{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hacker BJ"],"funding":["NIA NIH HHS","NINDS NIH HHS"],"pagination":["1883-1900"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11564847"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(15-16)"],"pubmed_abstract":["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 w"],"journal":["Journal of neurotrauma"],"pubmed_title":["Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning."],"pmcid":["PMC11564847"],"funding_grant_id":["R01 NS100973","R01 AG079957"],"pubmed_authors":["Imms PE","Zhu J","Chowdhury NF","Irimia A","Hacker BJ","Dharani AM","Chaudhari NN"],"additional_accession":[]},"is_claimable":false,"name":"Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.","description":"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 w","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Aug","modification":"2026-04-13T17:11:54.464Z","creation":"2026-04-07T13:34:27.589Z"},"accession":"S-EPMC11564847","cross_references":{"pubmed":["38482793"],"doi":["10.1089/neu.2023.0509"]}}