{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T20:22:16Z","timestamp":1792182136623,"version":"4.4.0"},"reference-count":75,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:00:00Z","timestamp":1705622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (<jats:italic>n<\/jats:italic>\u2009=\u200983) and healthy controls (HCs) (<jats:italic>n<\/jats:italic>\u2009=\u200940). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79\u201391.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings.<\/jats:p>","DOI":"10.3389\/fnins.2023.1333725","type":"journal-article","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T04:24:37Z","timestamp":1705638277000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging"],"prefix":"10.3389","volume":"17","author":[{"given":"Faezeh","family":"Vedaei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Najmeh","family":"Mashhadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Alizadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Zabrecky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Monti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nancy","family":"Wintering","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily","family":"Navarreto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chloe","family":"Hriso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew B.","family":"Newberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feroze B.","family":"Mohamed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,19]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/brb3.2261","article-title":"Atypical resting state functional connectivity in mild traumatic brain injury","volume":"11","author":"Amir","year":"2021","journal-title":"Brain Behav."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.cortex.2009.09.002","article-title":"Cognitive and affective disturbances following focal cerebellar damage in adults: a neuropsychological and SPECT study","volume":"46","author":"Baillieux","year":"2010","journal-title":"Cortex"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.neuroimage.2013.05.099","article-title":"The effect of scan length on the reliability of resting-state fMRI connectivity estimates","volume":"83","author":"Birn","year":"2013","journal-title":"NeuroImage"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"102936","DOI":"10.1016\/j.nicl.2022.102936","article-title":"Task-induced subjective fatigue and resting-state striatal connectivity following traumatic brain injury","volume":"33","author":"Bruijel","year":"2022","journal-title":"Neuroimage Clin."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3389\/fnene.2013.00013","article-title":"FDG-PET imaging in mild traumatic brain injury: a critical review","volume":"5","author":"Byrnes","year":"2014","journal-title":"Front. 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