ArticleNeurobiology of aging2020
Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease.
Article in Neurobiology of aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- BrainAGE, brain health, and mental disorders: A systematic review.Neuroscience and biobehavioral reviews · 2024Pooled it
- Tissue- and compartment-resolved brain age reveals vascular-metabolic and neurodegenerative aging.Research square · 2026Article
- Brain Age in Adult Patients With Early-Treated Phenylketonuria.Journal of inherited metabolic disease · 2026Article
- Brain Age as a Biomarker in Alzheimer's Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential.Brain sciences · 2025Review
- White matter brain age as a biomarker of cerebrovascular burden in the ageing brain.European archives of psychiatry and clinical neuroscience · 2025Article
- Advancing Research on Suicide and Mental Health in Autistic Adults Through a Research Domain Criteria (RDoC)-Inspired Framework.Autism in adulthood · 2025Article
- Distinct gut microbiota profiles and network properties in older Korean individuals with subjective cognitive decline, mild cognitive impairment, and Alzheimer's disease.Alzheimer's research & therapy · 2025Article
- Neuropsychiatric symptoms and neuroimaging-based brain age in mild cognitive impairment and early dementia: A multicenter study.Psychiatry and clinical neurosciences · 2025Article
- Complementary value of molecular, phenotypic, and functional aging biomarkers in dementia prediction.GeroScience · 2025Article
- Brain age mediates gut microbiome dysbiosis-related cognition in older adults.Alzheimer's research & therapy · 2025Article
- Preclinical Alzheimer's and vascular biomarkers alter brain aging in cognitively normal adults: a MRI-based study.Frontiers in aging neuroscience · 2025Article
- EEG-based brain age prediction in infants-toddlers: Implications for early detection of neurodevelopmental disorders.Developmental cognitive neuroscience · 2025Article
- Brain Age Is Not a Significant Predictor of Relapse Risk in Late-Life Depression.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- [European journal of nuclear medicine and molecular imaging · 2024Article
- Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.Tomography (Ann Arbor, Mich.) · 2024Review
- Prediction of chronological age from resting-state EEG power in the first three years of life.medRxiv : the preprint server for health sciences · 2024Article
- Investigating the impact of motion in the scanner on brain age predictions.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters.Journal of central nervous system disease · 2024Article
- Longitudinal accelerated brain age in mild cognitive impairment and Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- Probing multiple algorithms to calculate brain age: Examining reliability, relations with demographics, and predictive power.Human brain mapping · 2023Article
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8 authors.
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Abstract
Brain age prediction is a machine learning method that estimates an individual's chronological age from their neuroimaging scans. Brain age indicates whether an individual's brain appears "older" than age-matched healthy peers, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age models include older participants with amyloid pathology in their training sets and thus may be confounded when studying Alzheimer's disease (AD). We showed that amyloid status is a critical feature for brain age prediction models. We trained a model on T1-weighted MRI images participants without amyloid pathology. MRI data were processed to estimate gray matter density voxel-wise, which were then used to predict chronological age. Our model performed accurately comparable to previous models. Notably, we demonstrated more significant differences between AD diagnostic groups than other models. In addition, our model was able to delineate significant differences in brain age relative to chronological age between cognitively normal individuals with and without amyloid. Incorporation of amyloid status in brain age prediction models ultimately improves the utility of brain age as a biomarker for AD.
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