NeuroImage 50 (2010) 1017–1026

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Diffuse damage in pediatric traumatic brain injury: A comparison of automatedversus operator-controlled quantification methods

Erin D. Bigler a,b,⁎, Tracy J. Abildskov c, Elisabeth A. Wilde d,e,k, Stephen R. McCauley d,f, Xiaoqi Li d,Tricia L. Merkley c, Michael A. Fearing g, Mary R. Newsome d,k, Randall S. Scheibel d,k, Jill V. Hunter h,i,Zili Chu h,i, Harvey S. Levin d,j,k

a Department of Psychology and Neuroscience Center, Brigham Young University, Provo, UT, USAb Department of Psychiatry and the Utah Brain Institute, University of Utah, Salt Lake City, UT, USAc Department of Psychology, Brigham Young University, Provo, UT, USAd Physical Medicine and Rehabilitation Alliance of Baylor College of Medicine and the University of Texas-Houston Medical School, Houston, TX, USAe Departments of Radiology and Neurology, Baylor College of Medicine, Houston, TX, USAf Department of Pediatrics, Section of Hematology-Oncology, Baylor College of Medicine, Houston, TX, USAg Department of Medicine and Neuropsychology, Hebrew SeniorLife, Boston, MA, USAh Department of Radiology, Baylor College of Medicine, Houston, TX, USAi E.B. Singleton Department of Diagnostic Imaging, Texas Children's Hospital, Houston, TX, USAj Departments of Neurology, Neurosurgery, and Pediatrics, Baylor College of Medicine, Houston, TX, USAk Michael E. De Bakey Veterans Affairs Medical Center, Houston, Texas, USA

⁎ Corresponding author. Departments of PsychologYoung University, 1001 SWKT, Provo, UT 84602, USA. F

E-mail address: erin-bigler@byu.edu (E.D. Bigler).

1053-8119/$ – see front matter © 2010 Elsevier Inc. Adoi:10.1016/j.neuroimage.2010.01.003

a b s t r a c t

a r t i c l e i n f o

Article history:Received 1 September 2009Revised 5 December 2009Accepted 1 January 2010Available online 11 January 2010

This investigation had two main objectives: 1) to assess the comparability of volumes determined byoperator-controlled image quantification with automated image analysis in evaluating atrophic brainchanges related to traumatic brain injury (TBI) in children, and 2) to assess the extent of diffuse structuralchanges throughout the brain as determined by reduced volume of a brain structure or region of interest(ROI). Operator-controlled methods used ANALYZE® software for segmentation and tracing routines of pre-defined brain structures and ROIs. For automated image analyses, the open-access FreeSurfer program wasused. Sixteen children with moderate-to-severe TBI were compared to individually matched, typicallydeveloping control children and the volumes of 18 brain structures and/or ROIs were compared between thetwo methods. Both methods detected atrophic changes but differed in the magnitude of the atrophic effectwith the best agreement in subcortical structures. The volumes of all brain structures/ROIs were smaller inthe TBI group regardless of method used; overall effect size differences were minimal for caudate andputamen but moderate to large for all other measures. This is reflective of the diffuse nature of TBI and itswidespread impact on structural brain integrity, indicating that both FreeSurfer and operator-controlledmethods can reliably assess cross-sectional volumetric changes in pediatric TBI.

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Introduction

Generalized cerebral atrophy is a well-established consequence ofmoderate-to-severe traumatic brain injury (TBI) that can be quantifiedfrom magnetic resonance imaging (MRI) studies that assess total brainvolume (TBV; Blatter et al., 1997; Ding et al., 2008; Levine et al., 2008;Mamere et al., 2009;Nget al., 2008; Sidaros et al., 2009) or post-mortembrain weight (Maxwell et al., 2009). The degree of atrophy is related toinjury severity (Ghosh et al., 2009). Recently, Kim et al. (2008) applied atensor-based morphometry method to assess structural changes inadult subjects with TBI and observed the greatest reduction in white

matter (WM) in frontal and temporal lobe regions with localizedreductions in the thalamus, midbrain, corpus callosum, caudate,cerebellum, and cingulate cortex. Nonspecific ventricular enlargementhas been consistently shown to be proportional to the overallparenchymal loss (Trivedi et al., 2007), as also demonstrated by Kimet al. (2008). These findings suggest that TBI-related cerebral volumeloss occurs in widespread regions of the brain, with the largest volumechanges occurring in frontal and temporal lobe parenchyma.

Knowing the extent of diffuse structural changes throughout thebrain following TBI and which brain regions may be mostsusceptible to injury and atrophic change are critical factors inunderstanding the neurobehavioral and neurocognitive sequelae ofTBI (Levin et al., 2008; Riggio and Wong, 2009). However,volumetric TBI studies that have examined TBV and other regionsof interest (ROI) typically have examined only a few other ROIs, like

1018 E.D. Bigler et al. / NeuroImage 50 (2010) 1017–1026

the hippocampus (Ng et al., 2008; Wilde et al., 2007) or corpuscallosum (Bendlin et al., 2008; Johnson et al., 1996; Sidaros et al.,2009). One reason why few other brain structures or ROIs have beenexamined in past TBI volumetric studies is that until recently, incontrast to automated methods that utilize voxel-based morphom-etry techniques, volumetric methods required time-intensive oper-ator-controlled procedures making it impractical to analyze multipleROIs. However, automated segmentation and cortical and subcorticalparcellation procedures are now available, permitting the volumetriccomputation of all major brain structures and ROIs (Bozzali et al.,2008). Furthermore, examining volumetric changes in the brainstructure-by-structure enhances understanding of the degree andextent of TBI-related damage (i.e., volume loss) throughout thebrain and provides various metrics to explore neurobehavioral andneurocognitive sequelae.

Operator-controlled methods have been considered the ‘goldstandard’ in volumetric analyses, but with the rapid image processingand ROI quantification that can be achieved with automatedtechniques, these more time- and cost-efficient procedures have anadvantage if they approximate the ‘gold-standard’ operator-con-trolled methods. While comparison of operator-controlled andautomated methods have been examined in healthy adult controlsubjects, as well as in some clinical populations, comparisons withsubjects having sustained TBI have not been done. In a healthycontrol sample without known neurological disease or disorder,Morey et al. [(2009a)—see also Morey et al. (2009b) and Hasan andPedraza (2009)] compared FreeSurfer (Athinoula A. Martinos Centerfor Biomedical Imaging, http://surfer.nmr.mgh.harvard.edu/) versusoperator-controlled hand tracing methods for quantification of theamygdala and hippocampus. While absolute volume differenceswere found for both structures, the volumetric results of the twoprocedures were highly interrelated with comparable volumeoverlap and similar size estimates despite some boundary differencesfor inclusion/exclusion criteria. Morey et al. (2009a) concluded thatboth techniques reliably measured amygdala and hippocampalvolumes, but volumes computed by the two techniques cannot becompared because of the differences in absolute volumes derivedfrom each method. Cherbuin et al. (2009) have shown similarresults, but in a clinical sample of patients with dementia wherehippocampal atrophy was present, indicating that even with anatrophic structure, both techniques effectively distinguished thevolume reduction in the dementia subjects. Again, the absolutevolumes did differ significantly between the two quantificationtechniques, but hippocampal atrophy was identified regardless ofthe method used. Also, in a sample of Alzheimer's disease andsemantic dementia, Lehmann et al. (2009) have shown the compa-rability of FreeSurfer to manual volumetric measurements of thetemporal lobe.

In a cohort of children and adolescents with moderate-to-severeTBI, individually matched on age, sex, maternal education, race/ethnicity and socioeconomic status to typically developing children,we have shown widespread volume reduction across all ROIsexamined using ANALYZE® (Robb, 1995, 2001) based on conven-tional operator-controlled techniques (Fearing et al., 2008; Spanos etal., 2007; Wilde et al., 2006, 2007, 2005). This carefully matchedcohort with already established operator-controlled volumes formajor brain structures/ROIs represents an ideal dataset to compareto FreeSurfer calculated volumes in a TBI sample. The abovementioned studies that have compared operator-controlled methodsto FreeSurfer were in adults and this cohort is comprised of childrenwith typical development and brain injury. Developmental changesand brain injury could add additional variability which, in turn, couldinfluence the comparability of operator-controlled versus automatedmethods, which represents another reason to compare the twomethods in this pediatric group. Operator-controlled and automatedprocedures will differ in how an ROI is defined and therefore as

already shown by Morey et al. (2009a) and Cherbuin et al. (2009),direct comparison of absolute volumes derived from each method isproblematic for some structures/ROIs. However, one solution wouldbe to examine the magnitude of difference per structure/ROI betweenthe TBI and control subjects by determining effect size differences(Cohen, 1988) between the mean volume of each structure/ROI andtechnique for volume estimation. Using effect-size differences as themetric to compare operator-controlled to FreeSurfer methodsachieves two objectives. First, such an approach uses a uniformmetric across both methods for volume estimation and all structures/ROIs assessed to demonstrate volume reduction reflected in standarddeviation units, which also reflects the magnitude of differencesbetween the TBI and control means. Secondly, if effect size differencesbetween the TBI and control group are similar between the twomethods, the inference would be that atrophic changes are beingsimilarly assessed between the two methods even if their absolutevolumes differ.

For this investigation, we utilized the aforementioned dataset ofchildren with moderate-to-severe TBI and a group of typicallydeveloping children where established operator-controlled volumet-ric data had already been computed. Automated image analysis wasthen performed using FreeSurfer to generate ROI volumes forcomparison with operator-controlled volumes. There were twomajor aims of this investigation: (1) to compare volumetric findingsfrom automated (FreeSurfer) versus operator-controlled (ANALYZE®)quantitative image analysis methods in pediatric TBI and, (2) to assessthe generalized nature of TBI-induced atrophic changes reflected involume loss across different ROIs. To accomplish these two aims therewere four specific objectives: (1) to compare the similarities anddifferences between the twomethods (while controlling for age) usingCohen's f effect sizes, (2) to compare the actual volumes obtainedbetween the time-intensive (i.e., ‘gold-standard’) operator-controlledROI volumetric tracingmethod and the automated analysismethod onstructures or regions with comparable boundaries, (3) to determinethe extent of TBI-related proportional volume loss between the twomethods on all structures throughout the brain and, (4) to assess howvolumetric measures demonstrate the diffuse nature of TBI-relatedatrophic changes in the brain.

We anticipated that for structures where the automated andoperator-controlled methods had the most similar boundary defini-tions, absolute volume comparisons would be similar and highlycorrelated between the twomethods, and also yield similar effect sizeestimates. Additionally, we anticipated that even for brain structures/ROIs where substantial differences existed in boundary definitionsand hence lacked comparability of absolute volumes; the direction ofall effect size differences would indicate atrophic changes in the TBIgroup. However, we expected that where boundary differencesbetween the two methods were greatest, the magnitude of atrophicchange in TBI structures/ROIs would be reflected in higher effect sizedisparities. Despite these anticipated differences, it was expected thatboth methods would detect TBI-related atrophy.

Materials and methods

Subjects

We have fully characterized these subjects in previous studies(Fearing et al., 2008; Spanos et al., 2007;Wilde et al., 2007, 2005) withTable 1 summarizing demographic information of the TBI group aswell as the typically developing group. Briefly, the TBI group consistedof 16 children (8 male, 8 female) who had sustained moderate-to-severe TBI (initial Glasgow Coma Scale score=3–12) and wereinjured as a result of motor vehicle, bicycle, or pedestrian versusvehicle accident. The mean post-injury interval was 3.1 years(SD=2.4, range 1.0–10.1), and mean age at the time of scanningwas 12.9 years (SD=2.5, range 9.0–16.8). Patients with TBI were

Table 1Demographic characteristics of TBI and typically developing (TD) groups based on previously published studies (Wilde et al., 2005).

Demographics TBI group (n=16) TD group (n=16)

Age at testing (years) M=12.9; SD=2.5; range 9.0–16.8 M=12.8; SD=2.4; range 9.0–16.4Age at injury (years) M=9.75, SD=3.0, range 3.7–13.8 N/ATime post injury (years) M=3.1, SD=2.4, range 1.0–10.1 N/AGender distribution 8 males, 8 females 8 males, 8 femalesEthnicity distribution 6 Caucasian, 10 African American 6 Caucasian, 6 African American, 3 Hispanic, 1 AsianMaternal education (years) M=13.2, SD=1.8, range 10–17 M=13.2, SD=1.6, range 12–16Handedness 14 right-handed, 2 left-handed 14 right-handed, 2 left-handedMechanism of injury (accident type) 7 auto-pedestrian, 1 auto-bicycle, 5 MVA, 1 motorcycle, 2 RV, N/AGlasgow Coma Score M=5.7, SD=2.8, range 3–11 N/A

TBI=traumatic brain injury; TD=typically developing; MVA=motor vehicle accident; RV=recreation vehicle accident; M=mean; SD=standard deviation.

1019E.D. Bigler et al. / NeuroImage 50 (2010) 1017–1026

selected to obtain MRIs from a larger cohort of patients who had beenrecruited for an ongoing project investigating the neurobehavioraloutcome of TBI. Inclusion criteria also included an Abbreviated InjuryScale (AIS) score b4 for areas of the body other than the head, and nohistory of sustained (b30 minute) post-resuscitation hypoxia orhypotension. Sixteen typically developing children (TDC, 8 males,8 females) were selected to match the TBI patients for age (within 6months; mean=12.8, SD=2.4; range 9.0–16.4 years), sex, handed-ness, socioeconomic status, andmaternal education. No child in eithergroup had a pre-existing history of head injury, neurologic disorder,psychiatric disorder, or child abuse. The imaging and related datawere collected with the understanding and written consent of eachsubject's guardian or legally authorized representative, with theassent of each child subject, with the approval of the appropriateinstitutional review boards, and in compliance with nationallegislation and the Code of Ethical Principles for Medical ResearchInvolving Human Subjects of the World Medical Association (Decla-ration of Helsinki).

Clinical descriptions of lesion type and volume on MRI

Because the TBI subjects all had sustained moderate-to-severebrain injury, areas of abnormal MRI signal were detected in all of theTBI patients, with total lesion size for each child ranging from 0.25 to24.92 cm3 (mean=6.71 cm3±8.94). General location and estimatedsize of focal lesions in the TBI group are summarized in Table 2.Consistent with lesion findings fromMRI studies in adult (Pierallini etal., 2000) and child TBI patients (Levin et al., 1997), themajority of thelesions were in the frontal (52.8%) and temporal areas (19.5%), andthese also represent regions with the largest lesion volumes in ourcohort. Eighty-nine percent of patients with focal frontal pathology on

Table 2Neuroanatomic site and lesion volume in TBI Group based on previously publishedstudies (see Wilde et al., 2005).

Region of interest(ROI)

TBI groupwith lesion

Lesion volume for area (cm3)

n (%) Mean (SD) Range

Whole brain 16 (100) 6.71 (8.94) 0.25–25.17Right hemisphere 15 (93.8) 1.74 (2.47) 0–7.37Left hemisphere 14 (87.5) 2.95 (5.35) 0–20.31

Total temporal lobes 9 (56.3) 0.93 (2.51) 0–9.94Total frontal lobes 9 (56.3) 2.88 (6.36) 0–21.96

Lateral frontal 6 (37.5) 0.50 (1.67) 0–6.70Superior medial 8 (50) 0.89 (2.13) 0–7.99Ventromedial 5 (31.3) 0.81 (1.87) 0–6.79

Posterior areas 8 (50) 0.78 (1.53) 0–5.38Corpus callosum 4 (25) 0.10 (0.19) 0–0.57Parietal lobes 4 (25) 0.19 (0.52) 0–1.84Occipital lobes 3 (18.8) 0.11 (0.41) 0–1.67Cerebellum 4 (25) 0.32 (0.82) 0–2.89Brain stem 0 (0) N/A N/A

TBI=traumatic brain injury.

MRI exhibited more than one lesion. The clinically defined MRIpathology of most lesions was hemosiderin deposit (56.3%) andgliosis (29.9%).

MRI acquisition

MRI was performed on Philips 1.5 T Intera scanners (Philips,Cleveland, OH) at two pediatric medical centers using identicalscanners and protocols. T1-weighted (T1-w) (15 ms TR, 4.6 ms TE,1.0 mm slices, reconstructed voxel size of 1×1×1 mm) and T2-weighted (T2-w) (3500 ms TR, 114 ms TE, 1.5 mm slices, recon-structed voxel size of 1×1×1.5 mm) 3D sagittal acquisition serieswith a 256 mm field of view were used for volumetric analysis in theoperator-controlled analysis, and the T1-w image set was used for theautomated analysis method. A dual echo acquisition proton density/T2-w (2200 mm TR, 20/120 mm TE, 5.0 mm slices, and reconstructedvoxel size of .47×.47×5.0 mm) axial sequence with a 240mm field ofview was used for whole brain quantification and determination oftotal intracranial volume in the operator-controlled analysis. Regularquality assurance testing was performed on both scanners includingAmerican College of Radiology (ACR) phantom sequences, and bothscanners were consistently noted to be within an acceptable rangethroughout the study. Children from both groupswere imaged at eachsite, and no unanticipated systematic differences were noted in theacquisition of imaging data between the sites.

Operator-controlled ROI volumetric analysis

Using operator-controlled ANALYZE® 7.0 (Biomedical ImagingResource, Mayo Foundation, Rochester, MN), each data set wasconverted into a three-dimensional volume, and re-sliced to create1 mm isovoxels. T1-weighted and T2-weighted image sets were co-registered after realignment to the anterior to posterior commissure(AC–PC) line and the interhemispheric fissure. Interactive manualsegmentation of GM, WM, and cerebrospinal fluid (CSF) wasperformed using the ANALYZE® multispectral tool. Manual tracingsfor regions of interest (ROIs) were performed using ANALYZE®trace and editing tools. Finally, volumes (cm3) of GM,WM, and CSF foreach ROI were calculated by summing pixels designated as GM, WM,or CSF across the slices. Anatomic protocols and ROI details have beenfully reported elsewhere, including reliability estimates for each ROI(Fearing et al., 2008; Spanos et al., 2007; Wilde et al., 2007, 2005).

FreeSurfer based automated image analysis

Cortical reconstruction and volumetric segmentation were per-formed with the FreeSurfer image analysis version 4.0.4 (Athinoula A.Martinos Center for Biomedical Imaging, c2005; http://surfer.nmr.mgh.harvard.edu/). Details of the procedures are described in priorpublications (Fischl et al., 2004a; Jovicich et al., 2009). Briefly, thisprocessing includes the removal of non-brain tissue using a hybridwatershed/surface deformation procedure, automated Talairach

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transformation, segmentation of the subcortical WM and deep GMvolumetric structures (Fischl et al., 2002, 2004a), intensity normal-ization (Sled et al., 1998) tessellation of the GM–WM boundary,automated topology correction (Fischl et al., 2001; Segonne et al.,2007), and surface deformation following intensity gradients tooptimally place the GM/WM and GM/CSF borders at the locationwhere the greatest shift in intensity defines the transition to the othertissue class (Dale et al., 1999; Dale and Sereno, 1993; Fischl and Dale,2000). The resulting cortical models were registered to a sphericalatlas, utilizing individual cortical folding patterns to match corticalgeometry across subjects (Fischl et al., 1999) The cerebral cortex wasparcellated into regions based on gyral and sulcal structure (Desikanet al., 2006; Fischl et al., 2004b). Results for each subject were visuallyinspected to ensure accuracy of registration, skull stripping, segmen-tation, and cortical surface reconstruction. Manual editing, wherenecessary, was performed to optimize accuracy. The surface inaccu-racies involving skull stripping or frank exclusion of brain parenchy-mawere edited either by (1) adding control points to aid FreeSurfer inthe identification of whitematter (since it uses theWM/GMboundaryas a starting place for reconstructing the pial surface) (10 cases), (2)by fixing the skull strip by removing remaining dura (6 cases) or (3)by adding back in the sections of brain that were inadvertentlyautomatically removed (3 cases). In all cases these modificationsinvolved only the cortical surface of the cerebrum or cerebellum andwould not have affected any of the automated calculations ofsubcortical structures. A comparison of the segmented image usingFreeSurfer compared to ANALYZE® is presented in Fig. 1. As shown inthis figure for a single axial slice at the level of the anterior horn of thelateral ventricle, differences are present between the two methods inWM classification.

Anatomical protocols

There are potentially hundreds of volumetric measurements thatcan be computed in automated image analysis programs like Free-Surfer (Jovicich et al., 2009). Based on our prior operator-controlledvolumetric methods that had already been performed (Fearing et al.,

Fig. 1. Comparison of white matter (WM) classification by FreeSurfer versus operator-contlarge, cystic left frontal lobe lesion (image is in radiological convention). For both FreeSurfewhite matter (WM) classification. For the comparison image, the two images were fused witcolorized in red.Where the two classifications were similar is outlined in yellow. Areas whereFor the FreeSurfer analysis only the left and right cerebral and cerebellar applications along wclassification difference noted in the fused comparison image (areas of red) in the region oaforementioned structures, the operator-controlledmethodwould have been further segmenas WM. In FreeSurfer, volume calculation of those structures would be done by applying thexpected. As can be seen at the outer boundaries of WM classification, some differences octhis illustration shows good agreement between the two methods.

2008; Spanos et al., 2007; Wilde et al., 2007, 2005) as well as otherstudies reviewed in the introduction that have reported atrophicchanges associated with TBI, we narrowed the analysis to severalspecific structures and ROIs as well as computation of lobar volumes.We determined that therewere six brain regions that could be directlycompared between our operator-controlled and FreeSurfer methodsbecause of similar boundary definitions: globus pallidus, amygdala,caudate, putamen, thalamus and brainstem. For these structures,direct absolute volumetric comparisons were undertaken, withstatistical differences assessed by paired t-test, bivariate scatter plotsand intra-class correlation coefficients.

For lobar comparisons, different definitional boundaries existedbetween the two methods, particularly involving trajectories delin-eating WM between the lobes, along with differences in howFreeSurfer handles WM and GM differentiation within the corticalmantle. As such, direct volumetric comparisons were impracticablefor lobar measurements and were not performed, but the relativeeffect sizes of volume differences for these ROIs between TBI andcontrol subjects were calculated and compared. It must be kept inmind that for these comparisons, the absolute values used to calculatethe effect sizes were based on different volumes. To reduce thenumber of comparisons, and since our prior work with these datausing both methods did not indicate unexpected hemisphericasymmetry, or any systematic lateralization bias unique to eitherthe automated or operator controlled method, all structures/ROIsincluded combined left and right volumes to yield a singular structureor lobar measure for each brain region, except for the brainstem. Forlobar calculations, WM and GM volumes of the frontal and temporallobes were separately computed according to our establishedoperator-controlled methods and by FreeSurfer. Parietal and occipitalvolumes were collapsed into single occipitoparietal GM and WMvolumes. Corpus callosum cross-sectional surface area at the midline(rather than volume) was computed and compared across method.

Inter-rater and intra-rater agreementExperienced raters performed operator-controlled segmentation

and tracing under the supervision of a neuroradiologist (JVH) and an

rolled (ANALYZE®) methods in a traumatic brain injury (TBI) subject with an obviousr and the operator-controlled methods, what is shown represents the unedited initialh the FreeSurfer image colorized in green and the operator-controlled WM classificationthe primary color still exists demonstrate areas with lack of similarity between the two.ith the corpus callosum and optic chiasm were operational. This is why there is a majorf the lenticular nucleus, thalamus and caudate. For computation of the volumes of theted by the operator to identify those structures, and theywould not have been classifiede subcortical classification routine. So the ‘red’ differences at the subcortical level werecurred right at the WM-gray matter (GM) junction. For most of the WM classification,

Table 3Group analyses differences and Method×Group interactions.

Region ofinterest(ROI) analysismethod

TBIgroupa

(meancm3)

Controlgroupa

(meancm3)

Method×Groupinteraction

F p-value F p-value

AmygdalaOp-Contr 2.92 3.44 11.99 0.002 1.20 0.28FreeSurfer 2.90 3.28 8.71 0.006

Brain stemOp-Contr 20.77 22.79 32.61 0.009 0.02 0.90FreeSurfer 18.89 20.94 8.36 0.007

CaudateOp-Contr 7.30 7.72 2.07 0.16 0.32 0.58FreeSurfer 6.14 6.64 0.42 0.52

Globus pallidusOp-Contr 2.76 3.04 5.64 0.02 0.51 0.48FreeSurfer 2.63 3.03 5.24 0.03

PutamenOp-Contr 9.12 9.87 5.28 0.03 0.72 0.40FreeSurfer 9.05 9.43 1.00 0.32

ThalamusOp-Contr 12.21 13.90 11.54 0.002 1.46 0.24FreeSurfer 13.23 15.54 15.05 0.0006

Note. Op-Contr=operator-controlled ANALYZE®.a Using a repeated measures ANCOVA, least-squares means adjusted for age are

shown for both TBI and Control groups.

1021E.D. Bigler et al. / NeuroImage 50 (2010) 1017–1026

expert in volumetric imaging (EDB). Although the raters were notgiven information about the group identity of the children, thepresence of focal brain lesions and other injury-related atrophicchanges were indicative of the TBI patients in some cases. Intra- andinter-rater reliabilities were computed on each measure, details ofwhich are reported in the original publications. In all cases, intraclasscorrelation coefficients of both intra- and inter-rater reliabilityexceeded 0.90 for all measures used in this study, with many of theintra-class correlations for both inter- and intra-rater reliabilityexceeding 0.95.

We also performed reliability estimates on data analyzed using theFreeSurfer automated method by comparing data analyzed on twoseparate occasions. Here the intraclass correlation coefficients (ICC)exceeded 0.97 for all structures/ROIs used in this study.

Comparison of operator-controlled versus automated ROI quantification

ROI volumes for both the operator-controlled and the automatedROI analyses are in cubic centimeters (cm3) except for the operator-controlled method for corpus callosum analysis, where the compu-tation is performed as a mid-sagittal surface area measurement insquared centimeters (cm2). Direct volumetric comparison of thehippocampus was not performed because boundaries utilized in theoperator-controlled method included only the anterior portions, yetthe FreeSurfer method included the entire structure. We were unableto perform direct comparisons for cortical WM and GM again due todifferences in the demarcation of boundaries. For example, in theoperator-controlled method, only the prefrontal areas anterior to thegenu of the corpus callosum were included in the frontal lobeswhereas in the FreeSurfer method, the entire frontal lobes wereincluded. As already mentioned, certain subcortical structures hadanalogous and clearly circumscribed boundaries enabling directcomparison of the actual volumes generated by both methods.Although it would have been ideal to perform direct volumecomparisons for all cortical and subcortical regions, importantdifferences between the two methods in the boundaries used forselected ROIs precluded direct comparison other than effect sizedifferences.

Design and statistical analysis

Demographic data were examined using a series of chi-square andFisher exact analyses for categorical variables and t-tests forcontinuous variables. Paired t-tests were used to test the directvolume difference between methods for comparable structures/ROIs,and then repeated measures analysis of covariance (repeatedANCOVA) followed to test the interaction of group by methods withage controlled. In addition, correlations between the volumesobtained by each method for the structures with comparableboundaries were illustrated by scatter plots. Intra-class correlationcoefficients to measure the agreement between the two methodswere also calculated.

Effect sizes of Cohen's f (which controlled for age) were computedfor group differences on each structure/ROI using each method. Theeffect sizes were compared for each comparable structure/ROI forboth methods, this permitted ordering of the effect size differences toshow which structures exhibit the least to greatest change (i.e.,amount of atrophy).

To determine the degree of atrophy in each of the discrete brainstructures, proportional volume loss was assessed by using the meanvolumes for each cortical and subcortical ROI of the TBI group dividedby that of control group. For this comparison, 1.0 reflects no atrophy,and scores b1.0 reflect the proportion of atrophy within the TBI groupcompared to the control sample, with lower scores indicating greateratrophy. This permitted a comparison of the proportionality of volumedifferences across each brain structure/ROI determined by the

FreeSurfer and operator-controlled methods. Since the expectationwas that TBI would result in atrophic changes, it would have beenunlikely that the TBI sample would exhibit a proportional gain incomparison to the control sample for any structure/ROI. Using thisapproach, it is conceivable that a ratio of 1.0 could be obtained,thereby reflecting no atrophic change in that brain structure/ROI,assuming that both methods were accurately assessing each struc-ture/ROI. Although no ratios were expected to exceed 1.0, we didanticipate that the two methods would yield some differences in theratios of proportional volume loss. For the last specific objective, boththe effect size and proportional volume calculations were used toevaluate the diffuseness of atrophy. Presence of at least a small effectsize (Cohen's f=.10) signifies the presence of atrophy as does anyratio b1.0 for the proportional volume comparison. Using thisapproach, the number of brain structures/ROIs that met or exceededthese standards would specify the extent of cerebral atrophy in TBIand identify the brain structures that are most affected by injury.Lastly, the effect of age and GCS-determined injury severity on thetwo methods of volume estimation was examined using Spearmancorrelations.

Results

Group comparisons and relation of age and GCS tostructure/ROI volumes

Volumetric findings between the TBI and control groups compar-ing FreeSurfer and operator-controlled methods for brain structures/ROIs with comparable boundaries (globus pallidus, amygdala,caudate, putamen, thalamus and brainstem) are summarized inTable 3. The between-group comparisons showed that the TBI groupexhibited significant atrophy in the amygdala, brainstem, globuspallidus, and thalamus, regardless of technique used. Although theTBI group consistently had smaller volumes, neither techniquedemonstrated statistically significant atrophy in the caudate; theputamen was significantly atrophic in the TBI group assessed only bythe operator-controlled method but not for FreeSurfer. As shown inTable 4, there were some significant correlations between age and/or GCS and method of analysis, therefore age was controlled in themodel for comparison between groups.

Table 4Correlations between regional volumes and age and GCS.

Region ofinterest (ROI)analysis method

Correlations for TBI Correlations for control

Age rho(p-value)

GCS rho(p-value)

Age rho(p-value)

GCS rho(p-value)

Whole brain WMOp-Contr –0.29 (0.28) –0.22 (0.40) –0.19 (0.49) N/AFreeSurfer 0.55 (0.03)a –0.20 (0.45) 0.54 (0.03)a N/A

Whole brain GMOp-Contr 0.31 (0.24) –0.27 (0.30) 0.44 (0.08) N/AFreeSurfer –0.40 (0.13) –0.20 (0.46) –0.10 (0.72) N/A

Frontal WMOp-Contr –0.08 (0.78) –0.54 (0.03)a 0.48 (0.06) N/AFreeSurfer 0.50 (0.05)a –0.21 (0.44) 0.44 (0.08) N/A

Frontal GMOp-Contr –0.38 (0.15) –0.20 (0.45) 0.17 (0.54) N/AFreeSurfer –0.46 (0.08) –0.05 (0.86) 0.04 (0.90) N/A

Temporal WMOp-Contr –0.27 (0.31) –0.23 (0.39) –0.32 (0.22) N/AFreeSurfer 0.55 (0.03)a –0.04 (0.87) 0.46 0.07) N/A

Temporal GMOp-Contr –0.08 (0.78) 0.05 (0.86) –0.07 (0.79) N/AFreeSurfer 0.06 (0.81) –0.30 (0.26) 0.06 (0.81) N/A

Occipital/parietal WMOp-Contr –0.10 (0.71) –0.07 (0.78) –0.28 (0.30) N/AFreeSurfer 0.51 (0.05)a –0.27 (0.32) 0.61 (0.01)a N/A

Occipital/parietal GMOp-Contr 0.54 (0.03)a –0.18 (0.51) 0.05 (0.85) N/AFreeSurfer –0.70 (0.003)a –0.14 (0.61) –0.32 (0.23) N/A

Cerebellar WMOp-Contr –0.24 (0.38) –0.27 (0.32) 0.26 (0.32) N/AFreeSurfer –0.24 (0.37) –0.24 (0.38) 0.18 (0.50) N/A

Cerebellar GMOp-Contr 0.09 (0.74) –0.24 (0.37) 0.33 (0.21) N/AFreeSurfer –0.02 (0.95) –0.16 (0.55) 0.39 (0.13) N/A

AmygdalaOp-Contr –0.18 (0.50) 0.03 (0.92) –0.08 (0.76) N/AFreeSurfer 0.15 (0.58) –0.13 (0.64) 0.57 (0.02)a N/A

Brain stemOp-Contr 0.25 (0.36) –0.13 (0.63) 0.53 (0.03)a N/AFreeSurfer 0.35 (0.18) –0.08 (0.76) 0.58 (0.02)a N/A

CaudateOp-Contr –0.38 (0.14) 0.14 (0.61) –0.12 (0.66) N/AFreeSurfer –0.03 (0.91) –0.57 (0.02)a 0.35 (0.19) N/A

Corpus callosumOp-Contr 0.03 (0.89) 0.24 (0.37) 0.23 (0.39) N/AFreeSurfer –0.11 (0.70) 0.31 (0.24) 0.31 (0.24) N/A

Globus pallidusOp-Contr –0.71 (0.002)a 0.16 (0.56) –0.08 (0.77) N/AFreeSurfer 0.07 (0.80) –0.38 (0.15) 0.11 (0.68) N/A

HippocampusOp-Contr –0.34 (0.20) 0.13 (0.62) 0.21 (0.44) N/AFreeSurfer –0.28 (0.30) –0.07 (0.80) 0.28 (0.29) N/A

PutamenOp-Contr –0.33 (0.22) 0.00 (1.00) 0.09 (0.74) N/AFreeSurfer –0.07 (0.80) 0.03 (0.92) 0.20 (0.46) N/A

ThalamusOp-Contr –0.45 (0.08) –0.10 (0.71) 0.25 (0.35) N/AFreeSurfer –0.37 (0.16) –0.23 (0.39) 0.24 (0.37) N/A

Op-Contr=operator-controlled ANALYZE®; GCS=Glasgow Coma Scale score; TBI=traumatic brain injury; WM=white matter; GM=gray matter; N/A=not applicable.

a Indicates significance at uncorrected pb0.05. When corrected for multiplecomparisons, no correlations remained significant.

Fig. 2. Comparison of effect size indices of traumatic brain injury (TBI)-induced atrophyfor specific brain structures between operator-controlled (ANALYZE®) and FreeSurferimage quantification methods. Cohen's f are reported where the convention forinterpretation of small, medium, and large effect sizes is defined at the .10, .25, and .40levels, respectively (Cohen, 1988). In general measurement of specific structures andtheir atrophic changes, comparing the two methods showed good agreement, exceptfor cerebellar white matter (WM), where there was in excess of a 0.6 effect size unitdifference. All other differences were ≤0.3 units difference and five structures were≤0.1 units difference. All measures regardless of technique were reflective of significantatrophic changes in the TBI patients compared to their matched controls in thesespecific brain structures.

Fig. 3. Comparison of effect size indices of traumatic brain injury (TBI)-induced atrophyfor whole brain and lobar measurements between operator-controlled (ANALYZE®)and FreeSurfer image quantification methods. Cohen's f are reported where theconvention for interpretation of small, medium, and large effect sizes is defined at the.10, .25, and .40 levels, respectively (Cohen, 1988). As explained in the Materials andmethods section, lobar analyses were anticipated to yield somewhat different resultsbetween the operator-controlled and FreeSurfer methods because of differences inboundary definitions. Given differences in the absolute volumes, it was anticipated thatthe effect sizes reflective of atrophic change would also differ between the twomethods. Thus, even though each effect size difference reaches a medium level,reflective of substantial atrophy, there appears to be more variability between thesemethods when it comes to determining lobar volume and change related to TBI thanwhat was observed with specific structures as shown in Fig. 2. As can be seen in thisfigure, of the eight regions of interest (ROIs), half equaled or exceeded 0.4 units ofdifference between effect sizes for the same structure, with three showing N2.0 unitsand one at approximately 1.5 units difference.

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Objective 1: Pattern similarities between FreeSurfer andoperator-controlled image analyses

As shown in Fig. 2 for specific structures/ROIs, all effect sizedifferences reflective of TBI-related atrophy were medium to largeexcept for a small effect size found for the caudate with bothmethods,and a negligible effect size for the putamen using the FreeSurfermethod. Fig. 2 also shows that in terms of effect size differences, TBI-related atrophywas roughly comparable between the twomethods ofvolumetric analysis, with the exception of the putamen, cerebellarWM, and hippocampus, where in each case the observed effect sizewas substantially larger in the operator-controlled method. Impor-

tantly, decreased volumewas reflected in the TBI sample compared tocontrols for every measure regardless of method. As shown in Fig. 3,for whole brain and lobar comparisons, all effect sizes were at least

Fig. 5. Bar graph demonstrating the patterns of proportional tissue volume loss ofcortical structures/ROIs as determined through FreeSurfer and operator-controlled(ANALYZE®) methods of image quantification.

1023E.D. Bigler et al. / NeuroImage 50 (2010) 1017–1026

medium to large regardless of the method of image quantificationwith the exception of the temporal WM (which had a small effect sizeusing the FreeSurfer method) and the occipital/parietal white matter(which had a small effect size using the operator-controlledANALYZE® method). However, the magnitude of effect size differ-ences appeared to be more variable for these ROIs than that whichwas observed in the specific structures examined in Fig. 2. Table 3 alsoshows the least-squares mean volumes employing FreeSurfer andoperator-controlled image analysis methods using repeatedmeasuresANCOVA adjusting for age. Group×Method interactions were notsignificant for amygdala, brainstem, caudate, globus pallidus, puta-men and thalamus suggesting that the two methods demonstrated asimilar pattern of volumetric results in terms of group difference forthese structures.

Objective 2: Direct comparison of absolute volumes between FreeSurferand operator-controlled methods of quantification for selectstructures/ROI

As already mentioned, Table 3 shows that the quantificationmethod by group interaction was not significant for the six specificstructures/ROIs with comparable boundaries; however, when thevolumes between the two methods were assessed with paired t-tests,the absolute volumes were statistically different (pb .0001) forcaudate, thalamus and brainstem but not for putamen, globus pallidusand amygdala for which the effect size between the two methodswere small. Next, as shown in Fig. 4, bivariate scatterplots comparedthe two methods for each of these brain structures. In these analyses,robust linear relationships between these two methods existed forbrainstem with a moderate linear relationship for the thalamus. Therelationship between the twomethods for amygdala was weaker, andweakest for the caudate, putamen, and globus pallidus (see Fig. 4).Additionally, intraclass correlations (ICC; Shrout and Fleiss, 1979)were calculated for each structure/ROI (amygdala=0.74, brainstem=0.97, caudate=0.43, globus pallidus=0.50, putamen=0.46,

Fig. 4. Scatterplots demonstrating the correspondence between volume

and thalamus=0.88), again demonstrating a greater pattern ofconsistency for the brainstem and thalamus, with less agreement forthe amygdala and least agreement for basal ganglia structures.

Objective 3. Proportional TBI-related volume loss of each structure/ROIcompared between FreeSurfer and operator-controlled image analyses

Figs. 5 and 6 depict another comparison that assessed atrophicchanges in the TBI group by calculating the proportional volumereduction or loss for each structure/ROI (mean TBI volume divided bythe mean control volume) and compares these findings between thetwo methods of volume calculation. Generally, as shown by visual

s as measured by operator-controlled (ANALYZE®) and FreeSurfer.

Fig. 6. Bar graph demonstrating patterns of proportional tissue volume loss ofsubcortical structures/ROIs as determined through FreeSurfer and operator-controlled(ANALYZE®) methods of image quantification.

1024 E.D. Bigler et al. / NeuroImage 50 (2010) 1017–1026

inspection of Figs. 5 and 6, reasonable agreement in proportionalvolume loss between the two methods was observed where only four(temporal and occipitoparietal lobe WM, cerebellar WM andhippocampus) of the 18 comparisons differed by more than 10%.

Objective 4: How diffuse is the atrophy in TBI?Table 3 and Figs. 2–3 and 5–6 address this objective and show that

atrophic changes in the brain are widespread and generalized,affecting both WM and GM structures and ROIs. Parametrically, onlythe caudate was not significantly different with at least one of themethods. However, regardless of the image quantification methodused, the majority of effect-size differences were large, reflective ofprominent atrophic changes in the TBI group. The only structures/ROIs with small effect sizes were the caudate and only with theFreeSurfer-based analyses, temporal lobe WM (FreeSurfer methodonly), and occipitoparietal WM (ANALYZE® method only), and theonly structure with a negligible effect size was the putamen (for theFreeSurfer method only). In the proportional analyses as shown inFigs. 5 and 6, a 1.0 would indicate the TBI and control structure/ROIvolumes were equivalent and therefore no atrophy detected. As canbe seen, all structures/ROIs examined were b1.0, reflecting atrophicchanges in all structures with temporal lobe GM and corpus callosumshowing the greatest proportional volume loss regardless of method.

Discussion

There is considerable interest in how well automated MRIquantification methods compare to manual tracing procedures,especially for analyses of large databases (Cherbuin et al., 2009;Hasan, 2009; Hasan and Pedraza, 2009; Khan et al., 2008; Morey et al.,2009a,b; Shen et al., 2009; Tae et al., 2008). The current findingswithin a sample of pediatric patients with TBI suggest comparabilityof the twomethods for a variety of structures and in each case reducedvolume in the TBI group was observed regardless of method. For themost comparable structures/ROIs the two methods did result insignificant volume differences for the putamen, global pallidus andamygdala but not for caudate, thalamus and brainstem but effect sizedifferences were generally similar indicating substantial atrophicchanges in most structures. Most encouragingly, there was not asingle instance of disagreement in the direction of atrophic differ-ences between the two methods. In each case, the TBI group had lessvolume for all structures/ROIs examined; the only differences were in

terms of the magnitude of effect size. These differences in themagnitude of atrophy detected between the two methods are likelyexplicable by divergence between the two methods in inclusion/exclusion criteria along with start-stop points for boundaries thatdelineate a given structure.

It is instructive to examine in some detail where the greatestdifferences occurred between the automated and operator-controlledtracing methods. First, as shown in Fig. 1 the two procedures differ inhow tissue classification is achieved, ensuring that there will bedifferences. Also, as shown in Figs. 2 and 6, considerable effect sizedisparities in cerebellar WM were noted but the two methods differsubstantially in boundary and inclusion/exclusion criteria. Thecerebellum represents a unique challenge for image quantificationbecause of its numerous folia, where in some regions within folia theWM width may be just a few pixels (Diedrichsen et al., 2009; Filipek,1995; Makris et al., 2003). Classification that differs by just a fewpixels in the transition between WM and GM may lead to verydifferent results (Jack et al., 1995), which is particularly a problem forsmall convoluted structures, using either technique. It may be that forthe cerebellum, the operator-controlled method is superior to theautomated method. Another source of difference for cerebellarquantification between the operator-controlled and FreeSurfer meth-ods has to do with the boundary definitions for inclusion/exclusion ofthe three cerebellar peduncles, as the two methods differedsubstantially in those boundaries. Lastly, because of its pathologicaleffects on brain parenchyma TBI may result in an overall alteration issignal intensity that creates potential differences in WM-GMdifferentiation (Thatcher et al., 1997) and these influences may alsocreate unique differences between automated and operator-con-trolled methods. However, these potential differences were notexplored in this study.

At the lobar level, the boundaries that define the separationbetween the frontal, temporal, and occipitoparietal lobes and how thecortical mantle is classified also related to differences between thetwo methods, and therefore, differences in the magnitude of effectsizes between the two procedures were expected. Accordingly, asshown in Fig. 3, although whole brain WM volume was consistentbetween the two methods of volume estimation substantial differ-ences were present between the two methods in lobar WM and GMvolumes for all lobar calculations.

Definitional differences also likely contributed to the disparity ofthe two methods in assessing hippocampal volumes and TBI-relatedatrophy (see Fig. 2). In the operator-controlled method, the posteriorboundary of the hippocampus is operationally defined by the locationof the colliculi in the coronal plane, whereas the FreeSurfer boundaryincludes more of the crus fornix. Also, the precise start-stop pointsbetween the head of the hippocampus and the posterior aspect of theamygdala represents another location of classification challenge forboth image analysis methods. Despite these differences, thesefindings indicate that bothmethods are sensitive in detecting atrophicchanges associated with TBI in every ROI assessed, including the oneswhere there was the greatest discrepancy.

In addition to the objective of comparing the two methods, thesefindings also address the extent of diffuse injury in moderate-to-severe pediatric TBI. Every measurement reflected relative volumedecrease in the TBI subjects regardless of the method by which an ROIwas measured. These findings also support the idea that moderate-to-severe TBI is truly a diffuse brain injury resulting in generalizedforebrain to hindbrain atrophic changes, affecting all brain structuresanalyzed (Mamere et al., 2009; Ng et al., 2008; Suskauer and Huisman,2009). Small effect sizes were observed in basal ganglia structureswith the smallest being found for the caudate (using both methods)and putamen (but only when assessed with the FreeSurfer method)and in the occipital/parietal lobes when assessed with the operator-controlled method and the temporal white matter when assessedwith the automatedmethod. All other effect sizes were in themedium

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to large range for all structures/ROIs. As for the caudate and putamen(with either method of analysis) the distinctions of what to include todefine these structures can be challenging because of the GMstriations between the two and the ventral stop point where theputamen and caudate fuse into the ventral striatum and extendedamygdala. Regardless of these differences, both methods demonstrat-ed atrophic changes associated with brain injury.

The atrophic brain changes documented in these children withmoderate-to-severe TBI are likely a combination of direct injury aswell as a host of secondary injury factors including vascular,excitotoxic, transneuronal, and Wallerian degeneration (Povlishockand Katz, 2005). The neurobehavioral implications of such wide-spread damage in pediatric TBI indicates that extensive damage hasoccurred in thematuring brain and the recent studies by Ewing-Cobbset al. (2008) and by Yuan et al. (2007) implicate the permanency ofthis damage where future brain development does not override theoriginal structural damage from injury. This has widespread implica-tions for how compensatory neurodevelopmental adaptations to theinjury occur and how this relates to recovery of function and long-term outcome.

Conclusion

Both operator-controlled and automated methods of quantitativeimage analysis provide sensitive measures capable of detectingatrophic changes in TBI. Because the FreeSurfer automated methodoffers: 1) greater time savings, efficiency and uniformity in imageanalysis, and, 2) exhibited equivalent estimation of volume losscompared to the gold-standard operator-controlled ROI method forstructures with comparable boundaries and directionally similareffect size differences for all structures examined, these findingssupport automated volumetric assessment in the routine use forvolumetric analysis of pediatric TBI, particularly for large datasets.However, operator-controlled methods will continue to be necessaryfor certain structures and their subunits.

Acknowledgments

This research was supported by grant NS-21889 awarded toHarvey S. Levin by the National Institutes of Health. We alsoacknowledge the generous support of Mission Connect of the TIRRFoundation. Erin D. Bigler was partially supported by grant 1 RO1HD048946-01AZ. We acknowledge the contribution of Stacey K.Martin and Jo Ann Petrie for assistance in manuscript preparation. Wewould also like to thank Paul Swank, PhD, for his helpful adviceregarding statistical methodology. Finally, we thank the participantsand their families for their interest and involvement in this study.

References

Athinoula A. Martinos Center for Biomedical Imaging: FreeSurfer [Internet], c2005.Harvard-Massachusetts Institute of Technology (MIT) Division of Health Sciences &Technology (HST) and theMassachusetts GeneralHospital (MGH); [last updated2009Sept 08; cited 2009 Dec 02]. Available from: http://surfer.nmr.mgh.harvard.edu/.

Bendlin, B.B., Ries, M.L., Lazar, M., Alexander, A.L., Dempsey, R.J., Rowley, H.A., Sherman,J.E., Johnson, S.C., 2008. Longitudinal changes in patientswith traumatic brain injuryassessed with diffusion-tensor and volumetric imaging. Neuroimage 42, 503–514.

Blatter, D.D., Bigler, E.D., Gale, S.D., Johnson, S.C., Anderson, C.V., Burnett, B.M., Ryser, D.,Macnamara, S.E., Bailey, B.J., 1997. MR-based brain and cerebrospinal fluidmeasurement after traumatic brain injury: correlation with neuropsychologicaloutcome. AJNR Am. J. Neuroradiol. 18, 1–10.

Bozzali, M., Cercignani, M., Caltagirone, C., 2008. Brain volumetrics to investigate agingand the principal forms of degenerative cognitive decline: a brief review. Magn.Reson. Imaging 26, 1065–1070.

Cherbuin, N., Anstey, K.J., Reglade-Meslin, C., Sachdev, P.S., 2009. In vivo hippocampalmeasurement and memory: a comparison of manual tracing and automatedsegmentation in a large community-based sample. PLoS One 4, e5265.

Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. LawrenceEarlbaum Associates, Hillsdale, NJ.

Dale, A.M., Sereno, M.I., 1993. Improved localization of cortical activity by combiningEEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cog.Neurosci. 5, 162–176.

Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis: I. Segmentationand surface reconstruction. Neuroimage 9, 179–194.

Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L.,Dale, A.M., Maguire, R.P., Human, B.T., Albert, M.S., Killiany, R.J., 2006. An automatedlabeling system for subdividing the human cerebral cortex on MRI scans into gyralbased regions of interest. Neuroimage 31, 968–980.

Diedrichsen, J., Balsters, J.H., Flavell, J., Cussans, E., Ramnani, N., 2009. A probabilistic MRatlas of the human cerebellum. Neuroimage 46, 39–46.

Ding, K., Marquez de la Plata, C., Wang, J.Y., Mumphrey, M., Moore, C., Harper, C.,Madden, C.J., McColl, R., Whittemore, A., Devous, M.D., Diaz-Arrastia, R., 2008.Cerebral atrophy after traumatic white matter injury: correlation with acuteneuroimaging and outcome. J. Neurotrauma 25, 1433–1440.

Ewing-Cobbs, L., Prasad, M.R., Swank, P., Kramer, L., Cox Jr., C.S., Fletcher, J.M., Barnes,M., Zhang, X., Hasan, K.M., 2008. Arrested development and disrupted callosalmicrostructure following pediatric traumatic brain injury: relation to neurobeha-vioral outcomes. Neuroimage 42, 1305–1315.

Fearing, M.A., Bigler, E.D., Wilde, E.A., Johnson, J.L., Hunter, J.V., Xiaoqi, L., Hanten, G.,Levin, H.S., 2008. Morphometric MRI findings in the thalamus and brainstem inchildren after moderate to severe traumatic brain injury. J. Child. Neurol. 23,729–737.

Filipek, P.A., 1995. Quantitative magnetic resonance imaging in autism: the cerebellarvermis. Curr. Opin. Neurol. 8, 134–138.

Fischl, B., Dale, A.M., 2000. Measuring the thickness of the human cerebral cortex frommagnetic resonance images. Proc. Nat. Acad. Sci. 97, 11050–11055.

Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M., 1999. High-resolution intersubjectaveraging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8,272–284.

Fischl, B., Liu, A., Dale, A.M., 2001. Automated manifold surgery: constructinggeometrically accurate and topologically correct models of the human cerebralcortex. IEEE Trans. Med. Imag. 20, 70–80.

Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, J.C., van der Kouwe,A.J., Killiany, R.J., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale,A.M., 2002. Whole brain segmentation: automated labeling of neuroanatomicalstructures in the human brain. Neuron 33, 341–355.

Fischl, B., Salat, D.H., van der Kouwe, A.J., Makris, N., Segonne, F., Quinn, B.T., Dale, A.M.,2004a. Sequence-independent segmentation of magnetic resonance images.Neuroimage 23, S69–S84.

Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E.,Seidman, L.J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., Dale, A.M.,2004b. Automatically parcellating thehuman cerebral cortex. Cereb. Cortex14, 11–22.

Ghosh, A., Wilde, E.A., Hunter, J.V., Bigler, E.D., Chu, Z., Li, X., Vasquez, A.C., Menefee, D.,Yallampalli, R., Levin, H.S., 2009. The relation betweenGlasgow Coma Scale score andlater cerebral atrophy in paediatric traumatic brain injury. Brain Inj. 23, 228–233.

Hasan, K.M., 2009. A questionable gold standard for hippocampus volume andasymmetry. Neuroradiology 51, 201–202.

Hasan, K.M., Pedraza, O., 2009. Improving the reliability of manual and automatedmethods for hippocampal and amygdala volume measurements. Neuroimage[Epub date 2009/05/16]. doi:S1053-8119(09)00498-4 [pii]10.1016/j.neuroimage.2009.05.004.

Jack Jr., C.R., Theodore, W.H., Cook, M., McCarthy, G., 1995. MRI-based hippocampalvolumetrics: data acquisition, normal ranges, and optimal protocol. Magn. Reson.Imaging 13, 1057–1064.

Johnson, S.C., Pinkston, J.B., Bigler, E.D., Blatter, D.D., 1996. Corpus callosummorphologyin normal controls and traumatic brain injury. Sex differences, mechanisms ofinjury, and neuropsychological correlates. Neuropsychol. 10, 408–415.

Jovicich, J., Czanner, S., Han, X., Salat, D., van der Kouwe, A., Quinn, B., Pacheco, J., Albert,M., Killiany, R., Blacker, D., Maguire, P., Rosas, D., Makris, N., Gollub, R., Dale, A.,Dickerson, B.C., Fischl, B., 2009. MRI-derived measurements of human subcortical,ventricular and intracranial brain volumes: reliability effects of scan sessions,acquisition sequences, data analyses, scanner upgrade, scanner vendors and fieldstrengths. Neuroimage 46, 177–192.

Khan, A.R., Wang, L., Beg, M.F., 2008. FreeSurfer-initiated fully-automated subcorticalbrain segmentation in MRI using large deformation diffeomorphic metric mapping.Neuroimage 41, 735–746.

Kim, J., Avants, B., Patel, S., Whyte, J., Coslett, B.H., Pluta, J., Detre, J.A., Gee, J.C., 2008.Structural consequences of diffuse traumatic brain injury: a large deformationtensor-based morphometry study. Neuroimage 39, 1014–1026.

Lehmann, M., Douiri, A., Kim, L.G., Modat, M., Chan, D., Ourselin, S., Barnes, J., Fox, N.C.,2009. Atrophy patterns in Alzheimer's disease and semantic dementia: acomparison of FreeSurfer and manual volumetric measurements. Neuroimage[Epub date 2009/10/31]. doi:S1053-8119(09)01127-6 [pii]10.1016/j.neuroimage.2009.10.056.

Levine, B., Kovacevic, N., Nica, E.I., Cheung, G., Gao, F., Schwartz, M.L., Black, S.E., 2008.The Toronto traumatic brain injury study: injury severity and quantified MRI.Neurol. 70, 771–778.

Levin, H.S., Mendelsohn, D., Lilly, M.A., Yeakley, J., Song, J., Scheibel, R.S., Harward, H.,Fletcher, J.M., Kufera, J.A., Davidson, K.C., Bruce, D., 1997. Magnetic resonanceimaging in relation to functional outcome of pediatric closed head injury: a test ofthe Ommaya-Gennarelli model. Neurosurg. 40, 432–440.

Levin, H.S., Hanten, G., Roberson, G., Li, X., Ewing-Cobbs, L., Dennis, M., Chapman, S.,Max, J.E., Hunter, J., Schachar, R., Luerssen, T.G., Swank, P., 2008. Prediction ofcognitive sequelae based on abnormal computed tomography findings in childrenfollowing mild traumatic brain injury. J. Neurosurg. Pediatr. 1, 461–470.

1026 E.D. Bigler et al. / NeuroImage 50 (2010) 1017–1026

Makris, N., Hodge, S.M., Haselgrove, C., Kennedy, D.N., Dale, A., Fischl, B., Rosen, B.R., Harris, G.,Caviness Jr., V.S., Schmahmann, J.D., 2003. Human cerebellum: surface-assisted corticalparcellation and volumetry with magnetic resonance imaging. J. Cogn. Neurosci. 15,584–599.

Mamere, A.E., Saraiva, L.A., Matos, A.L., Carneiro, A.A., Santos, A.C., 2009. Evaluation ofdelayed neuronal and axonal damage secondary to moderate and severe traumaticbrain injury using quantitative MR imaging techniques. AJNR Am. J. Neuroradiol.30, 947–952.

Maxwell, W.L., Mackinnon, M.A., Stewart, J.E., Graham, D.I., 2009. Stereology of cerebralcortex after traumatic brain injury matched to the Glasgow Outcome Score. Brain[Epub date 2009/11/10]. doi:awp264 [pii]10.1093/brain/awp264.

Morey, R.A., Petty, C.M., Xu, Y., Hayes, J.P.,Wagner II, H.R., Lewis, D.V., LaBar, K.S., Styner,M.,McCarthy, G., 2009a.A comparison of automated segmentation andmanual tracing forquantifying hippocampal and amygdala volumes. Neuroimage 45, 855–866.

Morey, R.A., Petty, C.M., Xu, Y., Hayes, J.P., Wagner II, H.R., Lewis, D.V., Labar, K.S., Styner,M., McCarthy, G., 2009b. Rebuttal to Hasan and Pedraza in comments andcontroversies: “Improving the reliability of manual and automated methods forhippocampal and amygdala volumemeasurements”. Neuroimage [Epub date 2009/07/21]. doi:S1053-8119(09)00782-4 [pii]10.1016/j.neuroimage.2009.07.013.

Ng, K., Mikulis, D.J., Glazer, J., Kabani, N., Till, C., Greenberg, G., Thompson, A., Lazinski,D., Agid, R., Colella, B., Green, R.E., 2008. Magnetic resonance imaging evidence ofprogression of subacute brain atrophy inmoderate to severe traumatic brain injury.Arch. Phys. Med. Rehabil. 89, S35–S44.

Pierallini, A., Pantano, P., Fantozzi, L.M., Bonamini, M., Vichi, R., Zylberman, R., Pisarri, F.,Colonnese, C., Bozzao, L., 2000. Correlation between MRI findings and long-termoutcome in patients with severe brain trauma. Neuroradiol. 42, 860–867.

Povlishock, J.T., Katz, D.I., 2005. Update of neuropathology and neurological recoveryafter traumatic brain injury. J. Head. Trauma Rehabil. 20, 76–94.

Riggio, S.,Wong,M., 2009. Neurobehavioral sequelae of traumatic brain injury.Mt. Sinai.J. Med. 76, 163–172.

Robb,R., 1995.ANALYZE:Three-Dimensional Biomedical Imaging.VCHPublishers,NewYork.Robb, R.A., 2001. ANALYZE: the biomedical imaging resource at Mayo Clinic. IEEE Trans.

Med. Imag. 20, 854–867.Segonne, F., Pacheco, J., Fischl, B., 2007. Geometrically accurate topology-correction of

cortical surfaces using nonseparating loops. IEEE Trans. Med. Imag. 26, 518–529.Shen, L., Firpi, H.A., Saykin, A.J., West, J.D., 2009. Parametric surface modeling and

registration for comparison of manual and automated segmentation of thehippocampus. Hippocampus 19, 588–595.

Shrout, P.E., Fleiss, J.L., 1979. Intraclass correlations: uses in assessing rater reliability.Psychol. Bull. 86, 420–428.

Sidaros, A., Skimminge, A., Liptrot, M.G., Sidaros, K., Engberg, A.W., Herning, M., Paulson,O.B., Jernigan, T.L., Rostrup, E., 2009. Long-term global and regional brain volumechanges following severe traumatic brain injury: a longitudinal study with clinicalcorrelates. Neuroimage 44, 1–8.

Sled, J.G., Zijdenbos, A.P., Evans, A.C., 1998. A nonparametric method for automaticcorrection of intensity nonuniformity in MRI data. IEEE Trans. Med. Imag. 17,87–97.

Spanos, G.K., Wilde, E.A., Bigler, E.D., Cleavinger, H.B., Fearing, M.A., Levin, H.S., Li, X.,Hunter, J.V., 2007. Cerebellar atrophy after moderate-to-severe pediatric traumaticbrain injury. AJNR Am. J. Neuroradiol. 28, 537–542.

Suskauer, S.J., Huisman, T.A., 2009. Neuroimaging in pediatric traumatic brain injury:current and future predictors of functional outcome. Dev. Disabil. Res. Rev. 15,117–123.

Tae, W.S., Kim, S.S., Lee, K.U., Nam, E.C., Kim, K.W., 2008. Validation of hippocampalvolumes measured using a manual method and two automated methods(FreeSurfer and IBASPM) in chronic major depressive disorder. Neuroradiol. 50,569–581.

Thatcher, R.W., Camacho, M., Salazar, A., Linden, C., Biver, C., Clarke, L., 1997.Quantitative MRI of the gray-white matter distribution in traumatic brain injury.J. Neurotrauma 14, 1–14.

Trivedi, M.A., Ward, M.A., Hess, T.M., Gale, S.D., Dempsey, R.J., Rowley, H.A., Johnson,S.C., 2007. Longitudinal changes in global brain volume between 79 and 409 daysafter traumatic brain injury: relationship with duration of coma. J. Neurotrauma 24,766–771.

Wilde, E.A., Hunter, J.V., Newsome, M.R., Scheibel, R.S., Bigler, E.D., Johnson, J.L., Fearing,M.A., Cleavinger, H.B., Li, X., Swank, P.R., Pedroza, C., Roberson, G.S., Bachevalier, J.,Levin, H.S., 2005. Frontal and temporal morphometric findings on MRI inchildren after moderate to severe traumatic brain injury. J. Neurotrauma 22,333–344.

Wilde, E.A., Bigler, E.D., Haider, J.M., Chu, Z., Levin, H.S., Li, X., Hunter, J.V., 2006.Vulnerability of the anterior commissure in moderate to severe pediatric traumaticbrain injury. J. Child. Neurol. 21, 769–776.

Wilde, E.A., Bigler, E.D., Hunter, J.V., Fearing, M.A., Scheibel, R.S., Newsome, M.R.,Johnson, J.L., Bachevalier, J., Li, X., Levin, H.S., 2007. Hippocampus, amygdala, andbasal ganglia morphometrics in children after moderate-to-severe traumatic braininjury. Dev. Med. Child. Neurol. 49, 294–299.

Yuan, W., Holland, S.K., Schmithorst, V.J., Walz, N.C., Cecil, K.M., Jones, B.V.,Karunanayaka, P., Michaud, L., Wade, S.L., 2007. Diffusion tensor MR imagingreveals persistent white matter alteration after traumatic brain injury experiencedduring early childhood. AJNR Am. J. Neuroradiol. 28, 1919–1925.