{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T23:01:05Z","timestamp":1789340465095,"version":"build-2803163510"},"reference-count":32,"publisher":"American Medical Association (AMA)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n The identification of brain activity\u2013based concussion subtypes at time of injury has the potential to advance the understanding of concussion pathophysiology and to optimize treatment planning and outcomes.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To investigate the presence of intrinsic brain activity\u2013based concussion subtypes, defined as distinct resting state quantitative electroencephalography (qEEG) profiles, at the time of injury.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n In this retrospective, multicenter (9 US universities and high schools and 4 US clinical sites) cohort study, participants aged 13 to 70 years with mild head injuries were included in longitudinal cohort studies from 2017 to 2022. Patients had a clinical diagnosis of concussion and were restrained from activity by site guidelines for more than 5 days, with an initial Glasgow Coma Scale score of 14 to 15. Participants were excluded for known neurological disease or history of traumatic brain injury within the last year. Patients were assessed with 2 minutes of artifact-free EEG acquired from frontal and frontotemporal regions within 120 hours of head injury. Data analysis was performed from July 2021 to June 2023.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Quantitative features characterizing the EEG signal were extracted from a 1- to 2-minute artifact-free EEG data for each participant, within 120 hours of injury. Symptom inventories and days to return to activity were also acquired.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n From the 771 participants (mean [SD] age, 20.16 [5.75] years; 432 male [56.03%]), 600 were randomly selected for cluster analysis according to 471 qEEG features. Participants and features were simultaneously grouped into 5 disjoint subtypes by a bootstrapped coclustering algorithm with an overall agreement of 98.87% over 100 restarts. Subtypes were characterized by distinctive profiles of qEEG measure sets, including power, connectivity, and complexity, and were validated in the independent test set. Subtype membership showed a statistically significant association with time to return to activity.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this cohort study, distinct subtypes based on resting state qEEG activity were identified within the concussed population at the time of injury. The existence of such physiological subtypes supports different underlying pathophysiology and could aid in personalized prognosis and optimization of care path.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2023.55910","type":"journal-article","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T11:34:13Z","timestamp":1707824053000},"page":"e2355910","source":"Crossref","is-referenced-by-count":7,"title":["Identification of Concussion Subtypes Based on Intrinsic Brain Activity"],"prefix":"10.1001","volume":"7","author":[{"given":"Ruben","family":"Arma\u00f1anzas","sequence":"first","affiliation":[{"name":"BrainScope Company, Chevy Chase, Maryland"},{"name":"Institute of Data Science and Artificial Intelligence, Universidad de Navarra, Pamplona, Spain"},{"name":"Tecnun School of Engineering, Universidad de Navarra, Donostia-San Sebasti\u00e1n, 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