Jared A. Rowland

Objective: To define Military Occupational Blast Exposure (MOBE) as a standardized construct of cumulative occupational blast exposure spanning low-level blast and high-level blast, and propose reproducible MOBE risk classification groups based on military occupations when objective blast records or detailed lifetime interviews are unavailable. Setting: Data were drawn from 2 Long-term Impact of Military-relevant Brain Injury Consortium–Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC) studies: a retrospective cohort study and a multisite prospective longitudinal cohort. Participants: The Phenotype Study included 2 522 880 post-9/11 service members and Veterans. The Prospective Longitudinal Study (PLS) included 1666 service members and Veterans who had experienced combat. Design: The Phenotype Study was used to operationalize MOBE risk classification and characterize demographic and military correlates. PLS data were used to evaluate whether MOBE risk classification aligned with blast exposure history and blast traumatic brain injury (TBI) history. Main Measures: MOBE risk classification was compared with the generalized blast exposure value (GBEV) and blast exposure category scores from the Blast Exposure Threshold Survey, as well as blast TBI history from clinical interview data. Results: The Phenotype study classified 31.1% of military occupational codes into high-risk MOBE (MOBE+). In the PLS, MOBE+ individuals were more likely than MOBE− individuals to report blast TBI history (43.7% vs. 27.0%) and exceed the GBEV threshold of 200 000 (66.0% vs. 42.2%; both P < .001). MOBE+ individuals also reported higher mean blast exposure scores across all categories, with significant differences for small arms and large explosives. Occupational-level analyses showed meaningful heterogeneity in blast exposure profiles across high-risk occupations. Conclusions: MOBE risk classification differentiated meaningful patterns of blast exposure and blast-related injury in a well-characterized cohort. Findings support MOBE as a practical, scalable, standardized approach for estimating occupational blast exposure risk and improving comparability across studies when detailed individual exposure histories are unavailable.

Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) frequently co-occur in Veterans, producing overlapping symptoms and shared autonomic dysregulation. Heart rate variability (HRV) offers a noninvasive measure of autonomic function. Univariate HRV analyses often fail to capture complex, multivariate patterns associated with comorbidity. This study applied machine learning to HRV features extracted from MEG-derived electrocardiogram (M-ECG) signals to differentiate Veterans with TBI alone (TBI-alone; n = 42) from those with comorbid PTSD (TBI+PTSD; n = 40). Time-domain, frequency-domain, geometric, and nonlinear HRV metrics were analyzed using nested cross-validated Random Forest and XGBoost classifiers, with Boruta-based feature selection and SHapley Additive exPlanations for model interpretability. Both classifiers achieved above-chance discrimination (Random Forest AUC = 0.663; XGBoost AUC = 0.635). Multivariate models identified distributed autonomic signatures in TBI+PTSD, including altered sympathovagal balance, increased low-frequency proportion, and greater heart rate complexity. In contrast, univariate HRV differences were subtle and did not survive correction for multiple comparisons. These findings demonstrate how using multivariate machine learning HRV analysis could help with detecting comorbidity-specific autonomic patterns, suggesting that HRV-derived signatures may serve as exploratory biomarkers for risk assessment and targeted interventions in Veterans with TBI and PTSD.

Objective: To determine the characteristics of subconcussive blast exposure associated with differences in brain structure and brain function. Setting: Veterans Affairs Health Care System. Participants: Combat-exposed veterans (n = 107) without history of blast-related traumatic brain injury (TBI) or military TBI volunteered to participate. Design: Observational study. Main Outcomes and Measures: Connectome metrics describing the functional brain connectome, the unique brain network present at rest for individuals, were measured using magnetoencephalography. Regional brain volumes were calculated from anatomical magnetic resonance imaging using FreeSurfer. The Salisbury Blast Interview evaluated lifetime blast exposure. Results: Several blast characteristics were associated with the functional connectome. The average severity of exposures was related to slowing of oscillatory communication (average pressure, parameter estimate = −4.41, P < .05 corrected). By contrast, the frequency of exposures was associated with topological differences including the number of active brain regions (number of blast exposures, parameter estimate = 0.01, P < .05 corrected) and the composition of core subnetworks (number of close-range blast exposures, parameter estimate = 0.0005, P < .05 corrected). Subconcussive blast exposure was unrelated to brain volumes. Conclusion: These results demonstrate that cumulative burden of subconcussive blast exposures is associated with long-term brain function. Independent relationships with the functional connectome were observed for both the average severity and the frequency of subconcussive blast exposures. This contrasts with previous work combining concussive and subconcussive blast demonstrating the highest severity across all exposures was most relevant to long-term brain function. A critical implication of these results is that long-term brain function may be associated with blast exposure, even in the absence of acute clinical effects or noticeable symptoms. This renders subconcussive blast exposure an invisible neurological insult with potential long-term implications for brain function. As military occupational blast exposure (MOBE) is primarily subconcussive in nature, this has direct implications for its conceptualization, regulation, and monitoring.

Identifying historical mild traumatic brain injury (TBI) is important for many clinical care reasons; however, diagnosing mild TBI is inherently challenging and utility of screening is unknown. This study compares a standardized research process to an established clinical process for screening and diagnosis of historical mild TBI during combat deployment in a military/Veteran cohort. Using validated instruments, the Long-term Impact of Military-relevant Brain Injury Consortium–Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC) prospective longitudinal study (PLS) screens for all potential concussive events (PCEs) and conducts structured concussion diagnostic interviews for each PCE. The Veterans Health Administration (VHA) systematically screens all Veterans with a post-9/11 combat deployment for historical TBI and offers a comprehensive TBI evaluation (CTBIE) for those who screen positive. This study evaluates the agreement between these two systems on both PCE and mild TBI identification during combat deployment and identifies features of Veterans who were negative clinically but positive in research. VHA TBI screen and CTBIE data were obtained from Veterans Affairs Informatics and Computing Infrastructure and linked to the LIMBIC-CENC PLS dataset. VHA screen positive for PCE was defined as a positive response for the first two VHA TBI screen questions of that query mechanism and immediate signs/symptoms of TBI. The PLS identified more positive PCE screens during combat deployment (86% vs. 41%) than the VHA PCE screen, and overall agreement was poor (kappa = 0.113). Participants had higher odds of being VHA negative/PLS positive on the PCE screen if they were of older age, female sex, had more years of military service, more months in combat deployment, officer rank, or <50% service-connected disability rating and lower odds if they had less education attainment, higher combat intensity, or higher Neurobehavioral Symptom Inventory scores. The LIMBIC-CENC PLS method also identified more Veterans with mild TBI during combat deployment compared with the VHA CTBIE (81% vs. 72%) with minimal overall agreement (kappa = 0.311). Participants had higher odds of being VHA negative/PLS positive for mild TBI diagnosis if they never married or were Air Force and had lower odds if they had higher combat intensity. The LIMBIC-CENC PLS research structured interview protocol identified higher rates of TBI than the VHA TBI assessment system both for positive PCE screens and positive mild TBI diagnosis during combat deployment. Agreement was far higher for TBI determinations compared with the PCE screening. Significant characteristics of PLS-positive/VHA-negative mismatches included demographic variables, military service variables, and current symptom levels. Further research is needed to better understand whether there is a clinical value to adjust the VHA TBI screening process and how these characteristics could be considered. Providers should be aware that some Veterans may have undocumented, positive mild TBI histories even if they underwent screening and/or CTBIE processes.

U.S. Service members and Veterans (SM/V) experience elevated rates of traumatic brain injury (TBI), chronic pain, and other non-pain symptoms. However, the role of non-pain factors on pain interference levels remains unclear among SM/Vs, particularly those with a history of TBI. The primary objective of this study was to identify factors that differentiate high/low pain interference, given equivalent pain intensity among U.S. SM/V participating in the ongoing Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC) national multi-center prospective longitudinal observational study. An explainable machine learning was used to identify key predictors of pain interference conditioned on equivalent pain intensity. The final sample consisted of n = 1,577 SM/Vs who were predominantly male (87%), and 83.6% had a history of mild TBI(s) (mTBI), while 16.4% were TBI negative controls. The sample was categorized according to pain interference level (Low: 19.9%, Moderate: 52.5%, and High: 27.6%). Both pain intensity scores and pain interference scores increased with the number of mTBIs ( p < 0.001), and there was evidence of a dose response between the number of injuries and pain scores. Machine learning models identified fatigue and anxiety as the most important predictors of pain interference, whereas emotional control was protective. Partial dependence plots identified that marginal effects of fatigue and anxiety were associated with pain interference ( p < 0.001), but the marginal effect of mTBI was not significant in models considering all variables ( p > 0.05). Non-pain factors are associated with functional limitations and disability experience among SM/V with an mTBI history. The functional effects of pain may be mediated through multiple other factors. Pain is a multi-dimensional experience that may benefit most from holistic treatment approaches that target comorbidities and build supports that promote recovery.

Objective: To examine the impact of low-level blast (LLB) exposure on cognitive functioning in combat-exposed service members and Veterans (SM/Vs), and its interaction with posttraumatic stress disorder (PTSD) and deployment-related mild traumatic brain injury (TBI). Setting: Multi-site Department of Defense and Veterans Affairs research centers participating in the LIMBIC-CENC Prospective Longitudinal Study. Participants: 1036 SM/Vs who deployed in support of combat operations and completed comprehensive baseline assessments between 2015 and 2023. Design: Cross-sectional observational study using multivariate linear regression and interaction models to evaluate associations between LLB, PTSD, deployment-related mild TBI, and cognitive outcomes. Main Measures: LLB exposure was assessed using the generalized blast exposure value from the Blast Exposure Threshold Survey. Cognitive functioning was evaluated using neuropsychological tests assessing memory, attention, processing speed, executive function, and global performance. PTSD was assessed using the PTSD Checklist for DSM-5. Results: LLB was not independently associated with poorer cognitive performance. However, LLB moderated the relationship between PTSD and memory outcomes, with significantly worse visual memory in those with PTSD and high LLB exposure. Significant interaction effects were also observed between PTSD and deployment-related TBI on verbal memory, working memory, and processing speed. PTSD demonstrated the most consistent independent associations across cognitive domains. Conclusions: Although LLB exposure alone was not associated with cognitive deficits, it interacted with PTSD and TBI to influence cognitive performance. These findings support the need for integrated assessments of blast exposure, PTSD, and TBI history in evaluating cognitive health among SM/Vs.

Objective: Military service over the last several decades has been associated with an increased risk of injuries, including traumatic brain injury (TBI). Veterans with a history of TBI often experience poor health outcomes and have higher rates of premature mortality. In this study, we examined whether accelerated biological aging could help explain negative health outcomes following TBI. Setting, participants, and design: We evaluated the association between TBI and rate of epigenetic aging (assessed using DunedinPACE) using data from post-9/11 veterans ( N = 1152) enrolled in the VA Mid-Atlantic (VISN 6) MIRECC Post-Deployment Mental Health cohort study. Main measures: TBI was assessed using self-report during a clinical interview categorized into three TBI groups (none, 1, 2 +), epigenetic aging was assessed using DunedinPACE derived from DNA methylation data. Results: Veterans who reported more lifetime TBI (β = 0.07, 95% CI [0.01, 0.14], P = .029) or deployment-related TBI (β = 0.09, 95% CI [0.01, 0.18], P = .046) had faster epigenetic aging. TBI during and after military service was more strongly associated with accelerated aging than TBI prior to military service, and deployment-related TBI was more strongly associated with accelerated aging for women veterans. Overall, associations were small to moderate in size. Conclusion: These findings show TBI could increase risk for accelerated aging and underscores its potential utility in identifying veterans who may face aging-related health issues. Early identification of TBI-related accelerated aging could inform interventions that mitigate long-term health risks as post-9/11 veterans transition into middle and older age.

An extensive library of symptom inventories has been developed over time to measure clinical symptoms of traumatic brain injury (TBI), but this variety has led to several long-standing issues. Most notably, results drawn from different settings and studies are not comparable. This creates a fundamental problem in TBI diagnostics and outcome prediction, namely that it is not possible to equate results drawn from distinct tools and symptom inventories. Here, we present an approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories by ranking item text similarities according to their conceptual likeness. We tested the ability of four pretrained deep learning models to screen thousands of symptom description pairs for related content—a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. Correlation and factor analysis found the properties of the scales were broadly preserved under conversion. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding broad gains for the harmonization of TBI assessment.

Physical activity may be an important factor for understanding health and psychosocial outcomes following military service. The purpose of this analysis was to evaluate the association between physical activity and biopsychosocial outcomes in post-9/11 Veterans. We also evaluated the interaction of physical activity with posttraumatic stress disorder (PTSD) diagnosis and traumatic brain injury (TBI) history. Data were cross-sectional from 549 post-9/11 Veterans. Hierarchical linear regression models were used to investigate relationships between self-reported physical activity and biopsychosocial outcomes. Physical activity was significantly associated with better outcomes beyond PTSD and TBI, including depression ( B = −1.03), neurobehavioral symptoms ( B = −1.06), sleep quality ( B = −0.36), pain interference ( B = −0.18), and quality of life ( B = 1.38). Promoting physical activity could enhance mental and physical health in Veterans, independent of PTSD diagnosis or TBI history. Exercise programs may be particularly beneficial for addressing specific biopsychosocial concerns among Veterans.