{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:39:31Z","timestamp":1760146771245,"version":"build-2065373602"},"reference-count":81,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Pison Technology","award":["H9240124C0001"],"award-info":[{"award-number":["H9240124C0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>The accurate, repeatable, and cost-effective quantitative characterization of mild traumatic brain injuries (mTBIs) is crucial for safeguarding the long-term health and performance of high-risk groups, including athletes, emergency responders, and military personnel. However, gaps remain in optimizing mTBI assessment methods, especially regarding the integration of neuromechanical metrics such as reaction time (RT) in predictive models. Background\/Objectives: This review synthesizes existing research on the use of neuromechanical probabilistic models as tools for assessing mTBI, with an emphasis on RT\u2019s role in predictive diagnostics. Methods: We examined 57 published studies on recent sensing technologies such as advanced electromyographic (EMG) systems that contribute data for probabilistic neural imaging, and we also consider measurement models for real-time RT tracking as a diagnostic measure. Results: The analysis identifies three primary contributions: (1) a comprehensive survey of probabilistic approaches for mTBI characterization based on RT, (2) a technical examination of these probabilistic algorithms in terms of reliability and clinical utility, and (3) a detailed outline of experimental requirements for using RT-based metrics in psychomotor tasks to advance mTBI diagnostics. Conclusions: This review provides insights into implementing RT-based neuromechanical metrics within experimental frameworks for mTBI diagnosis, suggesting that such metrics may enhance the sensitivity and utility of assessment and rehabilitation protocols. Further validation studies are recommended to refine RT-based probabilistic models for mTBI applications.<\/jats:p>","DOI":"10.3390\/jcm13247648","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T03:46:02Z","timestamp":1734407162000},"page":"7648","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neuromechanical Models of Mild Traumatic Brain Injury Conditioned on Reaction Time: A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"13","author":[{"given":"Avinash","family":"Baskaran","sequence":"first","affiliation":[{"name":"Mechanical Engineering Department, Auburn University, Auburn, AL 36849, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ross D.","family":"Hoehn","sequence":"additional","affiliation":[{"name":"Pison Technology, Boston, MA 02111, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8616-393X","authenticated-orcid":false,"given":"Chad G.","family":"Rose","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Auburn University, Auburn, AL 36849, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2219444","DOI":"10.1001\/jamanetworkopen.2022.19444","article-title":"Employment and economic outcomes of participants with mild traumatic brain injury in the TRACK-TBI study","volume":"5","author":"Gaudette","year":"2022","journal-title":"JAMA Netw. 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