mlsanityData to 14 Oct 2026

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

arXiv 1612.02572 ↗First posted 8 Dec 2016stat.ML cs.CV cs.LGcited by 72

Not on the map. Why?

Abstract (arXiv)

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people and deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of "brain-predicted age" as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. Brain-predicted age represents an accurate, highly reliable and genetically-valid phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.

Key facts. Cited by 72 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Dec 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 16; 2022: 12; 2023: 16; 2024: 6; 2025: 13; 2026: 9.

72 papers.

Sort:

By area:

  1. 2022Brain Imaging Generation with Latent Diffusion Modelscited by 268
  2. 2021Global-Local Transformer for Brain Age Estimationcited by 34
  3. 2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 22
  4. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  5. 2021Brain Age Estimation From MRI Using Cascade Networks with Ranking Losscited by 17
  6. 2022Contrastive learning for regression in multi-site brain age predictioncited by 14
  7. 2023How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?cited by 14
  8. 2022Deep Learning for Brain Age Estimation: A Systematic Reviewcited by 13
  9. 2022Deep Relation Learning for Regression and Its Application to Brain Age Estimationcited by 12
  10. 2023Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIscited by 12
  11. 2021Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settingscited by 11
  12. 2021Improved Brain Age Estimation with Slice-based Set Networkscited by 11
38 more
  1. 2022Improving the repeatability of deep learning models with Monte Carlo dropoutcited by 11
  2. 2021Scaling Neuroscience Research using Federated Learningcited by 10
  3. 2025Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnosescited by 10
  4. 2021Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learningcited by 6
  5. 2021Ten years of image analysis and machine learning competitions in dementiacited by 5
  6. 2026FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matchingcited by 5
  7. 2023Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric researchcited by 4
  8. 2023Analysing race and sex bias in brain age predictioncited by 4
  9. 2024IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR imagescited by 4
  10. 2023Ordinal Classification with Distance Regularization for Robust Brain Age Predictioncited by 4
  11. 2026BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learningcited by 4
  12. 2021Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networkscited by 3
  13. 2021An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modelingcited by 3
  14. 2023Multimodal and multicontrast image fusion via deep generative modelscited by 3
  15. 2023Multimodal brain age estimation using interpretable adaptive population-graph learningcited by 3
  16. 2023A voxel-level approach to brain age prediction: A method to assess regional brain agingcited by 3
  17. 2025Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspectivecited by 3
  18. 2022Estimating Brain Age with Global and Local Dependenciescited by 2
  19. 2022Efficient brain age prediction from 3D MRI volumes using 2D projectionscited by 2
  20. 2023Brain Structure Ages -- A new biomarker for multi-disease classificationcited by 2
  21. 2023Robust Brain Age Estimation via Regression Models and MRI-derived Featurescited by 2
  22. 2023A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regressioncited by 2
  23. 2023A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templatescited by 2
  24. 2024Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasetscited by 2
  25. 2025Explainable Multimodal Regression via Information Decompositioncited by 2
  26. 2021Uncertainty-Based Biological Age Estimation of Brain MRI Scanscited by 1
  27. 2022Brain Structural Saliency Over The Agescited by 1
  28. 2022XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learningcited by 1
  29. 2023Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learningcited by 1
  30. 2024SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutionscited by 1
  31. 2024MRI Volume-Based Robust Brain Age Estimation Using Weight-Shared Spatial Attention in 3D CNNscited by 1
  32. 2025Three-dimensional end-to-end deep learning for brain MRI analysiscited by 1
  33. 2026Statistical Opportunities in Neuroimagingcited by 1
  34. 2026WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesiscited by 1
  35. 2021Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learningcited by 0
  36. 2021Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age predictioncited by 0
  37. 2021Predicc~ao da Idade Cerebral a partir de Imagens de Ressonancia Magn'etica utilizando Redes Neurais Convolucionaiscited by 0
  38. 2021Infant Brain Age Classification: 2D CNN Outperforms 3D CNN in Small Datasetcited by 0

50 most cited of 72 All 72 (JSON)