ArticleNature communications2022
A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Cortical thickness changes precede high levels of amyloid by at least 7 years.Nature neuroscience · 2026Article
- Generating synthetic tau-PET scans in Alzheimer's disease from MRI, blood biomarkers and demographics with deep learning.medRxiv : the preprint server for health sciences · 2026Article
- Neuropathologically validated MRI to tau PET synthesis via Covariate-modulated attention networks.bioRxiv : the preprint server for biology · 2025Article
- Solving the 'Goldilocks problem' in dementia clinical trials with multimodal AI.The journal of prevention of Alzheimer's disease · 2025Article
- Patterns of pathological tau deposition reflect the dynamics of cortical brain activity.Cell reports · 2025Article
- AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer's Disease clinical trial.Nature communications · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Connectivity, Pathology, and ApoE4 Interactions Predict Longitudinal Tau Spatial Progression and Memory.Human brain mapping · 2024Article
- Global brain activity and its coupling with cerebrospinal fluid flow is related to tau pathology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings.EClinicalMedicine · 2024Article
- Successful cognitive aging is associated with thicker anterior cingulate cortex and lower tau deposition compared to typical aging.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
- Profiling and predicting distinct tau progression patterns: An unsupervised data-driven approach to flortaucipir positron emission tomography.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Advancing Tau-PET quantification in Alzheimer's disease with machine learning: introducing THETA, a novel tau summary measure.Research square · 2023Article
- Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank.Communications medicine · 2023Article
- Automatic brain structure segmentation forQuantitative imaging in medicine and surgery · 2023Article
- Harnessing the potential of machine learning and artificial intelligence for dementia research.Brain informatics · 2023Review
- Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.Journal of the Royal Society, Interface · 2023Review
- Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia.Wiley interdisciplinary reviews. Data mining and knowledge discoveryReview
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The early stages of Alzheimer's disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.
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Registered trials
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 Socratic graph.