{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T09:10:37Z","timestamp":1789463437809,"version":"build-2803163510"},"reference-count":65,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Research Council of Finland","doi-asserted-by":"publisher","award":["351849"],"award-info":[{"award-number":["351849"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Flagship of Advanced Mathematics for Sensing Imaging and Modelin","award":["358944"],"award-info":[{"award-number":["358944"]}]},{"name":"PRIMAL","award":["346934"],"award-info":[{"award-number":["346934"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Neuron loss is a hallmark of neurodegenerative diseases and leads to brain atrophy detectable with magnetic resonance imaging (MRI). Accurate prediction of future atrophy is valuable for research in Alzheimer's disease (AD) and related dementias.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to predict annualized percentage changes in hippocampal, ventricular, and total gray matter (TGM) volumes in individuals ranging from cognitively normal to dementia, and to evaluate whether longitudinal MRI-derived change measures improve prediction performance compared with single-time-point MRI information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Using elastic net regression, we compared baseline models based on single-timepoint MRI information with longitudinal models incorporating prior MRI-derived change measures. Both approaches were evaluated as MRI-only and MRI\u2009+\u2009risk-factor variants, with risk factors including age, sex,\n                      <jats:italic toggle=\"yes\">APOE4<\/jats:italic>\n                      , and diagnostic status.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In cross-validated analyses using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, the longitudinal MRI\u2009+\u2009risk-factor model performed best, yielding Pearson correlations of 0.62 for hippocampal atrophy, 0.51 for ventricular enlargement, and 0.41 for TGM atrophy. Longitudinal models consistently outperformed single time-point models, and adding risk factors improved predictive performance beyond MRI alone. External validation using the Australian Imaging, Biomarkers and Lifestyle cohort confirmed these findings. Predicted atrophy outperformed present-day regional volumes in identifying individuals progressing from normal cognition to MCI\/dementia and from MCI to dementia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>MRI-derived longitudinal features enhance atrophy prediction, and predicted atrophy rates provide sensitive markers of future cognitive decline. These findings support the potential utility of predicted atrophy for cohort enrichment and therapeutic trial design.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261471049","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:04:05Z","timestamp":1785247445000},"page":"796-812","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting future brain atrophy based on longitudinal MRI"],"prefix":"10.1177","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-7513","authenticated-orcid":false,"given":"Maryam","family":"Hadji","sequence":"first","affiliation":[{"name":"A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elaheh","family":"Moradi","sequence":"additional","affiliation":[{"name":"A.I. 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