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Emotional Resilience Predicts Preserved White Matter Microstructure Following Mild Traumatic Brain Injury

Open AccessPublished:September 20, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.08.015

      Abstract

      Background

      Adult patients with mild traumatic brain injury (mTBI) exhibit distinct phenotypes of emotional and cognitive functioning identified by latent profile analysis of clinical neuropsychological assessments. When discerned early after injury, these latent clinical profiles have been found to improve prediction of long-term outcomes from mTBI. The present study hypothesized that white matter (WM) microstructure is better preserved in an emotionally resilient mTBI phenotype compared with a neuropsychiatrically distressed mTBI phenotype.

      Methods

      The present study used diffusion magnetic resonance imaging to investigate and compare WM microstructure in major association, projection, and commissural tracts between the two phenotypes and over time. Diffusion magnetic resonance images from 172 patients with mTBI were analyzed to compute individual diffusion tensor imaging maps at 2 weeks and 6 months after injury.

      Results

      By comparing the diffusion tensor imaging parameters between the two phenotypes at global, regional, and voxel levels, emotionally resilient patients were shown to have higher axial diffusivity compared with neuropsychiatrically distressed patients early after mTBI. Longitudinal analysis revealed greater compromise of WM microstructure in neuropsychiatrically distressed patients, with greater decrease of global axial diffusivity and more widespread decrease of regional axial diffusivity during the first 6 months after injury compared with emotionally resilient patients.

      Conclusions

      These results provide neuroimaging evidence of WM microstructural differences underpinning mTBI phenotypes identified from neuropsychological assessments and show differing longitudinal trajectories of these biological effects. These findings suggest that diffusion magnetic resonance imaging can provide short- and long-term imaging biomarkers of resilience.

      Key words

      Traumatic brain injury (TBI) affects tens of millions of people worldwide annually, with the vast majority classified as mild TBI (mTBI). Postinjury neuropsychiatric conditions include posttraumatic stress disorder, anxiety disorders, and major depressive disorder (MDD) (
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      ), patients exhibit wide variation in postinjury recovery that is unexplained by TBI severity. For example, any two patients who sustain an injury of comparable severity, for example, mTBI defined as Glasgow Coma Scale score 13 to 15, may or may not manifest neuropsychiatric difficulties after injury. They may experience different clinical symptom presentations, e.g., posttraumatic stress disorder versus MDD (
      • Stein M.B.
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      Exploring the convergence of posttraumatic stress disorder and mild traumatic brain injury.
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      ). It is therefore critical to better understand neuropsychiatric symptoms following mTBI by investigating changes that underlie risk for these postinjury disabilities.
      Using latent profile analysis, Brett et al. (
      • Brett B.L.
      • Kramer M.D.
      • Whyte J.
      • McCrea M.A.
      • Stein M.B.
      • Giacino J.T.
      • et al.
      Latent profile analysis of neuropsychiatric symptoms and cognitive function of adults 2 weeks after traumatic brain injury: Findings from the TRACK-TBI study.
      ) recently showed that 1757 participants with TBI (mild to severe) could be classified into clinically distinct phenotypes based on emotional and cognitive functioning at 2 weeks postinjury assessed using 12 different tests included in the National Institutes of Health–endorsed common data elements. Latent profile analysis is a mixture modeling method that assumes the presence of underlying, unmeasured phenotypes (subgroups of participants) that can be identified from distinct patterns of observed variables (here, symptom and cognitive performance measures). These acute TBI phenotypes strongly predicted 6-month postinjury outcomes across functional, clinical, and quality-of-life domains using standard tests different from those used to define the phenotypes. A 4-group solution included 2 distinct profiles that differentiated patients experiencing postinjury neuropsychiatric distress (ND) (n = 350) from patients exhibiting emotional resilience (ER) (n = 419). Another 2 profiles were characterized by cognitive difficulties (n = 368 patients) versus cognitive resilience (n = 620 patients). The ER group stood out as having the best prognosis for functional, clinical, and quality-of-life outcomes at 6 months following injury, while the ND group had the worst prognosis.
      Resilience is a salient area of interest in neuroscience, psychology, and sociology, encompassing the capacity to respond to adverse life and health experiences with adaptation, flexibility, and persistence (
      • Southwick S.M.
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      Bouncing back again, and again: A qualitative study of resilience in people with multiple sclerosis.
      ). Given the influence of emotional resilience on 6-month outcomes after TBI, it is critical to understand its biological mechanisms (
      • Brett B.L.
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      • Whyte J.
      • McCrea M.A.
      • Stein M.B.
      • Giacino J.T.
      • et al.
      Latent profile analysis of neuropsychiatric symptoms and cognitive function of adults 2 weeks after traumatic brain injury: Findings from the TRACK-TBI study.
      ). This represents the emerging viewpoint that devising better TBI rehabilitation requires understanding not just “what the injury brings to the brain” but also “what the brain brings to the injury.”
      Diffusion tensor imaging (DTI) has been widely applied to characterize white matter (WM) pathology in mTBI because of its sensitivity to diffuse axonal injury (DAI) (
      • Bazarian J.J.
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      • et al.
      Diffusion-tensor imaging implicates prefrontal axonal injury in executive function impairment following very mild traumatic brain injury.
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      Diffusion tensor imaging of mild traumatic brain injury.
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      • Singh M.
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      • Gruen P.
      Novel diffusion tensor imaging methodology to detect and quantify injured regions and affected brain pathways in traumatic brain injury.
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      • Sun X.
      • et al.
      Diffusion tensor imaging reveals elevated diffusivity of white matter microstructure that is independently associated with long-term outcome after mild traumatic brain injury: A TRACK-TBI study.
      ). Using DTI, Schmidt et al. (
      • Schmidt A.T.
      • Lindsey H.M.
      • Dennis E.
      • Wilde E.A.
      • Biekman B.D.
      • Chu Z.D.
      • et al.
      Diffusion tensor imaging correlates of resilience following adolescent traumatic brain injury.
      ) recently found that resilience-promoting factors (e.g., community support, close interpersonal relationships) were associated with intact WM microstructural integrity in a small adolescent sample of TBI of all severities (
      • Schmidt A.T.
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      • Dennis E.
      • Wilde E.A.
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      • Chu Z.D.
      • et al.
      Diffusion tensor imaging correlates of resilience following adolescent traumatic brain injury.
      ). Other studies indicate that WM microstructure correlates with resilience in adolescents (
      • Galinowski A.
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      • Martinot M.L.P.
      • Artiges E.
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      • et al.
      Resilience and corpus callosum microstructure in adolescence.
      ,
      • Burt K.B.
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      • Banaschewski T.
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      • Bokde A.L.
      • et al.
      Structural brain correlates of adolescent resilience.
      ). WM structural network efficiency derived from diffusion magnetic resonance imaging (MRI) predicts resilience to cognitive decline in adults at risk for Alzheimer’s disease (
      • Fischer F.U.
      • Wolf D.
      • Tüscher O.
      • Fellgiebel A.
      Alzheimer’s Disease Neuroimaging Initiative
      Structural network efficiency predicts resilience to cognitive decline in elderly at risk for Alzheimer’s disease.
      ). Conversely, reduced integrity of WM tracts has been observed in individuals with psychiatric diagnoses or higher levels of psychiatric symptoms (
      • Podwalski P.
      • Szczygiel K.
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      • Misiak B.
      • Samochowiec J.
      Magnetic resonance diffusion tensor imaging in psychiatry: A narrative review of its potential role in diagnosis.
      ). Taken together, WM microstructure represents a promising biological marker to elucidate emotional resilience versus neuropsychiatric distress following mTBI.
      Our objectives were to investigate 1) WM differences in ER versus ND phenotypes in acute mTBI and 2) longitudinal WM changes across the two phenotypes up to 6 months after injury. We studied a subset of the TRACK-TBI (Transforming Research and Clinical Knowledge in Traumatic Brain Injury) participants examined by Brett et al. (
      • Brett B.L.
      • Kramer M.D.
      • Whyte J.
      • McCrea M.A.
      • Stein M.B.
      • Giacino J.T.
      • et al.
      Latent profile analysis of neuropsychiatric symptoms and cognitive function of adults 2 weeks after traumatic brain injury: Findings from the TRACK-TBI study.
      ) 17 to 60 years of age who met criteria for mTBI and underwent DTI at both 2 weeks and 6 months after injury. We focused on the ER and ND groups because they had the greatest divergence in recovery after mTBI (
      • Brett B.L.
      • Kramer M.D.
      • Whyte J.
      • McCrea M.A.
      • Stein M.B.
      • Giacino J.T.
      • et al.
      Latent profile analysis of neuropsychiatric symptoms and cognitive function of adults 2 weeks after traumatic brain injury: Findings from the TRACK-TBI study.
      ) and because emotional resilience has significance in many other neuropsychiatric disorders. Figure 1 shows a schematic of hypothesized roles of resilience and DAI and expected observations.
      Figure thumbnail gr1
      Figure 1Hypothesized roles of resilience versus diffuse axonal injury (DAI) on white matter (WM) microstructural integrity and clinical outcomes after mild traumatic brain injury (mTBI). Solid boxes represent observed variables; dashed boxes represent unobserved variables. Red arrows and boxes denote negative effects; green arrows/boxes denote positive effects; gold arrows/boxes reflect transitions between observed variables. Larger arrows signify a larger effect. (A) What the injury brings to the brain. Patients with emotional resilience (ER) and patients with neuropsychiatric distress (ND) are assumed to have no difference in preinjury resilience. Differing intensities of DAI are postulated to cause the latent profile analysis (LPA) cluster segregation of the two phenotypes as well as the expected diffusion tensor imaging (DTI) differences (red arrows) that gradually increase between 2 weeks and 6 months after injury due to group differences in axonal degeneration (red box) that impact clinical outcome. (B) What the brain brings to the injury. The ER and ND patients are assumed to be different in preinjury resilience, but not DAI severity. In this scenario, the initial differences of DTI metrics at 2 weeks are largely due to premorbid differences in resilience, and the enlarging differences expected at 6 months are due to adaptive versus maladaptive responses to mTBI among the ER versus ND patients (green box).
      We hypothesized that the ER group would exhibit greater WM integrity acutely (2 weeks) following TBI and exhibit less decrease at 6 months after injury. Axial diffusivity (AD) was selected as the primary DTI metric because it represents the component of WM microstructural integrity along the principal axonal fiber orientation. A higher AD is often linked with greater WM microstructural integrity, and decrease of AD over time is often linked with WM deterioration. Its response to TBI is more monophasic from the acute to chronic phase of injury than other commonly used DTI metrics such as fractional anisotropy (FA) and mean diffusivity in both animal experimental models (
      • Mac Donald C.L.
      • Dikranian K.
      • Bayly P.
      • Holtzman D.
      • Brody D.
      Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury.
      ) and human studies (
      • Newcombe V.F.
      • Correia M.M.
      • Ledig C.
      • Abate M.G.
      • Outtrim J.G.
      • Chatfield D.
      • et al.
      Dynamic changes in white matter abnormalities correlate with late improvement and deterioration following TBI: A diffusion tensor imaging study.
      ).

      Methods and Materials

      Participants

      The present study included participants with mTBI from the TRACK-TBI study (
      • Nelson L.D.
      • Temkin N.R.
      • Dikmen S.
      • Barber J.
      • Giacino J.T.
      • Yuh E.
      • et al.
      Recovery after mild traumatic brain injury in patients presenting to US Level I trauma centers: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study.
      ). The participants were enrolled between 2014 and 2018 at 11 academic level I trauma centers across the United States within 24 hours of injury and were evaluated in the emergency department or hospital inpatient unit. All participants provided written consent to the study protocol approved by the Institutional Review Board at University of California, San Francisco, and the institutional review boards at other participating sites. Additional enrollment and inclusion criteria are reported in the Supplement. Of the 1132 patients with mTBI in the cohort, 391 from 17 to 60 years of age underwent MRI at both 2-week and 6-month time points. Of these 391 patients, 94 were classified as ER and 78 were classified as ND based on the latent profile analysis of their responses to a comprehensive battery of neuropsychological assessments/inventories at 2 weeks after injury (
      • Brett B.L.
      • Kramer M.D.
      • Whyte J.
      • McCrea M.A.
      • Stein M.B.
      • Giacino J.T.
      • et al.
      Latent profile analysis of neuropsychiatric symptoms and cognitive function of adults 2 weeks after traumatic brain injury: Findings from the TRACK-TBI study.
      ). The ER and ND phenotypes were not equivalent to any diagnosis from traditional approaches, e.g., DSM and ICD. In addition, a demographically matched control group of 148 uninjured volunteers was enrolled using the same inclusion and exclusion criteria except for those related to head injury.

      Clinical Outcomes at 6 Months

      Patients were assessed at 6 months after injury. The Glasgow Outcome Scale–Extended score measures diverse changes in daily functioning after traumatic injuries; a score <8 indicates incomplete recovery (
      • Jennett B.
      • Snoek J.
      • Bond M.
      • Brooks N.
      Disability after severe head injury: Observations on the use of the Glasgow Outcome Scale.
      ,
      • Nelson L.D.
      • Temkin N.R.
      • Dikmen S.
      • Barber J.
      • Giacino J.T.
      • Yuh E.
      • et al.
      Recovery after mild traumatic brain injury in patients presenting to US Level I trauma centers: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study.
      ,
      • Wilson J.T.
      • Pettigrew L.E.
      • Teasdale G.M.
      Structured interviews for the Glasgow Outcome Scale and the extended Glasgow Outcome Scale: Guidelines for their use.
      ,
      • Wilson L.
      • Boase K.
      • Nelson L.D.
      • Temkin N.R.
      • Giacino J.T.
      • Markowitz A.J.
      • et al.
      A Manual for the Glasgow Outcome Scale-Extended Interview.
      ,
      • Maas A.I.
      • Harrison-Felix C.L.
      • Menon D.
      • Adelson P.D.
      • Balkin T.
      • Bullock R.
      • et al.
      Common data elements for traumatic brain injury: Recommendations from the interagency working group on demographics and clinical assessment.
      ). TBI-related symptoms were assessed using the Rivermead Post Concussion Symptoms Questionnaire (score range, 0–64), with higher scores indicating more severe injury-related symptoms (
      • King N.S.
      • Crawford S.
      • Wenden F.J.
      • Moss N.E.
      • Wade D.T.
      The Rivermead Post Concussion Symptoms Questionnaire: A measure of symptoms commonly experienced after head injury and its reliability.
      ).

      MRI Acquisition

      Whole-brain MRI with diffusion sequences were conducted using 3T MRI scanners with phased-array head radiofrequency coils. Measures were standardized across sites by using a consistent acquisition protocol and a calibration process with a traveling diffusion phantom and human volunteers (
      • Palacios E.M.
      • Martin A.J.
      • Boss M.A.
      • Ezekiel F.
      • Chang Y.S.
      • Yuh E.L.
      • et al.
      Toward precision and reproducibility of diffusion tensor imaging: a multicenter diffusion phantom and traveling volunteer study.
      ). For each patient at each time point, multislice single-shot spin echo echo-planar pulse sequences were acquired at b = 1300 s·mm−2 for 64 diffusion-encoding directions and at b = 0 s·mm−2 for 8 acquisitions, with slices 2.7 mm thick and no gaps, matrix of 128 × 128, and field of view of 350 mm. The resulting voxel size is 2.7 mm in all 3 dimensions.

      DTI Processing and Analysis

      DTI preprocessing and tract-based spatial statistics were performed using the FMRIB Software Library (
      • Smith S.M.
      • Jenkinson M.
      • Woolrich M.W.
      • Beckmann C.F.
      • Behrens T.E.
      • JohansenBerg H.
      • et al.
      Advances in functional and structural MR image analysis and implementation as FSL.
      ). DTI parameters (FA, mean diffusivity, AD, and radial diffusivity) were calculated after correction of susceptibility- and eddy current–induced distortions by registering each volume to the B0 volume, without using reverse phase-encoding acquisitions. Individual FA maps were skeletonized and registered to the FMRIB58 FA template in Montreal Neurological Institute 152 standard space. Global mean DTI parameters were computed over the entire brain WM skeleton. Region-of-interest (ROI) values of WM tracts were obtained by masking the individual WM skeletons with Johns Hopkins University ICBM-DTI-81 Atlas (
      • Oishi K.
      • Faria A.
      • Jiang H.
      • Li X.
      • Akhter K.
      • Zhang J.
      • et al.
      Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer’s disease participants.
      ) WM regions in Montreal Neurological Institute 152 space and averaging across voxels in each region. Whole-brain voxelwise group comparison was implemented with permutation testing and corrected for multiple voxelwise comparisons using threshold-free cluster enhancement at p ≤ .05 (
      • Smith S.M.
      • Nichols T.E.
      Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference.
      ).

      Statistical Analyses

      For comparing global AD between ER and ND groups, 1) an unpaired t test was performed to evaluate the significance of differences between groups at each time point, 2) a paired t test was performed to evaluate the significance of within-group longitudinal differences, and 3) an unpaired t test was performed to evaluate the phenotype differences of longitudinal changes. For the phenotype comparison of regional DTI parameters at each time point, bilaterally measured regions were averaged, then an unpaired heteroscedastic t test was used for statistical inference, and Cohen’s d was computed to evaluate the effect size. The Benjamini-Hochberg false discovery rate–adjusted p value was computed to correct for multiple comparisons (
      • Benjamini Y.
      • Hochberg Y.
      Controlling the false discovery rate: A practical and powerful approach to multiple testing.
      ). To explore the longitudinal changes in regional AD, a paired t test was performed to evaluate the significance of within-group longitudinal change, and a repeated-measures analysis of variance was performed with time set as the within-subject factor, phenotype set as the between-subject factor (excluding control group for lacking phenotype assignments), and an interactive term of phenotype × time. For the comparison of 6-month clinical outcomes, an unpaired heteroscedastic t test was performed.

      Results

      The demographic, clinical, and CT results of the ER and ND groups are provided in Table S1. There was no age difference between ER patients (36.1 ± 13.4 years) and ND patients (35.1 ± 11.7 years). The ND group had twice as many women (43.6%) as the ER group (21.3%). ER patients had more years of education (14.9 ± 2.8) than ND patients (12.9 ± 2.1). There were trends toward higher rates of loss of consciousness, posttraumatic amnesia, and acute TBI findings on CT in ER patients than ND patients. There was also a trend toward a higher rate of prior neuropsychiatric diagnosis in ND patients.
      Results comparing WM microstructure between ER and ND groups are presented in ascending order of spatial resolution, from global WM (Figure 2) to regional WM tracts (Figure 3; Table 1, Table 2, Table 3) to voxelwise WM (Figure 4).
      Figure thumbnail gr2
      Figure 2Global axial diffusivity (AD) comparison between patients with emotional resilience (ER) (blue dots) and patients with neuropsychiatric distress (ND) (orange dots). The black bars show the mean and 95% confidence intervals for the group of dots. (A) ER patients (1.112 ± 0.045 × 10−3 mm·s−1) had higher AD than ND patients (1.098 ± 0.047 × 10−3 mm·s−1) at 2 weeks after mild traumatic brain injury. (B) ER patients (1.111 ± 0.044 × 10−3 mm·s−1) had higher AD than ND patients (1.091 ± 0.046 × 10−3 mm·s−1) at 6 months after mild traumatic brain injury. (C) Longitudinal change of global AD computed as the value at 6 months minus the value at 2 weeks. ND patients (−0.671 ± 1.944 × 10−5 mm·s−1) showed more negative changes than ER patients (−0.124 ± 1.582 × 10−5 mm·s−1; not significantly different from zero).
      Figure thumbnail gr3
      Figure 3The longitudinal change of axial diffusivity (AD) computed as the value at the second time point minus the value at the first time point. A red star on the abscissa denotes a significant longitudinal change after false discovery rate correction. Association tract: sagittal stratum (SS). Projection tracts: posterior corona radiata (PCR), posterior limb internal capsule (PLIC), and posterior thalamic radiation (PTR). Commissural tracts: body of corpus callosum (BCC) and genu of corpus callosum (GCC). Brainstem and cerebellar tracts: cerebral peduncle (CP), inferior cerebellar peduncle (ICP), middle cerebellar peduncle (MCP), superior cerebellar peduncle (SCP), and pontine crossing tract (PCT). ER, emotionally resilient; ND, neuropsychiatrically distressed.
      Table 1Differences of Diffusion Tensor Imaging Regional Values at 2 Weeks Postinjury Between ER and ND Patients
      RegionAxial Diffusivity (× 10−3 mm·s−1)Fractional AnisotropyMean Diffusivity (× 10−3 mm·s−1)Radial Diffusivity (× 10−3 mm·s−1)
      μER ± σμND ± σdpμER ± σμND ± σdpμER ± σμND ± σdpμER ± σμND ± σdp
      Association Tracts
      CGC1.19 ± 0.081.18 ± 0.080.17.260.50 ± 0.040.50 ± 0.04−0.09.580.73 ± 0.060.73 ± 0.050.08.620.51 ± 0.050.50 ± 0.050.22.15
      CGH1.11 ± 0.121.10 ± 0.070.08.600.44 ± 0.070.42 ± 0.070.29.060.73 ± 0.080.73 ± 0.07−0.04.770.54 ± 0.070.56 ± 0.07−0.22.16
      EC1.12 ± 0.061.10 ± 0.060.34
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .030.40 ± 0.040.39 ± 0.040.29.060.75 ± 0.060.75 ± 0.040.980.58 ± 0.040.58 ± 0.040.07.66
      FXST1.71 ± 0.151.67 ± 0.140.33
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .030.44 ± 0.050.44 ± 0.04−0.04.821.13 ± 0.171.12 ± 0.150.10.530.85 ± 0.160.85 ± 0.160.01.95
      FX1.24 ± 0.101.20 ± 0.070.48
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .002
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      0.51 ± 0.050.50 ± 0.050.25.110.77 ± 0.040.75 ± 0.050.34
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .030.53 ± 0.050.53 ± 0.05−0.03.86
      SFO1.01 ± 0.091.00 ± 0.070.15.320.44 ± 0.030.44 ± 0.040.21.180.65 ± 0.080.64 ± 0.060.06.720.48 ± 0.060.47 ± 0.050.14.36
      SLF1.09 ± 0.051.07 ± 0.080.33
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .030.48 ± 0.030.48 ± 0.030.11.460.69 ± 0.060.69 ± 0.03−0.02.870.49 ± 0.050.49 ± 0.03−0.15.34
      SS1.25 ± 0.101.24 ± 0.060.08.600.51 ± 0.040.51 ± 0.040.14.360.76 ± 0.060.75 ± 0.080.13.410.52 ± 0.040.52 ± 0.060.10.53
      UNC1.20 ± 0.071.17 ± 0.080.36
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .020.46 ± 0.060.45 ± 0.050.22.160.77 ± 0.040.75 ± 0.080.26.090.55 ± 0.070.56 ± 0.06−0.16.31
      Projection Tracts
      ACR1.12 ± 0.081.12 ± 0.070.05.760.46 ± 0.030.45 ± 0.050.15.310.72 ± 0.050.72 ± 0.040.02.880.52 ± 0.050.51 ± 0.050.06.71
      PCR1.16 ± 0.081.15 ± 0.060.03.850.47 ± 0.030.46 ± 0.040.40
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .009
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      0.73 ± 0.080.74 ± 0.07−0.09.540.53 ± 0.040.53 ± 0.05−0.02.88
      SCR1.08 ± 0.061.06 ± 0.060.32
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .040.48 ± 0.040.48 ± 0.040.02.920.66 ± 0.090.67 ± 0.04−0.24.120.48 ± 0.050.47 ± 0.050.14.36
      ALIC1.20 ± 0.101.17 ± 0.080.32
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .040.54 ± 0.040.54 ± 0.04−0.06.680.69 ± 0.070.69 ± 0.060.05.740.45 ± 0.060.45 ± 0.040.98
      PLIC1.29 ± 0.091.26 ± 0.070.36
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .020.67 ± 0.040.68 ± 0.03−0.27.080.67 ± 0.070.65 ± 0.040.21.170.37 ± 0.040.35 ± 0.040.37
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .02
      RLIC1.26 ± 0.091.23 ± 0.120.28.070.55 ± 0.050.55 ± 0.040.16.290.74 ± 0.070.73 ± 0.070.14.360.48 ± 0.040.48 ± 0.050.05.73
      CST1.14 ± 0.121.13 ± 0.090.08.580.58 ± 0.060.57 ± 0.060.18.240.66 ± 0.080.66 ± 0.07−0.06.720.42 ± 0.060.43 ± 0.06−0.10.51
      PTR1.30 ± 0.091.30 ± 0.060.01.950.57 ± 0.040.56 ± 0.040.19.220.76 ± 0.040.75 ± 0.060.06.690.48 ± 0.050.48 ± 0.05−0.14.37
      Commissural Tracts
      BCC1.55 ± 0.081.53 ± 0.070.26.090.64 ± 0.040.64 ± 0.04−0.06.690.83 ± 0.060.82 ± 0.060.18.250.47 ± 0.060.46 ± 0.060.13.40
      GCC1.53 ± 0.091.51 ± 0.090.26.090.68 ± 0.040.67 ± 0.040.16.310.79 ± 0.050.78 ± 0.050.16.300.41 ± 0.050.42 ± 0.06−0.06.68
      SCC1.52 ± 0.061.50 ± 0.080.20.200.76 ± 0.030.75 ± 0.040.25.110.72 ± 0.040.72 ± 0.05−0.01.980.31 ± 0.050.32 ± 0.05−0.19.22
      Brainstem and Cerebellar Tracts
      CP1.36 ± 0.091.34 ± 0.090.27.080.65 ± 0.040.65 ± 0.040.08.620.72 ± 0.050.71 ± 0.050.19.210.40 ± 0.040.40 ± 0.060.08.62
      ICP1.10 ± 0.051.10 ± 0.050.14.370.51 ± 0.050.50 ± 0.050.26.090.68 ± 0.040.68 ± 0.04−0.03.840.46 ± 0.050.46 ± 0.050.05.75
      MCP1.02 ± 0.041.01 ± 0.050.26.090.50 ± 0.030.50 ± 0.030.25.110.63 ± 0.030.63 ± 0.040.15.320.44 ± 0.040.44 ± 0.04−0.07.76
      SCP1.42 ± 0.081.38 ± 0.080.50
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .001
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      0.59 ± 0.060.58 ± 0.060.17.280.80 ± 0.050.78 ± 0.070.37
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .020.49 ± 0.070.49 ± 0.06−0.01.94
      ML1.26 ± 0.061.22 ± 0.130.37
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .020.60 ± 0.040.59 ± 0.040.26.090.70 ± 0.040.68 ± 0.070.37
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .020.43 ± 0.040.42 ± 0.050.11.49
      PCT1.05 ± 0.081.03 ± 0.070.24.110.49 ± 0.040.47 ± 0.040.34
      For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      .030.67 ± 0.040.66 ± 0.050.07.660.48 ± 0.050.48 ± 0.05−0.07.64
      ACR, anterior corona radiata; ALIC, anterior limb part of internal capsule; BCC, body of corpus callosum; CGC, cingulum (cingulate gyrus); CGH, cingulum (hippocampus); CP, cerebral peduncle; CST, corticospinal tract; EC, external capsule; ER, emotional resilience; FX, fornix; FXST, fornix stria terminalis; GCC, genu of corpus callosum; ICP, inferior cerebellar peduncle; MCP, middle cerebellar peduncle; ML, medial lemniscus; ND, neuropsychiatric distress; PCR, posterior corona radiata; PCT, pontine crossing tract; PLIC, posterior limb part of internal capsule; PTR, posterior thalamic radiation; RLIC, retrolenticular part of internal capsule; SCC, splenium of corpus callosum; SCP, superior cerebellar peduncle; SCR, superior corona radiata; SFO, superior fronto-occipital fasciculus; SLF, superior longitudinal fasciculus; SS, sagittal stratum; UNC, uncinate fasciculus.
      a For Cohen’s d when p < .05; for p value when false discovery rate–adjusted p value is also < .05.
      b For Cohen's d when p < .01 in t test.
      Table 2Differences of Diffusion Tensor Imaging Regional Values at 6 Months Postinjury Between ER and ND Patients
      RegionAxial Diffusivity (× 10−3 mm·s−1)Fractional AnisotropyMean Diffusivity (× 10−3 mm·s−1)Radial Diffusivity (× 10−3 mm·s−1)
      μER ± σμND ± σdpμER ± σμND ± σdpμER ± σμND ± σdpμER ± σμND ± σdp
      Association Tracts
      CGC1.19 ± 0.081.18 ± 0.080.13.400.50 ± 0.040.50 ± 0.040.01.930.73 ± 0.060.73 ± 0.040.05.750.51 ± 0.050.50 ± 0.040.13.40
      CGH1.12 ± 0.121.09 ± 0.080.31
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .040.45 ± 0.040.43 ± 0.070.27.080.73 ± 0.070.72 ± 0.070.16.300.54 ± 0.070.54 ± 0.06−0.10.52
      EC1.12 ± 0.061.09 ± 0.060.43
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .005
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.40 ± 0.040.39 ± 0.040.19.220.76 ± 0.040.75 ± 0.050.38
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .010.58 ± 0.040.58 ± 0.050.19.21
      FXST1.71 ± 0.171.65 ± 0.140.36
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .02
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.44 ± 0.040.44 ± 0.050.08.581.12 ± 0.181.11 ± 0.150.08.580.85 ± 0.160.84 ± 0.150.09.57
      FX1.24 ± 0.091.19 ± 0.080.62
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .001 in t test.
      < .001
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.51 ± 0.050.50 ± 0.040.24.120.77 ± 0.040.75 ± 0.040.54
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .001 in t test.
      <.001
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.53 ± 0.050.53 ± 0.040.15.34
      SFO1.02 ± 0.070.98 ± 0.070.51
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .001
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.44 ± 0.040.42 ± 0.050.29.060.67 ± 0.050.64 ± 0.060.46
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .003
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.48 ± 0.050.47 ± 0.070.32
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .04
      SLF1.09 ± 0.051.07 ± 0.050.38
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .01
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.48 ± 0.030.47 ± 0.030.15.340.69 ± 0.040.68 ± 0.030.17.260.49 ± 0.040.49 ± 0.030.99
      SS1.26 ± 0.071.24 ± 0.060.36
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .02
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.51 ± 0.040.51 ± 0.030.20.190.76 ± 0.080.76 ± 0.050.02.920.52 ± 0.060.52 ± 0.04−0.13.40
      UNC1.20 ± 0.081.17 ± 0.070.37
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .02
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.46 ± 0.040.44 ± 0.050.27.080.76 ± 0.040.76 ± 0.050.12.440.55 ± 0.070.56 ± 0.05−0.20.19
      Projection Tracts
      ACR1.12 ± 0.071.10 ± 0.100.31
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .040.45 ± 0.040.45 ± 0.050.09.550.72 ± 0.040.71 ± 0.050.22.160.52 ± 0.040.52 ± 0.040.14.37
      PCR1.17 ± 0.051.14 ± 0.090.32
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .040.46 ± 0.040.46 ± 0.040.20.190.74 ± 0.060.74 ± 0.04−0.05.730.54 ± 0.060.54 ± 0.04−0.13.39
      SCR1.07 ± 0.081.06 ± 0.060.21.170.48 ± 0.030.48 ± 0.040.01.940.67 ± 0.070.69 ± 0.03−0.03.850.48 ± 0.050.47 ± 0.040.09.55
      ALIC1.19 ± 0.081.16 ± 0.080.39
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .01
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.54 ± 0.030.54 ± 0.030.11.480.69 ± 0.070.69 ± 0.04−0.01.970.46 ± 0.050.45 ± 0.030.08.62
      PLIC1.28 ± 0.091.25 ± 0.080.40
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .01
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.67 ± 0.040.67 ± 0.03−0.07.660.66 ± 0.070.65 ± 0.060.25.110.36 ± 0.050.35 ± 0.040.24.11
      RLIC1.27 ± 0.061.23 ± 0.100.45
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .004
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.55 ± 0.030.55 ± 0.030.990.75 ± 0.030.74 ± 0.040.31
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .040.49 ± 0.050.49 ± 0.040.99
      CST1.14 ± 0.081.12 ± 0.060.17.280.57 ± 0.040.58 ± 0.05−0.11.480.67 ± 0.050.66 ± 0.050.26.090.43 ± 0.060.42 ± 0.060.08.60
      PTR1.31 ± 0.091.30 ± 0.060.17.270.57 ± 0.040.56 ± 0.040.10.530.76 ± 0.040.76 ± 0.040.11.460.48 ± 0.050.48 ± 0.05−0.04.81
      Commissural Tracts
      BCC1.54 ± 0.081.52 ± 0.080.35
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .030.64 ± 0.040.64 ± 0.05−0.01.970.83 ± 0.060.81 ± 0.060.27.080.47 ± 0.060.46 ± 0.060.20.20
      GCC1.52 ± 0.091.50 ± 0.090.30.050.68 ± 0.040.67 ± 0.050.09.550.78 ± 0.050.77 ± 0.040.21.180.41 ± 0.050.41 ± 0.06−0.01.98
      SCC1.52 ± 0.061.50 ± 0.070.32
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .040.75 ± 0.040.75 ± 0.030.16.310.72 ± 0.040.71 ± 0.050.12.450.32 ± 0.050.32 ± 0.05−0.08.62
      Brainstem and Cerebellar Tracts
      CP1.36 ± 0.091.32 ± 0.080.46
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .003
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.65 ± 0.040.65 ± 0.040.04.780.72 ± 0.050.70 ± 0.060.41
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .009
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.40 ± 0.050.39 ± 0.050.19.21
      ICP1.10 ± 0.061.09 ± 0.050.25.110.51 ± 0.040.50 ± 0.050.08.590.67 ± 0.060.67 ± 0.04−0.05.760.46 ± 0.060.46 ± 0.05−0.08.59
      MCP1.02 ± 0.041.00 ± 0.050.28.070.50 ± 0.040.50 ± 0.030.18.250.63 ± 0.030.62 ± 0.040.16.310.44 ± 0.040.44 ± 0.040.98
      SCP1.41 ± 0.071.36 ± 0.120.54
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .001 in t test.
      <.001
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.60 ± 0.050.59 ± 0.050.09.560.80 ± 0.060.78 ± 0.050.31
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .040.48 ± 0.080.49 ± 0.06−0.02.89
      ML1.25 ± 0.061.21 ± 0.130.41
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      ,
      For Cohen's d when p < .01 in t test.
      .009
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      0.60 ± 0.050.59 ± 0.060.25.110.70 ± 0.040.78 ± 0.080.34
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .030.42 ± 0.050.42 ± 0.050.01.93
      PCT1.04 ± 0.071.02 ± 0.070.33
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .030.48 ± 0.030.47 ± 0.040.32
      For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      .040.67 ± 0.040.66 ± 0.050.08.590.48 ± 0.040.48 ± 0.05−0.08.63
      ACR, anterior corona radiata; ALIC, anterior limb part of internal capsule; BCC, body of corpus callosum; CGC, cingulum (cingulate gyrus); CGH, cingulum (hippocampus); CP, cerebral peduncle; CST, corticospinal tract; EC, external capsule; ER, emotional resilience; FX, fornix; FXST, fornix stria terminalis; GCC, genu of corpus callosum; ICP, inferior cerebellar peduncle; MCP, middle cerebellar peduncle; ML, medial lemniscus; ND, neuropsychiatric distress; PCR, posterior corona radiata; PCT, pontine crossing tract; PLIC, posterior limb part of internal capsule; PTR, posterior thalamic radiation; RLIC, retrolenticular part of internal capsule; SCC, splenium of corpus callosum; SCP, superior cerebellar peduncle; SCR, superior corona radiata; SFO, superior fronto-occipital fasciculus; SLF, superior longitudinal fasciculus; SS, sagittal stratum; UNC, uncinate fasciculus.
      a For Cohen’s d when p < .05 in t test; for p value when false discovery rate–adjusted p value is also < .05.
      b For Cohen's d when p < .01 in t test.
      c For Cohen's d when p < .001 in t test.
      Table 3Results of Repeated Measures Analysis of Variance for Regions With Significant Longitudinal Changes and/or Interactions Between Phenotype and Time
      RegionFactorsSum of SquaresdfMean Square
      Sum of squares divided by df.
      Fp Value
      SSTime1.01 × 10−1111.01 × 10−110.01.93
      Phenotype × time5.24 × 10−915.24 × 10−93.98.05
      PCRTime2.06 × 10−1112.06 × 10−110.01.92
      Phenotype × time9.36 × 10−919.36 × 10−94.24.04
      p < .05 from analysis of variance.
      PLICTime8.29 × 10−918.29 × 10−928.4<.001
      p < .05 from analysis of variance.
      Phenotype × time3.55 × 10−1013.55 × 10−101.22.27
      PTRTime1.13 × 10−1111.13 × 10−110.04.84
      Phenotype × time3.12 × 10−913.12 × 10−911.5<.001
      p < .05 from analysis of variance.
      BCCTime8.06 × 10−918.06 × 10−911.6<.001
      p < .05 from analysis of variance.
      Phenotype × time8.17 × 10−1018.17 × 10−101.17.28
      GCCTime1.28 × 10−811.28 × 10−819.7<.001
      p < .05 from analysis of variance.
      Phenotype × time2.02 × 10−1012.02 × 10−100.31.58
      CPTime1.01 × 10−811.01 × 10−814.9<.001
      p < .05 from analysis of variance.
      Phenotype × time5.52 × 10−915.52 × 10−98.12.005
      p < .05 from analysis of variance.
      ICPTime6.07 × 10−916.07 × 10−99.92.002
      p < .05 from analysis of variance.
      Phenotype × time6.86 × 10−1016.86 × 10−101.12.29
      MCPTime3.00 × 10−913.00 × 10−98.83.003
      p < .05 from analysis of variance.
      Phenotype × time3.36 × 10−1113.36 × 10−110.10.75
      SCPTime1.66 × 10−811.66 × 10−86.81.01
      p < .05 from analysis of variance.
      Phenotype × time2.44 × 10−912.44 × 10−91.00.32
      PCTTime9.97 × 10−919.97 × 10−99.99.002
      p < .05 from analysis of variance.
      Phenotype × time7.32 × 10−1017.32 × 10−100.73.39
      BCC, body of corpus callosum; CP, cerebral peduncle; GCC, genu of corpus callosum; ICP, inferior cerebellar peduncle; MCP, middle cerebellar peduncle; PCR, posterior corona radiata; PCT, pontine crossing tract; PLIC, posterior limb part of internal capsule; PTR, posterior thalamic radiation; SCP, superior cerebellar peduncle; SS, sagittal stratum.
      a Sum of squares divided by df.
      b p < .05 from analysis of variance.
      Figure thumbnail gr4
      Figure 4Voxelwise statistics of axial diffusivity (AD) comparison between the emotional resilience and neuropsychiatric distress groups at 2 weeks (A) and 6 months (B). The color bar on the right shows the range of p values from 0.05 to 0 corrected for multiple voxelwise comparisons: red is marginally significant, while yellow is highly significant. The statistical significance represents emotional resilience patients with higher AD than neuropsychiatric distress patients in a given white matter voxel. In each row, 9 slices of the axial view of brain are laid out sequentially, with the z coordinate labeled at the bottom.

      Global AD

      For both time points, the ND group had lower global AD than the ER group (p = .045 at 2 weeks, p = .005 at 6 months). The ER group showed no significant longitudinal change (p = .45, Cohen’s d = 0.08), whereas the ND group exhibited a significant longitudinal decrease in global AD (p = .003, Cohen’s d = 0.35). The longitudinal global AD change was greater in the ND group than in the ER group (p = .043, Cohen’s d = 0.31).

      Phenotype Difference of Regional DTI

      To identify which WM tracts contributed most to this global AD difference between ER and ND groups, post hoc ROI analysis was performed at 2 weeks and 6 months, correcting for multiple comparisons (Tables 1 and 2). At 2 weeks, the ND group had significantly lower AD than the ER group in fornix and superior cerebellar peduncle. The AD group differences increased from 2 regions at 2 weeks to 13 regions at 6 months. The ND group showed significantly lower AD in association tracts (external capsule, fornix stria terminalis, superior fronto-occipital fasciculus, sagittal stratum, and uncinate fasciculus) and projection tracts of the internal capsule (anterior limb, posterior limb, and retrolenticular part of internal capsule) as well as brainstem (cerebral peduncle and medial lemniscus). In the ND group, regions with lower AD at 2 weeks postinjury showed increased effect size by 6 months (fornix and superior cerebellar peduncle).

      Longitudinal Change of Regional AD

      Table 3 reports longitudinal change of regional AD using repeated-measures analysis of variance. Figure 3 shows that the ND group had significant longitudinal reductions of AD in 7 regions, while the ER group had them only in posterior limb of internal capsule and genu of corpus callosum. The ER group also trended toward increased AD in sagittal stratum, posterior corona radiata, and posterior thalamic radiation. No significant change was found in uninjured control participants for any of the tracts. ND patients showed deterioration of WM microstructure as progressively reduced AD in more WM regions (Table S2) compared with ER patients, in whom significant interval AD decreases were limited to the internal capsule and genu of corpus callosum (Table S3).

      Voxelwise Analysis of AD

      Significant voxelwise AD differences between ER and ND groups at 2 weeks after injury were mostly in central regions, which extended to more peripheral and posterior regions over the following 6 months (Figure 4), such as posterior corona radiata and posterior thalamic radiation where longitudinal ROI analysis showed that AD was trending upward in the ER group but was significantly decreasing in the ND group (Figure 3). We also performed the longitudinal voxelwise t statistics within each group but did not find any significant changes after multiple voxelwise comparison correction.

      Clinical Outcomes at 6 Months

      ER patients predominantly demonstrated full functional recovery (Glasgow Outcome Scale–Extended score 8), whereas ND patients had significantly lower Glasgow Outcome Scale–Extended scores (p < .001) representing incomplete recovery, with a shallow distribution over the range of 4 to 8 (Figure 5A). Most ER patients had a score of zero on the Rivermead Post Concussion Symptoms Questionnaire (Figure 5B), implying no remaining TBI symptoms (
      • King N.S.
      • Crawford S.
      • Wenden F.J.
      • Moss N.E.
      • Wade D.T.
      The Rivermead Post Concussion Symptoms Questionnaire: A measure of symptoms commonly experienced after head injury and its reliability.
      ), whereas ND patients had significantly higher Rivermead Post Concussion Symptoms Questionnaire scores over a wide distribution (p < .001).
      Figure thumbnail gr5
      Figure 5Histograms of outcomes of the Glasgow Outcome Scale–Extended (GOSE) measure of disability (A) and the Rivermead Post Concussion Questionnaire (RPQ) measure of traumatic brain injury symptoms (B) at 6 months after injury, stratified by clinical phenotypes identified at 2 weeks after injury. Blue represents the emotionally resilient (ER) cohort, and orange represents the neuropsychiatrically distressed (ND) cohort.

      Discussion

      This prospective, natural history study of patients with mTBI is the first, to our knowledge, to interrogate neural pathways of resilience associated with distinct neuropsychiatric phenotypes following injury. WM microstructural differences between ER and ND phenotypes of mTBI were clearly identified at 2 weeks after injury and became larger and more widespread at 6 months. DTI revealed lower WM AD and therefore possibly reduced microstructural integrity in ND patients compared with their ER counterparts. This difference increased from 2 to 13 major WM tracts during the first 6 months after injury, indicating that greater neuropsychiatric distress interacts with injury-related processes to confer worse biological responses to mTBI, whereas greater emotional resilience may serve as a protective factor, both early and especially later during mTBI recovery. These findings support the novel phenotypic classification of Brett et al. (
      • Brett B.L.
      • Kramer M.D.
      • Whyte J.
      • McCrea M.A.
      • Stein M.B.
      • Giacino J.T.
      • et al.
      Latent profile analysis of neuropsychiatric symptoms and cognitive function of adults 2 weeks after traumatic brain injury: Findings from the TRACK-TBI study.
      ), with a striking group difference in 6-month outcomes. Whereas the majority of the ER group identified early after injury had little or no long-term disability or symptoms, the majority of the ND group had poor long-term outcomes. This cannot be explained by differences in standard clinical and CT measures of injury severity, all of which indicated more severe TBI in the ER group rather than the ND group (Table S1). This implies that emotional resilience is a major determinant of recovery after mTBI or that yet-to-be-determined (likely noninjury) factors drive long-term emotional and neurobiological outcomes. Given the high prevalence of mTBI worldwide, elucidating the neuroscientific underpinnings of resilience and leveraging this knowledge to improve diagnosis and treatment of patients with mTBI should be a major focus of research, especially if these neural mechanisms are also shared with other neurological and psychiatric disorders.
      The evident association between mTBI phenotypes and WM integrity implicates particular neurobiological mechanisms and may prove useful as diagnostic, prognostic, and/or predictive biomarkers for clinical trials and patient management. The ER and ND phenotypes contrast along multiple psychiatric symptom dimensions following TBI, including internalizing factors (depression, anxiety, and fear) and somatic factors (sleep problems, physical difficulty, and pain) (
      • Nelson L.D.
      • Kramer M.D.
      • Joyner K.J.
      • Patrick C.J.
      • Stein M.B.
      • Temkin N.
      • et al.
      Relationship between transdiagnostic dimensions of psychopathology and traumatic brain injury (TBI): A TRACK-TBI study.
      ). Distinct patterns of WM changes associated with these symptoms may contribute to the feature segregation between phenotypes. Axonal damage of tracts related to emotional functions may predict neuropsychiatric manifestations after TBI. Aldossary et al. (
      • Aldossary N.M.
      • Kotb M.A.
      • Kamal A.M.
      Predictive value of early MRI findings on neurocognitive and psychiatric outcomes in patients with severe traumatic brain injury.
      ) showed that patients with severe TBI and DAI were more likely to exhibit personality changes, aggression, and MDD, implicating emotional regulation neurotransmitter circuits of the frontal and anterior temporal lobes. We found greater reduction of diffusivity in superior fronto-occipital fasciculus, superior longitudinal fasciculus, uncinate fasciculus, and fornix, corroborating the hypothesized disrupted neurotransmitter circuits.
      In the broader neurosciences, studies have linked limbic and neocortical association tracts with internalizing mental illness. The fornix and cingulum are associated with emotional dysfunction in bipolar disorder (
      • Kurumaji A.
      • Itasaka M.
      • Uezato A.
      • Takiguchi K.
      • Jitoku D.
      • Hobo M.
      • Nishikawa T.
      A distinctive abnormality of diffusion tensor imaging parameters in the fornix of patients with bipolar II disorder.
      ). Decreased uncinate fasciculus integrity was found in MDD (
      • Zheng K.Z.
      • Wang H.N.
      • Liu J.
      • Xi Y.B.
      • Li L.
      • Zhang X.
      • et al.
      Incapacity to control emotion in major depression may arise from disrupted white matter integrity and OFC-amygdala inhibition.
      ). Jenkins et al. (
      • Jenkins L.M.
      • Barba A.
      • Campbell M.
      • Lamar M.
      • Shankman S.A.
      • Leow A.D.
      • et al.
      Shared white matter alterations across emotional disorders: A voxel-based meta-analysis of fractional anisotropy.
      ) studied shared WM microstructural abnormalities of patients across various emotion disorders using DTI and found reduced FA in uncinate fasciculus and superior longitudinal fasciculus. This is germane to the current study, as the classification of ER and ND was based on a transdiagnostic approach comprising various dimensions of internalizing and somatic psychiatric symptoms. We observed significantly lower AD in fornix, uncinate fasciculus, and superior longitudinal fasciculus in ND patients versus ER patients, concordant with previous findings, and lower AD in the ND group in other neocortical association tracts, including external capsule, superior fronto-occipital fasciculus, and sagittal stratum, by 6 months after injury.
      Compromised WM microstructure of commissural and projection tracts might also correlate with emotional deficits. Jenkins et al. (
      • Jenkins L.M.
      • Barba A.
      • Campbell M.
      • Lamar M.
      • Shankman S.A.
      • Leow A.D.
      • et al.
      Shared white matter alterations across emotional disorders: A voxel-based meta-analysis of fractional anisotropy.
      ) reported reduced FA in the genu of corpus callosum, anterior thalamic radiation, and superior corona radiata. Corpus callosum and anterior limb of internal capsule has lower FA in patients with MDD relative to control subjects (
      • Chen G.
      • Guo Y.
      • Zhu H.
      • Kuang W.
      • Bi F.
      • Ai H.
      • et al.
      Intrinsic disruption of white matter microarchitecture in first-episode, drug-naive major depressive disorder: A voxel-based meta-analysis of diffusion tensor imaging.
      ). We observed cross-sectional and/or longitudinal differences of AD in the ND group versus the ER group in the anterior limb of internal capsule, but also in many more tracts at 6 months after injury. Interestingly, posterior fibers of the posterior corona radiata, posterior thalamic radiation, and sagittal stratum in ER patients trended toward an increased AD over time, suggesting possible recovery of axonal integrity.
      Damage to the cerebellum might be important for the deleterious effects of mTBI, as it is sensitive to timing and has been postulated as the hub within the network for attentional prediction (
      • Ghajar J.
      • Ivry R.
      The predictive brain state: Timing deficiency in traumatic brain injury?.
      ,
      • Gatti D.
      • Rinaldi L.
      • Ferreri L.
      • Vecchi T.
      The human cerebellum as a hub of the predictive brain.
      ). AD of the cerebellar peduncles was reduced in both collegiate athletes and emergency department patients with mTBI compared with control subjects (
      • Mallott J.M.
      • Palacios E.M.
      • Maruta J.
      • Ghajar J.
      • Mukherjee P.
      Disrupted white matter microstructure of the cerebellar peduncles in scholastic athletes after concussion.
      ). In the current study, AD of the superior cerebellar peduncle was reduced in the ND group versus the ER group at both time points. Both cerebral and cerebellar peduncles (cerebral peduncle and inferior cerebellar peduncle) showed reduction of AD over time in the ND group, but not the ER group. Therefore, microstructural plasticity of cerebellar input and output WM pathways via its peduncles, which is vital for maintaining precise spike timing (
      • Fields R.D.
      A new mechanism of nervous system plasticity: Activity-dependent myelination.
      ,
      • Fields R.D.
      • Woo D.H.
      • Basser P.J.
      Glial regulation of the neuronal connectome through local and long-distant communication.
      ,
      • Sampaio-Baptista C.
      • Johansen-Berg H.
      White matter plasticity in the adult brain.
      ), may be an important mechanism of mTBI resilience in addition to causing postconcussive symptoms when damaged. Hence, individuals with greater preinjury microstructural integrity of the cerebellar peduncles might tolerate the same severity of injury with fewer symptoms and less disability than individuals without this advantage.
      These neurobiological correlates of ER versus ND are dynamic over time, leading to different potential interpretations at 2 weeks versus 6 months after injury. Higher AD at 2 weeks after injury may indicate that ER patients had less severe injury to axonal density and less disruption of axonal orientation coherence relative to ND patients. Higher AD 6 months later may indicate that the ER patients had recovered better and/or that the ND patients had more WM degeneration. However, a high AD does not necessarily mean better WM microstructural integrity in the ER patients at 2 weeks. Acute neural deformation edema can also have a transient effect on local AD, which may explain the finding by Brett et al. (
      • Brett B.L.
      • Kramer M.D.
      • Whyte J.
      • McCrea M.A.
      • Stein M.B.
      • Giacino J.T.
      • et al.
      Latent profile analysis of neuropsychiatric symptoms and cognitive function of adults 2 weeks after traumatic brain injury: Findings from the TRACK-TBI study.
      ) that the latent profiles did not intuitively cohere with TBI severity scores. They found that lower admission Glasgow Coma Scale scores (<13) were observed more commonly in ER patients (7.9%) than ND patients (5.5%), indicating that ER patients tended to have greater injury severity. The ND group had a higher percentage of women than the ER group; however, sex differences cannot explain the different WM AD changes between the ER and ND groups across the two time points. Interestingly, differences of longitudinal AD changes between the ER and ND groups were evident in the ROI analysis, but not in the voxelwise analysis, likely because voxelwise analysis has more stringent multiple comparison corrections and thus lower statistical power.
      Resilience can exist before TBI as a premorbid host factor. A minority of patients in the ER and ND groups reported preinjury psychiatric problems, which were only slightly more common in the ND group. McCauley et al. (
      • McCauley S.R.
      • Wilde E.A.
      • Miller E.R.
      • Frisby M.L.
      • Garza H.M.
      • Varghese R.
      • et al.
      Preinjury resilience and mood as predictors of early outcome following mild traumatic brain injury.
      ) evaluated preinjury clinical/functional resilience in patients with mTBI post hoc by using the Connor-Davidson Resilience Scale based on the patients’ memory of their functioning a month before the injury. These authors observed that preinjury resilience and preinjury depressed mood predicted postinjury outcomes. Another study of post-TBI resilience using the Connor-Davidson Resilience Scale found that premorbid host factors (e.g., minority group membership, preinjury substance abuse, and higher levels of anxiety and disability) were related to reduced resilience during the first year after injury (
      • Marwitz J.H.
      • Sima A.P.
      • Kreutzer J.S.
      • Dreer L.E.
      • Bergquist T.F.
      • Zafonte R.
      • et al.
      Longitudinal examination of resilience after traumatic brain injury: A traumatic brain injury model systems study.
      ). TBI patients with MDD are more likely to have a history of mood and anxiety disorders than TBI patients without depression, linking lower resilience to emotional deficits after TBI (
      • Jorge R.E.
      • Robinson R.G.
      • Moser D.
      • Tateno A.
      • Crespo-Facorro B.
      • Arndt S.
      Major depression following traumatic brain injury.
      ). Premorbid somatization symptoms influence clinical recovery after sport-related concussion (
      • Nelson L.D.
      • Tarima S.
      • LaRoche A.A.
      • Hammeke T.A.
      • Barr W.B.
      • Guskiewicz K.
      • et al.
      Preinjury somatization symptoms contribute to clinical recovery after sport-related concussion.
      ). Manic symptoms after TBI were more frequent in patients with a positive family history of bipolar disorder, suggesting that neuropsychiatric risk factors existed before injury (
      • Ahmed S.
      • Venigalla H.
      • Mekala H.M.
      • Dar S.
      • Hassan M.
      • Ayub S.
      Traumatic brain injury and neuropsychiatric complications.
      ). An earlier study also showed a strong relationship between severe mental illness after TBI and family histories of schizophrenia or bipolar disorder (
      • Malaspina D.
      • Goetz R.R.
      • Friedman J.H.
      • Kaufmann C.A.
      • Faraone S.V.
      • Tsuang M.
      • et al.
      Traumatic brain injury and schizophrenia in members of schizophrenia and bipolar disorder pedigrees.
      ).
      Alternatively, resilience after TBI may develop in response to injury. Schmidt et al. (
      • Schmidt A.T.
      • Lindsey H.M.
      • Dennis E.
      • Wilde E.A.
      • Biekman B.D.
      • Chu Z.D.
      • et al.
      Diffusion tensor imaging correlates of resilience following adolescent traumatic brain injury.
      ) suggested that the protective effects of resilience in adolescent patients with TBI may be a result of less disrupted WM tracts combined with quality of support from family and caregivers. Accordingly, resilience may be responsive and not just innate, although a positive correlation may exist between preinjury resilience and superior family/caregiver environments that can further enhance resilience after injury. Task-oriented coping and perceived social support, but not premorbid intelligence, predicted high resilience on the Connor-Davidson Resilience Scale after TBI (
      • Hanks R.A.
      • Rapport L.J.
      • Waldron Perrine B.
      • Millis S.R.
      Correlates of resilience in the first 5 years after traumatic brain injury.
      ).
      As premorbid resilience cannot be ascertained in most TBI study designs, our study is unable to distinguish between altered microstructural WM integrity due to DAI and that due to resilience. The conventional explanation for the group differences (Figure 1A) is that more severe DAI accounts for the lower WM integrity at 2 weeks in the ND group and that continued Wallerian axonal degeneration produces the widening gap between ND and ER groups at 6 months. However, there is no clinical evidence for greater DAI in the ND group; rather, the ER group trended toward higher proportions of loss of consciousness, posttraumatic amnesia, and acute intracranial injury on CT, which are all factors associated with greater injury severity. The alternative explanation (Figure 1B) is that differences in preinjury resilience account for the differences in DTI metrics at 2 weeks and that persistent adaptive behaviors among the ER group, in contrast to maladaptive behaviors among the ND group, explain the relatively preserved WM microstructure of ER patients by 6 months after injury, similar to that of the uninjured control subjects, in contrast to the deteriorating WM integrity of the ND patients. This view is consistent with the finding of higher educational levels in the ER group. Rates of premorbid psychopathology did not differ between ER and ND groups, indicating that this construct is not simply due to preexisting neuropsychiatric history. Furthermore, the greatest variations in WM microstructure between the two groups were in tracts implicated in resilience and neuropsychiatric function, as opposed to the more uniform and diffuse group differences that would be postulated by the DAI hypothesis. However, there are likely interactions between DAI and preinjury resilience during the recovery from mTBI, as DAI can affect WM tracts required for clinical/functional resilience, and, in turn, resilience can promote adaptive responses to injury that might potentially prevent further WM degeneration induced by DAI. This latter interaction is supported by the 1-year follow-up DTI data from a recent pilot study of resilience-promoting factors in adolescents with complicated mild, moderate, and severe TBI (
      • Schmidt A.T.
      • Lindsey H.M.
      • Dennis E.
      • Wilde E.A.
      • Biekman B.D.
      • Chu Z.D.
      • et al.
      Diffusion tensor imaging correlates of resilience following adolescent traumatic brain injury.
      ).
      Given the enlarging differences in WM microstructure between ER and ND over time, improving post-TBI intervention is urgent, demanding clear identification of modifiable factors. Patients with mTBI presenting to the emergency department frequently receive limited education and follow-up care (
      • Seabury S.A.
      • Gaudette É
      • Goldman D.P.
      • Markowitz A.J.
      • Brooks J.
      • McCrea M.A.
      • et al.
      Assessment of follow-up care after emergency department presentation for mild traumatic brain injury and concussion: Results from the TRACK-TBI study.
      ). Variation in TBI clinical care practices conceivably contributes to resilient versus adverse clinical and neurobiological outcomes. Advancing mTBI biological and phenotypic classification is critical to better precision medicine treatment approaches, as more precisely and accurately characterizing population heterogeneity will help ensure that interventions are tailored to the unique needs and preferences of individual patients (
      • Nelson L.D.
      • Temkin N.R.
      • Dikmen S.
      • Barber J.
      • Giacino J.T.
      • Yuh E.
      • et al.
      Recovery after mild traumatic brain injury in patients presenting to US Level I trauma centers: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study.
      ,
      • Manley G.T.
      • Mac Donald C.L.
      • Markowitz A.J.
      • Stephenson D.
      • Robbins A.
      • Gardner R.C.
      • et al.
      The Traumatic Brain Injury Endpoints Development (TED) initiative: Progress on a public-private regulatory collaboration to accelerate diagnosis and treatment of traumatic brain injury.
      ,
      • Bowman K.
      • Matney C.
      • Berwick D.M.
      Improving traumatic brain injury care and research: A report from the National Academies of Sciences, Engineering, and Medicine.
      ). Incorporating neuroscience-informed resilience theory and positive psychology into TBI rehabilitation promises to improve long-term quality of life (
      • Rabinowitz A.R.
      • Arnett P.A.
      Positive psychology perspective on traumatic brain injury recovery and rehabilitation.
      ,
      • Howe E.I.
      • Langlo K.P.S.
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      • Røe C.
      • Schanke A.K.
      • Søberg H.L.
      • et al.
      Combined cognitive and vocational interventions after mild to moderate traumatic brain injury: Study protocol for a randomized controlled trial.
      ,
      • Maas A.I.
      • Menon D.K.
      • Adelson P.D.
      • Andelic N.
      • Bell M.J.
      • Belli A.
      • et al.
      Traumatic brain injury: Integrated approaches to improve prevention, clinical care, and research.
      ,
      • Savulich G.
      • Menon D.K.
      • Stamatakis E.A.
      • Pickard J.D.
      • Sahakian B.J.
      Personalised treatments for traumatic brain injury: Cognitive, emotional and motivational targets.
      ). Patients’ perception of the injury might lead to differentiated neuropsychiatric outcomes (
      • Dilley M.
      • Avent C.
      Long-term neuropsychiatric disorders after traumatic brain injury.
      ). Postinjury social and environmental challenges also have psychological impacts. For example, a patient and family may be unable to work to their full capacity following injury, which consequently stretches their financial resources (
      • Gaudette É
      • Seabury S.A.
      • Temkin N.
      • Barber J.
      • DiGiorgio A.M.
      • Markowitz A.J.
      • et al.
      Employment and economic outcomes of mild traumatic brain injury patients presenting to US Level 1 trauma centers: A TRACK-TBI Study.
      ). Professional guidance should be integrated into rehabilitation to support coping with all these stressors (
      • Mikolić A.
      • Polinder S.
      • Helmrich I.R.R.
      • Haagsma J.A.
      • Cnossen M.C.
      Treatment for posttraumatic stress disorder in patients with a history of traumatic brain injury: A systematic review.
      ).

      Limitations and Future Directions

      To control for all potential confounding factors (genetics, sex, education, lifestyles, socioeconomics) would have required a larger sample size. More studies would help to determine the effect of these different demographic characteristics. DTI data from 2 time points were inadequate to quantify the effect of acute edema on AD. Future studies incorporating earlier imaging time points as well as more advanced diffusion models (e.g., neurite orientation dispersion and density imaging) would help elucidate these acute effects of TBI.
      Tract-based spatial statistics has limited anatomical specificity due to its reliance on FA and neglecting orientation information in the diffusion tensor (
      • Bach M.
      • Laun F.B.
      • Leemans A.
      • Tax C.M.
      • Biessels G.J.
      • Stieltjes B.
      • Maier-Hein K.H.
      Methodological considerations on tract-based spatial statistics (TBSS).
      ). Also, the impact of heterogeneity in image acquisition on the results (e.g., due to scanner types) might have been reduced by advanced harmonization methods.
      Regarding interpretation of the results, the reported effect sizes are not large enough to predict the outcomes of individual patients. Also, the present analyses cannot determine if particular neuropsychiatric factors (e.g., sleep, depression, anxiety, fear, sleep problems, physical difficulty, pain) were predominantly associated with AD to inform treatment recommendations. These limitations should inspire future hypothesis-driven investigations with more advanced diffusion MRI acquisition and analysis methodology.

      Acknowledgments and Disclosures

      This work was supported by the National Institute of Neurological Disorders and Stroke (Grant No. R01 NS110856 [to LDN]) and National Institute on Aging (Grant No. K23 AG073528-01 [to BLB]). The TRACK-TBI study is sponsored by the National Institutes of Health, National Institute of Neurological Disorders and Stroke (Grant No. U01 NS086090), U.S. Department of Defense (Grant No. W81XWH-14-2-0176), Abbott Laboratories, and One Mind. Abbott Laboratories is supported by the U.S. Army Medical Research and Development Command.
      This paper is in memory of our coauthor Dr. Harvey S. Levin, who was a pioneer in the application of neuroimaging and neuropsychology to the study of mTBI, including the role of resilience in mTBI patient outcomes. Regarding International Committee of Medical Journal Editors authorship criteria, we note that, due to his illness, Dr. Levin was not able to approve the final submitted version of this manuscript.
      ELY has a patent for United States Patent and Trademark Office No. 62/269,778 pending. GTM received grants from the National Institute of Neurological Disorders and Stroke during the conduct of the study; research funding from the U.S. Department of Energy, grants from the Department of Defense, research funding from Abbott Laboratories, grants from the National Football League Scientific Advisory Board, and research funding from One Mind outside the submitted work; in addition, GTM had a patent for Interpretation and Quantification of Emergency Features on Head Computed Tomography issued. He served for 2 seasons as an unaffiliated neurologic consultant for home games of the Oakland Raiders; he was compensated $1500 per game for 6 games during the 2017 season but received no compensation for this work during the 2018 season. MBS received personal fees from Aptinyx, Bionomics, Janssen, and Neurocrine as well as personal fees and stock options from Oxeia Biopharmaceuticals outside the submitted work. RD-A received personal fees and research funding from Neural Analytics, Inc., and travel reimbursement from Brain Box Solutions, Inc., outside the submitted work. DG received personal fees from Amgen, Avanir Pharmaceuticals, Acadia Pharmaceuticals, Aspen Health Strategy Group, and Celgene outside the submitted work. NK received personal fees from Portola outside the submitted work. PM received grants from GE Healthcare and nonfinancial support from the General Electric–National Football League Head Health Initiative outside the submitted work; in addition, PM had a patent for United States Patent and Trademark Office No. 62/269,778 pending. JR received personal fees from Boehringer Ingelheim and New Beta Innovations outside the submitted work. RDZ received royalties from Oakstone Publishing for an educational CD (Physical Medicine and Rehabilitation: A Comprehensive Review) and Demos Medical Publishing for serving as coeditor of Brain Injury Medicine. RDZ serves or served on the scientific advisory boards of Myomo, Oxeia Biopharma, Biodirection, and Elminda. He also evaluates patients in the Massachusetts General Hospital Brain and Body–TRUST Program, which is funded by the National Football League Players Association. RDZ served on the National Football League Players Association Mackey White Health and Safety Committee. None of these funding organizations influenced the scientific content of this paper. All other authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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