Insights into the Heterogeneity of Cognitive Aging: A Comparative Analysis of Two Data-Driven Clustering Algorithms
Journal
The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences
Journal Volume
80
Journal Issue
7
ISSN
1079-5014
1758-5368
Date Issued
2025-02-13
Author(s)
Nguyen, Truc Tran Thanh
Editor(s)
Germine, Laura Thi
Abstract
Objectives: Cognitive aging entails diverse patterns of cognitive profiles, brain imaging, and biomarkers. Yet, few studies have explored the performance of multiple clustering algorithms on a single data set. Here, we employ data-driven methods to analyze neuropsychological performance in older individuals with normal cognition (NC) and mild cognitive impairment (MCI). Methods: A total of 311 older adults without dementia completed a comprehensive assessment, consisting of 17 cognitive tests and a memory complaint questionnaire. We utilized 2 clustering algorithms: nonnegative matrix factorization (NMF) and model-based clustering (MBC). Cluster characteristics were examined in demographic, clinical, and brain morphometric data. Results: Both NMF and MBC uncovered two- and three-cluster solutions, with satisfactory data fit. The two-cluster profiles encompassed a cognitively intact (CI) group and a cognitively suboptimal (CS) group, distinguished by cognitive performance. The 3-cluster solutions included CI–memory proficient, CI–nonmemory proficient, and CS groups. Remarkably, patterns of cognitive heterogeneity and their association with demographic and neuroimaging variables were highly comparable across NMF and MBC. Phenotypic homogeneity improved after identifying participants with consistent and mismatched memberships from the 2 algorithms. Discussion: The results indicate that 2 distinct data-driven algorithms, with different heuristics, generated comparable patterns regarding cognitive heterogeneity within NC and MCI. These findings may inform future subtyping studies in cognitive aging, where replication of stratifications found across different methods is strongly recommended.
Subjects
Cluster analysis
Cognition
Machine learning
Neuropsychology
Publisher
Oxford University Press (OUP)
Type
journal article