{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T09:45:22Z","timestamp":1782812722963,"version":"3.54.5"},"reference-count":42,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Predicting post-stroke cognitive impairment (PSCI) remains challenging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study validated two brain age metrics\u2014Gray Matter Brain Age (GMBA) and Predicted Age Difference (PAD)\u2014as independent predictors of 12-month cognitive decline following ischemic stroke.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We analyzed 39 patients with ischemic stroke. Brain age was estimated using a machine learning model trained on healthy controls (n\u2009=\u2009362). Baseline assessments, including demographics, clinical severity, and thick-sliced (7\u2005mm) 2D T1- and T2-weighted MRI, were performed during acute hospitalization (median 5 days post-stroke). Change in Clinical Dementia Rating-Sum of Boxes (\n                      <jats:bold>\u0394<\/jats:bold>\n                      CDR-SB) was measured between baseline and 12 months. Multiple linear regression (MLR) and receiver operating characteristic (ROC) analysis identified predictors of\n                      <jats:bold>\u0394<\/jats:bold>\n                      CDR-SB.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      MLR identified two significant models. In Model 1, PAD (\u03b2\u2009=\u20090.109, p\u2009=\u20090.035) and chronological age (\u03b2\u2009=\u20090.082, p\u2009=\u20090.038) were independent predictors (Adjusted R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.183). In Model 2, GMBA emerged as the sole robust predictor (\u03b2\u2009=\u20090.092, p\u2009=\u20090.002; Adjusted R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.201). ROC analysis showed GMBA possessed the highest discriminative ability (AUC\u2009=\u20090.717, p\u2009=\u20090.026), followed by PAD (AUC\u2009=\u20090.691, p\u2009=\u20090.050), while chronological age was not significant (AUC\u2009=\u20090.629, p\u2009=\u20090.188).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Both GMBA and PAD are independent predictors of PSCI. Notably, these metrics maintained high predictive utility even when derived from real-world, thick-sliced MRI protocols. Integrating brain age estimation into routine clinical workflows can potentially facilitate early identification of patients at high risk for PSCI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261450602","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T07:16:34Z","timestamp":1779779794000},"page":"313-321","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Gray matter brain age predicts cognitive outcome one year after ischemic stroke"],"prefix":"10.1177","volume":"112","author":[{"given":"Wen-Yih Isaac","family":"Tseng","sequence":"first","affiliation":[{"name":"AcroViz, Inc., Taipei, Taiwan, (R.O.C)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1863-287X","authenticated-orcid":false,"given":"Yi-Chen","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Taipei Medical 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