Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals

2026-04-25 · Source: Machine learning : nature.com subject feeds · Field: Science & Research — Health & Medical Research, Mathematics & Computational Sciences, Life Sciences & Biology · Depth: Expert, short

Summary

A new machine learning method, Coupled Cross-sectional and Longitudinal Non-negative Matrix Factorization (CCL-NMF), has been developed to identify dominant brain aging patterns by integrating both cross-sectional and longitudinal neuroimaging data. This approach allows individuals to exhibit multiple patterns simultaneously, better reflecting mixed neuropathologic processes. When applied to neuroimaging data from 48,949 individuals within the harmonized iSTAGING study, CCL-NMF successfully identified seven distinct, reproducible, and biologically relevant neuroanatomical patterns. The study found that subject-specific loading coefficients, which quantify individual expression of these patterns, correlate distinctly with cognitive function, genetic factors, and lifestyle choices. A regression-based tool was also created to estimate these loadings in external cohorts without requiring a full rerun of the framework, enhancing its practical applicability.

Key takeaway

For AI Scientists and Research Scientists developing diagnostic tools for neurodegenerative diseases, CCL-NMF offers a robust framework to identify individualized brain aging trajectories. Your models can leverage this method to capture complex, mixed neuropathologies by integrating diverse data types, potentially improving the accuracy of risk stratification and therapeutic response prediction. Consider implementing the regression-based tool for efficient application in new patient cohorts.

Key insights

CCL-NMF integrates cross-sectional and longitudinal data to reveal distinct, reproducible brain aging patterns.

Principles

• Brain aging is heterogeneous.
• Mixed neuropathologies co-exist.
• Longitudinal data improves disease progression insight.

Method

CCL-NMF simultaneously analyzes cross-sectional and longitudinal neuroimaging data to identify co-expressed, non-negative matrix factorization patterns, with a regression tool for external cohort application.

In practice

• Estimate individual brain aging patterns.
• Improve neurodegenerative risk assessment.
• Evaluate therapeutic efficacy.

Topics

• Coupled Cross-sectional Longitudinal NMF
• Brain Aging Trajectories
• Neurodegenerative Diseases
• Neuroimaging Data Analysis
• iSTAGING Study

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine learning : nature.com subject feeds.