Original Articles

Prognostic Value of Diffusion Tensor ImagingParameters in Severe Traumatic Brain Injury

Joshua Betz,1–3 Jiachen Zhuo,1,2 Anindya Roy,3

Kathirkamanthan Shanmuganathan,2 and Rao P. Gullapalli1,2

Abstract

Diffusion tensor imaging (DTI) has recently emerged as a useful tool for assessing traumatic brain injury (TBI). Inthis study, the prognostic value of the relationship between DTI measures and the clinical status of severe TBIpatients, both at the time of magnetic resonance imaging (MRI), and their discharge to acute TBI rehabilitation,was assessed. Patients (n = 59) admitted to the trauma center with severe closed head injuries were retrospec-tively evaluated after approval from the institution’s institutional review board, to determine the prognosticvalue of DTI measures. The relationship of DTI measures, including apparent diffusion coefficient (ADC),fractional anisotropy (FA), axial (kk) and radial diffusivity (kt) from the whole brain white matter, internalcapsule, genu, splenium, and body of the corpus callosum, were compared with neurological status at MRI andat discharge to acute TBI rehabilitation. Whole brain white matter averages of ADC, kk, and kt, and theircoefficient of variation (CV) were significantly correlated with the Glasgow Coma Scale (GCS) score on the dayof MRI. The average kk was significantly correlated with GCS scores on the day of MRI in all measured brainregions. Outcomes were associated with whole brain white matter averages of ADC and kk, and the CVs of FA,ADC, kk, and kt; and the averages and CVs of FA and kk in all corpus callosum regions. The inclusion ofregional and global DTI measures improved the accuracy of prognostic models, when adjusted for admissionGCS score and age ( p < 0.05). Whole brain white matter and regional DTI measures are sensitive markers of TBI,and correlate with neurological status both at MRI and discharge to rehabilitation. The addition of DTI measuresadjusted for age, gender, and admission GCS score significantly improved prognostic models.

Key words: diffusion tensor imaging; Glasgow Coma Scale; magnetic resonance imaging; prognosis; severetraumatic brain injury

Introduction

Traumatic brain injury (TBI) is a major cause of dis-ability, morbidity, and mortality (Sosin et al., 1989). An

estimated 1.7 million cases of TBI occur every year in theUnited States (Faul et al., 2010), with an estimated 52,000 fa-talities and an additional 70,000–90,000 persons incurringsignificant decrements from premorbid functioning (Cor-onado et al., 2011; Langlois et al., 2004). The estimated numberof individuals living with the neurological, neuropsychiatric,and cognitive sequelae of TBI is between 2.5 and 6.5 million.Due to its high incidence and debilitating consequences, TBI isa disorder of major public health significance, entailing sub-stantial economic and social costs (Finkelstein and Corso,2006; National Institutes of Health Consensus Development

Panel on Rehabilitation of Persons with Traumatic Brain In-jury, 1999; Thornhill et al., 2000).

It is common for patients with TBI to have moderate-to-severe disabilities following head injury (Thornhill et al.,2000). In cases in whom cerebral lesions are present, thesedisabilities are appreciable even 15 years post-injury, withprofound effects on quality of life (Teasdale and Engberg,2005). However, even TBI patients with normal-appearingconventional magnetic resonance imaging (MRI) scans maydemonstrate decrements in performance on tests of cognition(Kurca et al., 2006). TBI can have lasting effects on attention,cognitive speed, learning and memory, executive function,verbal ability, intellect, motivation, affect, and neurologicalfunction, with the degree of impairment and disability de-pendent on the nature and severity of the insult (Meythaler

1Magnetic Resonance Research Center, and 2Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School ofMedicine, Baltimore, Maryland.

3Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, Maryland.

JOURNAL OF NEUROTRAUMA 29:1292–1305 (May 1, 2012)ª Mary Ann Liebert, Inc.DOI: 10.1089/neu.2011.2215

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et al., 2001; National Institutes of Health Consensus Devel-opment Panel on Rehabilitation of Persons with TraumaticBrain Injury, 1999; Vaishnavi et al., 2009). The effects ofphysical insults can be immediate, or can manifest over sev-eral days following the initial injury (Meythaler et al., 2001;National Institutes of Health Consensus Development Panelon Rehabilitation of Persons with Traumatic Brain Injury,1999). Secondary injury to the brain shortly follows the initialinsult, and may include ischemia, cerebral hypotension, ede-ma, elevated intracranial pressure, and altered metabolism.

Early in the course of TBI, prognosis can be complicated bythe limitations of traditional clinical measures, such as theGlasgow Coma Scale (GCS) and computed tomography (CT)features. The GCS score is a less reliable indicator of TBI se-verity in mild or moderate TBI. Further, the validity of the GCSin severe TBI can be undermined by intoxication or medicalintervention (intubation, sedation, or administration of para-lytics) prior to admission or transfer to rehabilitation (Gabbeet al., 2003; Marshall et al., 1991; Moppett, 2007; Murray et al.,1999; Saatman et al., 2008; Stochetti et al., 2004). Due to thesefactors, the European Brain Injury Consortium (EBIC) foundthat a full GCS assessment was only testable in 56% of patientson admission to neurosurgical units (Murray et el., 1999). TheGCS score can decline due to evolving pathology over the first72 h post-injury, and up to one-third of patients who die of TBIwill talk or obey commands before ultimately dying (Moppett,2007). Injury classification systems based on CT findings, suchas the Marshall classification, are able to predict importantclinical end-points such as the risk of mortality or rising in-tracranial pressure (ICP), but qualitative scoring systems cansuffer from significant inter-observer variability (Moppett,2007; Saatman et al., 2008). In addition, patients can havenormal-appearing CT and MRI results and still be profoundlycomatose and have poor functional outcomes, or a normal CTand neurological status depressed by intoxication (Saatmanet al., 2008). Only 10% of diffuse axonal injury (DAI) cases willpresent with the typical hemorrhagic pattern observed on CT,and more than 80% of DAI is non-hemorrhagic, leading to anunder-appreciation of axonal injury (Meythaler et al., 2001).The diffuse nature of these injuries may not be apparent in theacute phase of injury on conventional diagnostic imaging,which may limit its prognostic value (Meythaler et al., 2001).Additionally, measures that are associated with survival maynot necessarily be associated with good functional outcome asmeasured on the Glasgow Outcome Scale Extended (GOSE;van der Naalt et al., 1999).

There has been significant interest recently in the use ofdiffusion tensor imaging (DTI) in the evaluation of TBI pa-tients. Diffusion MRI has shown sufficient sensitivity tovisualize lesions that may be inconspicuous or absent onconventional MRI sequences (Arfanakis et al., 2002), and thusmay better depict diffuse injury in TBI. Since DTI is a quan-titative, physiologically-derived parameter, it may provide amore objective measure of axonal injury for use in TBI clas-sification and prognosis. Decreased fractional isotropy (FA) inthe lobar white matter, corpus callosum, internal capsule, andother white matter structures has been reported in acute TBIpatients (Arfanakis et al., 2002; Huisman et al., 2004), whichappears to persist or evolve in the sub-acute and chronicphases of TBI (Bendlin et al., 2008; Greenberg et al., 2008;Inglese et al., 2005). These decreases in FA have been linked topoor clinical outcomes or performance on cognitive testing in

the chronic phases of injury (Kennedy et al., 2009; Kumaret al., 2009; Nakayama et al., 2006; Sidaros et al., 2008). Al-though there are some reports on the use of DTI among se-verely injured TBI patients in the acute stage (Meythaler et al.,2001), the use of DTI as a quantitative prognostic marker hasnot been assessed among severely injured patients.

The goal of this study was to determine whether DTI markersat admission, including axial diffusivity (kk), radial diffusivity(kt), apparent diffusion coefficient (ADC), and FA at the whole-brain level and at the regional level provide prognostic infor-mation about the outcomes of severe TBI patients.

Methods

The University of Maryland’s institutional review boardapproved this study for retrospective evaluation, and thestudy was compliant with the requirements of the HealthInsurance Portability and Accountability Act.

Patients

Patients were selected and screened from a consecutive listof 126 individuals from the radiology database who receivedDTI as part of a standard clinical evaluation for blunt TBI on a1.5-Tesla scanner between September 2007 and May 2009.Patients who were 18 years or older were considered for in-clusion. Each patient’s GCS score was obtained at admission(admission GCS), on the day of MRI (scan GCS), and at dis-charge (discharge GCS). Since the patient’s GCS can be alteredby intoxication, medical intervention prior to admission, orevolving TBI pathology, we used both the admission GCS andscan GCS to determine the severity of injury. This allowedinclusion of patients whose GCS scores declined from the timeof admission to the MRI scan. Patients were considered to bein the severe TBI category if they had a low admission GCS orscan GCS score (GCS £ 8). For comparison purposes, a higher-GCS TBI reference group was also included, whose GCSscores remained high (GCS ‡ 13) throughout hospitalization,with negative CT and MRI findings.

Of the 126 patients screened, 30 patients who had severemotion artifacts or image distortion on diffusion-weightedimages were not included in this study. Three additional pa-tients were excluded due to the presence of stroke, as it wasunclear whether the stroke preceded the trauma or was aresult of the trauma. An additional 34 patients were not in-cluded because they did not meet the minimum age criteria,or because their GCS score was in the moderate range of 9–12,or because they met the mild-TBI reference group criteria, buthad positive CT or MRI scans. The total number of patientswho ultimately were included in this study was 59, with 41patients belonging to the severe TBI group and 18 patientsbelonging to the mild TBI reference group.

The total patient population (n = 59; age 37.2 – 16.8 years,range 18–83 years; 43 male, 16 female) that was included inthe study consisted of a heterogeneous mixture of closed headinjuries, from mechanisms including motor vehicle collisions(n = 26), falls (n = 15), struck pedestrians (n = 7), other vehicu-lar accidents (bicycles, motorcycles, and all-terrain vehicles:n = 5), assaults (n = 3), or other mechanisms (n = 3: founddown, sports injury, or struck in the head by thrown objects).Patients were imaged 3.7 – 6.1 days post-admission (max 28days), with half of the sample imaged within 1 day of ad-mission, and 81% imaged within 5 days of admission.

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The outcome for the severe TBI group (n = 41, age 38.0 – 17.0years; 33 males) was determined by discharge status from thehospital. The four outcome categories for the severe TBI groupincluded death (n = 10, age 50.9 – 17.1 years, 7 male, admissionGCS score 4.3 – 2.6), or neurological status at the time of dis-charge to the rehabilitation center: severe discharge GCS score(n = 8, age 30.8 – 16 years, 7 male, admission GCS score5.1 – 3.0), moderate discharge GCS score (n = 17, age 36.5 – 15.3years, 14 male, admission GCS score 5.0 – 3.0), or mild dis-charge GCS score (n = 6, age 30.2 – 13.0 years, 5 male, admis-sion GCS score 8.0 – 4.6). All surviving severe TBI patientswere discharged to rehabilitation between 4 and 50 days post-admission, with a median length of hospitalization of 20 days.

Imaging findings varied among study patients, and includedcontusions (n = 25), subarachnoid hemorrhages (n = 13), sub-dural hematomas (n = 23), diffuse axonal injury (n = 18), diffusecerebral edema (n = 14), herniation (n = 8), and midline shifts(n = 7). Some patients underwent ventriculostomy (n = 17) andcraniectomy (n = 12) prior to MRI.

The mild TBI reference group consisted of 18 individuals(age 35.3 – 16.6 years, 10 males) who were discharged fromthe hospital to home within 1.7 – 2.8 days. Example images ofthe patients belonging to each of the five outcome groups areshown in Figure 1.

Magnetic resonance imaging

All imaging was performed on a 1.5-T Avanto scanner (Sie-mens Medical Solutions, Erlangen, Germany) with parallelimaging capability. Conventional MR imaging included axialT2 using turbo spin echo (TEeff/TR/ETL = 113/5900 msec/15,

5-mm slices with 1-mm inter-slice gap, 0.6 · 0.4-mm in-planeresolution), fluid attenuated inversion recovery (FLAIR; TEeff/TI/TR/ETL = 102/2500/8000/13 msec, 5-mm slices with 1-mminter-slice gap, 1.2 · 0.9-mm in-plane resolution), volumetric T1(TE/TR = 4.76/11 msec with 20� flip angle, 1 · 1 · 2-mm voxels),and susceptibility weighted imaging (SWI; TE/TR = 40/50 msecwith 25� flip angle, 0.5 · 0.5 · 2-mm voxels).

DTI images were obtained using a double spin-echo echo-planar imaging technique over a 23-cm field of view (FOV), atan in-plane resolution of 1.79 · 1.79 mm, and a slice thicknessof 2 mm (3 averages; TE/TR of 95/11,200 msec, parallel im-aging acceleration factor of 2). A total of 68 axial images wereacquired to cover the brain from the apex to the skull base.Diffusion gradients were sensitized in 12 collinear directionsat an effective b-value of 1000 sec/mm2.

Image processing and analysis

The DTI images were exported offline and processed usingFDT (FMRIB Diffusion Toolbox; FMRIB, Oxford, U.K.). Imageswere first corrected for eddy current-induced image distortion,following which the brain parenchyma was extracted using theBrain Extraction Tool (BET) available within the FSL (FMRIBSoftware Library, Oxford, U.K.), and the diffusion tensor wasestimated for each voxel (Smith, 2002; Smith et al., 2004).

The FA maps of all patients were aligned to the ICBMtemplate derived using data from 152 subjects, and segmentedinto gray matter, white matter, and cerebrospinal fluid (CSF)maps using SPM5 (Wellcome Department of Imaging Sciences;University College London, London, U.K.). Segmented imageswere visually inspected to confirm the accuracy of white matter

FIG. 1. Example conventional images (T1-MPRAGE/FLAIR/SWI) for each outcome group. (a) Images of a severe TBIpatient imaged within a day of admission who presented with multiple hemorrhagic contusions resulting in death thefollowing day. (b) Images of a patient with extensive contusions, subdural hematoma, and edema following a pole-vaultingaccident was discharged with a GCS score of 7 after 43 days in the hospital. (c) Images of a patient involved in a motor vehiclecollision with multiple hemorrhagic contusions and evidence of hemorrhage in the gray matter consistent with diffuse axonalinjury discharged with a GCS score of 11 after spending 19 days in the hospital. (d) Images of a patient struck by a car withleft epidural and right subdural hematomas and indications of diffuse axonal shear injury in the left external capsule, whowas discharged from the hospital after 1 week with a GCS score of 14. (e) Images of a patient belonging to the referencegroup, who had an altered level of consciousness after being involved in motor vehicle accident who was admitted with aGCS score of 15, and who was discharged from the hospital within 2 days (FLAIR, fluid attenuated inversion recovery; SWI,susceptibility weighted imaging; MPRAGE, magnetization-prepared rapid acquisition with gradient echo; GCS, GlasgowComa Scale; TBI, traumatic brain injury).

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segmentation results. The segmented white-matter imageswere used to obtain information on whole-brain white matterADC, FA, kk, and kt values. Regions of interest with varyinggeometry to best fit the regions as shown in Figure 2 weredrawn on the genu, splenium, and body of the corpus callo-sum, and the internal capsule. Summary statistics such as themean, standard deviation (SD), and coefficient of variation(CV) for each of the above parameters were calculated usingMATLAB (Mathworks, Natick, MA). These summary mea-sures were used in statistical models for predicting a patient’soutcome category as described below.

Statistical analysis

Nonparametric correlation coefficients (Spearman’s partialrho) adjusted for age and gender were used to examine therelationship in all patients between DTI parameters and thescan GCS and the discharge GCS. Further, the relationshipbetween each outcome category and DTI parameters on aglobal and regional level in the white matter in all patientswas also examined.

Prognostic models of severe TBI patient outcomes werecreated using ordinal logistic regression models. These mod-els were adjusted for age, gender, time to scan, and admissionGCS score to determine if DTI parameters significantly im-proved prediction of patient outcome status among severeTBI patients. Improvement in model fit was judged by com-paring the differences in model deviance to the critical valuesof the chi-square distribution. Regression models were chosenby best subset selection based on the score criterion, selectingfrom all whole-brain DTI measures. Statistical analysis wasconducted with SAS 9.2 for Windows XP (SAS Corporation,Cary, NC), and plots were produced using R (R Foundationfor Statistical Computing, Vienna, Austria). Correlations werecorrected for multiple comparisons using the FDR method ofBenjamini and Hochberg (1995). Statistical significance for allhypothesis testing procedures was set at p < 0.05.

Results

There was no difference in the median age between severeTBI patients and the mild TBI reference group. However,

there were significant differences between the median ages ofthe different outcome groups ( p = 0.016). Bonferroni-adjustedpost-hoc tests indicate that the median ages significantly dif-fered between severe TBI patients with mild discharge GCSscores (age 29.4 – 12.2 years), and severe TBI patients whodied (age 50.9 – 17.1 years). No other groups differed signifi-cantly in median age.

Regional and global DTI correlation with GCS scores

Table 1 lists the partial correlations, corrected for age andgender, between the DTI and scan GCS scores among all thepatients included in this study. The average ADC (r = 0.46;p < 0.0003), kk (r = 0.61; p < 0.0001), and kt (r = 0.33; p = 0.013),for the whole-brain white matter demonstrated a strongpositive relationship with GCS, indicating that global whitematter decrements in DTI parameters are associated withpoor clinical presentations. The CV of the above parameters,including those of FA, also demonstrated a significant nega-tive correlation with GCS, suggesting that variability in theDTI values increased with the severity of injury. Similar pat-terns of decrements in average DTI parameters and increasesin CV were observed in regional measures. The body of thecorpus callosum showed strong positive correlations of av-erage ADC (r = 0.36; p = 0.007), and kk (r = 0.63; p < 0.0001), andnegative correlations of the CV of kk (r = - 0.31; p = 0.04) withGCS. The genu (r = 0.56; p < 0.0001), splenium of the corpuscallosum (r = 0.35; p = 0.0075), and the internal capsule (r = 0.40;p = 0.0022), all demonstrated a strong correlation between kkand the clinical status of the patient. The CV of kk in the sple-nium (r = - 0.26; p = 0.049), and the CV of ADC in the internalcapsule (r = - 0.51; p < 0.0001) also exhibited significant nega-tive relationships with clinical status. The average FA (r = 0.40;p = 0.0019), and the CV of FA (r = - 0.40; p = 0.0072), demon-strated a strong correlation with the GCS only in the body ofthe corpus callosum, but not in other regions, or the whole-brain level.

DTI parameters and patient outcomes

Table 2 lists the average values of the various DTI parametersfor each of the four patient outcome groups and the mild TBI

FIG. 2. Example of typical regions of interest (ROIs) in the internal capsule (blue), genu (green), splenium (red), and body ofthe corpus callosum (orange), the areas where the diffusion tensor imaging parameters were obtained. Color image isavailable online at www.liebertonline.com/neu

PROGNOSTIC VALUE OF DTI IN SEVERE TBI 1295

reference group for the whole-brain white matter, internalcapsule, genu, splenium, and the body of the corpus callosum.At the whole-brain white matter level, reduced average ADCand kkwere associated with poor patient outcomes at discharge,while FA and kt were not significantly associated with patientoutcomes. Regional measurements at the splenium, the body ofthe corpus callosum, the genu, and the internal capsule ex-hibited similar relationships with patient outcomes, with theexception that the association of FA with patient outcomes wasmuch stronger on a regional basis. The variability of the DTIvalues within the regions of interest (ROI) were also associatedwith patient status, with greater heterogeneity in the whitematter being associated with poor patient outcomes.

The partial correlation coefficients, corrected for age andgender, between patient outcomes and DTI measures at thewhole-brain white matter and at the regional level for allpatients are listed in Table 3. Favorable patient outcomes wereassociated with higher mean ADC (r = 0.33, p = 0.011) in thewhole-brain white matter, as shown in Figure 3. An evenstronger relationship was observed between favorable patientoutcomes and higher values of kk (r = 0.58, p < 0.0001), sug-gesting that the association between water diffusion changesand patient outcomes is primarily driven by changes in axialdiffusivity. Greater heterogeneity in the DTI values, as mea-sured by the CV of ADC (r = - 0.62, p < 0.0001), and kk (r = - 0.56,p < 0.0001), were strongly associated with poor patient out-comes. This association between the heterogeneity of the DTIvalues was also observed in the CV of FA (r = - 0.31, p = 0.01),and kt (r = - 0.53, p < 0.0001), although their average values(r = 0.16, p = 0.23 and r = 0.17, p = 0.22, respectively) did notexhibit a strong relationship with patient outcome.

In the genu of the corpus callosum, lower averages of ADC(r = 0.35, p = 0.0068), and kk (r = 0.59, p < 0.0001), were signifi-cantly related to poor patient outcomes (Fig. 4). In the sple-nium of the corpus callosum, lower averages of FA (r = 0.34,p = 0.01), and kk (r = 0.38, p = 0.0036), were significantly relatedto poor patient outcomes (Fig. 5). Additionally, higher CVs ofFA (r = - 0.32, p = 0.014) were significantly related to poorpatient outcomes. In the body of the corpus callosum, loweraverages of FA (r = 0.47, p = 0.0002), ADC (r = 0.32, p = 0.016),and kk (r = 0.62, p < 0.0001) were significantly related to poor

patient outcomes (Fig. 6). The corpus callosum also exhibitedhigher CV of ADC (r = - 0.30, p = 0.024), FA (r = - 0.52,p < 0.0001), and kk (r = - 0.38, p = 0.0037), that were signifi-cantly related to poor patient outcomes.

In the internal capsule, lower average kk (r = 0.41,p = 0.0017), and higher CV of ADC (r = - 0.52, p < 0.0001), andkt (r = - 0.28, p = 0.036), were significantly related to poorpatient outcomes.

Logistic models for patient outcomesusing DTI parameters

When predicting outcome utilizing logistic models using asingle whole-brain DTI summary measure, the CV for whole-brain white matter ADC or kk best predicted outcome. The useof any whole-brain ADC or axial diffusivity measure (averageor CV) significantly improved model prediction ( p < 0.05),when adjusted for admission GCS, age, gender, and time fromadmission to scan. Among the combinations of various DTImetrics, the addition of kt had the least effect on modelprediction, while kk and ADC had the strongest influence onmodel prediction. Due to the correlation between the variousDTI measures at both the whole-brain and regional level, littleadditional improvement in model fit was observed by addingmore than three DTI measures.

Discussion

Conventional CT and MR imaging provide valuable in-formation for surgical planning in TBI patients, but are notadequate for the characterization, quantification, and deter-mination of the extent of axonal injury (Haacke et al., 2010;Meythaler et al., 2001). The GCS is a rough neurologicalmeasure with known limitations in classifying the true extentof TBI, including reduced sensitivity in the lobar white matter,ceiling effects, and questions of reliability in the presence ofintoxication or medical interventions prior to admission(Gabbe et al., 2003; Marshall et al., 1991; Moppett, 2007;Saatman et al., 2008; Stochetti et al., 2004). In this study, DTIvalues obtained in the acute phase of injury were associatedwith gradations in neurological status as measured by theGCS. Severe TBI patients with good outcomes had DTI values

Table 1. Partial Correlations between DTI Parameters and Scan GCS Scores in All Patients

for Global and Regional White Matter Volumes, Adjusted for Age and Gender

FA Avg FA CV ADC Avg ADC CV kk Avg kk CV kt Avg kt CV

Whole-brain white matter - 0.04405 20.27834 0.46414 20.54078 0.60974 20.47389 0.32594 20.47239(0.7449) (0.0360) (0.0003)* (< 0.0001)* (< 0.0001)* (0.0002)* (0.0134)* (0.0002)*

Genu of corpus callosum 0.05106 - 0.04879 0.40719 - 0.20985 0.56463 - 0.24847 0.16639 - 0.09546(0.7060) (0.7185) (0.0017)* (0.1172) (< 0.0001)* (0.0624) (0.2161) (0.4800)

Body of corpus callosum 0.40274 20.40421 0.35628 - 0.21985 0.62918 20.36548 - 0.07087 - 0.00670(0.0019)* (0.0018)* (0.0065)* (0.1003) (< 0.0001)* (0.0052)* (0.6004) (0.9605)

Splenium of corpus callosum 0.14082 - 0.24637 0.15481 - 0.14014 0.35065 20.26246 - 0.02252 0.01377(0.2961) (0.0647) (0.2502) (0.2985) (0.0075)* (0.0486) (0.8679) (0.9190)

Internal capsule 0.23926 - 0.11755 0.10731 20.51986 0.40026 - 0.21643 - 0.14264 - 0.17135(0.0757) (0.3882) (0.4312) (< 0.0001)* (0.0022)* (0.1091) (0.2943) (0.2067)

The internal capsule could not be measured in one patient due to the severity of her injuries, which prevented placement of regions ofinterest. The p values are shown in parenthesis for each of the correlations. Significant correlations are shown in bold, and those that remainedsignificant after corrections for multiple comparisons are marked with an asterisk.

FA, fractional anisotropy; CV, coefficient of variation; ADC, apparent diffusion coefficient; Avg, average; DTI, diffusion tensor imaging;GCS, Glasgow Coma Scale.

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Table 2. Values of DTI Parameters for the Five Categories of Patients from the Whole-Brain

White Matter, Genu, Body of the Corpus Callosum, Splenium, and Internal Capsule

Whole-brain white matter

Outcome category ADC Avg, *10 - 3 mm2/sec FA Avg kk Avg, *10 - 3 mm2/sec kt Avg, *10 - 3 mm2/sec

Dead (n = 10) 0.68 – 0.15 0.41 – 0.05 0.97 – 0.19 0.53 – 0.13Severe (n = 8) 0.69 – 0.05 0.41 – 0.03 0.99 – 0.05 0.53 – 0.04Moderate (n = 17) 0.71 – 0.04 0.42 – 0.03 1.05 – 0.05 0.55 – 0.04Mild (n = 6) 0.75 – 0.03 0.39 – 0.02 1.08 – 0.03 0.59 – 0.03Reference (n = 18) 0.73 – 0.02 0.43 – 0.01 1.08 – 0.02 0.55 – 0.02

Genu of corpus callosum

Dead (n = 10) 0.67 – 0.18 0.66 – 0.11 1.23 – 0.27 0.39 – 0.16Severe (n = 8) 0.64 – 0.08 0.61 – 0.08 1.14 – 0.22 0.38 – 0.0Moderate (n = 17) 0.65 – 0.10 0.71 – 0.07 1.28 – 0.17 0.33 – 0.09Mild (n = 6) 0.75 – 0.06 0.65 – 0.08 1.39 – 0.13 0.43 – 0.08Reference (n = 18) 0.73 – 0.04 0.70 – 0.03 1.43 – 0.06 0.37 – 0.04

Body of corpus callosum

Dead (n = 10) 0.70 – 0.21 0.66 – 0.10 1.33 – 0.34 0.39 – 0.17Severe (n = 8) 0.66 – 0.12 0.63 – 0.10 1.20 – 0.21 0.39 – 0.11Moderate (n = 17) 0.76 – 0.08 0.67 – 0.05 1.45 – 0.14 0.41 – 0.07Mild (n = 6) 0.76 – 0.06 0.68 – 0.06 1.46 – 0.08 0.40 – 0.07Reference (n = 18) 0.76 – 0.03 0.74 – 0.03 1.56 – 0.04 0.35 – 0.04

Splenium of Corpus Callosum

Dead (n = 10) 0.67 – 0.25 0.69 – 0.16 1.29 – 0.38 0.37 – 0.23Severe (n = 8) 0.73 – 0.13 0.64 – 0.09 1.34 – 0.21 0.42 – 0.12Moderate (n = 17) 0.68 – 0.11 0.75 – 0.09 1.41 – 0.15 0.31 – 0.12Mild (n = 6) 0.78 – 0.07 0.69 – 0.06 1.50 – 0.09 0.41 – 0.08Reference (n = 18) 0.73 – 0.05 0.75 – 0.03 1.52 – 0.10 0.34 – 0.04

Internal capsule

Dead (n = 10) 0.65 – 0.12 0.57 – 0.11 1.11 – 0.13 0.42 – 0.13Severe (n = 8) 0.62 – 0.09 0.56 – 0.06 1.05 – 0.16 0.41 – 0.07Moderate (n = 17) 0.70 – 0.06 0.55 – 0.07 1.17 – 0.10 0.46 – 0.07Mild (n = 6) 0.69 – 0.04 0.57 – 0.04 1.18 – 0.06 0.45 – 0.04Reference (n = 18) 0.68 – 0.03 0.60 – 0.05 1.21 – 0.06 0.42 – 0.04

Values shown are mean – standard deviation.FA, fractional anisotropy; ADC, apparent diffusion coefficient; Avg, average; DTI, diffusion tensor imaging.

Table 3. Partial Correlations between DTI Parameters and Outcome Category in All Patients

for Global and Regional White Matter Volumes, Adjusted for Age and Gender

FA Avg FA CV ADC Avg ADC CV kk Avg kk CV kt Avg kt CV

Whole-brain white matter 0.16147 20.31465 0.33422 20.62271 0.58041 20.55518 0.16513 20.53409(0.2301) (0.0171)* (0.0111)* (< 0.0001)* (< 0.0001)* (< 0.0001)* (0.2196) < 0.0001)*

Genu of corpus callosum 0.25548 - 0.23022 0.35460 - 0.14901 0.59130 - 0.24818 0.04010 0.07818(0.0551) (0.0849) (0.0068)* (0.2686) (< 0.0001)* (0.0627) (0.7671) (0.5632)

Body of corpus callosum 0.47258 20.52232 0.31663 20.29849 0.62184 20.37808 - 0.16093 - 0.03183(0.0002)* (< 0.0001)* (0.0164)* (0.0241) (< 0.0001)* (0.0037)* (0.2317) (0.8142)

Splenium of corpus callosum 0.33730 20.32488 0.08709 - 0.00242 0.37979 - 0.22236 - 0.16802 0.22118(0.0103) (0.0137) (0.5195) (0.9857) (0.0036)* (0.0964) (0.2116) (0.0982)

Internal capsule 0.12978 - 0.22727 0.13712 20.51692 0.40970 - 0.22923 - 0.04608 20.28069(0.3404) (0.0921) (0.3136) (< 0.0001)* (0.0017)* (0.0892) (0.7359) (0.0361)

The internal capsule could not be measured in one patient due to the severity of her injuries, which prevented placement of regions ofinterest. The p values are provided in parentheses for each of the correlations. Significant correlations are shown in bold, and correlations thatremained significant after corrections for multiple comparisons are marked with an asterisk.

FA, fractional anisotropy; CV, coefficient of variation; ADC, apparent diffusion coefficient; Avg, average; DTI, diffusion tensor imaging.

PROGNOSTIC VALUE OF DTI IN SEVERE TBI 1297

similar to those of mild TBI patients. In models of severe TBIpatient outcomes, DTI values provide prognostic informa-tion about a patient’s discharge status, independent of fac-tors that may make the GCS unreliable (Brain TraumaFoundation et al., 2000; Gill et al., 2004; Kornbluth and

Bhardwaj, 2010). The relationship between DTI and severeTBI outcomes persists even after adjusting for age and ad-mission GCS, two of the strongest prognostic indicators ofmortality and functional outcome (Lingsma et al., 2010, vander Naalt et al., 1999).

FIG. 3. Diffusion tensor imaging (DTI) parameters, including average mean diffusity (MD), fractional anisotropy (FA), axialdiffusivity (kk), and radial diffusivity (kt), and their coefficient of variation (CV) for the whole-brain white matter for thedifferent outcome groups (ADC, apparent diffusion coefficient).

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FIG. 4. Diffusion tensor imaging (DTI) parameters, including average mean diffusity (MD), fractional anisotropy (FA), axialdiffusivity (kk), and radial diffusivity (kt), and their coefficient of variation (CV) for the genu of the corpus callosum (CC) forthe different outcome groups (ADC, apparent diffusion coefficient).

PROGNOSTIC VALUE OF DTI IN SEVERE TBI 1299

FIG. 5. Diffusion tensor imaging (DTI) parameters, including average mean diffusity (MD), fractional anisotropy (FA), axialdiffusivity (kk), and radial diffusivity (kt), and their coefficient of variation (CV) for the splenium of the corpus callosum (CC)for the different outcome groups (ADC, apparent diffusion coefficient).

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FIG. 6. Diffusion tensor imaging (DTI) parameters, including average mean diffusity (MD), fractional anisotropy (FA), axialdiffusivity (kk), and radial diffusivity (kt), and their coefficient of variation (CV) for the body of the corpus callosum (CC) forthe different outcome groups (ADC, apparent diffusion coefficient).

PROGNOSTIC VALUE OF DTI IN SEVERE TBI 1301

Poor clinical outcomes were associated with acute globalreductions in the averages of both kk and ADC. While theassociation between global white matter FA averages andclinical outcomes did not reach statistical significance, the CVdid exhibit a strong association with clinical outcomes. TheCV appears to be a powerful and sensitive summary measureof DTI alterations in TBI. Investigation of ADC as a traumabiomarker have been mixed, with trauma being associatedwith increased ADC (Bendlin et al., 2008; Kumar et al., 2009;Lipton et al., 2008; Salmond et al., 2006; Shanmugnathan et al.,2004), decreased ADC (Huisman et al., 2004), or no change inADC relative to controls (Arfanakis et al., 2002). This hetero-geneity may be due to differences in the anatomical regionssampled, quantitative methodological differences, or tempo-ral trends in ADC following TBI, as seen in various animalstudies (MacDonald et al., 2007a,2007b).

Similar investigations into whole-brain DTI have alsofound relationships between DTI parameters and trauma.Benson and colleagues (2007) have reported associations be-tween the DTI metrics and neurological status and post-traumatic amnesia. In particular, they found higher-ordermoments of the FA distribution to be better indicators of in-jury than the whole-brain FA mean.

Several investigators have reported regional ADC in dif-ferent white matter regions following trauma, with mixedresults. Some investigators have found increased ADC in TBIpatients relative to controls (Bendlin et al., 2008; Kumar et al.,2009; Lipton et al., 2008; Salmond et al., 2006; Shanmugnathanet al., 2004), while others have found either decreased ADC inpatients (Huisman et al., 2004), or no relationship to injurystatus (Arfanakis et al., 2002). Animal studies may shed somelight on these mixed results, for which the relationship be-tween ADC and trauma has been shown to be highly time-dependent, with decreased diffusivity in the acute phase, andincreased diffusivity in the chronic phase (MacDonald et al.,2007a). Significant differences in data analysis methodolo-gies across the literature may also account for some of themixed results, with some investigators using voxel-basedmorphometry (Lipton et al., 2008; Salmond et al., 2006),others sampling normal-appearing white matter (Arfanakiset al., 2002), and still others measuring pre-specified regionsregardless of appearance on conventional MRI (Bendlinet al., 2008; Kumar et al., 2009).

In our study, the association between the average FA of thewhole-brain white matter and discharge neurological statusdid not reach statistical significance. However, regional FAmeasures in the corpus callosum demonstrated greater sen-sitivity. In the corpus callosum, average axial diffusivity andaverage FA were significantly related to clinical outcomes,with worse outcomes associated with increasing decrementsin these parameters. The relationship between decreased FAand trauma severity in several regions of the brain includingthe corpus callosum has been reiterated by several other in-vestigations of TBI, regardless of injury severity and the timesince injury (Bendlin et al., 2008; Greenberg et al., 2008;Huisman et al., 2004; Kumar et al., 2009; Rutgers et al., 2008a;Sidaros et al., 2008; Sugiyama et al., 2009; Tollard et al., 2009),suggesting that changes in the fractional anisotropy withinthe corpus callosum are both a sensitive and stable marker ofinjury in TBI. Some investigators found relationships betweenchanges in FA and outcome status in severe TBI patients(Bendlin et al., 2008; Sidaros et al., 2008; Tollard et al., 2009),

while at least one study found no association between FAchanges and either the post-resuscitation GCS or the GOS(Newcombe et al., 2007). Controlled cortical impact (CCI)animal models of TBI help elucidate the pathological mecha-nisms underlying DTI changes. Mac Donald and colleagues(2007a) observed acute and subacute (4 h to 4 days post-injury) reductions in ADC, FA, and axial diffusivity, in thecortex ipsilateral and contralateral to the lesion, which wereassociated with primary axonal injury. Subacute (1 week to 1month post-injury) increases in ADC and axial diffusivitywere associated with edema, demyelination, and gliosis. Re-duced ADC and axial diffusivity at the acute stage followingCCI were also reported by Xu and associates (2011), as well asby Zhuo and colleagues (2012). However, they also observedsignificant changes in axial diffusivity at the very acute stage,and a normalization of DTI changes toward subacute stages.In yet another CCI study, by Mac Donald and co-workers(2007b), reduced relative anisotropy and axial diffusivitywere seen within 4–6 h of injury, while T2 relaxation time,ADC, and radial diffusivity were not significantly altered.Taken together, these studies support the notion that changesin FA and ADC, which are primarily driven by changes inaxial diffusivity, may be most indicative of the extent of ax-onal injury during the initial hours following injury. Since themajority of our study group was imaged within 5 days ofinjury (acute to subacute stage), it is likely that our findingsof reduced axial diffusivity are reflective of a combination ofprimary axonal injury and the early effects of secondary injury(Mac Donald et al., 2007a), and that the extent of this axonalinjury could be predictive of patient outcome.

Few other studies have focused on the utility of DTI mea-sures in evaluating severe TBI. In concordance with our study,Newcombe and associates (2007) found decreased FA in thewhite matter among 33 severely injured TBI patients who re-quired mechanical ventilation. In contrast to our study, how-ever, they found increased ADC relative to control subjects,and the changes in FA and ADC were driven by changes inradial diffusivity, not axial diffusivity as in the present study.They also found no association with either the post-resuscita-tion GCS or GOS and the DTI parameters. Bendlin and col-leagues (2008) observed a progressive decrease in FA, anincrease in mean diffusivity, and a reduction in brain volume,over 1 year, although significant improvements were observedon neuropsychological testing. However, this study only ad-dressed the longitudinal DTI changes seen in gray and whitematter, and did not address patient outcomes. Perlbarg andcolleagues (2009) investigated 30 TBI patients with varyinginjury severity and observed lower average FA in the posteriorlimb of the internal capsule and posterior corpus callosumamong the patients with poor GOS scores 1 year after injury,and found that DTI parameters offered prognostic value aboveand beyond clinical measures. Sidaros and co-workers (2008)observed a reduced axial diffusivity and increased radial dif-fusivity among 30 severe TBI patients in many regions, in-cluding the corpus callosum and the internal capsule, whichappeared to normalize by 12 months among patients withgood GOS scores at 1 year after injury. Tollard and associates(2009) observed significantly reduced FA in several regionsamong 43 severe TBI patients with poor outcomes, comparedto those who had favorable outcomes at 1 year based on theGOSE scale, although the FA was still reduced among the fa-vorable outcome group compared to normal control subjects.

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While most prior research in TBI compares TBI patients tohealthy volunteers, the present study investigated the asso-ciation between DTI values in the acute phase of injury andthe natural history of severe TBI. A group of mild TBI patientswith negative CT and MRI scans were included to providecontext for the DTI values of severe TBI patients, and thesepatients were not used in any models of patient outcomes.This should be taken into consideration when comparing ourresults with prior research. Other differences between thepresent study and prior research include time of the MRI scanfrom the time of injury, and the method of outcome classifi-cation. Despite these differences, there appears to be a con-sensus among these studies and ours, that changes in thecorpus callosum may be predictive of outcomes from severeTBI (Newcombe et al., 2007; Perlbarg et al., 2009; Rutgerset al., 2008b).

The development of accurate diagnostic and prognostic mark-ers in TBI is an important step in developing effective treatmentand rehabilitation strategies (Haacke et al., 2010; Moppett, 2007;Saatman et al., 2008). Diffusion tensor MRI appears to provideseveral biomarkers that relate to the physiological conditions ofthe white matter, which are correlated with existing clinicalmeasures, yet provide more information about a patient’s dis-charge status. Furthermore, these markers are often obtainedwithin a week of injury, and their measurement is not affected byintoxication, swelling around the eyes, medical interventionsprior to admission, or other factors that may limit the usefulnessof the GCS score. DTI also has been shown to detect abnormalitiesthat are not visualized or appreciated on conventional imaging.Neurological status was related to decreases in the mean DTImeasures, in particular the axial diffusivity and the average ADCin the whole-brain white matter, and in the body of the corpuscallosum in the subacute stage of the injury. These changes wereaccompanied by significant increases in their CV with injury se-verity, and were strongly associated with patient outcomes, evenafter adjusting for other prognostic factors, including age andadmission GCS score. The quantitative protocol used in this studycould easily be translated to the clinical setting, and the regionalmeasures could help extend the usefulness of DTI to patientswhose injuries prohibit the use of whole-brain segmentation al-gorithms to separate gray and white matter. The usefulness ofthese DTI parameters as prognostic markers needs to be furtherextended to longitudinal studies relating DTI findings to long-term biological, psychological, and social outcomes.

Since our patients were retrospectively evaluated in theacute setting at discharge to TBI rehabilitation, rehabilitationoutcome measures such as the GOS were not available (Liptonet al., 2008). While the GCS score on admission to rehabilita-tion is associated with functional recovery (Avesani et al.,2011), further research is necessary to associate acute DTI withlong-term functional outcomes. Without established scoringcriteria for MRI, analogous to the Marshall classification forCT (Marshall et al., 1991), it is difficult to compare the prog-nostic value of DTI to conventional imaging. While our studyinvolved only patients with head injuries, future studiesshould involve healthy volunteers to determine an absolutescale between normal human variability and the range of DTIvalues seen across the spectrum of TBI.

Our study investigated severe TBI patients in the acute set-ting, who are under-represented in the literature. Patients wereevaluated with DTI early in the course of injury using a simplewhole-brain and ROI methodology that could readily be

translated for clinical use. Prognostic models showed that axialdiffusivity and FA provided prognostic information about pa-tient outcomes in severe TBI, which likely reflects the degree ofunderlying axonal injury. The CV of these DTI measures is apowerful summary measure, capturing both decreases in meanvalues and increases in variance within ROIs.

Our study included patients in the age range of 18–83 years.DTI parameters are known to be affected by age. The results ofthis study demonstrate that the DTI parameters provide sig-nificant prognostic value, even after adjusting for age. Fur-ther, a re-analysis of the data (not shown) from patientssampled from a more stringent subset of patients in the agerange of 18–65 years, who were scanned within 5 days post-injury, essentially provided the same results. Once again theseobservations suggest that DTI parameters could play a sig-nificant role in the evaluation of the patient at the acute stage,while providing valuable prognostic information.

The results of this study have to be taken in the context of itslimitations. Due to the retrospective nature of the study, wewere limited to data that were collected with only 12 diffu-sion-encoding gradient directions per the existing clinicalprotocol. It is well known that variability in DTI parameters isreduced if larger numbers of diffusion directions are used(Papadakis et al., 2000). While we do not think this had a largeeffect on the outcome of our results, future studies should usemore diffusion directions to minimize any variability in DTIparameter estimation.

DTI may prove to be a valuable complement to conventionalimaging, and may help us better appreciate and quantify pa-thology. Unfortunately, no widely-validated MRI scoring sys-tem for TBI severity currently exists. Without a healthy controlpopulation, it is difficult to compare DTI findings near lesionsvisualized on conventional imaging, as occult pathology may bepresent in the homologous region contralateral to the lesion.This study only included patients during their acute hospitali-zation for TBI. The GOSE is typically used for measuring clinicaloutcomes in TBI. It measures a patient’s independence in andoutside the home, as well as participation in social and leisureactivities, which only apply to outpatient or rehabilitation set-tings (Wilson et al., 1998). Since not all patients attended affili-ated rehabilitation facilities, these data could not be collected forall individuals. In spite of this fact, neurological status on ad-mission to acute TBI rehabilitation, as represented by the dis-charge GCS score, is a strong prognostic marker of functionalrecovery (Avesani et al., 2011). Future research is necessary tosee if these results generalize to outcomes in the subacute andchronic phases of TBI, which are measured by the GOSE andother measures of cognitive, social, and occupational recovery.

However, the above limitations should also be viewed in thecontext of several key aspects of the study. First, while otherstudies have investigated the possibility of using DTI to quan-tify brain injury after trauma, many of these studies are limitedby long and highly variable intervals between injury and im-aging, sometimes spanning the acute, subacute, and chronicphases of injury. The vast majority of our sample was imagedwithin 5 days of injury, measuring the acute effects of severeTBI. Second, while mild TBI has been relatively well studied, wefocused on severe TBI patients, which are underrepresented inthe DTI literature. Third, most other studies examine FA orADC, and do not consider radial or axial diffusivity, the latter ofwhich appears to be a promising marker of injury. Fourth, whileother studies have looked at averages of DTI parameters, we

PROGNOSTIC VALUE OF DTI IN SEVERE TBI 1303

found that the variability and CV may be even stronger indi-cators of injury severity. Finally, our study looked at both re-gional and global changes, the combination of which may betterdepict the diffuse and heterogeneous nature of TBI.

Conclusion

Our study demonstrated that DTI parameters at the whole-brain level and regional level can provide prognostic infor-mation about the discharge status of a patient, while cir-cumventing many problems associated with currently usedclinical measures, including the GCS. The relationship be-tween DTI and discharge neurological status remained sig-nificant, even after adjusting for two of the strongestprognostic factors in TBI. Axial diffusivity appears to providethe most prognostic information about outcome status, onboth regional and global scales. The CV captures informationabout both the mean and the variability in the data, making ita parsimonious summary measure for DTI values. Whilethese results are promising, prospective longitudinal studiesare necessary to validate these findings.

Acknowledgment

The authors thank Brigitte Pocta for reviewing the manu-script. This work was partly supported by a grant from theU.S. Army (W81XWH-08-1-0725).

Author Disclosure Statement

No competing financial interests exist.

References

Arfanakis, K., Haughton, V.M., Carew, J.D., Rogers, B.P., Demp-sey, R.J., and Meyerand, M.E. (2002). Diffusion tensor MR im-aging in diffuse axonal injury. Am. J. Neuroradiol. 23, 794–802.

Avesani, R., Fedeli, M., Ferraro, C., and Khansefid, M. (2011).Use of early indicators in rehabilitation process to predictfunctional outcomes in subjects with acquired brain injury.Eur. J. Phys. Rehabil. Med. 471–410.

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

Benjamini., Y., and Hochberg, Y. (1995). Controlling the falsediscovery rate: a practical and powerful approach to multipletesting. J. R. Statistical Soc. Series B, 57, 289–300.

Benson, R.R., Meda, S.A., Vasudevan, S., Kou, Z., Govindarajan,K.A., Hanks, R.A., Millis, S.R., Makki, M., Latif, Z., Coplin, W.,Meythaler, J., and Haacke, E.M. (2007). Global white matteranalysis of diffusion tensor images is predictive of injury se-verity in traumatic brain injury. J. Neurotrauma 24, 446–459.

Brain Trauma Foundation. The American Association of Neu-rological Surgeons. The Joint Section on Neurotrauma andCritical Care. (2000). Glasgow Coma Scale Score. J. Neuro-trauma 17, 563–571.

Coronado, V.G., Xu, L., Basavaraju, S.V., McGuire, L.C., Wald,M.M., Faul, M.D., Guzman, B.R., and Hemphill, J.D. (2011).Centers for Disease Control and Prevention (CDC). Surveil-lance for Traumatic Brain Injury-related Deaths—UnitedStates, 1997–2007. MMWR Surveill. Summ. 60, 1–32.

Faul, M., Xu, L., Wald, M.M., and Coronado, V.G. (2010).Traumatic Brain Injury in the United States: Emergency De-

partment Visits, Hospitalizations and Deaths 2002–2006.Centers for Disease Control and Prevention, National Centerfor Injury Prevention and Control: Atlanta.

Finkelstein, E.A., and Corso, P.S.M.T. (2006). The Incidence andEconomic Burden of Injuries in the United States. Oxford Uni-versity Press: Oxford.

Gabbe, B.J., Cameron, P.A., and Finch, C.F. (2003). The status ofthe Glasgow Coma Scale. Emerg. Med. 15, 353–360.

Gill, M.R., Reiley, D.G., and Green, S.M. (2004). Interrater reli-ability of Glasgow Coma Scale Scores in the emergency de-partment. Ann. Emerg. Med. 43, 215–223.

Greenberg, G., Mikulis, D.J., Ng, K., DeSouza, D., and Green,R.E. (2008). Use of diffusion tensor imaging to examine sub-acute white matter injury progression in moderate to severetraumatic brain injury. Arch. Phys. Med. Rehabil. 89(12Suppl.), S45–S50.

Haacke, E.M., Duhaime, A.C., Gean, A.D., Riedy, G., Wintermark,M., Mukherjee, P., Brody, D.L., DeGraba, T., Duncan, T.D., Elo-vic, E., Hurley, R., Latour, L., Smirniotopoulos, J.G., and Smith,D.H. (2010). Common data elements in radiologic imaging oftraumatic brain injury. J. Magn. Reson. Imaging 32, 516–543.

Huisman, T.A.G.M., Schwamm, L.H., Schaefer, P.W., Koroshetz, W.J.,Shetty-Alva, N., Ozsunar, Y., Wu, O., and Sorensen, A.G. (2004).Diffusion tensor imaging as potential biomarker of white matterinjury in diffuse axonal injury. Am. J. Neuroradiol. 25, 370–376.

Inglese, M., Makani, S., Johnson, G., Cohen, B.A., Silver, J.A.,Gonen O., and Grossman, R.I. (2005). Diffuse axonal injury inmild traumatic brain injury: A diffusion tensor imaging study.J. Neurosurg. 103, 298–303.

Kennedy, M.R.T., Wozniak, J.R., Muetzel, R.L., Mueller, B.A.,Chiou, H.-H., Pantekoek, K., and Lim., K.O. (2009). Whitematter and neurocognitive changes in adults with chronictraumatic brain injury. J. Int. Neuropsychol. Soc. 15, 130–136.

Kornbluth, J., and Bhardwaj, A. (2010). Evaluation of coma: Acritical appraisal of popular scoring systems. Neurocrit. Care.134–143.

Kumar, R., Gupta, R.K., Husain, M., Chaudhry, C., Srivastava,A., Saksena, S., and Rathnore, R.K. (2009). Comparativeevaluation of corpus callosum DTI metrics in acute mild andmoderate traumatic brain injury: Its correlation with neu-ropsychometric tests. Brain Inj. 23, 675–685.

Kurca, E., Sivak, S., and Kucera, P. (2006). Impaired cognitivefunctions in mild traumatic brain injury patients with normaland pathologic magnetic resonance imaging. Neuroradiology48, 661–669.

Langlois, J.A., Rutland-Brown, W., and Thomas, K.E. (2004).Traumatic Brain Injury in the United States: Emergency De-partment Visits, Hospitalizations, and Deaths. Centers forDisease Control and Prevention, National Center for InjuryPrevention and Control: Atlanta.

Lingsma H.F., Roozenbeek B., Steyerberg E.W., Murray G.D.,Maas A.I. (2010). Early prognosis in traumatic brain injury:from prophecies to predictions. Lancet Neurol. 9, 543–554.

Lipton, M.L., Gellella, E., Lo, C., Gold, T., Ardekani, B.A., Shif-teh, K., Bello, J.A., and Branch, C.A. (2008). Multifocal whitematter ultrastructural abnormalities in mild traumatic braininjury with cognitive disability: a Voxel-wise analysis of dif-fusion tensor imaging. J. Neurotrauma 25, 1335–1342.

Mac Donald, C.L., Dikranian, K., Bayly, P., Holtzman, D., andBrody, D. (2007a). Diffusion tensor imaging reliably detectsexperimental traumatic axonal injury and indicates approxi-mate time of injury. J. Neurosci. 27, 11869–11876.

Mac Donald, C.L., Dikranian, K., Song, S.K., Bayly, P.V.,Holtzman, D.M., and Brody, D.L. (2007b). Detection of trau-

1304 BETZ ET AL.

matic axonal injury with diffusion tensor imaging in a mousemodel of traumatic brain injury. Exp. Neurol. 205, 116–131.

Marshall, L.F., Mashall, S.B., Klauber, M.R., and Clark, M.V.(1991). A new classification of head injury based on compu-terised tomography. J. Neurosurg. (Suppl.)75, S14–S20.

Meythaler, J.M., Peduzzi, J.D., Eleftheriou, E., and Novack, T.A.(2001). Current concepts: diffuse axonal injury-associated trau-matic brain injury. Arch. Phys. Med. Rehabil. 82, 1461–1471.

Moppett, I.K.(2007). Traumatic brain injury: Assessment, resus-citation and early management. Br. J. Anaesth. 99, 18–31.

Murray, G.D., Teasdale, G.M., Braakman, R ., Cohadon, F., Dear-den, M., Iannotti, F., Karimi, A., Lapierre, F., Maas, A., Ohman,J., Persson, L., Servadei, F., Stocchetti, N., Trojanowski, T., andUnterberg, A. (1999). The European Brain Injury ConsortiumSurvey of Head Injuries. Acta Neurochir. (Wien.) 141, 223–236.

Nakayama, N., Okumura, A., Shinoda, J., Yasokawa, Y.-T., Miwa,K., Yoshimura, S.-I., and Iwama, T. (2006). Evidence for whitematter disruption in traumatic brain injury without macroscopiclesions. J. Neurol. Neurosurg. Psychiatry 77, 850–855.

National Institutes of Health Consensus Development Panel onRehabilitation of Persons with Traumatic Brain Injury. (1999)Rehabilitation of persons with traumatic brain injury. JAMA282, 974–983.

Newcombe, V.F.J., Williams, G.B., Nortje, J., Bradley, P.G.,Harding, S.G., Smielewski, P., Coles, J.P., Maiya, B., Gillard,J.H., Hutchinson, P.J., Pickard, J.D., Carpenter, T.A., and Me-non, D.K. (2007). Analysis of acute traumatic axonal injuryusing diffusion tensor imaging. Br. J. Neurosurg. 21, 340–348.

Papadakis, N.G., Murrills, C.D., Hall, L.D., Huang, C.L., andAdrian Carpenter, T. (2000). Minimal gradient encoding forrobust estimation of diffusion anisotropy. Magn. Reson. Ima-ging 18, 671–679.

Perlbarg, V., Puybasset, L., Tollard, E., Lehericy, S., Benali, H.,and Galanaud, D. (2009). Relation between brain lesion loca-tion and clinical outcome in patients with severe traumaticbrain injury: a diffusion tensor imaging study using voxel-based approaches. Hum. Brain Mapp. 30, 3924–3933.

Rutgers, D.R., Fillard, P., Paradot, G., Tadie, M., Lasjaunias, P.,and Ducreux, D. (2008a). Diffusion tensor imaging character-istics of the corpus callosum in mild, moderate, and severetraumatic brain injury. Am. J. Neuroradiol. 29, 1730–1735.

Rutgers, D.R., Toulgoat, F., Cazejust, J., Fillard, P., Lasjaunias, P.,and Ducreux, D. (2008b). White matter abnormalities in mildtraumatic brain injury: a diffusion tensor imaging study. Am.J. Neuroradiol. 29, 514–519.

Saatman, K.E., Duhaime, A.C., Bullock, R., Maas, A.I., Valadka,A., and Manley, G.T. (2008). Workshop Scientific Team andAdvisory Panel Members. Classification of Traumatic BrainInjury for Targeted Therapies. J. Neurotrauma 25, 719–738.

Salmond, C.H., Menon, D.K., Chatfield, D.A., Williams, G.B.,Pena, A., Sahakian, B.J., and Pickard, J.D. (2006). Diffusiontensor imaging in chronic head injury survivors: Correlationswith learning and memory indices. Neuroimage 29, 117–124.

Shanmuganathan, K., Gullapalli, R.P., Mirvis, S.E., Roys, S., andMurthy, P. (2004) Whole-brain apparent diffusion coefficientin traumatic brain injury: correlation with Glasgow ComaScale score. Am. J. Neuroradiol. 25, 539–544.

Sidaros, A., Engberg, A.W., Sidaros, K., Liptrot, M.G., Herning,M., Petersen, P., Paulson, O.B., Jernigan, T.L., and Rostrup, E.(2008). Diffusion tensor imaging during recovery from severetraumatic brain injury and relation to clinical outcome: alongitudinal study. Brain 131(Pt. 2), 559–572.

Smith, S.M. (2002). Fast robust automated brain extraction.Human Brain Mapping 17, 143–155.

Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F.,Behrens, T.E.J., Johansen-Berg, H., Bannister, P.R., De Luca,M., Drobnjak, I., Flitney, D.E., Niazy, R., Saunders, J., Vickers,J., Zhang, Y., De Stefano, N., Brady, J.M., and Matthews, P.M.(2004). Advances in functional and structural MR image ana-lysis and implementation as FSL. Neuroimage 23, 208–219.

Sosin, D.M., Sacks, J.J., and Smith, S.M. (1989). Head injury-associated deaths in the United States from 1979 to 1986.JAMA 262, 2251–2255.

Stochetti, N., Pagan, F., Calappi, E., Canavesi, K., Beretta, L.,Citerio, G., Cormio, M., and Colombo, A. (2004). Inaccurateearly assessment of neurological severity in head injury. J.Neurotrauma 21, 1131–1140.

Sugiyama, K., Kondo, T., Oouchida, Y., Suzukamo, Y., Higano,S., Endo, M., Watanabe, H., Shindo, K., and Izumi, S. (2009).Clinical utility of diffusion tensor imaging for evaluating pa-tients with diffuse axonal injury and cognitive disorders in thechronic stage. J. Neurotrauma 26, 1879–1890.

Teasdale, T.W., and Engberg, A.W. (2005). Subjective well-beingand quality of life following traumatic brain injury in adults: along-term population-based follow-up. Brain Inj. 19, 1041–1048.

Thornhill, S., Teasdale, G.M., Murray, G.D., McEwen, J., Roy,C.W., and Penny, K.I. (2000). Disability in young people andadults one year after head injury: Prospective cohort study.BMJ 320, 1631–1635.

Tollard, E., Galanaud, D., Perlbarg, V., Sanchez-Pena, P., Le Fur,Y., Abdennour, L., Cozzone, P., Lehericy, S., Chiras, J., andPuybasset L. (2009). Experience of diffusion tensor imaging and1H spectroscopy for outcome prediction in severe traumaticbrain injury: Preliminary results. Crit. Care Med. 37, 1448–1455.

Vaishnavi, S., Rao, V., and Fann, J.R. (2009). Neuropsychiatricproblems after traumatic brain injury: Unraveling the silentepidemic. Psychosomatics 50, 198–205.

van der Naalt, J., van Zomeren, A.H., Sluiter, W.J., and Mind-erhoud, J.M. (1999). One year outcome in mild to moderatehead injury: the predictive value of acute injury characteristicsrelated to complaints and return to work. J. Neurol. Neuro-surg. Psychiatry 66, 207–213.

Wilson, J.T., Pettigrew, L.E., and Teasdale, G.M. (1998). Struc-tured interviews for the Glasgow Outcome Scale and Ex-tended Glasgow Outcome Scale: Guidelines for their use. J.Neurotrauma 15, 573–585.

Xu, S., Zhuo, J., Racz, J., Shi, D., Roys, S., Fiskum, G., andGullapalli, R. (2011). Early microstructural and metabolicchanges following controlled cortical impact injury in rat: amagnetic resonance imaging and spectroscopy study. J. Neu-rotrauma 28, 2091–2102.

Zhuo, J., Xu, S., Proctor, J.L., Mullins, R.J., Simon, J.Z., Fiskum,G., and Gullapalli, R.P. (2012). Diffusion kurtosis as an in vivoimaging marker for reactive astrogliosis in traumatic braininjury. Neuroimage 59, 467–477.

Address correspondence to:Rao P. Gullapalli, Ph.D.

Department of Diagnostic Radiology and Nuclear MedicineUniversity of Maryland School of Medicine

22 South Greene StreetBaltimore, MD 21201

E-mail: rgullapalli@umm.edu

PROGNOSTIC VALUE OF DTI IN SEVERE TBI 1305

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