Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.

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

SUBMITTER: Hacker BJ 

PROVIDER: S-EPMC11564847 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature