{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T19:46:52Z","timestamp":1789588012228,"version":"build-2803163510"},"reference-count":71,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005224","name":"Deutsches Zentrum f\u00fcr Neurodegenerative Erkrankungen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005224","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Geriat Psychiatry"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non\u2010invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non\u2010invasive assessment and exhibit changes during AD development and preclinical stages.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In a cross\u2010sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting\u2010state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, A\u03b242\/40 ratio) in a multi\u2010class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE\u2010Longitudinal Cognitive Impairment and Dementia Study (DELCODE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at\u2010risk stages.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/gps.6007","type":"journal-article","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T00:50:45Z","timestamp":1696639845000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Machine learning\u2010based classification of Alzheimer's disease and its at\u2010risk states using personality traits, anxiety, and depression"],"prefix":"10.1002","volume":"38","author":[{"given":"Konrad F.","family":"Waschkies","sequence":"first","affiliation":[{"name":"German Center for Neurodegenerative Diseases (DZNE)  G\u00f6ttingen Germany"},{"name":"Department of Psychiatry and Psychotherapy University Medical Center G\u00f6ttingen  G\u00f6ttingen 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