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However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non\u2010negative matrix factorization (NMF) is a data\u2010driven approach that detects covarying patterns (components) within high\u2010dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self\u2010reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (<jats:italic>n<\/jats:italic>\u00a0=\u20091,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta\u2010Analysis) Military Brain Injury working group. Regressions were used to examine TBI\u2010 and mTBI\u2010related associations in NMF\u2010derived components while adjusting for age, sex, post\u2010traumatic stress disorder, depression, and data acquisition site\/scanner. We found significantly stronger age\u2010dependent effects of lower FA in Veterans with TBI than Veterans without in four components (<jats:italic>q<\/jats:italic>\u00a0&lt;\u20090.05), which are spatially unconstrained by traditionally defined WM tracts. One component, occupying the most peripheral location, exhibited significantly stronger age\u2010dependent differences in Veterans with mTBI. We found NMF to be powerful and effective in detecting covarying patterns of FA associated with mTBI by applying standard parametric regression modeling. Our results highlight patterns of WM alteration that are differentially affected by TBI and mTBI in younger compared to older military Veterans.<\/jats:p>","DOI":"10.1002\/hbm.25811","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T11:53:26Z","timestamp":1647345206000},"page":"2653-2667","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["<scp>Age\u2010dependent<\/scp> white matter disruptions after military traumatic brain injury: Multivariate analysis results from <scp>ENIGMA<\/scp> brain injury"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3646-6334","authenticated-orcid":false,"given":"Heather C.","family":"Bouchard","sequence":"first","affiliation":[{"name":"Duke\u2010UNC Brain Imaging and Analysis Center Duke University  Durham North Carolina USA"},{"name":"Mid\u2010Atlantic Mental Illness Research Education and Clinical Center Durham VA Medical Center  Durham North Carolina USA"},{"name":"Center for Brain, Biology &amp; Behavior University of Nebraska\u2010Lincoln  Lincoln Nebraska USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Delin","family":"Sun","sequence":"additional","affiliation":[{"name":"Duke\u2010UNC Brain Imaging and Analysis Center Duke University  Durham North Carolina USA"},{"name":"Mid\u2010Atlantic Mental Illness Research Education and Clinical Center Durham VA Medical Center  Durham North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7112-4009","authenticated-orcid":false,"given":"Emily L.","family":"Dennis","sequence":"additional","affiliation":[{"name":"Department of Neurology University of Utah  Salt Lake City Utah USA"},{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary R.","family":"Newsome","sequence":"additional","affiliation":[{"name":"Michael E. 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