{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:47:18Z","timestamp":1778539638065,"version":"3.51.4"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Apolipoprotein E (ApoE) \u03b54 carriers have a higher risk of developing Alzheimer's disease (AD) and show brain atrophy and cognitive decline even before diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To predict ApoE \u03b54 status using gray matter volume (GMV) obtained from magnetic resonance imaging images and demographic data with machine learning (ML) methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We recruited 74 participants (25 probable AD, 24 amnestic mild cognitive impairment, and 25 cognitively normal\u00a0older people) with known ApoE genotype (22 ApoE \u03b54 carriers and 52 noncarriers) and scanned them with three\u2010dimensional (3D) T1\u2010weighted (T1W) and 3D double inversion recovery (DIR) sequences. We extracted GMV from regions of interest\u00a0related to AD pathology and used them as features along with age and mini\u2013mental state examination (MMSE) scores to train different ML models. We performed both receiver operating characteristic\u00a0curve analysis and the prediction analysis of the ApoE \u03b54 carrier with different ML models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The best model of ML analyses was a cubic support vector machine (SVM3) that used age, the MMSE score, and DIR GMVs at the amygdala, hippocampus, and precuneus as features (AUC\u00a0=\u00a0.88). This model outperformed models using T1W GMV or demographic data alone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our results suggest that brain atrophy with DIR GMV and cognitive decline with aging can be useful biomarkers for predicting ApoE \u03b54 status and identifying individuals at risk of AD progression.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.3381","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T04:39:24Z","timestamp":1704688764000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting the apolipoprotein E \u03b54 allele carrier status based on gray matter volumes and cognitive function"],"prefix":"10.1002","volume":"14","author":[{"given":"Hyug\u2010Gi","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Radiology Kyung Hee University Hospital  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunan","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Medicine, Graduate School Kyung Hee University College of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sue Min","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information Kyung Hee University  Yongin\u2010si Gyeonggi\u2010do Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soonchan","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Radiology Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hak 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