{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T01:14:11Z","timestamp":1789434851978,"version":"build-2803163510"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["1R01AG080821, P30AG066546, 1U24AG074855"],"award-info":[{"award-number":["1R01AG080821, P30AG066546, 1U24AG074855"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100020065","name":"San Antonio Medical Foundation","doi-asserted-by":"publisher","award":["SAMF \u2013 1000003860"],"award-info":[{"award-number":["SAMF \u2013 1000003860"]}],"id":[{"id":"10.13039\/100020065","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Edmond J. Safra Fellowship"},{"DOI":"10.13039\/100007333","name":"Alzheimer\u2019s Disease Neuroimaging Initiative","doi-asserted-by":"publisher","award":["National Institutes of Health Grant U01 AG024904"],"award-info":[{"award-number":["National Institutes of Health Grant U01 AG024904"]}],"id":[{"id":"10.13039\/100007333","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DOD ADNI","award":["Department of Defense award number W81XWH-12-2-0012"],"award-info":[{"award-number":["Department of Defense award number W81XWH-12-2-0012"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\"><jats:title>Background<\/jats:title><jats:p>Recently, tau PET tracers have shown strong associations with clinical outcomes in individuals with cognitive impairment and cognitively unremarkable elderly individuals. flortaucipir PET scans to measure tau deposition in multiple brain areas as the disease progresses. This information needs to be summarized to evaluate disease severity and predict disease progression. We, therefore, sought to develop a machine learning-derived index, SPARE-Tau, which successfully detects pathology in the earliest disease stages and accurately predicts progression compared to a priori-based region of interest approaches (ROI).<\/jats:p><\/jats:sec><jats:sec id=\"sec002\"><jats:title>Methods<\/jats:title><jats:p>587 participants of the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) cohort had flortaucipir scans, structural MRI scans, and an A\u03b2 biomarker test (CSF or florbetapir PET) performed on the same visit. We derived the SPARE-Tau index in a subset of 367 participants. We evaluated associations with clinical measures for CSF p-tau, SPARE-MRI, and flortaucipir PET indices (SPARE-Tau, meta-temporal, and average Braak ROIs). Bootstrapped multivariate adaptive regression splines linear regression analyzed the association between the biomarkers and baseline ADAS-Cog13 scores. Bootstrapped multivariate linear regression models evaluated associations with clinical diagnosis. Cox-hazards and mixed-effects models investigated clinical progression and longitudinal ADAS-Cog13 changes. The A\u03b2 positive cognitively unremarkable participants, not included in the SPARE-Tau training, served as an independent validation group.<\/jats:p><\/jats:sec><jats:sec id=\"sec003\"><jats:title>Results<\/jats:title><jats:p>Compared to CSF p-tau, meta-temporal, and averaged Braak tau PET ROIs, SPARE-Tau showed the strongest association with baseline ADAS-cog13 scores and diagnosis. SPARE-Tau also presented the strongest association with clinical progression in cognitively unremarkable participants and longitudinal ADAS-Cog13 changes. Results were confirmed in the A\u03b2+ cognitively unremarkable hold-out sample participants. CSF p-tau showed the weakest cross-sectional associations and longitudinal prediction.<\/jats:p><\/jats:sec><jats:sec id=\"sec004\"><jats:title>Discussion<\/jats:title><jats:p>Flortaucipir indices showed the strongest clinical association among the studied biomarkers (flortaucipir, florbetapir, structural MRI, and CSF p-tau) and were predictive in the preclinical disease stages. Among the flortaucipir indices, the machine-learning derived SPARE-Tau index was the most sensitive clinical progression biomarker. The combination of different biomarker modalities better predicted cognitive performance.<\/jats:p><\/jats:sec>","DOI":"10.1371\/journal.pone.0276392","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T17:29:43Z","timestamp":1667496583000},"page":"e0276392","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline"],"prefix":"10.1371","volume":"17","author":[{"given":"Jon 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