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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 was quantified by training individual classifier instantiations using a single feature type. The classifier was tested on a discovery sample of 92 healthy controls (HCs; 26 females, age \u03bc\u2009\u00b1\u2009\u03c3: 39.8\u2009\u00b1\u200915.5 years) and 471 adult mTBI patients (158 females, age \u03bc\u2009\u00b1\u2009\u03c3: 38.4\u2009\u00b1\u20095.9 years). Results were replicated in an independent validation sample of 256 HCs (149 females, age \u03bc\u2009\u00b1\u2009\u03c3: 55.3\u2009\u00b1\u200912.1 years) and 126 patients with concussion (46 females, age \u03bc\u2009\u00b1\u2009\u03c3: 39.0\u2009\u00b1\u200917.7 years). Classifier accuracy exceeds 99% in both samples, suggesting robust generalizability to new samples. Notably, 13 bilateral cortico-cortical connection pairs predict diagnostic status with accuracy exceeding 99% in both discovery and validation samples. Many such connection pairs are between prefrontal cortex structures, fronto-limbic and fronto-subcortical structures, and occipito-temporal structures in the ventral (\u201cwhat\u201d) visual stream. This and related connectivity form a highly salient network of brain connections that is particularly vulnerable to concussion. Because these connections are important in mediating cognitive control, memory, and attention, our findings explain the high frequency of cognitive disturbances after concussion. Our classifier was trained and validated on concussed participants with cognitive profiles very similar to those of HCs. This suggests that the classifier can complement current diagnostics by providing independent information in clinical contexts where patients have quasi-normal cognition but where concussion diagnosis stands to benefit from additional evidence.<\/jats:p>","DOI":"10.1089\/neu.2023.0509","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T07:41:22Z","timestamp":1710402082000},"page":"1883-1900","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning"],"prefix":"10.1177","volume":"41","author":[{"given":"Benjamin J.","family":"Hacker","sequence":"first","affiliation":[{"name":"Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, Dana and David Dornsife College of Arts and Sciences, University of Southern California, Los Angeles, California, USA."},{"name":"Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, Dana and David Dornsife College of Arts and Sciences, University of Southern California, Los Angeles, California, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phoebe E.","family":"Imms","sequence":"additional","affiliation":[{"name":"Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, Dana and David Dornsife College of Arts and Sciences, University of Southern California, Los Angeles, California, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ammar M.","family":"Dharani","sequence":"additional","affiliation":[{"name":"Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, Dana and David Dornsife College of Arts and Sciences, University of Southern California, Los Angeles, California, USA."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jessica","family":"Zhu","sequence":"additional","affiliation":[{"name":"Corwin D. 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