{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:12:56Z","timestamp":1787019176534,"version":"build-2736575974"},"reference-count":55,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["P41EB015922"],"award-info":[{"award-number":["P41EB015922"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG054073"],"award-info":[{"award-number":["R01AG054073"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG058533"],"award-info":[{"award-number":["R01AG058533"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["U19AG078109"],"award-info":[{"award-number":["U19AG078109"]}],"id":[{"id":"10.13039\/100000049","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":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based AD classifiers have been developed in Non-Hispanic White (NHW) cohorts, limiting generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Assess ethnic differences in AD prediction using classification performance and feature importance derived from multimodal neuroimaging biomarkers across African American (AA), Hispanic, and NHW participants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Support vector machine classifiers were applied to multimodal neuroimaging data from a multi-ethnic cohort, incorporating structural magnetic resonance imaging measures, diffusion tensor imaging metrics, and positron emission tomography-based amyloid and tau measures. Models classified cognitively unimpaired (CU) versus cognitively impaired (CI) individuals and mild cognitive impairment (MCI) versus AD dementia, with and without adjustment for age, sex, and education.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Classification performance varied by ethnicity and disease stage. NHW participants showed the strongest overall performance, particularly for CU versus CI, while Hispanic participants demonstrated high sensitivity and balanced performance for MCI versus AD. AA participants exhibited lower AUC and accuracy across tasks but maintained high negative predictive value. Demographic adjustment improved performance primarily for AA and NHW participants. Feature importance analyses revealed shared and population-specific patterns: tau positron emission tomography (PET) measures, especially posterior cingulate and lateral parietal standardized uptake value ratios, consistently ranked highest for CU versus CI across groups, whereas MCI versus AD classification diverged, with amyloid PET predominating in AA participants, tau PET in NHW participants, and mixed medial temporal atrophy and white matter signatures in Hispanics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Shared early AD neuroimaging signatures exist across ethnic groups, but biomarker importance diverges at later disease stages, underscoring the need for ethnicity-aware ML models to improve prediction and equitable clinical translation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261426563","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:01:45Z","timestamp":1773230505000},"page":"1723-1740","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring feature importance in machine learning for neuroimaging traits in Alzheimer's disease across a multiethnic cohort"],"prefix":"10.1177","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4308-5849","authenticated-orcid":false,"given":"Daniel","family":"Gibbs","sequence":"first","affiliation":[{"name":"University of North Texas Health Science Center, Fort Worth"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Black","sequence":"additional","affiliation":[{"name":"University of North Texas Health Science Center, Fort Worth"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa","family":"Petersen","sequence":"additional","affiliation":[{"name":"University of North Texas Health Science Center, Fort Worth"},{"name":"University of North Texas Health Science Center, Fort Worth"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leigh","family":"Johnson","sequence":"additional","affiliation":[{"name":"University of North Texas Health Science Center, Fort Worth"},{"name":"University of North Texas Health Science Center, Fort Worth"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Hall","sequence":"additional","affiliation":[{"name":"University of North Texas Health Science Center, Fort Worth"},{"name":"University of North Texas Health Science Center, Fort Worth"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sid E.","family":"O\u2019Bryant","sequence":"additional","affiliation":[{"name":"University of North Texas Health Science Center, Fort Worth"},{"name":"University of North Texas Health Science Center, Fort Worth"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3502-1808","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of North Texas Health Science Center, Fort Worth"},{"name":"University of North Texas Health Science Center, Fort Worth"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Health and Aging Brain Study (HABS-HD) Study Team #","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"World Alzheimer Report 2023: Reducing Dementia Risk: Never too early, never too late","author":"Alzheimer's Disease International","year":"2023","unstructured":"Alzheimer's Disease International. 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