{"doi":"10.1002/alz.13164","title":"Profiling and predicting distinct tau progression patterns: An unsupervised data‐driven approach to flortaucipir positron emission tomography","abstract":"INTRODUCTION: How to detect patterns of greater tau burden and accumulation is still an open question. METHODS: An unsupervised data-driven whole-brain pattern analysis of longitudinal tau positron emission tomography (PET) was used first to identify distinct tau accumulation profiles and then to build baseline models predictive of tau-accumulation type. RESULTS: The data-driven analysis of longitudinal flortaucipir PET from studies done by the Alzheimer's Disease Neuroimaging Initiative, Avid Pharmaceuticals, and Harvard Aging Brain Study (N = 348 cognitively unimpaired, N = 188 mild cognitive impairment, N = 77 dementia), yielded three distinct flortaucipir-progression profiles: stable, moderate accumulator, and fast accumulator. Baseline flortaucipir levels, amyloid beta (Aβ) positivity, and clinical variables, identified moderate and fast accumulators with 81% and 95% positive predictive values, respectively. Screening for fast tau accumulation and Aβ positivity in early Alzheimer's disease, compared to Aβ positivity with variable tau progression profiles, required 46% to 77% lower sample size to achieve 80% power for 30% slowing of clinical decline. DISCUSSION: Predicting tau progression with baseline imaging and clinical markers could allow screening of high-risk individuals most likely to benefit from a specific treatment regimen.","journal":"Alzheimer s & Dementia","year":2023,"id":362299,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9209,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1115440,"name":"Pamela Thropp","orcid":"0009-0006-5371-8667","position":1,"is_corresponding":false},{"id":1115441,"name":"Sudeepti Southekal","orcid":"0000-0002-5540-5000","position":2,"is_corresponding":false},{"id":894060,"name":"Bruce Spottiswoode","orcid":"0000-0002-8807-923X","position":3,"is_corresponding":false},{"id":1115442,"name":"Rachid Fahmi","orcid":"0000-0001-8277-6125","position":4,"is_corresponding":false},{"id":300139,"name":"for the Alzheimer's Disease Neuroimaging Initiative","orcid":null,"position":5,"is_corresponding":false},{"id":301454,"name":"Duygu Tosun","orcid":"0000-0001-8644-7724","position":0,"is_corresponding":true}],"reference_count":60,"raw_metadata":null,"created_at":"2026-07-19T01:14:19.266946Z","pmid":"37288753","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}