ArticleeLife2020
Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 2 of them syntheses that pooled it.
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Who cites it
119 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- BrainAGE, brain health, and mental disorders: A systematic review.Neuroscience and biobehavioral reviews · 2024Pooled it
- The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging.Psychophysiology · 2023Pooled it
- Insulin sensitivity in mesolimbic pathways predicts and improves with weight loss in older dieters.eLife · 2022Trial
- Divergent Multimodal Age-Association Profiles Across the Human Brain.Nature communications · 2026Article
- Article
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.Nature human behaviour · 2026Article
- Deep neural networks and genome-wide associations reveal the polygenic architecture of local brain aging.GeroScience · 2026Article
- Psychosis as a multisystem disorder of aberrant aging.npj aging · 2026Review
- Mechanisms by Which Exercise Delays Brain Aging Through Regulation of the Mitochondrial Quality Control System.Biology · 2026Review
- DynoSys 2.0: Graph-Based Modeling of Dynamic Risk States and System Transitions in Human Behaviours Development.bioRxiv : the preprint server for biology · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Alzheimer's Disease Brain Phenotypes are Age-dependent.bioRxiv : the preprint server for biology · 2026Article
- Exposome-wide patterns predict brain health in aging.Nature communications · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Investigating biotypes and neural characteristics among anxiety, depression, and comorbidity via a novel noisy label learning method.Psychoradiology · 2026Article
- Shared genetic architecture of brain age gap across 30 cohorts worldwide.medRxiv : the preprint server for health sciences · 2025Article
- No causal links between estradiol and female's brain and mental health using Mendelian randomization.Nature communications · 2025Article
- Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers.bioRxiv : the preprint server for biology · 2025Article
- Regulatory Genomic Circuitry of Brain Age by Integrative Functional Genomic Analyses.Genomics, proteomics & bioinformatics · 2025Article
59 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single 'brain age' is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.