ArticleGeroScience2025
A multi-stage feature selection method to improve classification of potential super-agers and cognitive decliners using structural brain MRI data-a UK biobank study.
Article in GeroScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Income, diet, and cognitive function: observational analyses and candidate metabolomic pathways identified by Mendelian randomization.The journal of prevention of Alzheimer's disease · 2026Observational
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Authors and funding
15 authors.
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Abstract
Cognitive aging is described as the age-related decline in areas such as memory, executive function, reasoning, and processing speed. Super-Agers, adults over 80 years old, have cognitive function performance comparable to middle-aged adults. To improve cognitive reserve and potentially decrease Alzheimer's disease (AD) risk, it is essential to contrast changes in regional brain volumes between "Positive-Agers" who have superior cognitive performance compared to their age peers but are not 80 years old yet and aging adults who show cognitive decline (i.e., "Cognitive Decliners"). Using longitudinal cognitive tests over 7-9 years in UK Biobank, principal component analysis (PCA) was first applied to four cognitive domains to create a general cognition (GC) composite score. The GC score was then used to identify latent cognitive groups. Given cognitive groups as the target variable and structural magnetic resonance imaging (sMRI) data and demographics as predictors, we developed a multi-stage feature selection algorithm to identify the most important features. We then trained a Random Forest (RF) classifier on the final set of 54 selected sMRI and covariate predictors to distinguish between Positive-Agers and Cognitive Decliners. The RF model achieved an AUC of 73%. The top 6 features were age, education, brain total surface area, the area of pars orbitalis, mean intensity of the thalamus, and superior frontal gyrus surface area. Prediction of cognitive trajectory types using sMRI may improve our understanding of successful cognitive aging.
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