamage” as having any part to do with PCS was simplyunsupported “speculation” (Miller 1961a, p. 992).

Miller was a neurologist and in the pre-neuroimaging era,objective neurological findings, particularly those pathogno-monic for damaged neural systems (i.e., an abnormal reflex,hemiplegia) were the only “objective findings” worthy ofclinical demonstration of neurological damage or impairmentshort of something being observed at the time of neurosurgeryor autopsy. Understandably, the patient with mTBI and anentirely “normal” clinical neurological examination wouldnot be suspected to have any underlying impairment.Indeed, one of the classic and influential psychodynamictextbooks at the time of Miller’s publications, Laughlin’s(1956) ‘The Neuroses in Clinical Practice ’, characterized allneuroses following trauma, including concussion, as merely“neurotic reactions which have been attributed to or whichfollow a situational traumatic event ….” (p.633). Specific toresidual effects from concussion, Laughlin writes that“Evidence could not be found to validate scientifically thetheories of an organic basis….”, rather “…a large body of datahas been accumulated clearly establishing the basic


psychologic origins of the symptoms commonly seen in con-nection with these reactions” (p.635).

Without an independent biomarker of brain injury, thenatural conclusion is captured by the title of Jacobson’s(1995) review, The post -concussional syndrome :Physiogenesis , psychogenesis and malingering : An integra-tive model . Macleod (2010); Evans (2010) and Obermannet al. (2010), all provide updated reviews and perspectives ofthe functional versus the organic versus the malingeringsymptoms that may be associated with mTBI. For example,as reflected in the article title—Post concussion syndrome :The attraction of the psychological by the organic—Macleod(2010) makes the case that following concussion “…the po-tentially malignant influences of psychosocial factors cancompound the organic state…” since “…there is invariably apsychological component to any physical disease” (p. 1035).

Neuropsychological assessment entered the debate in 1974when Gronwall andWrightson (1974a) demonstrated reducedneuropsychological test performance in a group of individualswith concussion dependent on task complexity and attentiondemands (see also Gronwall and Wrightson 1974b, 1975).Gronwall and Wrightson and countless other investigatorshave been attempting to characterize PCS symptoms, espe-cially, “…inability to carry out normal work, poor concentra-tion, fatigue, irritability, and headache” (p. 605), since theoriginal 1974 publications. Gronwall and Wrightson wereimmediately criticized (see their commentaries in Gronwallet al. 1990; Wrightson and Gronwall 1999) and the controver-sy continues to this day (see Temme et al. 2013). However,there would likely not be controversy, or less of it, if objectivemeasures of neural abnormalities could be demonstrated withmTBI. The problem centers on the fact that PCS symptomsare simply not specific to having sustained a concussion; theyoccur from diverse sources and, in the individual with ahistory of head injury, are influenced by a host of pre-injuryas well as post-injury factors (Silver et al. 2009; Silver 2012).Given these ambiguities it is no surprise that neuropsycholog-ical outcome studies provide such a confusing picture.

Although memory impairment as a symptom followingmTBI is commonplace (Ruff et al. 2009), memory complaintsare ubiquitous symptoms across numerous neurological andneuropsychiatric disorders. Similarly, given the complexity ofneural systems that underlie memory, diverse structures andpathways have the potential to influence memory performanceon a neuropsychological task. Indeed, similar arguments canbe made about all symptoms/problems that form PCS—noneare unique to having sustained an mTBI and none have aunique neural underpinning that would be specific to mTBI.Unless there is some type of biomarker to link the symptomcomplaint (i.e., problems with memory, fatigue, inability toconcentrate, etc.) to the presenting problem and history, and towhich neural system is being affected, it becomes a statisticalquagmire to find relationships. As argued in this review, one

potential solution, or at least an improvement in researchdesign, would be to use neuroimaging as a type of biomarkerto define mTBI subgroups with common pathology. As al-ready shown in Fig. 2, this was the approach taken by Sorget al. (2013).

In addition, this is captured in the study by Niogi et al.(2008) which examinedmTBI patients who underwent DTI aswell as neuropsychological examination. A critical pathwayfor memory involves the uncinate fasiculus (UF; see Nestoret al. 2012) and Fig. 8 shows the relation of UF integrity basedon FA and memory performance. Note in Fig. 8 that the longdelay free recall (LDFR) trial of the California VerbalLearning Test (CVLT; Delis et al. 2000) positively relates withFA in the UF, but not in the anterior corona radiata (ACR). Avariety of factors may contribute to reduced memory perfor-mance and UF integrity as the UF is part of language andemotional processing networks and not just memory (Cataniet al. 2012), but the point is that it was the UF that related tomemory impairment in mTBI. Furthermore, note that overhalf of the mTBI subjects performed well within normativeranges of the standardization sample. So, unless the rightpathway is examined in mTBI patients with possible pertur-bation of that pathway related to a particular function, nogroup relationship would be found. No two mTBI patientswill ever have identical pathology; therefore, this limits howomnibus whole-brain neuroimaging metrics would be sensi-tive in detecting where unique pathology may reside in mTBI(however, see Kim et al. 2013).

Geary and colleagues (2010, 2011) similarly demonstratedthe importance of the UF in memory in mTBI but also exam-ined a novel way of assessing list-learning using the CVLT(Delis et al. 2000) in an mTBI sample. In 35 controls and 40mTBI subjects, Trial 1 was the only trial that reached signif-icance, but the mean (M ) and standard deviation (SD) valueswere clinically unimpressive (Control: M =7.63, SD =2.06;mTBI: M =6.58, SD =1.84); no other CVLT comparisonswere significant including no Group×List interaction. In atraditional sense these would be interpreted as potentiallymeaningless and inconsequential clinical findings—that is,no neuropsychological effect from mTBI. However, the cor-relation between UF and CVLTwas significant (it should alsobe noted that the superior longitudinal fasciculus was alsosignificant, likely a reflection of attentional networks in mem-ory), reflecting a distinct relation between reduced verballearning and UF integrity.

Another example comes from Little et al. (2010), but thisstudy focused on executive functioning tasks and examinedDTI metrics of thalamic integrity. Again, only those withabnormally low FA exhibited reduced neuropsychologicalfunctioning. In these investigations if neuropsychological testswere group averaged, irrespective of neuroimaging findings,none or just a few and clinically unimpressive differenceswould be observed. Geary et al. (2010) concluded, “Most


critically, the finding of diminished recall for Trial 1 wasobserved in well-motivated (i.e., as assessed by effort mea-sures), nonlitigating, nondepressed, and gainfully employedindividuals many years after sustaining a mild TBI” (p. 514),and that patients with mTBI demonstrated, “…diminishedverbal learning that is not often interpreted in standard neuro-psychological assessment.” (p. 506). Traditional neuropsy-chological methods did not distinguish the groups.

Psychogenic interpretations of mTBI effects have beendependent upon the absence of neurogenic explanations.However, there has been no systematic assessment that haspitted the so-called psychogenic versus neurogenic where ad-vanced neuroimaging has been used. Had not advanced neu-roimaging techniques been used, the interpretations of thememory problems in the mTBI patients described in the stud-ies by Niogi et al. (2008), Geary et al. (2010, 2011) and Littleet al. (2010), could have been given a psychogenic description.

Without definitive biomarkers of brain injury in mTBI, oneshould be skeptical of its proof and influence on cognition andbehavior (see M. P. Alexander 1998). However, skepticismover mTBI sequelae should form the foundation for critical,hypothesis driven research using the best in technology, notjust the rejection of the existence of the problem, especiallywhen the issues of underlying neuropathology have not beendetermined.

False Distinction of “Mild Complicated Traumatic BrainInjury” or mcTBI

Since its introduction as a clinical neuroimagingmethod, MRIhas proven to be superior in detecting abnormalities associatedwith any type of TBI (H. S. Levin et al. 1987). However,routine DOI clinical neuroimaging is almost exclusively donewith CT because the procedure is quick, clinically sensitive toneurosurgically important abnormalities, readily detects skullfracture and can be done on patients with life support equip-ment or metallic fragments in the head or body (Hunter et al.2012). Likewise, interpretation is typically based solely onclinical judgment of the radiologist, with no quantitative mea-sures applied. As might be expected, presence of DOI CTabnormality in mTBI has been associated with increasedlevels of neurobehavioral and neurocognitive sequelae (deGuise et al. 2010; Kashluba et al. 2008; Mounce et al. 2012;Iverson et al. 2000), although this is not always observed inmTBI samples (Lange et al. 2012; Deepika et al. 2012).Because of the objectivity of DOI CTabnormalities indicatingpresence of parenchymal injury, as noted before, this classifi-cation became known as “mild complicated TBI” or mcTBI.This specific classification clearly identifies those mTBI pa-tients with a definitive traumatic abnormality on the DOI CTscan, but the problem with this distinction is that because it is

Fig. 8 Two cognitive functions are specifically associated with whitematter (WM) microstructure in two distinct regions (uncinate fasciculus[UF] and anterior corona radiata [ACR]) in adults with mild TBI. aCorrelation of memory performance and average bilateral UF fractionalanisotropy (FA) in both hemispheres (r =0.52, p <0.001). b The ACR FAdoes not correlate significantly with long delay free recall (LDFR)

memory (r =−0.057, p =0.725). c The UF FA does not correlate signif-icantly with attentional control (r =0.133; p =0.41). d Correlation ofattentional control measured by conflict score and the left ACR FAweresignificant (r =0.47; p=0.001). This shows that cognitive effects frommTBI may be region and task specific. From Niogi et al. (2008) and usedwith permission from Oxford University Press


only CT based, it substantially underestimates the true pres-ence and type of TBI abnormalities.

Figure 9 is from a recent study by Yuh et al. (2012) thatdepicts the results from a prospective, multi-center study ofmTBI that included 135 patients who received early CTimaging and MRI within 3–9 days post-injury. Clinically,CT identified lesions in the form of contusion, hemorrhage,or edema were found in approximately 25 % (numbers con-sistent with other studies as reviewed by Yuh et al. 2012) butnote, over 40 % were identified with MRI. As reported bythese investigators, CT was especially poor at identifyingaxonal injury.

As important as Yuh et al. (2012) study is in demonstratinghow frequently neuroimaging abnormalities are found inmTBI, a particular limitation of this investigation is that itused conventional MRI and only examined T1, T2, T2*GREand FLAIR sequences and clinical readings. The SWI se-quence is superior to standard GRE sequences in detectinghemosiderin, where comparative studies show a two-to-threefold increase in detection of abnormalities with SWI overstandard GRE (Benson et al. 2012). Yuh et al. did not includequantitative analyses at different post-injury time-points,which show a high yield in detecting differences in mTBIbeyond just clinical findings (see Toth et al. 2013). Notably,none of the mTBI patients in the Toth et al. study had abnor-malities on DOI scanning, and clinical interpretation of theMRI findings was likewise negative. Only when the neuro-imaging data were subjected to quantitative analyses, weredifferences detected. It should be noted that Toth et al. at1 month post-injury, in their longitudinal, within subjectsdesign, showed amean 1% volume reduction in overall brain

volume and a 3.4 % increase in ventricular volume in indi-viduals with mTBI. Both changes were statistically significant(p <.05). In the acute phase, quantitative metrics using DTI,like FA may identify differences from controls in more than90 % of mTBI subjects (Wilde et al. 2008). Likewise, severalmTBI studies have shown that DTI findings over 3 monthspost-injury assist in identifying those with persistent neurobe-havioral and neurocognitive sequelae and, in turn, relate tothose who remain symptomatic (see Ling et al. 2012; Mayeret al. 2012a, 2011, 2012b; Messe et al. 2011, 2012).

When DOI CT is compared with conventional MRI, asshown in the studies reported above it may detect only half orfewer of the abnormalities visibly present with MRI. Further,with the greater sensitivity of SWI and DTI in detectingabnormalities, conservatively within any mTBI sample withreportedly “normal” CT, somewhere from one-quarter to athird of those will have abnormalities detected with MRI.An example of a normal DOI CT but abnormal MRI appearsin Fig. 5, demonstrating the importance of follow-up neuro-imaging in mTBI even in the absence of any CT findings onthe DOI scan. This young adult university student was struckby a car and thrown into the curb. Positive LOC of shortduration was independently verified by an observer at thescene. Emergency department (ED) assessment indicated aGCS of 13 but a negative CT. She was discharged from thehospital the following day; however, upon returning to hergraduate studies she complained of substantial problemswith attention and memory. Follow-up MRI demonstratedmultiple regions of hemosiderin deposition within the fron-tal white matter. Furthermore, these abnormalities remainedessentially unchanged over the next 5 years, demonstratingtheir permanency.

All past “markers” of mTBI such as LOC, DOI CT find-ings, PTA, GCS, and PCS symptoms have limitations, withLOC, PTA and GCS only reflecting indirect measures ofpossible neuropathology. Even clinical categorical ratings thatare determined to reveal no abnormalities on conventionalclinical MRI cannot detect what quantitative analyses can(Toth et al. 2013). As such, only a confusing picture ofmTBI outcome would come from incomplete or ineffectivemarkers like GCS, or “positive CT” assessed in the ED usedto define the event that represents mTBI; such is the state ofneuropsychological investigations of mTBI. A host of psy-chological variables may be at play as sequelae to mTBI(McNally et al. 2013). Before psychogenic theories areinvoked to explain the effects of mTBI, objective and con-trolled investigation of potential structural and functionalneuropathologies predictive of outcome is essential (Blaineet al. 2013).

An example of the potential misapplication of psychogenicinterpretations for poorly identified illness comes from mili-tary service men and women who served in the Gulf War inthe early 1990s and developed Gulf War Illness (GWI). When

Fig. 9 Incidence of computed tomography (CT) vs. magnetic resonanceimaging (MRI) traumatic brain injury common data element (TBI-CDE)abnormalities in 135 study participants with mTBI. For MRI evidence ofcontusion andMRI evidence of hemorrhagic axonal injury, progressivelydarker shades of red indicate larger numbers of lesions (gray legend).Study participants with CT evidence of brain contusion had, in mostcases, evidence of one or two hemorrhagic contusions, with no CTdemonstrating more than 3 convincing brain contusions. CT showedevidence of hemorrhagic axonal injury in 3 of 135 study participants,all with 1 to 3 foci of injury. Permission to be reproduced from Yuh et al.(2012) and Wiley


first reported GWI was widely touted as a classic psychogenicillness within the somatoform category of disorders (Gronseth2005); however, neuroimaging studies using techniques likeDTI have shown WM pathology in some individuals withGWI that relate to subjective symptoms like fatigue and pain(Rayhan et al. 2013). Such findings call into questionumbrella-type psychogenic classifications until potential neu-ropathological explanations are ruled out.

Limitations in Detecting Neuropsychological Impairmentin TBI

The most common problems from TBI are residual deficits incognitive speed, visuoconstruction, attention, and memoryabilities (Millis et al. 2001). The study by Bendlin et al.(2008) provides an ideal example in the demonstration ofresidual neuropsychological effects of TBI combined withthe most common areas of damage, in particular WM changesbased on DTI metrics (see Fig. 10). Note how the abnormal-ities depicted in Fig. 10 overlap with predicted areas of dam-age as shown in Figs. 1 and 7. Inclusion criteria in this inves-tigation involved documented positive loss of consciousness,positive DOI CT scan and a GCS of at least 13 with initialMRI obtained around 2 months post-injury, with follow uparound 1 year post-injury. Thus, the Bendlin et al. researchincluded mcTBI participants, although the overall samplewould be characterized as a moderate-to-severe TBI group.The control sample consisted of age and education matchedparticipants without injury. Figure 11 summarizes the neuro-psychological findings.

Neuropsychological results are presented in z -score formatwith the 0.0 horizontal line indicating the control reference z-score. Clearly notable is the distinct influence that sub-acutebrain injury has on speed of processing when first assessed butby 1 year post-injury, improved functioning occurs across the

board, including processing speed with all domains function-ing approximately 0.5 to 1.0 SD below control levels.

Figure 10 indicates that the brain was damaged as this TBIcohort exhibited degenerative changes from the sub-acutetime of initial testing (2 months post-injury) to follow-up(1 year). It is instructive to note that the greatest neuropatho-logical changes occurred in WM, in the areas predicted byDAI/TAI studies (see Figs. 1, 2, 3 and 7: note the overlap withareas acutely and chronically seen with just mTBI) and thatWM damage related most to the cognitive deficits (see alsoFarbota et al. 2012b from this same group; and Farbota et al.2012a). Concomitant with the neurodegenerative changesresulting in both gray matter (GM) andWM atrophy (definedas volume loss), all domains of cognitive functioningexhibited improvements over time. However, despite im-provements in cognitive functioning with time in a mostlymoderate-to-severe TBI sample, as a group no cognitive func-tion returned to presumed baseline as reflected by the control0.0 z -score.

From a psychometric standpoint the extent of cognitiveimpairment during the chronic phase seems to hover at around0.5 of a standard deviation difference. Translating this to effectsize differences, in TBI, including mcTBI with known grossneuropathological changes, neurocognitive function in mostdomains would reflect on average medium effect size differ-ences in comparison to a non-brain injured control sample. Ifonly medium effect size differences occur in mostly moderate-to-severe TBI with quantifiable brain volume loss from thebrain injury, what does this mean for those who sustain mTBI,where pathology may be much more subtle? Does this notmean that in using traditional neuropsychological measuresthat the expected effect-size differences would be small orminimal, if present at all (see discussion by Bigler et al.2013c)? In some studies involving moderate TBI, neuropsy-chological test findings cannot be differentiated from controlsor those with mTBI, even in those with positive neuroimaging

Fig. 10 Neuroimaging findings depicting the extent of gray matter (GM)and white matter (WM) damage in the Bendlin et al. (2008) study,showing changes (volume loss) from 2-months post-injury to 12 months.The TBI group showed extensive WM volume decline over time (shownin hot colors), in addition to a few regions of GM volume decline,including the thalamus and bilateral pallidum (shown in winter colors).

Patients also showed GM volume decline over time in the cingulum, rightpost-central gyrus, supplementary motor area, right precentral gyrus, andbilateral putamen (areas not shown here). Results are shown in neurolog-ical orientation (left is left). Color bars reflect t-statistics. Permission toreproduce from Elsevier and the authors


imaging findings (Lange et al. 2012). Does this not mean thatthe neuropsychological technique being used to assess cogni-tion in mTBI needs to be critiqued?

As noted, the CVLT memory task was sensitive to differ-ences on Trial 1, but on average this amounted to less than onefewer word retained in the mTBI group on the 16-item wordlist [Little et al. (2010) and Geary et al. (2010)]. Heitger et al.(2009) found a significantly lower retention of the distractorlist on the Rey Auditory Verbal Learning Test (RAVLT; Rey1958) in a group of symptomatic mTBI patients but in realterms this amounted, on average, to less than one wordrecalled by the PCS symptomatic mTBI group compared withthe symptomatic (again, effort and depression were controlledfor). Ponsford et al. (2011) examined memory performance at1 week and 3 months post-mTBI using the ImPACT comput-erized assessment method (Iverson et al. 2003) and observed asignificant difference at 3 months on a visual compositememory measure; however, the reported effect size differencewas small at 0.25. Again, from a clinical perspective these arenegligible to minimal differences for the clinical neuropsy-chologist to detect with traditional neuropsychological mea-sures in the individual with mTBI. Therefore, not only will theneuropathological substrate of mTBI be subtle for neuroim-aging techniques to detect, but the neuropsychological effectswill also be as equally subtle if traditional methods are used.

Figure 12 graphically shows this problem in an mTBI caseof a 17.5 year-old mTBI patient by plotting out CVLT-IIperformance over trials. This individual had initial negative

CT but positive follow-up MRI with multiple hemosiderindeposits noted particularly in the frontal area (this case isdiscussed in Bigler 2004). The patient complained of memoryproblems but as graphically depicted in Fig. 12, only on Trial2 of the CVLT-II does this patient perform outside the range ofthe normative sample, and on that trial only deviates less thanone word. Without the positive neuroimaging reflectingabnormalities within known memory/attentional networks,such findings from traditional neuropsychological test re-sults may simply be overlooked. The memory problemsassociated with mTBI may simply not be assessed by thesetraditional neuropsychological measures or they may sim-ply be silent anomalies.

Between Subject Heterogeneity of mTBI Neuropathology:If No Two TBIs are Ever Identical, How can a UniformNeuropsychological Outcome be Expected?

Given the uniqueness of individual brains, combined with theunique circumstances and biomechanics of any brain injury(Watanabe et al. 2012)—especially the random nature ofinjuries—it is unlikely that two brain injuries could ever beidentical (Rosenbaum and Lipton 2012). Even with the de-tailed controls and experimental rigor of animal TBI models,Turtzo et al. (2012) showed using an identical rat strain—mechanically injured in exactly the same fashion with a con-trolled cortical impact method—identifiable pathology was

Fig. 11 Summaryneuropsychological results fromBendlin et al. (2008) organized bygeneral domain of cognitivefunction. Note: COWAT=Controlled Oral WordAssociation Test; Trails A=TrailMaking Test, Part A, Trails B=Trail Making Test, Part B; LNSequence=Letter NumberSequence; CVLT=CaliforniaVerbal Learning Test, Tot=total,SD=short delay, LD=long delay;BVMT=Brief VisuospatialMemory Test. Controlperformance was standardizedwith the z-score statistic, showingthat the initial assessment atapproximately 2-months post-injury reflects the most significantdeficits, which by a year post-injury while improved, still isbelow the control reference level.Modified from Bendlin et al.2008, courtesy of Drs. Bendlinand Johnson


never identical. Similarly, Sullivan et al. (2013) in a porcinemodel demonstrated that sagittal rotations were most likely toinjure axons, sparing those in other planes. With human FEmodeling using individual MRI configured brain anatomy, notwo injuries could be identically simulated (see Bayly et al.2012).

The mcTBI component of the study by Bigler et al. (2013a)found no case where any of the lesions (areas of focalencephalomalacia, hemosiderin deposit, or WM signal abnor-mality) perfectly overlapped. The major inference of this isreflected in Fig. 13 from four mTBI cases from this study,three with a GCS of 15 and one a GCS score of 14. Thesecases were selected for this illustration as they unequivocally

have mcTBI-related focal pathology, but the marked differ-ences in the distribution of macroscopically identified lesionsare evident. Simple overview of this illustration and the non-overlapping nature of the lesions would implicate non-overlapping neuropathological influences likely resulting indifferent neurobehavioral consequences because differentneural pathways and networks were damaged in thesemcTBI patients.

Based on DTI-derived atlases (see Catani et al. 2012),lesions from any MRI sequence or CT scan can be overlaid,where various inferences can be made about specific path-ways and neural systems that may be disrupted or affected asshown in Fig. 14. Taking two of the cases from Fig. 13 andapplying the Catani and Thiebaut de Schotten (2012) atlas,the identifiable lesions disrupt different pathways. Withoutusing quantitative neuroimaging methods to identify abnor-malities and aggregate mTBI subjects into similar groups,why would there be any neuropsychological expectationthat similar deficits in cognitive and neurobehavioral func-tioning would emerge simply by having mTBI subjectswithin a group only defined by having sustained anmTBI? The lesions in Fig. 14a would affect the cingulumbundle and the uncinate, along with occipitofrontal fasciculiand interhemispheric pathways across the genu; whereas incase Fig. 14b, the coup -contre coup lesions would havelikely influenced both the superior and inferioroccipitofrontal fasciculi. Locus of such pathology undoubt-edly will differentially affect cognition.

Different lesion patterns with each mTBI case would likelyinfluence neuropsychological outcome in unique ways.Mixture of lesions and outcomes has the potential to washouteffects that otherwise are present within mTBI subgroups.Returning to the Niogi et al. (2008; see Fig. 8) investigation,it is only when memory is considered within the context of theUF integrity, that mTBI related memory impairments becomeapparent, and then only within a subset of those with a historyof mTBI.

The Concept of “Mild” Cognitive Impairmentand Neuropsychology

In terms of injury classification of TBI, mild, moderate andsevere categorization has been the standard since the origina-tion of the GCS metric in 1974. There is no dispute concus-sion is a TBI and, provided that no disruption or change inlevel of consciousness moves the GCS measure below a 13,that this level of TBI constitutes the mildest of injuries. If levelof injury is effectively described as ranging from mild tosevere and if all TBI is on a continuum, as argued in thisreview, why would the potential for residual cognitive effectsstop at the moderate range of TBI? As already stated, animalmodels of TBI may be graded from no discernible

Fig. 12 Performance on the California Verbal Learning Test (CVLT) inan mTBI patient with negative day-of-injury (DOI) computed tomogra-phy (CT) but positive magnetic resonance imaging (MRI) with multiplehemosiderin deposits within the frontal and temporal lobes. PatientCVLT-II performance (black diamond ) is plotted in comparison toCVLT-II normative data (gray bars) from the test manual. Note that thepatient’s performance, while on the low end of the normative distributionin each trial, only drops below 1 standard deviation (s.d.) of the compar-ison sample on Trial 2 in List A Immediate. This case is adapted fromBigler (2004)


pathophysiological or neurobehavioral effects, to showingincreasingly impaired cognitive performance with underlyinginjury severity and neuropathology. With neuroimaging andelectrophysiological studies consistently showing WM pa-thology and abnormalities in connectivity in mTBI (Moreyet al. 2012; Tallus et al. 2013; Smits et al. 2011; Kirov et al.2013; Messe et al. 2013), combined with animal models andhuman studies from multiple concussions showing histologi-cally confirmed brain pathology (McKee et al. 2010, 2012;Wall et al. 2006), why would it not be expected that suchpathology adversely affects cognition and behavior?

Historically, psychological theory of cognitive impairment,regardless of etiology, consistently has been reported in termsof mild, moderate-to-severe impairments in comparison tosome normative sample (Bornstein et al. 1987). This has beenestablished for all disorders, be it stroke, degenerative disease,demyelinating disorders, epilepsy, etc., in order to characterizeneurocognitive effects of the disorder (Beghi et al. 2006;Grau-Olivares and Arboix 2009; Snyder et al. 2011;Waldstein and Wendell 2010). The argument of this reviewis that mild neurocognitive and neurobehavioral sequelae dooccur with mTBI and should be characterized as such. Andfurther, although a rather benign clinical course of full returnof function in mTBI is the norm, neuroimaging methods that

provide objective indicators of underlying neuropathologyshould assist in better characterization of the neurocognitiveand neurobehavioral outcomes of mTBI.

Subjective Symptoms and the Term ‘Mild’ Maybe the biggestobstacle with the “mild” classification within the field ofneuropsychology comes with the subjectivity and non-descript nature of so many of mTBI symptoms. Much hasbeen written about subjective symptoms in mTBI because oftheir co-occurrence with so many other neurological andneuropsychiatric conditions (Chaput et al. 2009; Iversonet al. 2010; Jakola et al. 2007; Kennedy et al. 2007;Macleod 2010). This, coupled with issues related to researchdesign of mTBI studies, led the 2004 World HealthOrganization (WHO) to label much of the research in mTBIproblematic (see Carroll et al. 2004a). Without a definitivebiomarker that differentiates etiology of symptoms from otherdisorders, it should be no surprise that studies that examinegroup outcome in mTBI, mostly endorse biopsychosocialmodels, some of which minimize or do not discuss anyunderlying neuropathology (see discussions by Silver 2012;Silver et al. 2009; van Veldhoven et al. 2011; Williams et al.2010). On the other hand, expression of a subset of symptomsassociated with mTBI may be psychogenic (Mounce et al.

Fig. 13 Four cases of pediatricmild TBI (mTBI) showingdifferent locations of focal lesionsselected from the study by Bigleret al. (2013a, b, c). All had mTBIand only the participant in thelower right (d) had a GlasgowComa Scale (GCS) of 14 with therest having GCS scores of 15.Note the complete lack of overlapof any focal lesion. The childrenin C and D are highlighted inFig. 14 to show how differentpathways are affected. a , c and dare T2-weighted images andshow areas of old focal surfacecontusions with increased CSFsignal and B is a fluid attenuatedinversion recovery (FLAIR)sequence where the red arrowpoints to a white matter (WM)hyperintensity


2012; Macleod 2010); however, a search for underlying neu-ropathology should be undertaken before the psychogeniclabel is considered.

While subjective symptoms may be the bane of neuropsy-chology in terms of understanding mTBI (McLean et al.2009), understanding the nature of subjective symptoms haveplayed a key element of what occurs during the prodrome of

neurodegenerative disease. For example, when turning to theliterature on aging and dementia, neuroticism was once con-sidered simply a trait not linked in any way with dementia.However, life stressors as well as early-in-life neuroticism anddepression have a relationship with the development of de-mentia, potentially via stress-mediated neuroinflammationfactors (Wilson et al. 2007).

Fig. 14 a Sagittal views of thepathology demonstrated insubject 13-D is compared to theCatani and Thiebaut de Schotten(2012) atlas. Note that from thisatlas, pathways involving theanterior cingulum bundle alongwith projecting interhemisphericpathways from the genu of thecorpus callosum would beaffected by this lesiondistribution. [Outline Notes: Red=corpus callosum, Mauve=overlap of cingulum with corpuscallosum tracts, Blue=uncinatefasciculus] b Axial views of thepathology demonstrated insubject 13-B. Note that this is theT2-weighted image in the upperleft which clearly shows the WMhyperintensity as was visualizedin the fluid attenuated inversionrecovery (FLAIR) image inFig. 13. The bottom left image is agradient recalled echo (GRE)sequence and shows distinctpresence of hemosiderin. Notethese pathologies would influencethe inferior fronto-occipitalfasciculus (Green Outline) andsuperior longitudinal fasciculus(Green). Different lesions affectdifferent pathways and networks


Similarly, ill-defined prodromal cognitive complaints inolder individuals with subjective memory symptoms do relateto who develops mild cognitive impairment (MCI) and con-verts to Alzheimer’s disease (Jessen et al. 2010; Reisberg et al.2010). Subjective symptoms also appear to be harbingers ofprogressive change in other conditions, for example, multiplesclerosis (Marrie et al. 2005), Lupus erythematosus (Denburget al. 2003), Parkinson’s disease (Bugalho and Oliveira-Maia2012; Skogar et al. 2012). Most relevant to mTBI is thatrecently Lafrance and colleagues (2013) found that historyof TBI was a significant risk factor in development of psy-chogenic nonepileptic seizures. It seems intuitive for all neu-rological and neuropsychiatric disorders that factors heraldingvulnerabilities for developing depression and anxiety-baseddisorders provide a backdrop reflecting a different brain at thetime of sustaining an mTBI than the brain without suchvulnerabilities. Likewise, any of these pre-existing vulnerabil-ities probably diminishes the resources and resiliencies post-injury in coping with the effects of mTBI. In the case ofpsychogenic epilepsy, some diminished capacity for emotion-al coping because of the brain injury may be the path forincreased maladaptive emotional adjustment post-injury,resulting in the phenomena of pseudoseizures as found inthe Lafrance et al. (2013) study. Of course, all of the disordersjust mentioned (i.e., epilepsy) have their own definitive bio-markers, which then assist in ferreting out how subjectivesymptoms/complaints may relate to the disorder. Withoutbiomarkers for mTBI as offered by advanced neuroimagingmethods, the problems circle back to the imprecision of “mea-suring” subjective complaints and no method to tie them towhat may be bona fide neuropathology or absence thereof.

Subjective symptoms will remain nondescript and likelynot be helpful in discriminating mTBI patients from otherconditions nor useful in predicting who achieves good versuspoor recovery. Well-designed neuroimaging studies or studiesbased on other validated biomarkers are needed to address thisissue systematically (Zetterberg et al. 2013). For example,Chen et al. (2007) demonstrated a relationship betweenpersisting symptoms and PCS following mTBI and differentfMRI activation patterns in prefrontal regions in mTBI sub-jects. Bigler and Bazarian (2010) argue that studyingpersisting symptoms combined with neuroimaging bio-markers must be the next line of research examining theeffects of mTBI, including emotional outcome and occurrenceof non-specific symptoms.

The Problem of Measuring What may be “Mild” in Neuro-psychological Outcome Using traditional neuropsychologicalmeasures, one of the biggest challenges in clinical neuropsy-chology may be determining what the “mild” neurocognitiveeffects of any disorder are, including mTBI. This problem wasintroduced in the discussions involving Figs. 2, 5, 8, and 11. Ina stroke study that has in its title the detection of “subtle

memory impairment and attentional deficits….”, Duffinet al. (2012) show on average, that stroke patients thought tohave suffered a “mild [italics added] cerebrovascular acci-dent”, exhibited significantly reduced retention on theRAVLT of approximately one word. Note this is the samelevel of reduced performance that Geary et al. (2010, 2011),Little et al. (2010), and Heitger et al. (2009) found in mTBI onlist retention tasks like the RAVL or CVLT. Although signif-icant, a one or two word less retention on a 15-item word listcan only be viewed as a subtle, slight difference. Returning tothe Bendlin et al. (2008) study, it shows that effect sizedifferences overall on cognitive performance >12 monthspost-TBI in mostly moderate-to-severe TBI patients with neu-roimaging documented atrophy is only about 0.5. This trans-lates to an expected difference per trial on measures like theCVLT or RAVLT of approximately one less word recalled. Ifthose with well documented structural damage exhibit thiskind of subtleness to their cognitive profile on traditionalneuropsychological tests, what kind of impairment would bedetected in the mTBI patient with mild cognitive problems?

An even more important issue is alluded to by Millis(2009) with the following statement: “cognitive tests donot directly measure cognition: they measure behaviorfrom which we make inferences about cognition”(p.2410). Neuropsychological measures of cognition onlypermit inferences to be made about brain function (seeLezak et al. 2012). This becomes an even more importantissue when attempting to detect subtle deficits, given thecontrolled assessment environment that does not mimic anaturalistic setting. The majority of clinically administeredneuropsychological tests that are performed use highlyregimented standardized presentations of auditory and vi-sual stimuli, with hand-recording of responses and theirtabulation for scoring and interpreting all done within arigid administration of items, with no extraneous stimuli orsound. Likewise, potential differences are as good as arethe comparative norms and normative samples. This prob-lem is distinctly demonstrated in the mTBI case presentedin Fig. 5.

The college student previously depicted in Fig. 5 hadparticipated in a variety of routine working memory tasks asa volunteer participant for fMRI cognitive neuroscience re-search projects before she was injured. Therefore, pre-injurydata were available on not only her cognitive performance buther fMRI activation patterns in response to the cognitiveprobes (see Wu et al. 2010a; Allen et al. 2011). When re-assessed more than a year post-injury and having completedher degree and with full-time employment, although still withlingering PCS, cognitively on the paper-and-pencil and com-puter based tasks she was able to perform at similar levels aspre-injury. However, using an event-related fMRI paradigmwhere performance (response latency) was assessed in milli-seconds, reflective of true neural processing speed, her


performance level was slower by about ~135 milliseconds.Accuracy had not changed at all, only slowed processing.Thus, traditional neuropsychological test findings were nottapping underlying neural deficits and what the patient wassubjectively experiencing. She struggled with attentional pro-cesses, speed and working memory, and as can be viewed inFig. 5 had distinct frontal pathology (hemosiderin andWMHs) within known networks associated with attention,speed of processing and working memory, confirming thelikelihood that her mTBI related complaints were neurogenic.

Evolutionary Influences on Recovery from mTBI

Survivability from a concussion is true across the animalkingdom. There are numerous animal models of mTBI(Biasca and Maxwell 2007; Cohen et al. 2007; Leker et al.2002; Shaw 2002; Weber 2007), all demonstrating good re-covery of basic functioning following mTBI in distinct con-trast to moderate-to-severe TBI. In terms of the natural “re-covery” of function, most extensively studied in the athlete,basic motor, sensory and cerebellar functions predictably arethe first affected and likewise, the first to come back on-line.Evolutionarily, that makes sense because in the brutish fight-flight world of our ancestors, if you were on the losing end offisticuffs, one would need to sufficiently recover motor abilityto flee (see Bigler 2012) or survival was unlikely (Masel andDeWitt 2010). Likewise, in earlier eras to remain part of ahuman clan, mobility was certainly a key factor as well. Froma cognitive perspective, the concussed individual is capable of

performing basic mental tasks even in the earliest stages ofrecovery, just not as efficiently. But in earlier times from asurvival perspective memory demands would likely have beenmuch more naturalistic than what occurs with the modernchallenges of today’s technologically sophisticated world,and therefore, potentially not as noticeable.

Anatomically, there is likely a straightforward explanationfor differential evolutionary effects on motor over cognitiverecovery. Figure 15 shows the mostly vertically organizedmotor and sensory tracts of the corticospinal and spinocorticalsystems and cerebellar tracts. Efferent and afferent pathwaysof these systems that pass through the upper brainstem (com-pare to Fig. 16) have a common location associated with thereticular activating system (RAS). One look at the narrowtrajectory, see red arrows in Fig. 16, of these pathways throughthis upper midbrain region, see horizontal red lines in Fig. 16,shows the unique confluence of these tracts in relationship tothe large and weighty cerebral hemispheres. Furthermore, theupper brainstem remains more rigid being held in position bythe skull base than the cerebral hemispheres, and is tethered tothe spinal cord that is tightly held within the spinal column. Bydefinition, any effect at this upper brainstem level must beminimal in mTBI or otherwise the individual is left withlonger duration coma and thus a lower GCS than the minimalpost-resuscitation 13 score required for mTBI classification.Any prolonged disruption of the RAS would lead to longerduration coma and persistence of disorders of consciousness(Jellinger 2013). So, by definition these neural systems cannotbe seriously damaged in order to survive the brain injury andremain classified as an mTBI.

Fig. 15 The corticospinal and spinocortical tracts are oriented verticallywith afferent and efferent pathways coursing through the brainstem. Incontrast the interhemispheric pathways of the corpus callosum and theintrahemispheric pathways of the inferior occipitofrontal fasciculus have

very different trajectories. Adapted with permission from Catani andThiebaut de Schotten 2008 and Elsevier. See also Catani and Thiebautde Schotten (2012).


However, as shown in Figs. 15 and 16 the long coursingWM inter- and intra-hemispheric pathways are oriented quitedifferently from the primary motor and somatosensory path-ways and are, therefore (see Figs. 6 and 16 and compare theorientations of the fiber tracts that make up the corpuscallosum and inferior occipito-frontal fasciculus), differential-ly influenced by shear-strain effects and interhemispheric con-nections. Pathology within these inter-and intra-hemisphericWM tracts would less likely influence motor function butdifferentially influence cognitive ability, in particular workingmemory, attention and speed of processing. Hence, in mTBIthe injured individual returns to baseline motoric and sensoryprocessing ability before cognitive functions return.

Within-Subject Heterogeneity of mTBI Lesions, TheirSpecificity and Neuropathology

Given the prior discussion of inter-subject heterogeneity inmTBI combined with evolutionary adaptability of mTBI,what about within subject variability of lesions in mTBI?Figures 17 and 18 are from a single mTBI patient with WMhyperintensities (WMH). While WMH may be a normal var-iant and certainly not specific to TBI (see Hopkins et al. 2006;Iverson et al. 2011), in an otherwise healthy individual whoexperiences mTBI, their presence may reflect white matterdamage (Benson et al. 2012; Shenton et al. 2012). WMHmayalso represent vulnerability indicators since they are associat-ed with disorders like depression (Kempton et al. 2011),where pre-existing depression increases the likelihood ofpersisting symptoms following mTBI (Silver et al. 2009).Orrison et al. (2009) demonstratedWMH with increased rates

in unarmed combatants, presumed to be present as a reflectionof concussion history, and when such findings are within thegray-white junction or adjacent to the corpus callosum, the

Fig. 16 The left cerebral hemisphere is shown in the upper left as a 3-Drendered view from T1-weighted magnetic resonance imaging (MRI).The upper middle panel shows the much smaller brainstem (outlinedflesh-tone) with the ventricular system shown in aquamarine for orienta-tion. Notice the smallness of the brainstem in relation to the cerebralhemisphere, as well as the central location of the brainstem in relation tothe cerebral hemisphere. Mid-sagittal T1-weighted MRI is shown in theupper right panel. Particularly small is the midbrain region, outlined bythe horizontal red lines in the upper right image. Lower left is a coronal

T1-weighted image that shows the small midbrain and brainstem incomparison to the large cerebral hemispheres, and then highlighted withthe middle panel, with the arrows pointing to the region of the cerebralpeduncle andwhere the corticospinal tracts pass. Lower right panel showsa lateral view of the corpus callosum superimposed on the 3-D brain fromthe upper left, showing the orientation of projecting fibers. Colors repre-sent aggregate tract orientation—blue=vertically oriented tracts, green=anteriorly-posteriorly orientated tracts and warm colors (orange-red)=side-to-side or laterally projecting tracts

Fig. 17 [View this image in conjunction with Fig. 18]. The FLAIRimage in the upper right shows a distinct white matter hyperintensity(WMHs, red arrow) in the frontal white matter (WM). Plotting thelocation of all WMHs (as shown in yellow) in the 3-D DTI surfacerendered image of the brain (diffusion scan images are fuzzy, and that iswhy this image represents just a smooth surface outline of the brain), inthe upper left shows the multiple distribution of WMHs. However, usingtract-based spatial statistics (TBSS), differences in WM, compared to anormative sample, exhibit a somewhat different distribution of WMfindings as shown in red in the 3-D view (ventricular system is shownin aquamarine for orientation purposes)


likelihood of these signal abnormalities reflecting brain pa-thology from trauma is increased (Bigler and Maxwell 2011).WMHs are best seen on the FLAIR sequence as shown inFig. 17 (see also Figs. 5 and 13 and previous discussion of T2and FLAIR identified WMH).

Another method for assessing WM integrity based on DTIfindings is a technique that uses what is referred to as tract-based spatial statistics or TBSS (Grossman et al. 2012b). Incomparison with a normative sample, location of statisticallysignificant deviations in DTI metrics can be identified usingTBSS, pathological differences that do not necessarily matchwhere FLAIR-identified WMHs are located as shown inFigs. 17 and 18 (see also Dineen et al. 2009). This distinctlydemonstrates that these two MRI-derived WM findings—WMHandDTI tract abnormalities—in this mTBI patient likelyrepresent different aspects of WM anatomy. WhenWMHweresimultaneously plotted in conjunction with TBSS derived dif-ferences and overlaid on various white matter fasciculi, theheterogeneity of how such findings may influence differentneural substrates is immediately evident as shown in Fig. 18.

Because abnormalities findings like WMH occur in thenormal population, even in children (see Bigler et al. 2013a),their presence alone cannot be considered an unequivocalbiomarker of injury. However, because TBI is likely to occurin an individual under 40 years of age, presence of WMH inyounger individuals has a low base rate as do indicators formicro-bleeds (see Potchen et al. 2013), although the likeli-hood of these findings becomes much more frequent after age

60 (Pettersen et al. 2008). For example, in a sample of ortho-pedically injured children with no brain insult, had no evi-dence of hemosiderin although two had WMH (Bigler et al.2013a). Whether these WMH were trauma-related in any wayis unknown, but likely were just normal variants or incidentalfindings (Katzman et al. 1999; Vernooij et al. 2007) that pre-dated the mTBI.

If between-subject heterogeneity reflects the unique differ-ences between subjects in terms of injury biomechanics anddynamics (along with genetic, pre-injury, and complex-post-injury factors), then the within -subject heterogeneity of“how” and “where” lesions may influence neural systems, isjust as complex.What becomes evident when these lesions areplotted is not just whether a lesion is present, but whether thelesion significantly disrupts a network.

mTBI, Networks, and Resiliency

Consistently, the neurocognitive effects of TBI have beenshown to result in slower speed of processing and disruptionsin the central EF working memory system (Ciaramelli et al.2006; Willmott et al. 2009). While a full discussion of atten-tional and working memory networks is beyond the scope ofthis review, what has been referred to as the default modenetwork (DMN) has become fundamental in understandingthe effects of TBI on memory, attention, and processing speed(see Arenivas et al. 2012; Bonnelle et al. 2012; Palacios et al.

Fig. 18 [View this image in conjunction with Fig. 17]. White matterhyperintensities (WMHs, yellow) and tract-based spatial statistics (TBSS)white matter (WM) differences (red ) in an mTBI subject aresuperimposed on tubular generated diffuse tensor imaging (DTI) tractsfor ease in visualizing the tract, showing how depending on which tract isbeing examined, the WM finding occurs within different tracts. Note:

White=corpus callosum, Purple=arcuate fasciculus, Orange=uncinatefasciculus, Blue=corticospinal and spinocortical tracts, Dark Green=inferior occipito-frontal fasciculus, Light Green=occipito-temporal fas-ciculus, Lightest Green=Cingulum Bundle. Different techniques thathighlight different types ofWM findings depend on the imaging sequenceand the method used to identify the difference


2012; Zhou et al. 2012). Of particular relevance to mTBI, theDMN, in part, relies on patent and efficient pathways betweenparietal and frontal cortices during engagement involvingattentional tasks. These are some of the longestintrahemispheric pathways, but also require interhemisphericintegration via the corpus callosum. This means that as aconsequence of deformation/shear/strain influences that occurduring TBI, the DMN is especially vulnerable to injury, evenat the level of an mTBI. Andrews-Hanna et al. (2010) deriveda simplified network involving aspects of the DMN as shownin Fig. 19. What is critical about this figure is the interrela-tionship between key brain structures involving medial tem-poral lobe memory regions (hippocampus), with intra- andinterhemispheric pathways linking the entirety of the memorynetwork including the fornix and cingulum bundle. All ofthese brain regions in the DMN are particularly vulnerableto the biomechanical and neuropathological effects of TBI(Bigler 2007).

In association with the DMN, as partly shown in Fig. 19,are key connections, like the cingulum bundle (Wu et al.2010b), which receives input from the hippocampus via themammillary bodies and anterior thalamus. Figure 20, fromBonnelle et al. (2011) shows that in TBI lower FA, likelyreflecting abnormal WM integrity of the cingulum bundle,was associated with slower reaction time. In addition to thestudy by Bonnelle et al., others investigations that includedmTBI subjects, some of which exclusively examined only

mTBI subjects, have demonstrated that disruptions in theDMN relates to neurocognitive sequelae in TBI (Zhang et al.2012; Johnson et al. 2012a; Sharp et al. 2011; Caeyenberghset al. 2012; Mayer et al. 2012a). From a cognitive symptomperspective, attention deficits along with disrupted emotionalregulation are commonplace in mTBI. As shown in the abovementioned studies WM pathology within the DMN related toTBI, especially that which influences both cortical-corticaland corticolimbic connections may play a central role in theevolution of symptoms of memory and emotional dysfunctionfollowing mTBI.

The Human Connectome work of Van den Heuvel andSporns (2011) has emphasized critical hubs and nodes inwhat they refer to as “rich-club” connections within ma-jor networks. If damage does not involve a major hub ornode, then the brain probably has greater resiliency in re -networking around the “lesion.” However, returning toFig. 19 from Andrews-Hanna et al., if the anterior medialprefrontal cortex is damaged, such damage takes outeight critical links within the network. As Moretti et al.(2012) have shown, disruptions of a hub have far-reaching ipsilateral as well as contralateral adverse in-fluences. Damage in mTBI outside the rich-club networkmay have minimal to no effect and permit the brain to“re-wire”. However, any type of lesion disrupting a so-called rich-club network could have widespread influ-ence, even though the lesion itself is small and localized.

Fig. 19 a and b As demonstratedin the study by Andrews-Hannaet al. (2010), the functionalconnectivity of the default modenetwork comprises a midline coreand two distinct subsystems, asshown in d (dorsal medial andmedial temporal). c Depicts thefunctional correlation strengthsbetween 11 regions and all colorcodes reflect in bolder lines thesystems with strongerrelationships. Note : aMPFC=anterior medial prefrontal cortex,PCC=posterior cingulate cortex,dMPFC=dorsal medial prefrontalcortex, TPJ=temporoparietaljunction, LTC=lateral temporalcortex, TempP=temporal pole,vMPFC=ventral MPFC, pIPL=posterior inferior parietal lobule,Rsp=retrosplenial cortex, PHC=parahippocampal cortex, HF+=hippocampal formation.Reproduced with permissionfrom Elsevier


Are Traditional Neuropsychological Metrics Usedin mTBI Assessment Insensitive to the Cognitiveand Neurobehavioral Effects of the Injury?

With the emergence of clinical neuropsychology in the late1960s, early 1970s neuropsychological methods were consid-ered the gold standard for diagnosing mTBI (see Boll andBarth 1983; Barth et al. 1983). The premise was that theneurocognitive effects of mTBI were detected when neuro-psychological test performance deviated below some assumedbaseline or established norm. However, once factors such asage, education, time post-injury plus pre-injury psychosocialvariables, post-injury variables such as pain, sleep disorder,fatigue and emotional functioning were all considered, thecomplexity of the problem of mTBI assessment with onlyneuropsychological tests became evident (Silver 2012; Silveret al. 2009).

Within-subject designed sports concussion studies thatobtained pre-injury measures and then assessed neuropsycho-logical status post-injury, convincingly demonstrated thatsome traditional neuropsychological methods were sensitivein detecting acute effects of mTBI. However, over time thesestandard neuropsychological measures could show a return tobaseline, all-the-while electrophysiological and neuroimagingfindings may not, and subjectively, mTBI patients continue toreport PCS (Prichep et al. 2012; Gardner et al. 2012; Henryet al. 2011; Talavage et al. 2013; Slobounov et al. 2011a, b).

At 4 months post-injury using DTI, neuroimaging abnor-malities have been detected in mTBI, yet minimal-to-no neu-ropsychological differences were found on standard clinicalmeasures (Ling et al. 2012). The question reverts to whether

this is an absence of any residual neuropsychological effect,even though structural pathology may be present, or is it thelack of sensitivity of the test attempting to define aneurocognitive correlate? All of this implicates an insensitiv-ity of traditional neuropsychological methods in detectingsubtle abnormal neurological states that may exist in mTBI.

Levin et al. (2012) showed in a study that obtained initialpost-injury mTBI MRI findings within 24 to 96 h, then at1 week, 1 month and 3 months that traditional speed ofprocessing measures like the Symbol Digit Modalities Test(SDMT; Smith 1992) did not distinguish lasting sequelae,only computerized reaction time (RT), or “CodesubReaction Time”, the speed of processing task from theAutomated Neuropsychological Assessment Metrics(ANAM, Reeves et al. 2002) remained significantly differentafter 3 months. Traditional neuropsychological administrationof the SDMT is paper-and-pencil with time measured inseconds. Codesub RT is measured in milliseconds (msecs;Cernich et al. 2007).

Millisecond processing speed is more representative of‘real-time’ neural processing (Momjian et al. 2003) and po-tentially the type of task that best distinguishes between thosewith mTBI and controls (Hartikainen et al. 2010). In typical,developed, non-injured control subjects cognitive tasks thatpit speed versus accuracy show match versus mismatch per-formance to differ between the 100–200 msec range (Ho et al.2012). All timed traditional neuropsychological measures as-sess speed with a manually controlled stopwatch. Stopwatchaccuracy either between trials or between raters for an identi-cal event is about 100 msec (Vicente-Rodriguez et al. 2011).Measuring in seconds rather than msecs and the error

Fig. 20 Sustained attention asmeasured by reaction timewas found to becorrelated with cingulum bundle structure in TBI as shown by Bonnelleet al. (2011). Notice how this system involves the default mode networkand extensive connectiveness of what was previously shown in Fig. 19. aSagittal view (x=8) of the reference component selected for the function-al connectivity analysis. Anterior and posterior midline nodes of thedefault mode network (DMN) are shown in warm colors, and the midlinenode within the executive network (EN) is shown in blue. The right

cingulum bundle connecting the posterior and anterior parts of theDMN is shown in green. b Fractional anisotropy (FA) of the cingulumbundle in patients is plotted against the change in reaction time (RT)between the first and the last part of the task (N =28). Measures are agenormalized, i.e., age was regressed out from the measures using a linearregression, where residuals were saved as standardized values.Reproduced with permission from the Society for Neuroscience


introduced by turning on and off a hand initiated timer wouldobscure any subtle differences in true neural processing speedpotentially induced by mTBI. Combining this error variancewith only small-to-medium and often non-significant differ-ences in traditional neuropsychological test performance, evenin moderate-to-severe TBI as demonstrated by Bendlin et al.(2008), suggests that traditional neuropsychological assess-ment methods will be even less sensitive in detecting whatmay be genuine cognitive, yet subtle differences in those withmTBI.

Brenner et al. (2010) examined 45 soldiers with well doc-umented history of presumed blast-related mTBI divided intotwo groups—one that became asymptomatic and the otherremained symptomatic, yet neuropsychological measurescould not differentiate the groups. Brenner et al. offer thefollowing conclusion, “standard neuropsychological assess-ment may not increase understanding about impairment asso-ciated with mTBI symptoms” (p. 160). The longitudinal studyby Heitger et al. (2009) conducted in New Zealand, has beenreferred to several times in this review because of the qualityof the experimental design. It demonstrated the exact samefindings as Brenner et al., matching two groups on injuryseverity and all aspects of injury demographics where, at6 months and beyond, one group had “recovered” and wasasymptomatic, but the other group remained symptomatic. Inthe symptomatic group, neuropsychological testing identifieddeficits in only 5 of the 36 subjects despite all being symp-tomatic from a PCS standpoint. Heitger and colleagues con-cluded, “This limited ability of neuropsychological testing todocument ongoing impairment in brain function in mCHI[mild closed head injury; their term rather than mTBI, as usedin this review] was also reflected in our results” (p. 2865).Abnormalities in eye-movement in the form of slower eye-tracking did demonstrate consistent differences in the symp-tomatic mTBI group, reliably differentiating symptomaticfrom non-symptomatic mTBI subjects.

Such findings raise important questions about locus ofabnormality and whether traditional neuropsychological mea-sures are designed to detect such subtle abnormalities (Grindel2006). For example, in the Heitger et al. (2009) study thedeficits in eye-tracking in the symptomatic mTBI group wereconsidered to be of upper brainstem origin. There are notraditional neuropsychological tests that directly assess eye-movements or upper brainstem function.

Of course, the upper brainstem is contiguous with theventral diencephalon and likewise, in addition to theascending RAS that interfaces the brainstem with thethalamus, there is the diffuse thalamic projection system;all of which are important in arousal, attention, andhabituation, and potentially injured in TBI (Lifshitz andLisembee 2012). Neuropsychological measures may de-tect impairments in attention/concentration and workingmemory, but such techniques have no ability to localize

the origin of the deficit. Fortunately, using neuroimaging as abiomarker of thalamic integrity, there are now several studiesof mTBI patients showing abnormal connectivity and restingstate activity within the thalamus that relate to cognitiveimpairment (Grossman et al. 2012b; Nathan et al. 2012;Yang et al. 2012; Raz et al. 2011; L. Tang et al. 2011;Grossman et al. 2012a; Gosselin et al. 2011; Little et al.2010). Because of the central role played by the thalamusany small perturbation at this brain level could have potentialwidespread cortical effects (Izhikevich and Edelman 2008).Nonetheless, without neuroimaging or some electrophysio-logical measure of upper brainstem and/or thalamic integrity,neuropsychological techniques are limited in distinguishingdeficits associated with the type of pathology that might bepresent in these areas without independent confirmation vianeuroimaging.

So, what can it mean in an mTBI patient if neural process-ing is disrupted by 50 to 100 msec when engaging attentionalnetworks, such as the DMN? Mayer et al. (2009) demonstrat-ed that an mTBI group, which did not significantly differ fromcontrols on traditional neuropsychological measures of atten-tion or memory, was significantly slower to disengage andreorient to an auditory attention task. As a group, the mTBIparticipants were slower by the 50–100 msec range, timesbelow detection with traditional neuropsychological mea-sures. Given the increased likelihood of WM pathology asso-ciated with mTBI and the subtleness of injury effects, espe-cially at a thalamic level, neuropsychology as a field needs torethink the use of traditional neuropsychological methods tostudy the mildest forms of brain injury. Traditional methodswould not be sensitive in detecting real-world processingdeficits that could influence processing speed, arousal, andattention/concentration. For a variety of reasons clinical neu-ropsychology has been slow to embrace techniques like com-puter and virtual-based methods that may have much greaterapplication in detecting subtle impairments, including thoseassociated with mTBI (Schultheis et al. 2002; Slobounov et al.2011a; Parsons et al. 2011; Armstrong et al. 2012; Campbellet al. 2009).

In addition to the demonstration by Brenner et al. (2010)that traditional neuropsychological measures failed to differ-entiate bona fide cases of mTBI with residual symptoms;Geary et al. (2010) acknowledge at the outset of their investi-gation that while mTBI patients often report chronic memoryproblems, “…traditional neuropsychological assessments of-ten fail to find evidence for such complaints” (p. 514). Whatsome have done within neuropsychology is to concludethat performance in many of these patients, despite theircomplaints, is within the range of normal or a normativesample, and therefore does not represent a deficit or anytype of brain impairment. Instead of considering that thetraditional neuropsychometric approach may be insensitivein detecting deficits and searching for diagnostic solutions,


neuropsychology as a field has been too quick to dismissthese complaints as merely functional “noise” that is cog-nitively inconsequential.

Why has it Been so Challenging to Accept That Some mTBIsResult in Lasting Sequelae? Butler et al. (2009) provide amost interesting account in Neuropsychology Review of howthe conflict in American medicine between the disciplines ofpsychiatry and neurology, delayed the recognition of cog-nitive dysfunction in multiple sclerosis (MS). In the lasttwo decades, well designed neuropsychological studieshave unquestionably demonstrated neurocognitive deficitsas part of the spectrum of impairments associated with MS.Interestingly, as Butler et al. point out, a major reason whycognitive impairment was late in being recognized couldbe attributed to researchers using an insensitive metric—the Disability Status Scale (DSS; Kurtzke 1961) that onlyassessed the most rudimentary of cognitive functions inMS. The original DSS over-relied on physical and sensoryloss because the zeitgeist of the day conceptualized MS aspredominantly a sensory-motor disorder. As reviewed byButler et al. (2009), neuroimaging improvements in the1980s and 90s could demonstrate in vivo WM abnormal-ities outside of motor tracks; this provided the neededindependent classifier of underlying brain abnormalityto then show presence of impaired neuropsychologicalperformance (see Sepulcre et al. 2009). In other words,once neuroimaging became an established biomarker forthe study of MS, acceptance of non-motor cognitive andemotional impairments became straightforward.

Butler et al. (2009) discuss how the neuroticizing of MSsequelae influenced by the Freudian zeitgeist of the mid-20thCentury meant that any behavior that could not be organicallydefined was considered as some dimension of a neurosis(Gladstone 2009; Sims 1985; Weighill 1983). Because ofthese same influences, medicine and psychology also havethe distinction of not recognizing the underlying neurologicalbases of disorders like schizophrenia and autism until the latterhalf of the 20th Century. It was just 1979whenMalec’s (1978)neuropsychological review asked the question “…whether thepatient is brain damaged or schizophrenic?” (p. 507).

All of this sounds very familiar when considering theeffects of mTBI. Absence of a “lesion” combined with thelack of a cogent medical explanation other than those ofsomatization and associated psychological/psychiatric sequel-ae, meant that any bona fide effects from mTBI were to beviewed with skepticism. Now given our ability to use ad-vanced neuroimaging methods to assess mTBI, indisputablythese techniques show subgroups with positive neuroimagingand neurocognitive impairments (Van Boven et al. 2009;Matthews et al. 2011; Zhou et al. 2012; Grossman et al.2012b; Ross et al. 2012; Jorge et al. 2012; Messe et al.2013; Morey et al. 2012; Nakagawara et al. 2013).

Lastly, it is impossible to calculate what influence forensicneuropsychology has had, but given the amount of forensicactivity involving mTBI, it has the potential to influenceprevailing thoughts about mTBI outcome. Plainly, there areopposing camps in anything forensic (Bigler and Brooks2009; Ruff 2009), each with their supporting literature.Litigation involving mTBI is frequent and it is common tosee neuropsychological outcome studies utilize cases fromforensic sources, which never could be considered unbiasedsamples.

Integration of Neuroimaging with Neuropsychology

Neuropsychology’s embrace of neuroimaging began with im-aging’s ability to identify the in vivo “lesion” (Bigler et al.1989). Because of the limits of early neuroimaging techniquesin the 1970s and 80s only the most fundamental clinicaldescriptions of brain pathology (i.e., type, size, and locationof lesions) were possible during that era. Currently, a vastarray of neuroimaging metrics can be applied to image anal-ysis, many of them involving automated image analysismethods (Tate et al. 2012; Wilde et al. 2012; Hunter et al.2012; Shenton et al. 2012). As Vasterling and Dikmen (2012)point out, there are many clinical and conceptual complexitiesthat need to be attended to in designing neuropsychologicaloutcome studies of mTBI, one of which is how to objectivelydefine any brain pathology.

Neuropsychological assessment techniques can be adaptedfor presentation within a functional neuroimaging environ-ment. Adapting neuropsychological probes in this mannerpermits some appreciation of regional activation and neuralengagement. Similarly electrophysiological methods can beperformed off-line from neuroimaging, but then integratedwith 3-D imaging so the best of temporal and spatial resolu-tion may be achieved in plotting the activation source.Networks can now be examined using integrative techniquesfrom DTI and resting-state fMRI. As reviewed by Shentonet al. (2012) multi-modality sophisticated neuroimagingmethods can be used to identify subtle brain pathology asso-ciated with mTBI and should be a part of any neuropsycho-logical outcome study of mTBI.

Numerous neuroimaging methods are now available toassess mTBI (Hunter et al. 2012; Duhaime et al. 2012) andthe field of neuropsychology should take full advantage of theneuroimaging biomarker approach. By applying multi-modality neuroimaging methods as biomarkers for detectingunderlying abnormalities associated with mTBI, neuropsy-chology will have an objective measure to integrate withneurocognitive and neurobehavioral assessment of the mTBIpatient. The absence of an objective criterion as to whethersome type of neuropathology is independently present or not


in the mTBI patient is what has led to the confused picture ofclinical outcome in the neuropsychology of mTBI. Otherbiomarkers of TBI are likewise on the horizon (Zetterberget al. 2013), which also will undoubtedly improve understand-ing of mTBI outcome. Some of these serum biomarkers mayin fact be combined with neuroimaging and likewise; neuro-imaging studies are advancing along the lines of direct imag-ing of pathology like β-amyloid and tau proteins, as by-products of brain injury (Di Battista et al. 2013; Rostamiet al. 2012; Jeter et al. 2013).

Limitations

The intent of this review was to provide the basis for asensitive, objective metric to enhance detection of mTBIsequelae which could form the basis for improved and com-plementary research design for investigations of neuropsycho-logical outcome. This review is selective and focused onneuroimaging studies that identify pathology potentiallyrelated to residual impairments in cognitive and/or behav-ioral functioning post-mTBI in what must be consideredthe minority of all those who sustain an mTBI. Post-mTBI neuropsychological functioning reflects a host ofcomplex factors that includes all pre-morbid genetic andage-dependent influences present at the time of injuryincluding incidental findings (Katzman et al. 1999;Vernooij et al. 2007), biological and emotional resiliency,and vulnerability features associated with the injury itself,as well as a myriad of post-injury factors. Returning toFigs. 5, 6, 13, and 14 where a “lesion” is clearly visiblein these mTBI patients, the state-of-the-art in neuroimag-ing is that it can still only be inferred how a particularlesion disrupted pre-existing integrity because pre-injuryneuroimaging and neurocognitive/neurobehavioral assess-ments are mostly never available. Furthermore, all ofwhat is presented and discussed herein pertains to correl-ative findings with neuroimaging variables in cases ofmTBI. However, as typically stated, correlation is neversynonymous with causation, and given the complexitiesthat attend premorbid and post-injury neuropsychologicalfunctioning, more precise relations specific to mTBI mustbe considered unknown.

While this review highlights the worth of potential neuro-imaging biomarkers of mTBI, neuroimaging remains an ex-pensive study method. Extensive research studies are under-way for blood and other biomarkers of mTBI (Di Battista et al.2013). Thesemay turn out to be effective and far less expensivethan MRI. Regardless of whether neuroimaging and/or bloodbiomarkers become universally accepted as indicators of neuralinjury in mTBI, the field needs objectivity. While the price ofneuroimaging is a legitimate concern, getting the diagnosiscorrect is the objective of identifying an effective biomarker.

It may be the case that, within the mildest of TBI the braincould adapt to injury and therefore, positive imaging wouldpossibly reflect an abnormality to which the brain has adaptedand re-circuited without apparent ill effect—a classic Type 1error if the neuropsychologist concluded impairment wherenone existed. Nonetheless, there may be more to the story ofso-called “recovered” mTBI, even in the presence of a lesion.As Eisenberg et al. (2013) demonstrated in their prospectivecohort of those who sustain mTBI, a prior concussion distinct-ly and significantly prolonged symptoms. So, even if cogni-tive functions return to pre-injury levels, the mere fact of priorbrain injury may change the threshold for subsequent injury,therefore indicating residua from the supposed injury fromwhich the mTBI patient had “recovered.” These are veryserious issues for the researcher and clinician to contemplatebefore they would conclude that “no injury” had occurredbased on neuropsychological findings alone and that therewere “no untoward” effects of mTBI. Because of this it seemsevident that the best in technology, including biomarkers ofinjury, should be brought to bear on this important topic,before there is a blanket conclusion of no lasting effect frommTBI.

Conclusions and Future Directions

This review began with a simplified view of concussion butconcludes by showing the complexities of what needs to beaddressed in neuropsychological outcome research in mTBI.Restoration of neural function is likely the norm followingmTBI, but as this review demonstrates, contemporary neuro-imaging methods identify residual indicators of neuropathol-ogy in subsets of individuals with mTBI. Commenting on theentire TBI field, Chen and D’Esposito (2010) state,“Unfortunately, most tests of cognitive functioning, includingneuropsychological tests and most cognitive neurosciencemeasures, are not designed to reflect the complexities andlow structure of settings in the real world” (p.13). Integratingthis statement with an editorial in Nature (Editorial 2010) thatstates “…as biological insights develop, the crudity of currentpsychiatric diagnoses will become all too clear” (p. 9), meansthat we have reached a level in the investigation of mTBIsequelae where the entire role of traditional neuropsycholog-ical methods in mTBI needs refinement.

Neuroimaging provides biomarkers of underlying structur-al and physiological abnormalities in mTBI, and these patho-logical changes occur in regions and within neural systemsthat plausibly give rise to the common types of neurobehav-ioral and neurocognitive sequelae associated with mTBI.Neuroimaging methods provide objective biomarkers of inju-ry and damage that need to be incorporated into neuropsycho-logical outcome studies.


Acknowledgments The assistance of Jo Ann Petrie, Ph.D. in the prep-aration of this manuscript is gratefully acknowledged as is the assistanceof Tracy J. Abildskov in preparation of some of the illustrations.

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