Hospitalization for physical injury may contribute to recovery of invisible war wounds: Response to Peterson's (2021) commentary on Soumoff et al. (2021)

Military-related mild traumatic brain injury: clinical characteristics, advanced neuroimaging, and molecular mechanisms

Mild traumatic brain injury (mTBI) is a significant health burden among military service members. Although mTBI was once considered relatively benign compared to more severe TBIs, a growing body of evidence has demonstrated the devastating neurological consequences of mTBI, including chronic post-concussion symptoms and deficits in cognition, memory, sleep, vision, and hearing. The discovery of reliable biomarkers for mTBI has been challenging due to under-reporting and heterogeneity of military-related mTBI, unpredictability of pathological changes, and delay of post-injury clinical evaluations. Moreover, compared to more severe TBI, mTBI is especially difficult to diagnose due to the lack of overt clinical neuroimaging findings. Yet, advanced neuroimaging techniques using magnetic resonance imaging (MRI) hold promise in detecting microstructural aberrations following mTBI. Using different pulse sequences, MRI enables the evaluation of different tissue characteristics without risks associated with ionizing radiation inherent to other imaging modalities, such as X-ray-based studies or computerized tomography (CT). Accordingly, considering the high morbidity of mTBI in military populations, debilitating post-injury symptoms, and lack of robust neuroimaging biomarkers, this review (1) summarizes the nature and mechanisms of mTBI in military settings, (2) describes clinical characteristics of military-related mTBI and associated comorbidities, such as post-traumatic stress disorder (PTSD), (3) highlights advanced neuroimaging techniques used to study mTBI and the molecular mechanisms that can be inferred, and (4) discusses emerging frontiers in advanced neuroimaging for mTBI. We encourage multi-modal approaches combining neuropsychiatric, blood-based, and genetic data as well as the discovery and employment of new imaging techniques with big data analytics that enable accurate detection of post-injury pathologic aberrations related to tissue microstructure, glymphatic function, and neurodegeneration. Ultimately, this review provides a foundational overview of military-related mTBI and advanced neuroimaging techniques that merit further study for mTBI diagnosis, prognosis, and treatment monitoring.

Association of Traumatic Brain Injury Severity and Self-Reported Neuropsychiatric Symptoms in Wounded Military Service Members

The impact of traumatic brain injury (TBI) severity and loss of consciousness (LOC) on the development of neuropsychiatric symptoms was studied in injured service members (SMs; n = 1278) evacuated from combat settings between 2003 and 2012. TBI diagnoses of mild TBI (mTBI) or moderate-to-severe TBI (MS-TBI) along with LOC status were identified using International Classification of Diseases, Ninth Revision (ICD-9) codes and the Defense and Veterans Brain Injury Center Standard Surveillance Case Definition for TBI. Self-reported psychiatric symptoms were evaluated for post-traumatic stress disorder (PTSD) with the PTSD Checklist, Civilian Version for PTSD, the Patient Health Questionnaire-9 for major depressive disorder (MDD), and the Patient Health Questionnaire-15 for somatic symptom disorder (SSD) in two time periods post-injury: Assessment Period 1 (AP1, 0.0-2.5 months) and Assessment Period 2 (AP2, 3-12 months). mTBI, but not MS-TBI, was associated with increased neuropsychiatric symptoms: PTSD in AP1 and AP2; MDD in AP1; and SSD in AP2. A subgroup analysis of mTBI with and without LOC revealed that mTBI with LOC, but not mTBI without LOC, was associated with increased symptoms as compared to non-TBI: PTSD in AP1 and AP2; MDD in AP1; and SSD in AP1 and AP2. Moreover, mTBI with LOC was associated with increased MDD symptoms in AP2, and SSD symptoms in AP1 and AP2, compared to mTBI without LOC. These findings reinforce the need for the accurate characterization of TBI severity and a multi-disciplinary approach to address the devastating impacts of TBI in injured SMs.

An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network

An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. Injury Assessment. The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%.