ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2022
Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Alzheimer's Disease as a Multi-Layer Network Disorder: A Systems Biology Framework Integrating Multi-Omics Mechanisms.Biomedicines · 2026Review
- Encoding Discordance in the Alzheimer's Disease A/T/N Framework.medRxiv : the preprint server for health sciences · 2026Article
- Bridging Research, Policy, and Practice: Advancing Evidence-Informed Approaches for Dementia Care and Prevention: an expert review by the Health Policy Professional Interest Area from ISTAART.Alzheimer's & dementia. Behavior & socioeconomics of aging · 2026Article
- Uncovering Spatiotemporal and Functional Dynamics of Long Non-coding RNAs During Alzheimer's Progression in the Human Brain at Single-Cell Resolution.Molecular neurobiology · 2026Article
- Passive digital health technologies for Alzheimer's disease screening and diagnosis: a systematic review.NPJ digital medicine · 2026Article
- Alpha-synuclein co-pathology in a real-world early Alzheimer's disease cohort.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- A benchmark of text embedding models for semantic harmonization of Alzheimer's disease cohorts.The journal of prevention of Alzheimer's disease · 2025Article
- Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease.Brain informatics · 2025Article
- Heterogeneity Analysis of Associations Involving the Large-Scale Online MindCrowd Survey Memory Test.Gerontology · 2025Article
- Rethinking the residual approach: Leveraging machine learning to operationalize cognitive resilience in Alzheimer's disease.medRxiv : the preprint server for health sciences · 2024Article
- Article
- Multimodal subtypes identified in Alzheimer's Disease Neuroimaging Initiative participants by missing-data-enabled subtype and stage inference.Brain communications · 2024Article
- The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
- Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper.Journal of Alzheimer's disease : JAD · 2024Article
- Aging Brain from a Lifespan Perspective.Current topics in behavioral neurosciences · 2024Review
- Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials.Brain communications · 2024Article
- Data-Driven Thresholding Statistically Biases ATN Profiling across Cohort Datasets.The journal of prevention of Alzheimer's disease · 2024Article
- Characteristics of amnestic patients with hypometabolism patterns suggestive of Lewy body pathology.Brain : a journal of neurology · 2023Article
- Forecasting individual progression trajectories in Alzheimer's disease.Nature communications · 2023Article
- The Immune System as a Therapeutic Target for Alzheimer's Disease.Life (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
introductionGiven study-specific inclusion and exclusion criteria, Alzheimer's disease (AD) cohort studies effectively sample from different statistical distributions. This heterogeneity can propagate into cohort-specific signals and subsequently bias data-driven investigations of disease progression patterns.
methodsWe built multi-state models for six independent AD cohort datasets to statistically compare disease progression patterns across them. Additionally, we propose a novel method for clustering cohorts with regard to their progression signals.
resultsWe identified significant differences in progression patterns across cohorts. Models trained on cohort data learned cohort-specific effects that bias their estimations. We demonstrated how six cohorts relate to each other regarding their disease progression. DISCUSSION: Heterogeneity in cohort datasets impedes the reproducibility of data-driven results and validation of progression models generated on single cohorts. To ensure robust scientific insights, it is advisable to externally validate results in independent cohort datasets. The proposed clustering assesses the comparability of cohorts in an unbiased, data-driven manner.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.