Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals
Machine learning can unravel heterogeneous patterns of brain aging and neurodegeneration, but existing methods offer limited insights into disease progression due to reliance on cross-sectional data. We introduce Coupled Cross-sectional and Longitudinal Non-negative Matrix Factorization (CCL-NMF) to capture dominant brain aging patterns by simultaneously leveraging cross-sectional and longitudinal neuroimaging data. CCL-NMF allows individuals to co-express multiple patterns, capturing mixed neuropathologic processes. Applied to neuroimaging data from 48,949 individuals from the harmonized iSTAGING study, CCL-NMF identifies seven distinct, reproducible, and biologically relevant neuroanatomical patterns. Subject-specific loading coefficients quantifying the individual expression of these patterns show distinct associations with cognition, genetic, and lifestyle factors. To support broader application, a regression-based tool was developed to estimate loadings in external cohorts without rerunning the full framework. By enabling individualized estimation of distinct brain aging patterns, these findings may improve risk assessment and therapeutic evaluation in neurodegenerative diseases. Although demonstrated using structural MRI, this framework is generalizable to other imaging modalities and biomarker types.
Authors
- Susan M. Resnick (ORCID: https://orcid.org/0000-0003-1115-7145)
- Ilya M. Nasrallah (ORCID: https://orcid.org/0000-0003-2346-7562)
- Güray Erus (ORCID: https://orcid.org/0000-0001-6633-4861)
- Christos A. Davatzikos (ORCID: https://orcid.org/0000-0002-1025-8561)
- Michael R. Duggan (ORCID: https://orcid.org/0000-0002-1029-4423)
- Haochang Shou
- Keenan A. Walker (ORCID: https://orcid.org/0000-0002-5989-9853)
- Sindhuja Tirumalai Govindarajan (ORCID: https://orcid.org/0000-0003-1741-0906)
- Ioanna Skampardoni
- Alexander Getka (ORCID: https://orcid.org/0000-0002-9319-8479)
- Konstantina Nikita
- Zhijian Yang
- Randa Melhem
- Kyunglok Baik (ORCID: https://orcid.org/0009-0002-3645-9449)
Institutions
- National Technical University of Athens (GR)
- National Institute on Aging (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-04-25
- DOI
- https://doi.org/10.1038/s41467-026-72091-7
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Health and Human Services
- Alzheimer's Association
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- Biogen
- University of Southern California
- F. Hoffmann-La Roche
- Meso Scale Diagnostics
- BioClinica
- Novartis Pharmaceuticals Corporation
- Northern California Institute for Research and Education
- Bundesministerium für Bildung und Forschung
- Eisai
- Siemens Healthineers
- Servier
- H. Lundbeck A/S
- IXICO
- Universität Greifswald
- National Institutes of Health
- Genentech
- Canadian Institutes of Health Research
- National Institute on Aging