{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T06:01:08Z","timestamp":1790402468568,"version":"4.1.0"},"reference-count":92,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":63,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["A&amp;D Transl Res &amp; Clin Interv"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Brain\u2010age gap (BAG), the difference between predicted age and chronological age, is studied as a biomarker for the natural progression of neurodegeneration. The BAG captures brain atrophy as measured with structural magnetic resonance imaging (MRI). Electroencephalography (EEG) has also been explored for estimating the BAG (EEG\u2010BAG). However, studies showed mixed results for the EEG\u2010BAG including counterintuitive findings of younger predicted age in clinical populations, raising doubts about its utility as a clinical tool for mild cognitive impairment (MCI) and Alzheimer's disease (AD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>\n                      This study critically examined brain\u2010age estimation from spectral EEG power as a common measure of brain activity in two of the largest public EEG datasets containing heterogeneous clinical cases alongside controls including MCI and AD. EEG recordings were analyzed from individuals with heterogeneous neurological conditions (\n                      <jats:italic>n =<\/jats:italic>\n                      898, Temple University Hospital Abnormal EEG corpus [TUAB] data;\n                      <jats:italic>n =<\/jats:italic>\n                      417 MCI &amp;\n                      <jats:italic>n =<\/jats:italic>\n                      311 dementia, Chung\u2010Ang University Hospital EEG [CAU] data) and controls (\n                      <jats:italic>n =<\/jats:italic>\n                      1245, TUAB data;\n                      <jats:italic>n =<\/jats:italic>\n                      459, CAU data).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>We found that age\u2010prediction models trained on the reference population systematically underpredicted age in clinical conditions showing strong and systematic, age\u2010related differences in EEG power compared to controls. Data exploration and simulations revealed how diverging age\u2010related trends in specific EEG frequencies can account for a negative EEG\u2010BAG.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>The utility of brain age as an interpretable biomarker relies on the observation from structural MRI that progressive neurodegeneration often broadly resembles aging. This assumption can be violated for functional assessments such as EEG spectral power related to different neurological and psychiatric conditions or medications. The sign of the BAG may therefore not be meaningfully interpreted as an individual aging metric, hence hampering its utility as an endpoint or biomarker in MCI and AD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/trc2.70302","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T09:56:21Z","timestamp":1788342981000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinically altered brain activity may not look like aged brain activity: Implications for brain\u2010age modeling and biomarker strategies"],"prefix":"10.1002","volume":"12","author":[{"given":"Lukas A. W.","family":"Gemein","sequence":"first","affiliation":[{"name":"Neuroscience and Rare Diseases, Roche Innovation Center Basel, Roche Pharma Research and Early Development F. Hoffmann\u2013La Roche Ltd.  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