Persistent MRI Findings Unique to Blast and Repetitive Mild TBI: Analysis of the CENC/LIMBIC Cohort Injury Characteristics.

INTRODUCTION: MRI represents one of the clinical tools at the forefront of research efforts aimed at identifying diagnostic and prognostic biomarkers following traumatic brain injury (TBI). Both volumetric and diffusion MRI findings in mild TBI (mTBI) are mixed, making the findings difficult to interpret. As such, additional research is needed to continue to elucidate the relationship between the clinical features of mTBI and quantitative MRI measurements. MATERIAL AND METHODS: Volumetric and diffusion imaging data in a sample of 976 veterans and service members from the Chronic Effects of Neurotrauma Consortium and now the Long-Term Impact of Military-Relevant Brain Injury Consortium observational study of the late effects of mTBI in combat with and without a history of mTBI were examined. A series of regression models with link functions appropriate for the model outcome were used to evaluate the relationships among imaging measures and clinical features of mTBI. Each model included acquisition site, participant sex, and age as covariates. Separate regression models were fit for each region of interest where said region was a predictor. RESULTS: After controlling for multiple comparisons, no significant main effect was noted for comparisons between veterans and service members with and without a history of mTBI. However, blast-related mTBI were associated with volumetric reductions of several subregions of the corpus callosum compared to non-blast-related mTBI. Several volumetric (i.e., hippocampal subfields, etc.) and diffusion (i.e., corona radiata, superior longitudinal fasciculus, etc.) MRI findings were noted to be associated with an increased number of repetitive mTBIs versus. CONCLUSIONS: In deployment-related mTBI, significant findings in this cohort were only observed when considering mTBI sub-groups (blast mechanism and total number/dose). Simply comparing healthy controls and those with a positive mTBI history is likely an oversimplification that may lead to non-significant findings, even in consortium analyses.

Microstructural Organization of Distributed White Matter Associated With Fine Motor Control in US Service Members With Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) is the most common form of brain injury. While most individuals recover from mTBI, roughly 20% experience persistent symptoms, potentially including reduced fine motor control. We investigate relationships between regional white matter organization and subcortical volumes associated with performance on the Grooved Pegboard (GPB) test in a large cohort of military Service Members and Veterans (SM&Vs) with and without a history of mTBI(s). Participants were enrolled in the Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium. SM&Vs with a history of mTBI(s) (n = 847) and without mTBI (n = 190) underwent magnetic resonance imaging and the GPB test. We first examined between-group differences in GPB completion time. We then investigated associations between GPB performance and regional structural imaging measures (tractwise diffusivity, subcortical volumes, and cortical thickness) in SM&Vs with a history of mTBI(s). Lastly, we explored whether mTBI history moderated associations between imaging measures and GPB performance. SM&Vs with mTBI(s) performed worse than those without mTBI(s) on the non-dominant hand GPB test at a trend level (p < 0.1). Higher fractional anisotropy (FA) of tracts including the posterior corona radiata, superior longitudinal fasciculus, and uncinate fasciculus were associated with better GPB performance in the dominant hand in SM&Vs with mTBI(s). These findings support that the organization of several white matter bundles are associated with fine motor performance in SM&Vs. We did not observe that mTBI history moderated associations between regional FA and GPB test completion time, suggesting that chronic mTBI may not significantly influence fine motor control.

Discriminating Mild Traumatic Brain Injury and Posttraumatic Stress Disorder Using Latent Neuroimaging and Neuropsychological Profiles in Active-Duty Military Service Members.

OBJECTIVE: Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) commonly occur among military Service Members and Veterans and have heterogenous, but also overlapping symptom presentations, which often complicate the diagnoses of underlying impairments and development of effective treatment plans. Thus, we sought to examine whether the combination of whole brain gray matter (GM) and white matter (WM) structural measures with neuropsychological performance can aid in the classification of military personnel with mTBI and PTSD. METHODS: Active-Duty US Service Members ( n = 156; 87.8% male) with a history of mTBI, PTSD, combined mTBI+PTSD, or orthopedic injury completed a neuropsychological battery and T1- and diffusion-weighted structural neuroimaging. Cortical, subcortical, ventricular, and WM volumes and whole brain fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were calculated. Latent profile analyses were performed to determine how the GM and WM indicators, together with neuropsychological indicators, classified individuals. RESULTS: For both GM and WM, respectively, a 4-profile model was the best fit. The GM model identified greater ventricular volumes in Service Members with cognitive symptoms, including those with a diagnosis of mTBI, either alone or with PTSD. The WM model identified reduced FA and elevated RD in those with psychological symptoms, including those with PTSD or mTBI and comorbid PTSD. However, contrary to expectation, a global neural signature unique to those with comorbid mTBI and PTSD was not identified. CONCLUSIONS: The findings demonstrate that neuropsychological performance alone is more robust in differentiating Active-Duty Service Members with mTBI and PTSD, whereas global neuroimaging measures do not reliably differentiate between these groups.

Comparing resting-state connectivity of working memory networks in U.S. Service members with mild traumatic brain injury and posttraumatic stress disorder.

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) are prevalent among military populations, and both have been associated with working memory (WM) impairments. Previous resting-state functional connectivity (rsFC) research conducted separately in PTSD and mTBI populations suggests that there may be similar and distinct abnormalities in WM-related networks. However, no studies have compared rsFC of WM brain regions in participants with mTBI versus PTSD. We used resting-state fMRI to investigate rsFC of WM networks in U.S. Service Members (n = 127; ages 18-59) with mTBI only (n = 46), PTSD only (n = 24), and an orthopedically injured (OI) control group (n = 57). We conducted voxelwise rsFC analyses with WM brain regions to test for differences in WM network connectivity in mTBI versus PTSD. Results revealed reduced rsFC between ventrolateral prefrontal cortex (vlPFC), lateral premotor cortex, and dorsolateral prefrontal cortex (dlPFC) WM regions and brain regions in the dorsal attention and somatomotor networks in both mTBI and PTSD groups versus controls. When compared to those with mTBI, individuals with PTSD had lower rsFC between both the lateral premotor WM seed region and middle occipital gyrus as well as between the dlPFC WM seed region and paracentral lobule. Interestingly, only vlPFC connectivity was significantly associated with WM performance across the samples. In conclusion, we found primarily overlapping patterns of reduced rsFC in WM brain regions in both mTBI and PTSD groups. Our finding of decreased vlPFC connectivity associated with WM is consistent with previous clinical and neuroimaging studies. Overall, these results provide support for shared neural substrates of WM in individuals with either mTBI or PTSD.

Latent Neuropsychological Profiles to Discriminate Mild Traumatic Brain Injury and Posttraumatic Stress Disorder in Active-Duty Service Members.

OBJECTIVE: To determine whether cognitive and psychological symptom profiles differentiate clinical diagnostic classifications (eg, history of mild traumatic brain injury [mTBI] and posttraumatic stress disorder [PTSD]) in military personnel. METHODS: US Active-Duty Service Members ( N = 209, 89% male) with a history of mTBI ( n = 56), current PTSD ( n = 23), combined mTBI + PTSD ( n = 70), or orthopedic injury controls ( n = 60) completed a neuropsychological battery assessing cognitive and psychological functioning. Latent profile analysis was performed to determine how neuropsychological outcomes of individuals clustered together. Diagnostic classifications (ie, mTBI, PTSD, mTBI + PTSD, and orthopedic injury controls) within each symptom profile were examined. RESULTS: A 5-profile model had the best fit. The profiles differentiated subgroups with high (34.0%) or normal (21.5%) cognitive and psychological functioning, cognitive symptoms (19.1%), psychological symptoms (15.3%), and combined cognitive and psychological symptoms (10.0%). The symptom profiles differentiated participants as would generally be expected. Participants with PTSD were mainly represented in the psychological symptom subgroup, while orthopedic injury controls were mainly represented in the high-functioning subgroup. Further, approximately 79% of participants with comorbid mTBI and PTSD were represented in a symptomatic group (∼24% = cognitive symptoms, ∼29% = psychological symptoms, and 26% = combined cognitive/psychological symptoms). Our results also showed that approximately 70% of military personnel with a history of mTBI were represented in the high- and normal-functioning groups. CONCLUSIONS: These results demonstrate both overlapping and heterogeneous symptom and performance profiles in military personnel with a history of mTBI, PTSD, and/or mTBI + PTSD. The overlapping profiles may underscore why these diagnoses are often difficult to diagnose and treat, but suggest that advanced statistical models may aid in identifying profiles representing symptom and cognitive performance impairments within patient groups and enable identification of more effective treatment targets.

Advanced brain age in deployment-related traumatic brain injury: A LIMBIC-CENC neuroimaging study.

OBJECTIVE: To determine if history of mild traumatic brain injury (mTBI) is associated with advanced or accelerated brain aging among the United States (US) military Service Members and Veterans. METHODS: Eight hundred and twenty-two participants (mean age = 40.4 years, 714 male/108 female) underwent MRI sessions at eight sites across the US. Two hundred and one participants completed a follow-up scan between five months and four years later. Predicted brain ages were calculated using T1-weighted MRIs and then compared with chronological ages to generate an Age Deviation Score for cross-sectional analyses and an Interval Deviation Score for longitudinal analyses. Participants also completed a neuropsychological battery, including measures of both cognitive functioning and psychological health. RESULT: In cross-sectional analyses, males with a history of deployment-related mTBI showed advanced brain age compared to those without (t(884) = 2.1, p = .038), while this association was not significant in females. In follow-up analyses of the male participants, severity of posttraumatic stress disorder (PTSD), depression symptoms, and alcohol misuse were also associated with advanced brain age. CONCLUSION: History of deployment-related mTBI, severity of PTSD and depression symptoms, and alcohol misuse are associated with advanced brain aging in male US military Service Members and Veterans.

Distinct patterns of resting-state connectivity in U.S. service members with mild traumatic brain injury versus posttraumatic stress disorder.

Mild traumatic brain injury (mTBI) is highly prevalent in military populations, with many service members suffering from long-term symptoms. Posttraumatic stress disorder (PTSD) often co-occurs with mTBI and predicts worse clinical outcomes. Functional neuroimaging research suggests there are both overlapping and distinct patterns of resting-state functional connectivity (rsFC) in mTBI versus PTSD. However, few studies have directly compared rsFC of cortical networks in military service members with these two conditions. In the present study, U.S. service members (n = 137; ages 19-59; 120 male) underwent resting-state fMRI scans. Participants were divided into three study groups: mTBI only, PTSD only, and orthopedically injured (OI) controls. Analyses investigated group differences in rsFC for cortical networks: default mode (DMN), frontoparietal (FPN), salience, somatosensory, motor, auditory, and visual. Analyses were family-wise error (FWE) cluster-corrected and Bonferroni-corrected for number of network seeds regions at the whole brain level (pFWE < 0.002). Both mTBI and PTSD groups had reduced rsFC for DMN and FPN regions compared with OI controls. These group differences were largely driven by diminished connectivity in the PTSD group. rsFC with the middle frontal gyrus of the FPN was increased in mTBI, but decreased in PTSD. Overall, these results suggest that PTSD symptoms may have a more consistent signal than mTBI. Our novel findings of opposite patterns of connectivity with lateral prefrontal cortex highlight a potential biomarker that could be used to differentiate between these conditions.

Subcortical shape and neuropsychological function among U.S. service members with mild traumatic brain injury.

In a recent manuscript, our group demonstrated shape differences in the thalamus, nucleus accumbens, and amygdala in a cohort of U.S. Service Members with mild traumatic brain injury (mTBI). Given the significant role these structures play in cognitive function, this study directly examined the relationship between shape metrics and neuropsychological performance. The imaging and neuropsychological data from 135 post-deployed United States Service Members from two groups (mTBI and orthopedic injured) were examined. Two shape features modeling local deformations in thickness (RD) and surface area (JD) were defined vertex-wise on parametric mesh-representations of 7 bilateral subcortical gray matter structures. Linear regression was used to model associations between subcortical morphometry and neuropsychological performance as a function of either TBI status or, among TBI patients, subjective reporting of initial concussion severity (CS). Results demonstrated several significant group-by-cognition relationships with shape metrics across multiple cognitive domains including processing speed, memory, and executive function. Higher processing speed was robustly associated with more dilation of caudate surface area among patients with mTBI who reported more than one CS variables (loss of consciousness (LOC), alteration of consciousness (AOC), and/or post-traumatic amnesia (PTA)). These significant patterns indicate the importance of subcortical structures in cognitive performance and support a growing functional neuroanatomical literature in TBI and other neurologic disorders. However, prospective research will be required before exact directional evolution and progression of shape can be understood and utilized in predicting or tracking cognitive outcomes in this patient population.

Radiology: Imaging Trauma Patients in a Deployed Setting.

Medical imaging plays a critical role in the rapid diagnosis, effective triage, and management of complex poly-trauma patients. High-quality medical imaging can be accomplished successfully in a deployed or wartime setting. Due to advances in aggressive resuscitation techniques and the speed of the latest generation computed tomography scanners (64-detector and beyond), rapid trauma scans utilizing computed tomography and ultrasound imaging can routinely be performed prior to taking the patient to the operating room potentially providing the trauma team with lifesaving information. This clinical practice guideline provides an overview of the imaging modalities available in austere settings, the equipment required, and the role that each plays in triaging and diagnosis of the acutely injured poly-trauma patients.

ENIGMA MILITARY BRAIN INJURY: A COORDINATED META-ANALYSIS OF DIFFUSION MRI FROM MULTIPLE COHORTS.

Traumatic brain injury (TBI) is a significant cause of morbidity in military Veterans and Service Members. While most individuals recover fully from mild injuries within weeks, some continue to experience symptoms including headaches, disrupted sleep, and other cognitive, behavioral or physical symptoms. Diffusion magnetic resonance imaging (dMRI) shows promise in identifying areas of structural disruption and predicting outcomes. Although some studies suggest widespread structural disruption after brain injury, dMRI studies of military brain injury have yielded mixed results so far, perhaps due to the subtlety of mild injury, individual differences in injury location, severity and mechanism, and comorbidity with other disorders such as post-traumatic stress disorder (PTSD), depression, and substance abuse. We present preliminary dMRI results from the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) military brain injury working group. We found higher fractional anisotropy (FA) in participants with a history of TBI. Understanding the injury and recovery process, along with factors that influence these, will lead to improved diagnosis and treatment.

Relationships Between Subcortical Shape Measures and Subjective Symptom Reporting in US Service Members With Mild Traumatic Brain Injury.

OBJECTIVE: To assess interactions of subcortical structure with subjective symptom reporting associated with mild traumatic brain injury (mTBI), using advanced shape analysis derived from volumetric MRI. PARTICIPANTS: Seventy-six cognitively symptomatic individuals with mTBI and 59 service members sustaining only orthopedic injury. DESIGN: Descriptive cross-sectional study. MAIN MEASURES: Self-report symptom measures included the PTSD Checklist-Military, Neurobehavioral Symptom Inventory, and Symptom Checklist-90-Revised. High-dimensional measures of shape characteristics were generated from volumetric MRI for 7 subcortical structures in addition to standard volume measures. RESULTS: Several significant interactions between group status and symptom measures were observed across the various shape measures. These interactions were revealed in the right thalamus and globus pallidus for each of the shape measures, indicating differences in structure thickness and expansion/contraction for these regions. No relationships with volume were observed. CONCLUSION: Results provide evidence for the sensitivity of shape measures in differentiating symptomatic mTBI individuals from controls, while volumetric measures did not exhibit this same sensitivity. Disruptions to thalamic nuclei identified here highlight the role of the thalamus in the spectrum of symptoms associated with mTBI. Additional work is needed to prospectively, and longitudinally, assess these measures along with cognitive performance and advanced multimodal imaging methods to extend the utility of shape analysis in relation to functional outcomes in this population.

Diffusion Imaging Findings in US Service Members With Mild Traumatic Brain Injury and Posttraumatic Stress Disorder.

OBJECTIVE: Use diffusion tensor imaging to investigate white matter microstructure attributable to mild TBI (mTBI) and/or posttraumatic stress disorder (PTSD). PARTICIPANTS: Twenty-seven individuals with mTBI only, 16 with PTSD only, 42 with mTBI + PTSD, and 43 service members who sustained orthopedic injury. DESIGN: Descriptive cross-sectional study. MAIN MEASURES: Clinical diffusion tensor imaging sequence to assess fractional anisotropy, mean, axial, and radial diffusivity within selected regions of interest. RESULTS: Corrected analyses revealed a pattern of lower white matter integrity in the PTSD group for several scalar metrics. Regions affected included primarily right hemisphere areas of the internal capsule. These differences associated with the PTSD only cohort were observed in relation to all 3 comparison groups, while the mTBI + PTSD group did not exhibit any notable pattern of white matter abnormalities. CONCLUSION: Results suggest that lower resolution scan sequences are sensitive to post-acute abnormalities associated with PTSD, particularly in the right hemisphere. In addition, these findings suggest that ongoing PTSD symptoms are associated with differences in white matter diffusion that are more readily detected in a clinical scan sequence than mTBI abnormalities. Future studies are needed to prospectively assess service members prior to onset of injury to verify this pattern of results.

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities.

In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.

Susceptibility Weighted Imaging and White Matter Abnormality Findings in Service Members With Persistent Cognitive Symptoms Following Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) is a major health concern among active duty service members and Veterans returning from combat operations, and it can result in variable clinical and cognitive outcomes. Identifying biomarkers that can improve diagnosis and prognostication has been at the forefront of recent research efforts. The purpose of this study was to compare the sensitivity and specificity of abnormalities identified using more traditional magnetic resonance imaging (MRI) sequences such as fluid attenuation inversion recovery (FLAIR) to more advanced MRI sequences such as susceptibility weighted imaging (SWI) among a cohort of active duty service members experiencing persistent cognitive symptoms after mTBI. One-hundred and fifty-two active duty service members (77 mTBI, 58 orthopedically injured [OI] only, 17 post-traumatic stress disorder [PTSD] only) underwent MRI and neuropsychological evaluation at a large military treatment facility. Results demonstrated that FLAIR white matter hyperintensities (WMHs) were present in all three groups at statistically similar rates (41% mTBI, 49% OI, and 29% PTSD). With the exception of a single OI participant showing a small discrete SWI lesion, SWI abnormalities were overwhelmingly present in mTBI patients (22% mTBI, 1% OI, and 0% PTSD). Functionally, mTBI participants with and without SWI abnormalities did not differ in demographics, symptom reporting, or cognitive performance. However, mTBI participants with and without WMH did differ for on measures of working memory with the mTBI participants with WMH having worse cognitive performance. No other significant differences were noted for those participants with and without imaging abnormalities for either the OI or PTSD only cohorts. These results appear to illustrate the sensitivity and specificity of SWI findings though these results did not have any significant functional impact in this cohort. In contrast, WMHs noted on FLAIR imaging were not sensitive or specific findings, but functionally relevant among mTBI participants. These findings emphasize the complexity of injury and functional outcome in mTBI patients that requires additional examination.

Comparing Two Processing Pipelines to Measure Subcortical and Cortical Volumes in Patients with and without Mild Traumatic Brain Injury.

PURPOSE: To compare volumetric results from NeuroQuant® and FreeSurfer in a service member setting. Since the advent of medical imaging, quantification of brain anatomy has been a major research and clinical effort. Rapid advancement of methods to automate quantification and to deploy this information into clinical practice has surfaced in recent years. NeuroQuant® is one such tool that has recently been used in clinical settings. Accurate volumetric data are useful in many clinical indications; therefore, it is important to assess the intermethod reliability and concurrent validity of similar volume quantifying tools. METHODS: Volumetric data from 148 U.S. service members across three different experimental groups participating in a study of mild traumatic brain injury (mTBI) were examined. Groups included mTBI (n = 71), posttraumatic stress disorder (n = 22), or a noncranial orthopedic injury (n = 55). Correlation coefficients and nonparametric group mean comparisons were used to assess reliability and concurrent validity, respectively. RESULTS: Comparison of these methods across our entire sample demonstrates generally fair to excellent reliability as evidenced by large intraclass correlation coefficients (ICC = .4 to .99), but little concurrent validity as evidenced by significantly different Mann-Whitney U comparisons for 26 of 30 brain structures measured. CONCLUSION: While reliability between the two segmenting tools is fair to excellent, volumetric outcomes are statistically different between the two methods. As suggested by both developers, structure segmentation should be visually verified prior to clinical use and rigor should be used when interpreting results generated by either method.

Quantitative structural neuroimaging of mild traumatic brain injury in the Chronic Effects of Neurotrauma Consortium (CENC): Comparison of volumetric data within and across scanners.

BACKGROUND: An important component of the multicentre Chronic Effects of Neurotrauma Consortium (CENC) project is the development of improved quantitative magnetic resonance imaging (MRI) methods, including volumetric analysis. Although many studies routinely employ quality assurance (QA) procedures including MR and human phantoms to promote accuracy and monitor site differences, few studies perform rigorous direct comparisons of these data nor report findings that enable inference regarding site-to-site comparability. These gaps in evaluating cross-site differences are concerning, especially given the well-established differences that can occur between data acquired on scanners with different manufacturer, hardware or software. METHODS: This study reports findings on (1) a series of studies utilizing two MR phantoms to interrogate machine-based variability using data collected on the same magnet, (2) a human phantom repeatedly imaged on the same scanner to investigate within-subject, within-site variability and (3) a human phantom imaged on three different scanners to examine within subject, between-site variability. RESULTS: Although variability is relatively minimal for the phantom scanned on the same magnet, significantly more variability is introduced in a human subject, particularly when regions are relatively small or multiple sites used. CONCLUSION: Vigilance when combining data from different sites is suggested and that future efforts address these issues.

Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury.

BACKGROUND: White matter hyperintensities (WMHs) are foci of abnormal signal intensity in white matter regions seen with magnetic resonance imaging (MRI). WMHs are associated with normal ageing and have shown prognostic value in neurological conditions such as traumatic brain injury (TBI). The impracticality of manually quantifying these lesions limits their clinical utility and motivates the utilization of machine learning techniques for automated segmentation workflows. METHODS: This study develops a concatenated random forest framework with image features for segmenting WMHs in a TBI cohort. The framework is built upon the Advanced Normalization Tools (ANTs) and ANTsR toolkits. MR (3D FLAIR, T2- and T1-weighted) images from 24 service members and veterans scanned in the Chronic Effects of Neurotrauma Consortium's (CENC) observational study were acquired. Manual annotations were employed for both training and evaluation using a leave-one-out strategy. Performance measures include sensitivity, positive predictive value, [Formula: see text] score and relative volume difference. RESULTS: Final average results were: sensitivity = 0.68 ± 0.38, positive predictive value = 0.51 ± 0.40, [Formula: see text] = 0.52 ± 0.36, relative volume difference = 43 ± 26%. In addition, three lesion size ranges are selected to illustrate the variation in performance with lesion size. CONCLUSION: Paired with correlative outcome data, supervised learning methods may allow for identification of imaging features predictive of diagnosis and prognosis in individual TBI patients.

Volumetric and shape analyses of subcortical structures in United States service members with mild traumatic brain injury.

Mild traumatic brain injury (mTBI) is a significant health concern. The majority who sustain mTBI recover, although ~20 % continue to experience symptoms that can interfere with quality of life. Accordingly, there is a critical need to improve diagnosis, prognostic accuracy, and monitoring (recovery trajectory over time) of mTBI. Volumetric magnetic resonance imaging (MRI) has been successfully utilized to examine TBI. One promising improvement over standard volumetric approaches is to analyze high-dimensional shape characteristics of brain structures. In this study, subcortical shape and volume in 76 Service Members with mTBI was compared to 59 Service Members with orthopedic injury (OI) and 17 with post-traumatic stress disorder (PTSD) only. FreeSurfer was used to quantify structures from T1-weighted 3 T MRI data. Radial distance (RD) and Jacobian determinant (JD) were defined vertex-wise on parametric mesh-representations of subcortical structures. Linear regression was used to model associations between morphometry (volume and shape), TBI status, and time since injury (TSI) correcting for age, sex, intracranial volume, and level of education. Volumetric data was not significantly different between the groups. JD was significantly increased in the accumbens and caudate and significantly reduced in the thalamus of mTBI participants. Additional significant associations were noted between RD of the amygdala and TSI. Positive trend-level associations between TSI and the amygdala and accumbens were observed, while a negative association was observed for third ventricle. Our findings may aid in the initial diagnosis of mTBI, provide biological targets for functional examination, and elucidate regions that may continue remodeling after injury.

Advanced neuroimaging applied to veterans and service personnel with traumatic brain injury: state of the art and potential benefits.

Traumatic brain injury (TBI) remains one of the most prevalent forms of morbidity among Veterans and Service Members, particularly for those engaged in the conflicts in Iraq and Afghanistan. Neuroimaging has been considered a potentially useful diagnostic and prognostic tool across the spectrum of TBI generally, but may have particular importance in military populations where the diagnosis of mild TBI is particularly challenging, given the frequent lack of documentation on the nature of the injuries and mixed etiologies, and highly comorbid with other disorders such as post-traumatic stress disorder, depression, and substance misuse. Imaging has also been employed in attempts to understand better the potential late effects of trauma and to evaluate the effects of promising therapeutic interventions. This review surveys the use of structural and functional neuroimaging techniques utilized in military studies published to date, including the utilization of quantitative fluid attenuated inversion recovery (FLAIR), susceptibility weighted imaging (SWI), volumetric analysis, diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), positron emission tomography (PET), magnetoencephalography (MEG), task-based and resting state functional MRI (fMRI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS). The importance of quality assurance testing in current and future research is also highlighted. Current challenges and limitations of each technique are outlined, and future directions are discussed.

Subgroups of US IRAQ and Afghanistan veterans: associations with traumatic brain injury and mental health conditions.

U. S. veterans of Iraq and Afghanistan are known to have a high prevalence of traumatic brain injury (TBI), posttraumatic stress disorder (PTSD), and depression, which are often comorbid and share many symptoms. Attempts to describe this cohort by single diagnoses have limited our understanding of the complex nature of this population. The objective of this study was to identify subgroups of Iraq and Afghanistan veterans (IAVs) with distinct compositions of symptoms associated with TBI, PTSD, and depression. Our cross-sectional, observational study included 303,716 IAVs who received care in the Veterans Health Administration in 2010-2011. Symptoms and conditions were defined using International Classification of Diseases, Ninth Revision codes and symptom-clusters were identified using latent class analysis. We identified seven classes with distinct symptom compositions. One class had low probability of any condition and low health care utilization (HCU) (48 %). Other classes were characterized by high probabilities of mental health comorbidities (14 %); chronic pain and sleep disturbance (20 %); headaches and memory problems (6 %); and auditory problems (2.5 %). Another class had mental health comorbidities and chronic pain (7 %), and the last had high probabilities of most symptoms examined (3 %). These last two classes had the highest likelihood of TBI, PTSD, and depression and were identified as high healthcare utilizers. There are subgroups of IAVs with distinct clusters of symptom that are meaningfully associated with TBI, PTSD, depression, and HCU. Additional studies examining these veteran subgroups could improve our understanding of this complex comorbid patient population.