ArticleNeurobiology of aging2022
Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging.
Article in Neurobiology of aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 24 citations in OpenAlex.
- Robust Longitudinal Dementia Prediction under Systemic Missingness via Hierarchical Fusion and Test-Time Adaptation.medRxiv : the preprint server for health sciences · 2026Article
- Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain Data.medRxiv : the preprint server for health sciences · 2026Article
- Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.Frontiers in neuroimaging · 2026Article
- Cross-Dataset Evaluation of Dementia Longitudinal Progression Prediction Models.Human brain mapping · 2025Article
- Linking Individual Variability of Multi-modal Connectivity, Lifestyle, Psycho-Social Factors and Cognition in Older Adults.Aging and disease · 2025Article
- Cross-dataset Evaluation of Dementia Longitudinal Progression Prediction Models.medRxiv : the preprint server for health sciences · 2025Article
- Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.Human brain mapping · 2025Article
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Article
- Differential predictability of cognitive profiles from brain structure in older males and females.GeroScience · 2024Article
- Prediction of cognitive performance differences in older age from multimodal neuroimaging data.GeroScience · 2024Article
- Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features.Scientific reports · 2023Article
- Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach.Network neuroscience (Cambridge, Mass.) · 2023Article
- Unravelling the relationship between amyloid accumulation and brain network function in normal aging and very mild cognitive decline: a longitudinal analysis.Brain communications · 2022Article
- Baseline cognition is the best predictor of 4-year cognitive change in cognitively intact older adults.Alzheimer's research & therapy · 2021Article
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 5 countries.
Funding
Abstract
Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46-96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions.
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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.