Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain Data.

ABSTRACT: Aging is often accompanied by cognitive decline, but the extent, timing, and severity of this process is subject to large inter-individual variability. Predicting cognitive decline along a continuum, encompassing healthy age-related decline, mild cognitive impairment, and dementia, allows for more precise individual-level predictions. Previous work has demonstrated that machine learning (ML) models combining risk factors, clinical, neuropsychological, and structural magnetic resonance imaging (MRI) data can predict continuous cognitive decline. However, generalizability across independent datasets has rarely been evaluated. This study aimed to replicate previous findings using an independent dataset and to assess the models' generalizability across acquisition sites. Multi-target random fo

SUBMITTER: Huppi RM 

PROVIDER: S-EPMC12870485 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature