ArticleNature aging2022
Deep learning-based brain age prediction in normal aging and dementia.
Article in Nature aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 1 of them a synthesis that pooled it.
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Who cites it
117 citing papers in PubMed, 1 synthesis or guideline pooled it, 202 citations in OpenAlex.
- Adverse childhood experiences and subsequent impact on adulthood cognitive impairment: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial.Dialogues in clinical neuroscience · 2024Trial
- Metabolomic signatures of brain aging: A multimodal and genetic study.Molecular psychiatry · 2026Article
- Tissue- and compartment-resolved brain age reveals vascular-metabolic and neurodegenerative aging.Research square · 2026Article
- The Transdiagnostic Association Between Childhood Trauma and Accelerated Brain Aging in Two Independent Datasets.Schizophrenia bulletin · 2026Article
- Learning neuroimaging models from health system-scale data.Nature biomedical engineering · 2026Article
- Article
- Elevated BrainAGE precedes cognitive impairment and improves prediction of future cognitive decline.medRxiv : the preprint server for health sciences · 2026Article
- Multimodal Deep Learning Reveals the Modular Genetic Architecture of Cardiovascular Aging.medRxiv : the preprint server for health sciences · 2026Article
- OpenMAP-BrainAge: generalizable and interpretable brain age predictor from MRI.Radiology advances · 2026Article
- Deep learning-based MRI analysis reveals Lewy body co-pathology accelerates brain aging in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Prediction of Antipsychotic Drug Doses for BPSD in Alzheimer's Disease Using Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2026Article
- Association of imaging-defined brain age with disease severity and adverse outcomes in CADASIL.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Functional system-specific brain aging across the Alzheimer's disease continuum.Translational psychiatry · 2026Article
- Spatial amyloid-informed multimodal brain age as an early marker of Alzheimer's-related vulnerability and risk stratification.The journal of prevention of Alzheimer's disease · 2026Article
- Accelerated brain aging in methamphetamine use disorder revealed by functional connectivity.National science review · 2026Article
- A physically and mentally active lifestyle relates to younger brain and cognitive age.GeroScience · 2026Article
- Multimodal Assessment of Biological Age Following Radiation Therapy Among Patients With Early-Stage NSCLC.JAMA network open · 2026Article
- Brain age gap as biomarker linking cardiovascular diseases genetic susceptibility and causality.iScience · 2026Article
- Women's reproductive factors predict local brain aging profiles mapped using deep neural networks.GeroScience · 2026Article
57 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
Abstract
Brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction model using a large collection of fluorodeoxyglucose positron emission tomography and structural magnetic resonance imaging and tested how the brain age gap relates to degenerative syndromes including mild cognitive impairment, AD, frontotemporal dementia and Lewy body dementia. Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap was highly correlated with cognitive impairment and the AD biomarker. The higher gap also showed a longitudinal predictive nature across clinical categories, including cognitively unimpaired individuals who converted to a clinical stage. However, regions generating brain age gaps were different for each diagnostic group of which the AD continuum showed similar patterns to normal aging.
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Registered trials
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.