ArticleNature communications2019
Brain age prediction using deep learning uncovers associated sequence variants.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 171 papers, 1 of them a synthesis that pooled it.
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Who cites it
171 citing papers in PubMed, 1 synthesis or guideline pooled it, 406 citations in OpenAlex.
- Factors associated with brain ageing - a systematic review.BMC neurology · 2021Pooled it
- Metabolomic signatures of brain aging: A multimodal and genetic study.Molecular psychiatry · 2026Article
- Age estimation from pubic symphysis based on cinematic volume rendering: comparison between Suchey-Brooks staging and deep learning.International journal of legal medicine · 2026Article
- Genome-wide association study for refractive error imputed from fundus images via deep learning: a proof-of-concept study.Japanese journal of ophthalmology · 2026Article
- Genetically informed dissection of multimodal brain aging and ten major psychiatric disorders.Molecular psychiatry · 2026Article
- Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.Brain informatics · 2026Article
- Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major.iScience · 2026Article
- Hybrid deep learning model for brain age prediction using time-distributed convolutional and bidirectional LSTM networks.Scientific reports · 2026Article
- Deep neural networks and genome-wide associations reveal the polygenic architecture of local brain aging.GeroScience · 2026Article
- Brain age prediction in generalized anxiety disorder using a convolutional neural network.Translational psychiatry · 2026Article
- Aging beyond diagnosis: the MRI brain age gap across disorders.GeroScience · 2026Review
- superLPNet: a super lightweight parameter deep learning model for brain age estimation from structural MRI.Magma (New York, N.Y.) · 2026Article
- Exposome-wide patterns predict brain health in aging.Nature communications · 2026Article
- Brain age gap as biomarker linking cardiovascular diseases genetic susceptibility and causality.iScience · 2026Article
- Parameter-efficient convolutional neural network for drug treatment outcome studies of pediatric epilepsy.Scientific reports · 2026Article
- MDFNet: a multi-dimensional feature fusion model based on structural magnetic resonance imaging representations for brain age estimation.Magma (New York, N.Y.) · 2026Article
- Machine learning assessment of retinal blood flow links metabolic dysfunction and accelerated microvascular aging.Scientific reports · 2026Article
- Structural connectome analysis using a graph-based deep model for prediction of non-imaging variables.Frontiers in neuroscience · 2026Article
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Deep learning-based brain age predicts stroke recurrence in acute ischemic cerebrovascular disease.NPJ digital medicine · 2025Article
111 more citing papers are in PubMed but not listed here.
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9 authors at 2 institutions in 1 country.
Funding
Abstract
Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual's predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: [Formula: see text], replication set: [Formula: see text]) yielded two sequence variants, rs1452628-T ([Formula: see text], [Formula: see text]) and rs2435204-G ([Formula: see text], [Formula: see text]). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).
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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.