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Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep\u2010learning neural network method, 3D\u2010MEGNET, and applied it to resting\u2010state magnetoencephalography (rs\u2010MEG) source\u2010magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat\u2010deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All\u2010frequency model, which combined delta\u2010theta (1\u20137 Hz), alpha (8\u201312\u2009Hz), beta (15\u201330\u2009Hz), and gamma (30\u201380\u2009Hz) frequency bands, outperformed models based on individual bands. The optimized 3D\u2010MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9\u2009\u00b1\u20090.38%) and specificity (98.9\u2009\u00b1\u20091.54%). Receiver\u2010operator\u2010characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta\u2010theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta\u2010theta and gamma\u2010band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta\u2010band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source\u2010imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all\u2010frequency model offered more discriminative power than each frequency\u2010band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.<\/jats:p>","DOI":"10.1002\/hbm.25340","type":"journal-article","created":{"date-parts":[[2021,1,16]],"date-time":"2021-01-16T00:08:51Z","timestamp":1610755731000},"page":"1987-2004","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Resting\u2010state magnetoencephalography source magnitude imaging with deep\u2010learning neural network for classification of symptomatic combat\u2010related mild traumatic brain injury"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1025-1298","authenticated-orcid":false,"given":"Ming\u2010Xiong","family":"Huang","sequence":"first","affiliation":[{"name":"Radiology, Research, and Psychiatry Services VA San Diego Healthcare System  San Diego California USA"},{"name":"Department of Radiology 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