ArticleNeurobiology of aging2021
Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.
Article in Neurobiology of aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Metabolomic signatures of brain aging: A multimodal and genetic study.Molecular psychiatry · 2026Article
- Genetically informed dissection of multimodal brain aging and ten major psychiatric disorders.Molecular psychiatry · 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
- Hybrid deep learning model for brain age prediction using time-distributed convolutional and bidirectional LSTM networks.Scientific reports · 2026Article
- Article
- No causal links between estradiol and female's brain and mental health using Mendelian randomization.Nature communications · 2025Article
- Genome-wide analysis of brain age identifies 59 associated loci and unveils relationships with mental and physical health.Nature aging · 2025Article
- Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank.Human brain mapping · 2025Article
- Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A review of artificial intelligence-based brain age estimation and its applications for related diseases.Briefings in functional genomics · 2025Review
- Decoding brain aging trajectory: predictive discrepancies, genetic susceptibilities, and emerging therapeutic strategies.Frontiers in aging neuroscience · 2025Review
- Long-term night-shift work is associated with accelerates brain aging and worsens N3 sleep in female nurses.Sleep medicine · 2024Article
- The Genetic Architecture of Biological Age in Nine Human Organ Systems.medRxiv : the preprint server for health sciences · 2024Article
- Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.Annals of clinical and translational neurology · 2024Article
- Nonlinear DNA methylation trajectories in aging male mice.Nature communications · 2024Article
- The genetic architecture of multimodal human brain age.Nature communications · 2024Article
- Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning.Nature and science of sleep · 2024Article
- Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes.Journal of imaging · 2023Article
- The Genetic Architecture of Multimodal Human Brain Age.bioRxiv : the preprint server for biology · 2023Article
- Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections.Brain sciences · 2023Article
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8 authors.
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Abstract
To study genetic factors associated with brain aging, we first need to quantify brain aging. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age (PBA), using imaging data. Recent studies have refined these models to obtain a more accurate PBA, but research has yet to demonstrate the scientific value of doing so. Here, we show that a more accurate PBA leads to better characterization of genetic factors associated with brain aging. We trained a convolutional neural network (CNN) model on 16,998 UK Biobank subjects to derive PBA, then conducted a genome-wide association study on the PBA, in which we identified single nucleotide polymorphisms from four independent loci significantly associated with brain aging, three of which were novel. By comparing association results based on the CNN-derived PBA to those based on a linear regression-derived PBA, we concluded that a more accurate PBA enables the discovery of novel genetic associations. Our results may be valuable for identifying other lifestyle factors associated with brain aging.
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