ArticleScientific reports2020
Multimodal Phenotyping of Alzheimer's Disease with Longitudinal Magnetic Resonance Imaging and Cognitive Function Data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI-enabled digital phenotyping for Alzheimer's disease: a review of multimodal sensor integration and symptom trajectories.Alzheimer's research & therapy · 2026Review
- Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization.Journal of Alzheimer's disease : JAD · 2024Article
- Data-Driven Phenotyping of Alzheimer's Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning.Biomedicines · 2023Article
- Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment.BMC bioinformatics · 2022Article
- Deep phenotyping of Alzheimer's disease leveraging electronic medical records identifies sex-specific clinical associations.Nature communications · 2022Article
- Diagnostic Blood Biomarkers in Alzheimer's Disease.Biomedicines · 2022Article
- Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data.Emerging topics in life sciences · 2021Review
- Application of modern neuroimaging technology in the diagnosis and study of Alzheimer's disease.Neural regeneration research · 2021Article
- Temporal phenotyping for transitional disease progress: An application to epilepsy and Alzheimer's disease.Journal of biomedical informatics · 2020Article
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Authors and funding
8 authors.
Funding
Abstract
Alzheimer's disease (AD) varies a great deal cognitively regarding symptoms, test findings, the rate of progression, and neuroradiologically in terms of atrophy on magnetic resonance imaging (MRI). We hypothesized that an unbiased analysis of the progression of AD, regarding clinical and MRI features, will reveal a number of AD phenotypes. Our objective is to develop and use a computational method for multi-modal analysis of changes in cognitive scores and MRI volumes to test for there being multiple AD phenotypes. In this retrospective cohort study with a total of 857 subjects from the AD (n = 213), MCI (n = 322), and control (CN, n = 322) groups, we used structural MRI data and neuropsychological assessments to develop a novel computational phenotyping method that groups brain regions from MRI and subsets of neuropsychological assessments in a non-biased fashion. The phenotyping method was built based on coupled nonnegative matrix factorization (C-NMF). As a result, the computational phenotyping method found four phenotypes with different combination and progression of neuropsychologic and neuroradiologic features. Identifying distinct AD phenotypes here could help explain why only a subset of AD patients typically respond to any single treatment. This, in turn, will help us target treatments more specifically to certain responsive phenotypes.
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Registered trials
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