Key PointsQuestion听
Can the use of diffusion tensor imaging and neurite orientation dispersion and density imaging effectively detect white matter microstructural alterations in US military service members with a history of mild traumatic brain injury occurring more than 2 years ago, and can the results be used in monitoring neurobehavioral symptoms?
Findings听
In this case-control study of 98 military service members, both imaging tools detected widespread differences in white matter, with more sensitive results occurring with the use of neurite orientation dispersion and density imaging metrics through a region-of-interest approach. Notably, these white matter alterations were found to be associated with neurobehavioral symptoms.
Meaning听
These findings support the usefulness of advanced neuroimaging techniques in assessing microstructural changes related to military-related mild traumatic brain injury, and suggest that aberrant white matter properties can be used in monitoring progression or recovery during the chronic postinjury phase.
Importance听
Mild traumatic brain injury (mTBI) is the signature injury experienced by military service members and is associated with poor neuropsychiatric outcomes. Yet, there is a lack of reliable clinical tools for mTBI diagnosis and prognosis.
Objective听
To examine the white matter microstructure and neuropsychiatric outcomes of service members with a remote history of mTBI (ie, mTBI that occurred over 2 years ago) using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI).
Design, Setting, and Participants听
This case-control study examined 98 male service members enrolled in a study at the National Intrepid Center of Excellence. Eligible participants were active duty status or able to enroll in the Defense Enrollment Eligibility Reporting system, ages 18 to 60 years, and had a remote history of mTBI; controls were matched by age.
Exposures听
Remote history of mTBI.
Main Outcomes and Measures听
White matter microstructure was assessed using a region-of-interest approach of skeletonized diffusion images, including DTI (fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity) and NODDI (orientation dispersion index [ODI], isotropic volume fraction, intra-cellular volume fraction). Neuropsychiatric outcomes associated with posttraumatic stress disorder (PTSD) and postconcussion syndrome were assessed.
Results听
A total of 65 male patients with a remote history of mTBI (mean [SD] age, 40.5 [5.0] years) and 33 age-matched male controls (mean [SD] age, 38.9 [5.6] years) were included in analysis. Compared with the control cohort, the 65 service members with mTBI presented with significantly more severe PTSD-like symptoms (mean [SD] PTSD CheckList-Civilian [PCL-C] version scores: control, 19.0 [3.8] vs mTBI, 41.2 [11.6]; P鈥�&濒迟;鈥�.001). DTI and NODDI metrics were altered in the mTBI group compared with the control, including intra-cellular volume fraction of the right cortico-spinal tract (尾鈥�=鈥夆垝0.029, Cohen d鈥�=鈥�0.66; P鈥�<鈥�.001), ODI of the left posterior thalamic radiation (尾鈥�=鈥夆垝0.006, Cohen d鈥�=鈥�0.55; P鈥�<鈥�.001), and ODI of the left uncinate fasciculus (尾鈥�=鈥�0.013, Cohen d鈥�=鈥�0.61; P鈥�&濒迟;鈥�.001). In service members with mTBI, fractional anisotropy of the left uncinate fasciculus was associated with postconcussion syndrome (尾鈥�=鈥�5.4鈥壝椻��10鈭�3; P鈥�=鈥�.003), isotropic volume fraction of the genu of the corpus callosum with PCL-C (尾鈥�=鈥�4.3鈥壝椻��10鈭�4; P鈥�=鈥�.01), and ODI of the left fornix and stria terminalis with PCL-C avoidance scores (尾鈥�=鈥�1.2鈥壝椻��10鈭�3; P鈥�=鈥�.02).
Conclusions and Relevance听
In this case-control study of military-related mTBI, the results suggest that advanced magnetic resonance imaging techniques using NODDI can reveal white matter microstructural alterations associated with neuropsychiatric symptoms in the chronic phase of mTBI. Diffusion trends observed throughout widespread white matter regions-of-interest may reflect mechanisms of neurodegeneration as well as postinjury tissue scarring and reorganization.
Traumatic brain injury (TBI) is a significant health concern, particularly among military populations. According to the Defense and Veterans Brain Injury Center (DVBIC), more than 450鈥�000 TBIs among US service members worldwide have been reported between 2000 and 2022, with over 80% of those TBIs being classified as mild (mTBI).1 Mounting evidence has demonstrated the neuropsychiatric consequences of mTBI, including chronic postconcussion symptoms and serious long-term effects on cognition, memory, mood, sleep, vision, and hearing.2-4 Furthermore, individuals with a history of mTBI are at higher risk for dementia, neurodegenerative diseases, psychiatric illness, and even mortality, indicating long-term progression of subclinical pathology that can manifest later in life.5-7
Despite mTBI lacking obvious clinical neuroimaging findings, making the detection of postinjury neurological changes challenging, there is increasing recognition that advanced neuroimaging techniques are promising biomarkers for its diagnosis, prognosis, and treatment monitoring.8,9 Specifically, diffusion-weighted imaging (DWI) utilizes the diffusion of water within brain tissue to infer microstructural tissue properties. One such advanced technique is diffusion tensor imaging (DTI), which provides a measure of the microstructural integrity of white matter fiber tracts through modeling of the DWI data sets. Within each voxel, DTI estimates specific diffusion variables, including mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity (Table 1).29,30
Previous studies have demonstrated that white matter microstructural integrity, measured by DTI metrics, is perturbed following military-related mTBI.31-35 Additionally, DTI studies of mTBI have reported that microstructural white matter disruptions are associated with neurocognitive and behavioral deficits postinjury.36-38 However, studies of military mTBI have generally yielded varied findings on which white matter tracts are affected and whether fractional anisotropy is increased or decreased following injury.39 For instance, some studies report lower fractional anisotropy after remote mTBI,40,41 elevated fractional anisotropy,42 or a lack of significant mTBI effects on fractional anisotropy.31,43-46 These inconsistencies may be attributed to the variability in the mechanism (ie, different cellular alterations) and etiology of mTBI at different time points postinjury.
DTI metrics represent basic mathematical descriptions of diffusion that lack structural specificity and do not directly correspond to biophysically meaningful parameters of the underlying tissue. DTI assumes Gaussian diffusion within a single microstructural compartment and is thus insensitive to the complexity of white matter microstructure that is depicted through non-Gaussian models with multiple compartments.47 To address this limitation, a neurite orientation dispersion and density imaging (NODDI) model was created to offer more specific indices of tissue microstructure.48 NODDI leverages recent progress in high-performance magnetic field gradients for magnetic resonance imaging (MRI) scanners that can more specifically probe complex non-Gaussian properties of white matter diffusion (Table 1). A 2022 study22 on civilian subjects used NODDI to identify longitudinal white matter changes of declining neurite density after mTBI, suggesting axonal degeneration from diffuse axonal injury. The study concluded that NODDI metrics are more sensitive and specific biomarkers than DTI for white matter microstructural changes.
However, further investigation using both DTI and NODDI is needed in military populations to improve the health and well-being of service members at higher risk of TBIs and their associated consequences. To our knowledge, this case-control study is the first to leverage DTI and NODDI to examine subtle white matter neuropathological changes in military service members with mTBI as well as their association with neuropsychiatric symptomology in the chronic phase postinjury. We hypothesize that NODDI would be more sensitive and specific to microstructural changes than DTI and that diffusion metrics would be associated with neuropsychiatric symptoms. By combining advanced neuroimaging techniques and neuropsychiatric data, we aim to gain deeper insights into the underlying brain changes associated with military mTBI, ultimately enhancing our understanding and management of this complex condition.
All participants were US military service members enrolled in the Walter Reed National Military Medical Center institutional review board鈥揳pproved and HIPAA-compliant Neuroimaging Core project. All participants gave written informed consent. This cross-sectional study followed the Strengthening the Reporting of Observational Studies in Epidemiology () reporting guideline.
Participants and TBI Evaluation
Participants were scanned at the National Intrepid Center of Excellence at the Walter Reed National Military Medical Center. Inclusion criteria include active duty status or eligibility for the Defense Enrollment Eligibility Reporting system, age 18 to 60 years, male sex or female sex with no current pregnancy or breastfeeding. Exclusion criteria included patients with TBI unable to consent, actively enrolled in other treatment trials where this study would interfere, a history of prior severe neurologic or psychiatric conditions unrelated to the injury event or deployment (eg, meningioma, bipolar disorder), and patients with metal implants or shrapnel. Patients were targeted for recruitment and consent to the study if they had a history of mTBI (ie, mTBI group) or had no mTBI (ie, noninjured controls). Individuals were included in the mTBI group based on confirmed mTBI diagnosis with a history exceeding 2 years, male sex, and active duty status. Participants in the control group were matched for age, sex, and active duty status. Participants from both groups were excluded if they had incomplete PCL-C survey, DTI, or NODDI data. Diagnosis of TBI is described in eMethods in Supplement 1.
Neuropsychiatric Assessments
The assessment of commonly affected neuropsychiatric domains after mTBI involved self-report surveys administered prior to the scan. These surveys included the PTSD Checklist鈥揅ivilian version (PCLC-C),49 Neurobehavioral Symptom Inventory (NSI),50 Generalized Anxiety Disorder-7 (GAD-7),51 and Patient Health Questionnaire-9 (PHQ-9).52 The PCL-C is a 17-item measure modeled after the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision) (DSM-IV-TR)53 symptom criteria for PTSD. Three cluster scores corresponding to the DSM-IV-TR symptom criteria were calculated: criterion B (reexperiencing cluster), criterion C (avoidance cluster), and criterion D (hyperarousal cluster). The NSI is a 22-item measure designed to evaluate self-reported postconcussion symptoms, such as headache, balance issues, and nausea, rated on a 5-point scale. A total score was obtained by summing the ratings for the 22 items, and 4 cluster scores were calculated as outlined by Vanderploeg and colleagues54: vestibular, somatosensory, cognitive, and affective clusters. The GAD-7 is a self-report survey consisting of 7 items designed to assess the severity of generalized anxiety disorder symptoms in individuals over the past 2 weeks. The PHQ-9 is a self-report questionnaire comprising 9 items used to measure the severity of depression symptoms and aid in diagnosing depressive disorders. While participants in both the mTBI and control groups completed the PCL-C, only patients with mTBI completed the NSI, GAD-7, and PHQ-9 surveys.
MRI Acquisition and Image Processing
All subjects were scanned on a 3T MR750 scanner (General Electric) equipped with a 32-channel phased array head radiofrequency coil (MR Instruments). Whole-brain diffusion and structural MRI were obtained. Head motion was represented by framewise displacement measures of diffusion MRI (dMRI)55 and images were preprocessed with the protocol discussed in eMethods in Supplement 1.
DTI scalar images (ie, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) were created from dMRI with b-values between 0 and 1000 seconds/mm2 using the log-signal in 2 steps implemented in MRtrix3.56 Weighted least-squares with weights based on the empirical signal intensity followed by iterated weighted least-squares with weights determined by the signal predictions from the previous iteration using unconstrained optimization were applied to reconstructed DTI maps. NODDI parameters were calculated using the open-source tool AMICO,57 which yielded maps of intracellular volume fraction (ICVF), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF) for each participant.48
White Matter Region-of-Interest鈥揃ased Analysis
In a region-of-interest (ROI) analysis, the fractional anisotropy data were warped to the common FMRIB58 fractional anisotropy template in MNI152 standard space using the symmetric image normalization method implemented in the ANTs package58 using FMRIB Software Library鈥檚 toolbox tract-based spatial statistics (TBSS), a mean fractional anisotropy image was generated from all participant scans in this common space, creating a mean fractional anisotropy white matter skeleton representing common tracts across the entire group and thresholded at above 0.2 to exclude voxels containing gray matter and partial volume effects. The aligned fractional anisotropy volume was projected onto the skeleton by filling it with fractional anisotropy values from the nearest relevant tract center. Output images and the 0.2-thresholded skeleton were visually inspected for accuracy. The same nonlinear warping and skeleton projection steps were then applied to other whole brain mean diffusivity, radial diffusivity, axial diffusivity, ICVF, ODI, and ISOVF maps.
White matter main ROIs were examined by utilizing the Johns Hopkins University (JHU) ICBM-DTI-81 white matter Labeled Atlas59 in the standard MNI152 space. The JHU-ICBM-DTI-81 white matter labels atlas contains 46 white matter labels (eTable 1 in Supplement 1). To analyze these fasciculi, binary mask images corresponding to each tract were used to mask individual skeletonized maps that had been previously registered to the MNI (Montreal Neurological Institute) standard space. Regional values represented by the average voxel value within the selected JHU white matter tract masks were computed for each participant across all generated DTI and NODDI parameter maps.
All statistical analyses were performed using SPSS Statistics version 24.0 (IBM Corp) and R package 4.2.2 (R Project for Statistical Computing). Unpaired 2-sample t tests were used for demographic analysis between groups. 蠂2 tests compared the proportion of counts in each military-related category between groups with the expected proportions and the null hypothesis of equal proportions in each group. To compare DTI and NODDI parameters between mTBI and control groups, a generalized linear model (GLM) analysis was conducted with covariates of age and mean framewise displacement followed by nonparametric permutation test and randomization test (eMethods in Supplement 1). Variance inflation factor (VIF) was computed to assess the severity of multicollinearity in the OLS regression analysis.60 Finally, we performed a sensitivity analysis by comparing the variances between full and reduced OLS models and calculating the standardized regression coefficients (SRC), a global sensitivity indices based on linear or monotonic assumptions in the case of independent factors,61,62 and the Johnson indices, indices for correlated input relative importance by R2 decomposition for linear regression models (eMethods in Supplement 1).63,64 The dichotomous receiver operating characteristic (ROC) curve analysis is described in eAppendix in Supplement 1. A 2-tailed P value at .05 was considered statistically significant.
Demographic and Neuropsychiatric Assessment of Study Participants
A total of 98 study participants were included (all male; mean [SD] age, 40.0 [5.2] years); 33 participants had no history of mTBI (mean [SD] age, 39.1 [5.6] years) and 65 participants had a history of mTBI (mean [SD]age, 40.6 [5.0] years) (Table 2). Participants with a history of mTBI had significantly fewer education years compared with those in the control group (mean [SD] education, 14.7 [2.2] years vs 16.3 [2.8] years; P鈥�=鈥�.006). A significantly greater proportion of participants in the control group were in the Army compared with those in the mTBI group (22 of 33 [67%] vs 15 of 65 [23%]; P鈥�&濒迟;鈥�.001). A significantly higher proportion of participants with mTBI were enlisted (53 of 65 [82%] vs 17 of 33 [52%]; P鈥�=鈥�.002). The mean (SD) mTBI count was 2.1 (1.4), with a mean (SD) time since the most recent injury being 14.2 (9.3) years. Most of the mechanisms causing mTBI were attributed to impacts (24 [37%]) and falls (20 [31%]). Additionally, 9 injuries (14%) resulted from blasts, 10 (15%) from motor vehicle accidents, 1 (2%) from gunshot wounds, and 1 (2%) from various other causes.
Compared with the control group, participants with mTBI history had significantly higher PCL-C total scores (mean [SD] score, 40.9 [11.3] vs 19.0 [3.8]; P鈥�&濒迟;鈥�.001). The mTBI group had a mean (SD) NSI total score of 36.1 (12.4), a somatosensory subscore of 8.4 (4.4), an affective subscore of 12.1 (4.6), a cognitive subscore of 9.1 (3.0), and a vestibular subscore of 3.4 (2.0). The majority of participants with mTBI history had moderately severe (20 participants [31%]) and severe (20 [31%]) anxiety as indicated by the GAD-7 scores as well as moderate (25 [39%]), moderately severe (6 [9%]), and severe (4 [6%]) depression as indicated by the PHQ-9 scores (eTable 2 in Supplement 1). Three service members with mTBI (4.6%) did not have NSI scores, which were replaced by imputation using R MICE. NSI, GAD-7, and PHQ-9 were not obtained in healthy controls.
There was no significant group difference of dMRI framewise displacement. This suggests participants with mTBI history had no greater head motion than controls during MRI examination.
DTI and NODDI ROI-Based Comparisons and Their Association With Neuropsychiatric Symptoms
ROI analyses using GLMs with age as a covariate revealed widespread differences in DTI and NODDI metrics in various white matter regions (Table 3). Notably, more NODDI metrics were significantly different between control and mTBI groups compared with DTI metrics. Diffusion metrics of ROIs with the highest effect sizes between mTBI and control groups included ICVF of the right corticospinal tract (CST) (尾鈥�=鈥夆垝0.029, R2鈥�=鈥�0.136; P鈥�<鈥�.001), ODI of the left posterior thalamic radiation (PTR) (尾鈥�=鈥夆垝6鈥壝椻��10鈭�3, R2鈥�=鈥�0.253; P鈥�<鈥�.001) and ODI of the left uncinate fasciculus (UNC) (尾鈥�=鈥�0.013, R2鈥�=鈥�0.125; P鈥�&濒迟;鈥�.001).
When assessing the association with neuropsychiatric symptoms, NSI cognitive subscores were associated with fractional anisotropy of the left UNC (尾鈥�=鈥�5.4鈥壝椻��10鈭�3; P鈥�=鈥�.003); PCL-C total scores were associated with ISOVF of the genu of corpus callosum (尾鈥�=鈥�4.3鈥壝椻��10鈭�4; P鈥�=鈥�.01); PCL-C C avoidance subscores were associated with ODI of the left fornix (crus) and stria terminalis (尾鈥�=鈥�1.2鈥壝椻��10鈭�3; P鈥�=鈥�.02). VIF, an index measuring how much the variance of an estimated regression coefficient is increased because of collinearity, of all models were less than 1.2 (Table 4). Sensitivity analysis of the 3 regression models revealed that the full model (3 independent variables) explained the variances of diffusion metrics better than those of the reduced model (2 independent variables) with an SRC of 0.335, 0.266, and 1.000, and R2 of 0.091, 0.067, and 0.867 (Johnson indices) for NSI cognitive, PCL-C total scores and PCL-C C avoidance subscores, respectively (eTable 4 in Supplement 1). ROC curve analysis is available in eResults in Supplement 1.
This case-control study investigated diffusion parameters of white matter in military service members with and without remote mTBI. To the best of our knowledge, this is the first study to report white matter microstructural changes indicated by DTI and NODDI metrics using an ROI-based approach in the chronic phase of mTBI among military service members. Our study supports previous evidence that mTBI can have long-term effects on white matter microstructure and neuropsychiatric symptoms related to PTSD, postconcussion syndrome, anxiety, and depression.65-68
The ROI analysis yielded extensive white matter alterations in the mTBI group that were also linked to neuropsychiatric symptoms. Notably, the analysis revealed DTI and NODDI trends of increased anisotropic and parallel diffusion, implicating mechanisms of inflammation, glial cell activation, and tissue scarring or reorganization.69,70 For example, there was increased fractional anisotropy and axial diffusivity, as well as decreased ODI in the left PTR. It is often assumed that lower fractional anisotropy values may correspond to a reduction of the white matter microstructural integrity.71 However, emerging evidence suggests that higher fractional anisotropy and axial diffusivity values can be attributed to factors such as cytotoxic edema during the acute phase following injury72-74 as well as glial scarring and a manifestation of recovery and/or compensation in the chronic phase,69,72,74-76 or simply in the voxels with less fiber crossings. In these conditions, the interpretation of higher fractional anisotropy values as solely indicative of increased white matter integrity may not hold true. Some studies indicated that astrocytes undergoing structural remodeling postinjury, thereby driving glial scarring, lead to anisotropic tissue microstructure causing an increase in fractional anisotropy.69,70,77 Notably, a 2011 study69 performing a histological analysis showed that glial fibrillary acidic protein (GFAP), which is a marker for gliosis, was significantly positively correlated with fractional anisotropy and axial diffusivity. Furthermore, decreased mean diffusivity, radial diffusivity, ISOVF, and ODI were observed in the fornix, with ODI values being associated with PCL-C scores. Reduced ISOVF in the corpus callosum was also associated with PCL-C total. Altogether, our findings of compromised white matter in these key tracts complement previous findings that also showed white matter changes in military service members with a remote history of mTBI.41,78
Alternatively, our study also revealed diffusion trends implicating mechanisms of neurodegeneration. For example, decreased ICVF in right and left CST suggests reduced neurite density; reduced fractional anisotropy with increased ODI in the left UNC may suggest axonal degeneration with compensatory neural sprouting.79-81 These findings are consistent with our 2023 study82 investigating fiber-specific structural changes in a larger cohort of military service members after a remote brain injury. Importantly, fractional anisotropy was significantly reduced in the left UNC and was associated with cognitive-related PCS scores. This anatomically aligns with the significance of UNC in key neural circuitry involving the entorhinal and amygdala, which plays a pivotal role in memory formation and emotion regulation.83,84 Postconcussion symptoms, PTSD symptoms, and neuropsychological function have been shown to be associated with compromised fronto-limbic neurocircuitry in chronic mTBI.85,86
While statistical significance shows that an association exists in a study, effect size indicates the practical significance of a research outcome. R2 determines the proportion of variance in the dependent variable that can be explained by the independent variable, representing the goodness of fit. Our results show that the ICVF of the right CST had a high Cohen d for group difference but a low R2 value, while the ODI of left fornix and stria terminalis had a high SRC and R2 but a relatively low Cohen d. These findings might help explain why diffusion metrics exhibit low dichotomous discrimination.
Lastly, ROC curve analyses indicated that self-reported neuropsychiatric symptoms were more effective in distinguishing between the mTBI and control groups compared with imaging metrics, which did not provide significant discriminatory power. Particularly, PTSD symptoms were successful in classifying participants with mTBI history from controls in this patient population. Prior studies have highlighted the increased risk of comorbid PTSD and mTBI among military service members compared with civilians.87-90 This emphasizes the importance of assessing PTSD symptoms and associated neuropsychiatric symptoms in the management of military-related mTBI. Indeed, while neuroimaging metrics did not show superior discriminatory ability, they did facilitate the identification of potential lesions associated with neuropsychiatric outcomes, which can be used to predict clinical progression and determine the most suitable treatment approach.91,92
This study had several limitations. This was a cross-sectional case-control study with a relatively limited sample size, which offers only a snapshot of the neural factors associated with neuropsychiatric symptoms in the chronic phase postinjury. Although the sample size was similar to other studies using NODDI to examine mTBI,22,23,28,93,94 further analysis with a larger sample is necessary to validate the findings. Additionally, there were missing clinical data that might have influenced the results, including past medical history, preexisting conditions, medications, treatments, or interventions received. Finally, a causal relationship between white matter microstructural alterations and neuropsychiatric symptom presentation cannot be assumed. Thus, these findings should be considered as suggestive of a relationship that should be replicated in other studies.
In this case-control study of military-related mTBI, our results showed that DTI and NODDI can detect microstructural white matter alterations in the chronic phase of mTBI, implicating inflammatory and neurodegenerative processes. Moreover, our findings suggest that NODDI might offer greater sensitivity than DTI in identifying alterations during the chronic phase. Consequently, combining NODDI with DTI parameters in future research could yield valuable insights. Specifically, our results suggest that mTBI in military service members is characterized by widespread differences in diffusion parameters of white matter tracts important for cognitive and emotional processing, including the corpus callosum, CST, UNC, and fornix. Our findings indicate that DTI and NODDI metrics can provide valuable pathophysiological insights into the long-term neuropsychiatric consequences of mTBI.
Accepted for Publication: February 25, 2024.
Published: April 18, 2024. doi:10.1001/jamanetworkopen.2024.8121
Correction: This article was corrected on June 7, 2024, to add a coauthor who was omitted from the original version.
Open Access: This is an open access article distributed under the terms of the CC-BY License. 漏 2024 Kim S et al. 糖心vlog Open.
Corresponding Author: Ping-Hong Yeh, PhD, RM1128, Bldg 51, National Intrepid Center of Excellence (NICoE), Walter Reed National Military Medical Center, Bethesda, MD 20914 (pinghongyeh@gmail.com; ping-hong.yeh.civ@health.mil).
Author Contributions: Drs S. Kim and Yeh had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs S. Kim and Yeh contributed equally to this article.
Concept and design: S. Kim, Ollinger, H. Kim, Yeh.
Acquisition, analysis, or interpretation of data: S. Kim, Song, Raiciulescu, Seenivasan, Wolfgang, H. Kim, Werner, Yeh.
Drafting of the manuscript: S. Kim, Yeh.
Critical review of the manuscript for important intellectual content: All authors.
Statistical analysis: S. Kim, Ollinger, Raiciulescu, Yeh.
Administrative, technical, or material support: Song, Seenivasan, Yeh.
Supervision: Ollinger, Wolfgang, Werner, Yeh.
Conflict of Interest Disclosures: None reported.
Funding/Support: This project was funded and partly supported by US Army Medical Research and Materiel Command (USAMRMC) (award No. 203337).
Role of the Funder/Sponsor: The institutions with which the authors of this study are affiliated, including USAMRMC, National Intrepid Center of Excellence, Walter Reed National Military Medical Center, and Uniformed Services University of Health Sciences, played no part in the design or conduct of the study; collection, management, analysis, interpretation of the data, manuscript preparation, nor the decision to submit the manuscript for publication. The manuscript was reviewed and approved by the public affairs official at the Uniformed Services University of Health Sciences.
Disclaimer: The opinions and assertions expressed herein are those of the author(s) and do not reflect the official policy or position of the Uniformed Services University of the Health Sciences or the Department of Defense.
Data Sharing Statement: See Supplement 2.
Additional Contributions: The authors would like to acknowledge the efforts of the larger team of research coordinators, technical support, and senior management at the Neuroimaging Section of the Research Department, National Intrepid Center of Excellence (NICoE). Particular thanks to Dr Rujirutana Srikanchanat, PhD, Dr Cheng Guan Koay, PhD, Dr Wei Liu, PhD, Mr Adam Cliffton, BA, Mr Joseph Hennesy, BA, and Ms Rebecca Sandlain, BA, for their assistance in MRI data acquisition (affiliated with NICoE); Dr Gerard Riedy, MD, PhD, Dr Grant Bonavia, MD, PhD, and Dr Treven Pickett, PsyD, for administrative support (affiliated with NICoE); Dr Kimbra Kenney, MD, and Dr Chandler Rhodes, PhD, for administering OSU-TBI identification methods (affiliated with NICoE); Dr Hosung Kim, PhD (affiliated with University of South California), for his discussion of the manuscript, and all the clinicians at the NICoE for their hard work of administering interview and clinical care to all the service member participants. None of the contributors has received any compensation beyond terms of employment.
1.Military Health System. DOD TBI Worldwide Numbers. Accessed October 29, 2023.
2.Arciniegas
听DB锘�, Anderson
听CA锘�, Topkoff
听J锘�, McAllister
听TW锘�. 听Mild traumatic brain injury: a neuropsychiatric approach to diagnosis, evaluation, and treatment.听锘� 听Neuropsychiatr Dis Treat. 2005;1(4):311-327.
3.Management of Concussion/mTBI Working Group. 听VA/DoD clinical practice guideline for management of concussion/mild traumatic brain injury.听锘� 听J Rehabil Res Dev. 2009;46(6):CP1-CP68.
4.Taylor
听CA锘�, Bell
听JM锘�, Breiding
听MJ锘�, Xu
听L锘�. 听Traumatic brain injury鈥搑elated emergency department visits, hospitalizations, and deaths鈥擴nited States, 2007 and 2013.听锘� 听MMWR Surveill Summ. 2017;66(9):1-16. doi:
5.Smith
听DH锘�, Johnson
听VE锘�, Stewart
听W锘�. 听Chronic neuropathologies of single and repetitive TBI: substrates of dementia?听锘� 听Nat Rev Neurol. 2013;9(4):211-221. doi:
6.Wilson
听L锘�, Stewart
听W锘�, Dams-O鈥機onnor
听K锘�,
听et al. 听The chronic and evolving neurological consequences of traumatic brain injury.听锘� 听Lancet Neurol. 2017;16(10):813-825. doi:
7.Barnes
听DE锘�, Byers
听AL锘�, Gardner
听RC锘�, Seal
听KH锘�, Boscardin
听WJ锘�, Yaffe
听K锘�. 听Association of mild traumatic brain injury with and without loss of consciousness with dementia in US military veterans.听锘� 听闯础惭础 Neurol. 2018;75(9):1055-1061. doi:
8.Bigler
听ED锘�. 听Neuroimaging biomarkers in mild traumatic brain injury (mTBI).听锘� 听Neuropsychol Rev. 2013;23(3):169-209. doi:
9.Moyron
听RB锘�, Vallejos
听PA锘�, Fuller
听RN锘�, Dean
听N锘�, Wall
听NR锘�. 听Neuroimaging and advanced research techniques may lead to improved outcomes in military members suffering from traumatic brain injury.听锘� 听Trauma Surg Acute Care Open. 2021;6(1):e000608. doi:
10.Kraus
听MF锘�, Susmaras
听T锘�, Caughlin
听BP锘�, Walker
听CJ锘�, Sweeney
听JA锘�, Little
听DM锘�. 听White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study.听锘� 听叠谤补颈苍. 2007;130(Pt 10):2508-2519. doi:
11.Aoki
听Y锘�, Inokuchi
听R锘�, Gunshin
听M锘�, Yahagi
听N锘�, Suwa
听H锘�. 听Diffusion tensor imaging studies of mild traumatic brain injury: a meta-analysis.听锘� 听J Neurol Neurosurg Psychiatry. 2012;83(9):870-876. doi:
12.Dodd
听AB锘�, Epstein
听K锘�, Ling
听JM锘�, Mayer
听AR锘�. 听Diffusion tensor imaging findings in semi-acute mild traumatic brain injury.听锘� 听J Neurotrauma. 2014;31(14):1235-1248. doi:
13.Gardner
听A锘�, Kay-Lambkin
听F锘�, Stanwell
听P锘�,
听et al. 听A systematic review of diffusion tensor imaging findings in sports-related concussion.听锘� 听J Neurotrauma. 2012;29(16):2521-2538. doi:
14.Churchill
听NW锘�, Caverzasi
听E锘�, Graham
听SJ锘�, Hutchison
听MG锘�, Schweizer
听TA锘�. 听White matter microstructure in athletes with a history of concussion: comparing diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI).听锘� 听Hum Brain Mapp. 2017;38(8):4201-4211. doi:
15.Kumar
听R锘�, Gupta
听RK锘�, Husain
听M锘�,
听et al. 听Comparative evaluation of corpus callosum DTI metrics in acute mild and moderate traumatic brain injury: its correlation with neuropsychometric tests.听锘� 听叠谤补颈苍 Inj. 2009;23(7):675-685. doi:
16.Abdelrahman
听HAF锘�, Ubukata
听S锘�, Ueda
听K锘�,
听et al. 听Combining multiple indices of diffusion tensor imaging can better differentiate patients with traumatic brain injury from healthy subjects.听锘� 听Neuropsychiatr Dis Treat. 2022;18:1801-1814. doi:
17.Winklewski
听PJ锘�, Sabisz
听A锘�, Naumczyk
听P锘�, Jodzio
听K锘�, Szurowska
听E锘�, Szarmach
听A锘�. 听Understanding the physiopathology behind axial and radial diffusivity changes鈥攚hat do we know?听锘� 听Front Neurol. 2018;9:92. doi:
18.Kinnunen
听KM锘�, Greenwood
听R锘�, Powell
听JH锘�,
听et al. 听White matter damage and cognitive impairment after traumatic brain injury.听锘� 听叠谤补颈苍. 2011;134(Pt 2):449-463. doi:
19.Perez
听AM锘�, Adler
听J锘�, Kulkarni
听N锘�,
听et al. 听Longitudinal white matter changes after traumatic axonal injury.听锘� 听J Neurotrauma. 2014;31(17):1478-1485. doi:
20.Cubon
听VA锘�, Putukian
听M锘�, Boyer
听C锘�, Dettwiler
听A锘�. 听A diffusion tensor imaging study on the white matter skeleton in individuals with sports-related concussion.听锘� 听J Neurotrauma. 2011;28(2):189-201. doi:
21.Hagmann
听P锘�, Jonasson
听L锘�, Maeder
听P锘�, Thiran
听JP锘�, Wedeen
听VJ锘�, Meuli
听R锘�. 听Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond.听锘� 听搁补诲颈辞驳谤补辫丑颈肠蝉. 2006;26(suppl 1):S205-S223. doi:
22.Palacios
听EM锘�, Owen
听JP锘�, Yuh
听EL锘�,
听et al; TRACK-TBI Investigators. 听The evolution of white matter microstructural changes after mild traumatic brain injury: a longitudinal DTI and NODDI study.听锘� 听Sci Adv. 2020;6(32):eaaz6892. doi:
23.Churchill
听NW锘�, Caverzasi
听E锘�, Graham
听SJ锘�, Hutchison
听MG锘�, Schweizer
听TA锘�. 听White matter during concussion recovery: Comparing diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI).听锘� 听Hum Brain Mapp. 2019;40(6):1908-1918. doi:
24.Wu
听YC锘�, Mustafi
听SM锘�, Harezlak
听J锘�, Kodiweera
听C锘�, Flashman
听LA锘�, McAllister
听TW锘�. 听Hybrid diffusion imaging in mild traumatic brain injury.听锘� 听J Neurotrauma. 2018;35(20):2377-2390. doi:
25.Kamiya
听K锘�, Hori
听M锘�, Aoki
听S锘�. 听NODDI in clinical research.听锘� 听J Neurosci Methods. 2020;346:108908. doi:
26.Caron
听B锘�, Bullock
听D锘�, Kitchell
听L锘�,
听et al. Advanced mapping of the human white matter microstructure better separates elite sports participation. PsyArXiv. Preprint posted online January 3, 2020.
27.Mayer
听AR锘�, Ling
听JM锘�, Dodd
听AB锘�, Meier
听TB锘�, Hanlon
听FM锘�, Klimaj
听SD锘�. 听A prospective microstructure imaging study in mixed-martial artists using geometric measures and diffusion tensor imaging: methods and findings.听锘� 听叠谤补颈苍 Imaging Behav. 2017;11(3):698-711. doi:
28.Cao
听M锘�, Luo
听Y锘�, Wu
听Z锘�, Wu
听K锘�, Li
听X锘�. 听Abnormal neurite density and orientation dispersion in frontal lobe link to elevated hyperactive/impulsive behaviours in young adults with traumatic brain injury.听锘� 听叠谤补颈苍 Commun. 2022;4(1):fcac011. doi:
29.Yang
听E锘�, Nucifora
听PG锘�, Melhem
听ER锘�. 听Diffusion MR imaging: basic principles.听锘� 听Neuroimaging Clin N Am. 2011;21(1):1-25, vii. doi:
30.Tournier
听JD锘�, Mori
听S锘�, Leemans
听A锘�. 听Diffusion tensor imaging and beyond.听锘� 听Magn Reson Med. 2011;65(6):1532-1556. doi:
31.Hayes
听JP锘�, Miller
听DR锘�, Lafleche
听G锘�, Salat
听DH锘�, Verfaellie
听M锘�. 听The nature of white matter abnormalities in blast-related mild traumatic brain injury.听锘� 听狈别耻谤辞颈尘补驳别 Clin. 2015;8:148-156. doi:
32.Trotter
听BB锘�, Robinson
听ME锘�, Milberg
听WP锘�, McGlinchey
听RE锘�, Salat
听DH锘�. 听Military blast exposure, ageing and white matter integrity.听锘� 听叠谤补颈苍. 2015;138(Pt 8):2278-2292. doi:
33.Ware
听JB锘�, Biester
听RC锘�, Whipple
听E锘�, Robinson
听KM锘�, Ross
听RJ锘�, Nucifora
听PG锘�. 听Combat-related mild traumatic brain injury: association between baseline diffusion-tensor imaging findings and long-term outcomes.听锘� 听搁补诲颈辞濒辞驳测. 2016;280(1):212-219. doi:
34.Miller
听DR锘�, Hayes
听JP锘�, Lafleche
听G锘�, Salat
听DH锘�, Verfaellie
听M锘�. 听White matter abnormalities are associated with chronic postconcussion symptoms in blast-related mild traumatic brain injury.听锘� 听Hum Brain Mapp. 2016;37(1):220-229. doi:
35.Kim
听SY锘�, Yeh
听PH锘�, Ollinger
听JM锘�,
听et al. 听Military-related mild traumatic brain injury: clinical characteristics, advanced neuroimaging, and molecular mechanisms.听锘� 听Transl Psychiatry. 2023;13(1):289. doi:
36.Yuh
听EL锘�, Cooper
听SR锘�, Mukherjee
听P锘�,
听et al; TRACK-TBI Investigators. 听Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: a TRACK-TBI study.听锘� 听J Neurotrauma. 2014;31(17):1457-1477. doi:
37.Croall
听ID锘�, Cowie
听CJ锘�, He
听J锘�,
听et al. 听White matter correlates of cognitive dysfunction after mild traumatic brain injury.听锘� 听狈别耻谤辞濒辞驳测. 2014;83(6):494-501. doi:
38.Oehr
听L锘�, Anderson
听J锘�. 听Diffusion-tensor imaging findings and cognitive function following hospitalized mixed-mechanism mild traumatic brain injury: a systematic review and meta-analysis.听锘� 听Arch Phys Med Rehabil. 2017;98(11):2308-2319. doi:
39.Asken
听BM锘�, DeKosky
听ST锘�, Clugston
听JR锘�, Jaffee
听MS锘�, Bauer
听RM锘�. 听Diffusion tensor imaging (DTI) findings in adult civilian, military, and sport-related mild traumatic brain injury (mTBI): a systematic critical review.听锘� 听叠谤补颈苍 Imaging Behav. 2018;12(2):585-612. doi:
40.Petrie
听EC锘�, Cross
听DJ锘�, Yarnykh
听VL锘�,
听et al. 听Neuroimaging, behavioral, and psychological sequelae of repetitive combined blast/impact mild traumatic brain injury in Iraq and Afghanistan war veterans.听锘� 听J Neurotrauma. 2014;31(5):425-436. doi:
41.Davenport
听ND锘�, Lim
听KO锘�, Armstrong
听MT锘�, Sponheim
听SR锘�. 听Diffuse and spatially variable white matter disruptions are associated with blast-related mild traumatic brain injury.听锘� 听狈别耻谤辞颈尘补驳别. 2012;59(3):2017-2024. doi:
42.Dennis
听EL锘�, Wilde
听EA锘�, Newsome
听MR锘�,
听et al. 听Enigma military brain injury: a coordinated meta-analysis of diffusion MRI from multiple cohorts.听锘� 听Proc IEEE Int Symp Biomed Imaging. April 2018:1386-1389. doi:
43.Jorge
听RE锘�, Acion
听L锘�, White
听T锘�,
听et al. 听White matter abnormalities in veterans with mild traumatic brain injury.听锘� 听Am J Psychiatry. 2012;169(12):1284-1291. doi:
44.Sorg
听SF锘�, Schiehser
听DM锘�, Bondi
听MW锘�,
听et al. 听White matter microstructural compromise is associated with cognition but not PTSD symptoms in military veterans with traumatic brain injury.听锘� 听J Head Trauma Rehabil. 2016;31(5):297. doi:
45.Maruta
听J锘�, Palacios
听EM锘�, Zimmerman
听RD锘�, Ghajar
听J锘�, Mukherjee
听P锘�. 听Chronic post-concussion neurocognitive deficits. I. Relationship with white matter integrity.听锘� 听Front Hum Neurosci. 2016;10:35. doi:
46.Sorg
听SF锘�, Delano-Wood
听L锘�, Luc
听N锘�,
听et al. 听White matter integrity in veterans with mild traumatic brain injury: associations with executive function and loss of consciousness.听锘� 听J Head Trauma Rehabil. 2014;29(1):21-32. doi:
47.Jones
听DK锘�, Cercignani
听M锘�. 听Twenty-five pitfalls in the analysis of diffusion MRI data.听锘� 听NMR Biomed. 2010;23(7):803-820. doi:
48.Zhang
听H锘�, Schneider
听T锘�, Wheeler-Kingshott
听CA锘�, Alexander
听DC锘�. 听NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain.听锘� 听狈别耻谤辞颈尘补驳别. 2012;61(4):1000-1016. doi:
49.Blanchard
听EB锘�, Jones-Alexander
听J锘�, Buckley
听TC锘�, Forneris
听CA锘�. 听Psychometric properties of the PTSD Checklist (PCL).听锘� 听Behav Res Ther. 1996;34(8):669-673. doi:
50.King
听PR
听Jr锘�. 听A Psychometric Study of the Neurobehavioral Symptom Inventory. State University of New York at Buffalo; 2011.
51.L枚we
听B锘�, Decker
听O锘�, M眉ller
听S锘�,
听et al. 听Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population.听锘� 听Med Care. 2008;46(3):266-274. doi:
52.Levis
听B锘�, Benedetti
听A锘�, Thombs
听BD锘�. 听Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis.听锘� 听叠惭闯. 2019;365:l1476. doi:
53.Bell
听CC锘�. 听DSM-IV: diagnostic and statistical manual of mental disorders.听锘� 听闯础惭础. 1994;272(10):828-829. doi:
54.Vanderploeg
听RD锘�, Silva
听MA锘�, Soble
听JR锘�,
听et al. 听The structure of postconcussion symptoms on the Neurobehavioral Symptom Inventory: a comparison of alternative models.听锘� 听J Head Trauma Rehabil. 2015;30(1):1-11. doi:
55.Power
听JD锘�, Mitra
听A锘�, Laumann
听TO锘�, Snyder
听AZ锘�, Schlaggar
听BL锘�, Petersen
听SE锘�. 听Methods to detect, characterize, and remove motion artifact in resting state fMRI.听锘� 听狈别耻谤辞颈尘补驳别. 2014;84:320-341. doi:
56.Tournier
听J-D锘�, Smith
听RE锘�, Raffelt
听D锘�,
听et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. 听狈别耻谤辞滨尘补驳别. 2019;202:116鈥�137.
57.Daducci
听A锘�, Canales-Rodr铆guez
听EJ锘�, Zhang
听H锘�, Dyrby
听TB锘�, Alexander
听DC锘�, Thiran
听JP锘�. 听Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.听锘� 听狈别耻谤辞颈尘补驳别. 2015;105:32-44. doi:
58.Avants
听BB锘�, Tustison
听N锘�, Johnson
听H锘�. Advanced Normalization Tools release 2.x. July 10, 2014. Accessed October 29, 2023.
59.Mori
听S锘�, Wakana
听S锘�, Van Zijl
听PC锘�, Nagae-Poetscher
听L锘�. 听MRI Atlas of Human White Matter. Elsevier; 2005.
60.Kutner
听MH锘�, Nachtsheim
听CJ锘�, Neter
听J锘�, Li
听W锘�. 听Applied Linear Statistical Models. McGraw-Hill; 2005.
61.Iooss
听B锘�, Chabridon
听V锘�, Thouvenot
听V锘�. Variance-based importance measures for machine learning model interpretability. HAL Open Science. Published online August 1, 2022. Accessed October 29, 2023.
62.Saltelli
听A锘�, Chan
听K锘�. Scott
听EM锘�. 听Sensitivity Analysis. Wiley; 2000.
63.Johnson
听JW锘�. 听A heuristic method for estimating the relative weight of predictor variables in multiple regression.听锘� 听Multivariate Behav Res. 2000;35(1):1-19. doi:
64.Johnson
听JW锘�, LeBreton
听JM锘�. 听History and use of relative importance indices in organizational research.听锘� 听Organ Res Methods. 2004;7(3):238-257. doi:
65.Walker
听WC锘�, Hirsch
听S锘�, Carne
听W锘�,
听et al. 听Chronic Effects of Neurotrauma Consortium (CENC) multicentre study interim analysis: differences between participants with positive versus negative mild TBI histories.听锘� 听叠谤补颈苍 Inj. 2018;32(9):1079-1089. doi:
66.Verfaellie
听M锘�, Lafleche
听G锘�, Spiro
听A
听III锘�, Tun
听C锘�, Bousquet
听K锘�. 听Chronic postconcussion symptoms and functional outcomes in OEF/OIF veterans with self-report of blast exposure.听锘� 听J Int Neuropsychol Soc. 2013;19(1):1-10. doi:
67.Walker
听WC锘�, Franke
听LM锘�, McDonald
听SD锘�, Sima
听AP锘�, Keyser-Marcus
听L锘�. 听Prevalence of mental health conditions after military blast exposure, their co-occurrence, and their relation to mild traumatic brain injury.听锘� 听叠谤补颈苍 Inj. 2015;29(13-14):1581-1588. doi:
68.Waters
听AB锘�, Bottari
听SA锘�, Jones
听LC锘�, Lamb
听DG锘�, Lewis
听GF锘�, Williamson
听JB锘�. 听Regional associations of white matter integrity and neurological, post-traumatic stress disorder and autonomic symptoms in veterans with and without history of loss of consciousness in mild TBI.听锘� 听Front Neuroimaging. 2024;2. doi:
69.Budde
听MD锘�, Janes
听L锘�, Gold
听E锘�, Turtzo
听LC锘�, Frank
听JA锘�. 听The contribution of gliosis to diffusion tensor anisotropy and tractography following traumatic brain injury: validation in the rat using Fourier analysis of stained tissue sections.听锘� 听叠谤补颈苍. 2011;134(Pt 8):2248-2260. doi:
70.Laitinen
听T锘�, Sierra
听A锘�, Bolkvadze
听T锘�, Pitk盲nen
听A锘�, Gr枚hn
听O锘�. 听Diffusion tensor imaging detects chronic microstructural changes in white and gray matter after traumatic brain injury in rat.听锘� 听Front Neurosci. 2015;9:128. doi:
71.Arfanakis
听K锘�, Haughton
听VM锘�, Carew
听JD锘�, Rogers
听BP锘�, Dempsey
听RJ锘�, Meyerand
听ME锘�. 听Diffusion tensor MR imaging in diffuse axonal injury.听锘� 听AJNR Am J Neuroradiol. 2002;23(5):794-802.
72.Xu
听S锘�, Zhuo
听J锘�, Racz
听J锘�,
听et al. 听Early microstructural and metabolic changes following controlled cortical impact injury in rat: a magnetic resonance imaging and spectroscopy study.听锘� 听J Neurotrauma. 2011;28(10):2091-2102. doi:
73.Wilde
听EA锘�, McCauley
听SR锘�, Hunter
听JV锘�,
听et al. 听Diffusion tensor imaging of acute mild traumatic brain injury in adolescents.听锘� 听狈别耻谤辞濒辞驳测. 2008;70(12):948-955. doi:
74.Lo
听C锘�, Shifteh
听K锘�, Gold
听T锘�, Bello
听JA锘�, Lipton
听ML锘�. 听Diffusion tensor imaging abnormalities in patients with mild traumatic brain injury and neurocognitive impairment.听锘� 听J Comput Assist Tomogr. 2009;33(2):293-297. doi:
75.Jiang
听Q锘�, Qu
听C锘�, Chopp
听M锘�,
听et al. 听MRI evaluation of axonal reorganization after bone marrow stromal cell treatment of traumatic brain injury.听锘� 听NMR Biomed. 2011;24(9):1119-1128. doi:
76.Chary
听K锘�, Manninen
听E锘�, Claessens
听J锘�, Ramirez-Manzanares
听A锘�, Gr枚hn
听O锘�, Sierra
听A锘�. 听Diffusion MRI approaches for investigating microstructural complexity in a rat model of traumatic brain injury.听锘� 听Sci Rep. 2023;13(1):2219. doi:
77.Soni
听N锘�, Medeiros
听R锘�, Alateeq
听K锘�, To
听XV锘�, Nasrallah
听FA锘�. 听Diffusion tensor imaging detects acute pathology-specific changes in the P301L tauopathy mouse model following traumatic brain injury.听锘� 听Front Neurosci. 2021;15:611451. doi:
78.Morey
听RA锘�, Haswell
听CC锘�, Selgrade
听ES锘�,
听et al; MIRECC Work Group. 听Effects of chronic mild traumatic brain injury on white matter integrity in Iraq and Afghanistan war veterans.听锘� 听Hum Brain Mapp. 2013;34(11):2986-2999. doi:
79.Hutchinson
听EB锘�, Schwerin
听SC锘�, Avram
听AV锘�, Juliano
听SL锘�, Pierpaoli
听C锘�. 听Diffusion MRI and the detection of alterations following traumatic brain injury.听锘� 听J Neurosci Res. 2018;96(4):612-625. doi:
80.Armstrong
听RC锘�, Mierzwa
听AJ锘�, Marion
听CM锘�, Sullivan
听GM锘�. 听White matter involvement after TBI: clues to axon and myelin repair capacity.听锘� 听Exp Neurol. 2016;275(Pt 3):328-333. doi:
81.Yeh
听PH锘�, Lippa
听SM锘�, Brickell
听TA锘�, Ollinger
听J锘�, French
听LM锘�, Lange
听RT锘�. 听Longitudinal changes of white matter microstructure following traumatic brain injury in U.S. military service members.听锘� 听叠谤补颈苍 Commun. 2022;4(3):fcac132. doi:
82.Yeh
听PH锘�, Song
听C锘�, Rujirutana
听S锘�,
听et al. Brain white matter alterations in military service members after a remote mild traumatic brain injury. ISMRM & ISMRT Annual Meeting & Exhibition; June 6, 2023; Toronto, Canada.
83.Von Der Heide
听RJ锘�, Skipper
听LM锘�, Klobusicky
听E锘�, Olson
听IR锘�. 听Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis.听锘� 听叠谤补颈苍. 2013;136(Pt 6):1692-1707. doi:
84.Rincon
听S锘�, Gupta
听R锘�, Ptak
听T锘�. Imaging of head trauma. In: Masdeu
听JC锘�, Gonz谩lez
听RG锘�, eds. 听Handbook of Clinical Neurology. Elsevier; 2016:447-477.
85.Yeh
听PH锘�, Guan Koay
听C锘�, Wang
听B锘�,
听et al. 听Compromised neurocircuitry in chronic blast-related mild traumatic brain injury.听锘� 听Hum Brain Mapp. 2017;38(1):352-369. doi:
86.Santhanam
听P锘�, Teslovich
听T锘�, Wilson
听SH锘�, Yeh
听PH锘�, Oakes
听TR锘�, Weaver
听LK锘�. 听Decreases in white matter integrity of ventro-limbic pathway linked to post-traumatic stress disorder in mild traumatic brain injury.听锘� 听J Neurotrauma. 2019;36(7):1093-1098. doi:
87.Tanev
听KS锘�, Pentel
听KZ锘�, Kredlow
听MA锘�, Charney
听ME锘�. 听PTSD and TBI co-morbidity: scope, clinical presentation and treatment options.听锘� 听叠谤补颈苍 Inj. 2014;28(3):261-270. doi:
88.Carlson
听KF锘�, Nelson
听D锘�, Orazem
听RJ锘�, Nugent
听S锘�, Cifu
听DX锘�, Sayer
听NA锘�. 听Psychiatric diagnoses among Iraq and Afghanistan war veterans screened for deployment-related traumatic brain injury.听锘� 听J Trauma Stress. 2010;23(1):17-24. doi:
89.Lange
听RT锘�, French
听LM锘�, Lippa
听S锘�,
听et al. 听Risk factors for the presence and persistence of posttraumatic stress symptoms following traumatic brain injury in U.S. service members and veterans.听锘� 听J Trauma Stress. 2023;36(1):144-156. doi:
90.Loignon
听A锘�, Ouellet
听MC锘�, Belleville
听G锘�. 听A systematic review and meta-analysis on PTSD following TBI among military/veteran and civilian populations.听锘� 听J Head Trauma Rehabil. 2020;35(1):E21-E35. doi:
91.Zogas
听A锘�. 听鈥淲e have no magic bullet鈥�: diagnostic ideals in veterans鈥� mild traumatic brain injury evaluations.听锘� 听Patient Educ Couns. 2022;105(3):654-659. doi:
92.Elbin
听RJ锘�, Trbovich
听A锘�, Womble
听MN锘�,
听et al. 听Targeted multidomain intervention for complex mTBI: protocol for a multisite randomized controlled trial in military-age civilians.听锘� 听Front Neurol. 2023;14:1085662. doi:
93.Huang
听S锘�, Huang
听C锘�, Li
听M锘�, Zhang
听H锘�, Liu
听J锘�. 听White matter abnormalities and cognitive deficit after mild traumatic brain injury: comparing DTI, DKI, and NODDI.听锘� 听Front Neurol. 2022;13:803066. doi:
94.Oehr
听LE锘�, Yang
听JYM锘�, Chen
听J锘�, Maller
听JJ锘�, Seal
听ML锘�, Anderson
听JFI锘�. 听Investigating white matter tract microstructural changes at six鈥搕welve weeks following mild traumatic brain injury: a combined diffusion tensor imaging and neurite orientation dispersion and density imaging study.听锘� 听J Neurotrauma. 2021;38(16):2255-2263. doi: