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As a predominately young, physically active, and generally healthy population, service members (SMs) with vestibular dysfunction (VD) following a TBI may not be accurately represented by the current civilian reference ranges on assessments of VD. This study enrolled SMs who were referred for vestibular rehabilitation following a mild/moderate TBI. The participants self-reported VD using the Activities-specific Balance Confidence (ABC) scale and the Dizziness Handicap Inventory (DHI) followed by evaluation of vestibular performance using computerized dynamic posturography sensory organizational test (CDP–SOT). Retrospective analysis of these outcomes comparing the study sample of SMs to the reported civilian samples revealed SMs self-reported lower VD with significantly higher balance confidence (ABC: 77.11 ± 14.61, p < 0.05) and lower dizziness (DHI: 37.75 ± 11.74, p < 0.05) than civilians. However, the SMs underperformed in performance-based evaluations compared to civilians with significantly lower CDP–SOT composite and ratio scores (COMP: 68.46 ± 13.46, p < 0.05; VIS: 81.36 ± 14.03, p < 0.01; VEST: 55.63 ± 22.28, p < 0.05; SOM: 90.46 ± 10.17, p < 0.05). Correlational analyses identified significant relationships between the ABC and CDP–SOT composite (r = 0.380, p < 0.01) and ratio scores (VIS: r = 0.266, p < 0.05; VEST: r = 0.352, p < 0.01). These results highlight the importance of recognizing and understanding nuances in assessing VD in SMs to ensure they have access to adequate care and rehabilitation prior to returning to duty.

Combat‐related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active‐duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep‐learning neural network method, 3D‐MEGNET, and applied it to resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat‐deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All‐frequency model, which combined delta‐theta (1–7 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D‐MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver‐operator‐characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta‐theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta‐theta and gamma‐band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta‐band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all‐frequency model offered more discriminative power than each frequency‐band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders., This study developed a novel resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging method, 3D‐MEGNET, using deep learning. The optimized 3D‐MEGNET method combining rs‐MEG data from all frequency bands distinguished individuals with combat‐related mild traumatic brain injury (cmTBI) from combat‐deployed healthy controls with high sensitivity, specificity, and diagnostic accuracy. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI.

Background The present investigation tested the efficacy of the Tennant Biomodulator, a novel pain management intervention that uses biofeedback-modulated electrical stimulation, to reduce chronic pain and its psychosocial sequelae in a sample of current and former military service members. The Tennant Biomodulator used on its most basic setting was compared to two commonly used, non-pharmacological pain treatments—traditional Chinese acupuncture and transcutaneous electrical nerve stimulation (TENS)—in a comparative efficacy, randomized, open-label trial. Methods Participants included 100 active duty and retired service men and women with chronic pain undergoing treatment at the Brooke Army Medical Center in Texas, USA, randomly assigned to receive six, weekly sessions of either Tennant Biomodulator treatment, traditional Chinese acupuncture, or TENS, in addition to usual care. Recruitment was conducted between May 2010 to September 2013. Outcome measures were collected at intake, before and after each treatment session, and at a 1-month follow-up. Intent-to-treat analyses were used throughout, with mixed models used to investigate main effects of group, time, and group × time interactions with consideration given to quadratic effects. Outcomes measured included ratings of chronic pain, pain-related functional disability, and symptoms of post-traumatic stress disorder (PTSD) and depression. Results On average, regardless of their treatment group, participants exhibited a 16% reduction in pain measured by the Brooke Army Medical Center’s Clinic Pain Log [F(1, 335) = 55.7, P F(1, 84) = 28.3, P Conclusions Findings build on previous work suggesting that traditional Chinese acupuncture and TENS can reduce pain and its functional sequelae without risks associated with pharmacological pain management. The Tennant Biomodulator used on its most basic setting performs as well as these other interventions. Based on the present findings, large, randomized controlled trials on the Tennant Biomodulator are indicated. Future work should test this device using its full range of settings for pain-related psychological health. Trial registration Clincialtrials.gov (NCT01752010); registered December 14, 2012.

The use of music performance in music therapy with military service members is discussed as a vehicle for social transformation and reintegration. The use of performance in music therapy is not without controversy primarily because therapy is considered a process, not a product, and confidentiality and privacy are essential components of therapy. However, others have argued that public performances can validate therapeutic changes in clients, give voice to their experiences, raise awareness of social issues within their communities, transform perceptions of injury, or illness in audience members, and may result in the clients gaining support and validation from their communities. We discuss the potential of music performances to contribute to individual development, reinforce rehabilitation, enhance function, and facilitate change at the community level to support reintegration of military service members. We illustrate this through two brief case reports of service members who received music therapy as part of their treatment for post-traumatic stress disorder, traumatic brain injury, and other psychological health concerns at the National Intrepid Center of Excellence, a Directorate of the Walter Reed National Military Medical Center, Bethesda, MD, United States. The service members wrote, learned, and refined songs over multiple music therapy sessions and created song introductions to share with audiences the meanings and benefits gained from integrating performance in music therapy. The case reports also include excerpts of interviews conducted with these service members several months after treatment about their experiences of performing and the perceived impact of their performances on the audience and greater community.