ArticleNeurobiology of aging2018
Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods.
Article in Neurobiology of aging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature.BMC medicine · 2021Pooled it
- Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes.JAMA network open · 2023Trial
- Integration of neuroimaging signatures of aging and Alzheimer's with fluid biomarkers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Your brain doesn't look a day past 70! Cross-sectional associations with brain-predicted age in the cognitively-intact oldest-old.GeroScience · 2026Article
- Mapping Neuroanatomical Heterogeneity of Brain Aging Within a Clinically Defined Aging Reference Cohort.Bioengineering (Basel, Switzerland) · 2026Article
- A comparative evaluation of brain MRI modalities, modality-specific measures, brain regions, and cognitive tests for brain age prediction.GeroScience · 2026Article
- Depression and aging: insights from brain age prediction models.Psychological medicine · 2026Article
- Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals.Nature communications · 2026Article
- Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes.Brain research bulletin · 2026Article
- CSF Levels of NPTX2 Are Associated With Less Brain Atrophy Over Time in Cognitively Unimpaired Individuals.Annals of clinical and translational neurology · 2026Article
- The predictive power of brain-predicted age and leg strength on mobility decline in aging: findings from the Baltimore Longitudinal Study of Aging.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Cortical thickness subtypes in cognitively unimpaired individuals: Differential network and transcriptomic vulnerability to cortical thinning.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Modifiable traits and genetic associations with grey matter volume in mid-to-late adulthood: a population-based study in the UK biobank.npj aging · 2025Article
- Decomposing the effect of normal aging and Alzheimer's disease in brain morphological changes via learned aging templates.Scientific reports · 2025Article
- Joint Aging Patterns in Brain Function and Structure Revealed Using 27,793 Samples.Research (Washington, D.C.) · 2025Article
- Association of Self-Reported Sleep Characteristics With Neuroimaging Markers of Brain Aging Years Later in Middle-Aged Adults.Neurology · 2024Article
- Brain aging patterns in a large and diverse cohort of 49,482 individuals.Nature medicine · 2024Article
- Associations of Epigenetic Age Estimators With Cognitive Function Trajectories in the Women's Health Initiative Memory Study.Neurology · 2024Article
- Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study.Alzheimer's research & therapy · 2024Article
- Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum.Brain sciences · 2024Article
7 more citing papers are in PubMed but not listed here.
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12 authors.
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Abstract
Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns.
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