biostudies-literatureHacker BJNIA NIH HHSNINDS NIH HHS1883-1900https://www.ebi.ac.uk/biostudies/studies/S-EPMC11564847biostudies-literatureUnknown41(15-16)Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification wJournal of neurotraumaIdentification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.PMC11564847R01 NS100973R01 AG079957Imms PEZhu JChowdhury NFIrimia AHacker BJDharani AMChaudhari NNfalseIdentification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification w2024-01-01T00:00:00Z2024 Aug2026-04-13T17:11:54.464Z2026-04-07T13:34:27.589ZS-EPMC115648473848279310.1089/neu.2023.0509