{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T02:22:45Z","timestamp":1791426165136,"version":"4.3.3"},"reference-count":48,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000135","name":"NIH Blueprint for Neuroscience Research","doi-asserted-by":"publisher","award":["NIH P30AG066444, P30NS09857781, P01AG026276, UL1TR002345, P01AG003991, R01AG043434, UL1TR000448, R01EB009352"],"award-info":[{"award-number":["NIH P30AG066444, P30NS09857781, P01AG026276, UL1TR002345, P01AG003991, R01AG043434, UL1TR000448, R01EB009352"]}],"id":[{"id":"10.13039\/100000135","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>As we transition to disease-modifying treatment for Alzheimer's disease (AD), identifying individuals most at risk for future cognitive decline is crucial. Amyloid PET, cerebrospinal fluid and more recently blood-based biomarkers can identify the first stage of AD. However, changes detectable by PiB-PET may precede the onset of the dementia by 20\u201330 years. MRI is a widely available tool for detecting longitudinal changes in brain structure, such as cortical thickness and hippocampal volume and may provide additional insight into which patients are at greatest risk to develop cognitive decline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To determine how well the hippocampal volume and cortical thickness, without specific AD biomarkers, can predict cognitive decline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>MRI data from 344 participants (cognitively unimpaired or mild cognitive impairment, age 50\u201386) were used to evaluate if changes in cortical thickness and hippocampal volume predict cognitive decline, measured by a global cognitive composite score. A random coefficient model was employed to calculate longitudinal changes in cortical thickness and hippocampal volume and assess their ability to predict cognitive decline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Baseline cortical thickness as well as hippocampal volume predicted cognitive decline, regardless of baseline cognitive status. In individuals unimpaired at baseline, decreases in cortical thickness and hippocampal volume independently predicted cognitive decline. For participants with baseline mild impairment, decreases in hippocampal volume predicted further cognitive decline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings indicate that MRI could serve as an effective tool for identifying individuals at elevated risk of cognitive decline, a growing public health concern as global populations continue to age.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877251352202","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T05:54:03Z","timestamp":1752818043000},"page":"1452-1462","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Baseline and longitudinal changes in cortical thickness and hippocampal volume predict cognitive decline"],"prefix":"10.1177","volume":"106","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1879-9415","authenticated-orcid":false,"given":"Gengsheng","family":"Chen","sequence":"first","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA"},{"name":"Knight Alzheimer's Disease Research Center, Washington University in St Louis School of Medicine, St Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4123-2548","authenticated-orcid":false,"given":"Nicole S","family":"McKay","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, USA"},{"name":"Knight Alzheimer's Disease Research Center, Washington University in St Louis School of Medicine, St Louis, MO, 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