ArticleBiology methods & protocols2025
An explainable AI approach for mapping multivariate regional brain age and clinical severity patterns in Alzheimer's disease.
Article in Biology methods & protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi-Omics Integration.Aging cell · 2026Review
- Interpretable Feature-Transformer Framework for Cross-Subject MCI Detection Using Nonlinear Dynamical and Graph-Theoretic EEG Features.Research square · 2026Article
- Development of an explainable machine learning model for Alzheimer's disease prediction using clinical and behavioural features.MethodsX · 2025Article
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
Abstract
Age is a significant risk factor for mild cognitive impairment (MCI) and Alzheimer's disease (AD) and identifying brain age patterns is critical for comprehending the normal aging and MCI/AD processes. Prior studies have widely established the univariate relationships between brain regions and age, while multivariate associations remain largely unexplored. Herein, various artificial intelligence (AI) models were used to perform brain age prediction using an MRI dataset (
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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 OpenQuestion graph.