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U.S.A."],"published-print":{"date-parts":[[2026,8,11]]},"abstract":"\n Brain aging, the strongest risk factor for Alzheimer\u2019s disease (AD), varies across cortical regions. Global brain age (GBA), an imaging-derived measure of neuroanatomic decline, reduces structural aging to a single summary value. This can potentially obscure regional patterns of cognitive vulnerability preceding AD. This study introduces a deep-learning architecture trained on the\n \n \n \n T<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n <\/mml:math>\n <\/jats:inline-formula>\n -weighted MRIs of 14,748 cognitively normal (CN) participants from multiple sites to estimate local brain age (LBA) at voxel level. By mapping spatial variations in brain aging, the model reveals relatively advanced aging in frontal and temporal lobes compared to parietal and occipital regions. Beyond aging in CN aging adults (\n \n \n \n N<\/mml:mi>\n =<\/mml:mo>\n 1<\/mml:mn>\n ,<\/mml:mo>\n 102<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), findings reveal a pattern of progressively advanced frontotemporal aging as a function of neurodegeneration stage, ranging from mild cognitive impairment (MCI,\n \n \n \n N<\/mml:mi>\n =<\/mml:mo>\n 354<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ) to AD (\n \n \n \n N<\/mml:mi>\n =<\/mml:mo>\n 529<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ). Compared to CN adults, key cortical and subcortical structures known to manifest early AD pathology exhibit significantly older LBAs in both early MCI and AD (\n \n \n \n P<\/mml:mi>\n <<\/mml:mo>\n 0.05<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ). Deviations from normative regional aging are significantly associated with cognitive performance supported by neural processes linked to those regions (\n \n \n \n P<\/mml:mi>\n <<\/mml:mo>\n 0.05<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), thereby relating anatomic aging to functional outcomes. By quantifying regional variations in brain aging, this framework extends GBA models to provide anatomically interpretable measures that can improve characterization of typical and pathological aging.\n <\/jats:p>","DOI":"10.1073\/pnas.2532233123","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T19:06:16Z","timestamp":1785783976000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning maps local brain aging in relation to cognition across human adulthood"],"prefix":"10.1073","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3048-6710","authenticated-orcid":false,"given":"Nikhil N.","family":"Chaudhari","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03taz7m60","id-type":"ROR","asserted-by":"publisher"}],"name":"Alfred E. Mann Department of Biomedical Engineering, Corwin D. 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