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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 (p FWE  < 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., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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.

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.

Though cortical abnormalities have been demonstrated in moderate and severe traumatic brain injured (TBI) patients, there have been no studies examining cortical changes following blast related mild TBI (mTBI). The purpose of this study was to determine the effects and functional relevance of blast mTBI on cortical thickness in a small cohort of carefully screened blast injured US Service Members (SM). Twelve SM with mTBI acquired through blast injury were compared to 11 demographically matched control SM without TBI. Both mTBI and control participants were active duty and had completed a combat deployment. Subjects underwent MRI examination and the T1 weighted anatomic images were processed using the FreeSurfer suite of tools. Cortical thickness maps were compared between groups and examined for relationships with time since injury (TSI). Utilizing a large database of functional imaging results (BrainMap), significant regions of interest (ROI) were used to determine the behavioral profiles most consistently associated with the specific ROI. In addition, clinical variables were examined as part of post-hoc analysis of functional relevance. Group comparisons controlling for age demonstrated several significant clusters of cortical thinning for the blast injured SM. After multiple comparisons correction (False Discovery Rate (FDR)), two left hemisphere clusters remained significant (left superior temporal (STG) and frontal (SFG) gyri). No clusters were significantly correlated with TSI after FDR correction. Behavioral analysis for the STG and SFG clusters demonstrated three significant behavioral/cognitive sub-domains, each associated with audition and language. Blast injured SMs demonstrated distinct areas of cortical thinning in the STG and SFG. These areas have been previously shown to be associated with audition and language. Post-hoc analyses of clinical records demonstrated significant abnormal audiology reports for the blast injured SM suggesting that the thinning in these ROIs might be related to injury to the external auditory system rather than direct injury to the brain from the blast. It is clear that additional replication is needed in much larger cohorts. Importantly, the combination of imaging tools and methods in this study successfully demonstrated the potential to define unique ROIs and functional correlates that can be used to design future studies.

The demand for image-processing software for radiology applications has been increasing, fueled by advancements in both image-acquisition and image-analysis techniques. The utility of existing image-processing software is often limited by cost, lack of flexibility, and/or specific hardware requirements. In particular, many existing packages cannot directly utilize images formatted using the specifications in part 10 of the DICOM standard ("DICOM images"). We show how image analyses can be performed directly on DICOM images by using ImageJ, a free, Java-based image-processing package (http://rsb.info.nih.gov/ij/). We demonstrate how plug-ins written in our laboratory can be used along with the ImageJ macro script language to create flexible, low-cost, multiplatform image-processing applications that can be directed by information contained in the DICOM image header.