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EEG and MEG studies have demonstrated several population-level indicators of mTBI that could serve as objective markers of brain injury. However, deriving clinically useful biomarkers for mTBI and other brain disorders from EEG\/MEG signals is hampered by the large inter-individual variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as deviations with respect to the normal variation. To this end, a normative dataset comprising 621 healthy participants was used to determine the variation in power spectra across the cortex. In addition, we constructed normative datasets based on age-matched subsets of the full normative data. To discriminate patients from healthy control subjects, we trained support-vector-machine classifiers on the quantitative deviation maps for 25 mTBI patients and 20 controls not included in the normative dataset. The best performing classifier made use of the full normative data across the entire age and frequency ranges. This classifier was able to distinguish patients from controls with an accuracy of 79%. Inspection of the trained model revealed that low-frequency activity in the theta frequency band (4\u20138 Hz) is a significant indicator of mTBI, consistent with earlier studies. The results demonstrate the feasibility of using normative modeling of MEG data combined with machine learning to advance diagnosis of mTBI and identify patients that would benefit from treatment and rehabilitation. The current approach could be applied to a wide range of brain disorders, thus providing a basis for deriving MEG\/EEG-based biomarkers.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011613","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T18:52:45Z","timestamp":1699555965000},"page":"e1011613","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data"],"prefix":"10.1371","volume":"19","author":[{"given":"Veera","family":"It\u00e4linna","sequence":"first","affiliation":[]},{"given":"Hanna","family":"Kaltiainen","sequence":"additional","affiliation":[]},{"given":"Nina","family":"Forss","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9110-6788","authenticated-orcid":true,"given":"Mia","family":"Liljestr\u00f6m","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0130-0801","authenticated-orcid":true,"given":"Lauri","family":"Parkkonen","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"issue":"3","key":"pcbi.1011613.ref001","doi-asserted-by":"crossref","first-page":"e42","DOI":"10.1097\/JSA.0000000000000119","article-title":"Structural neuroimaging findings in mild traumatic brain injury","volume":"24","author":"ED Bigler","year":"2016","journal-title":"Sports Medicine and Arthroscopy Review"},{"issue":"8","key":"pcbi.1011613.ref002","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1089\/neu.2016.4618","article-title":"Mild traumatic brain injury: Longitudinal study of cognition, functional status, and post-traumatic symptoms.","volume":"34","author":"S Dikmen","year":"2017","journal-title":"Journal of Neurotrauma."},{"issue":"8","key":"pcbi.1011613.ref003","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1089\/neu.2014.3339","article-title":"A prospective biopsychosocial study of the persistent post-concussion symptoms following mild traumatic brain injury","volume":"32","author":"M W\u00e4ljas","year":"2015","journal-title":"Journal of Neurotrauma"},{"key":"pcbi.1011613.ref004","first-page":"109","article-title":"Single-subject-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mild traumatic brain injury. 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