Brain Age Prediction

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Brain age prediction trains models to estimate a person’s chronological age from brain measurements, then uses the difference between predicted and actual age to study development, aging, and possible disease-related change. Common approaches use 3D CNNs or vision transformers on structural MRI, combine MRI modalities or vascular signals, and increasingly learn representations from unlabeled MRI or EEG; some models instead track change across repeat scans. Performance is often assessed by mean absolute error and tested across cohorts such as UK Biobank, OpenBHB, and Cam-CAN, with applications including cognitive impairment and post-stroke outcomes. A central challenge is whether models generalize across scanners and populations, and whether an age gap reflects pathology rather than age-range mismatch, cohort differences, or other confounds.

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17 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

Interpretable Brain Age Prediction Using White Matter Fiber Orientation Distributions with Rotation-Equivariant Deep Learning
Oct 26, 2026Chenyang Fan, Robert D. Sanders, Elie Matar +5Brain Age Prediction Explainable Medical Image Analysis
School of Biomedical Engineering and Brain and Mind Centre at the University of Sydney, Sydney, NSW 2050 Australia · Brain and Mind Centre and Central Clinical School at the University of Sydney, Sydney, NSW, 2050 Australia · Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR 999077, China +5
Beyond Feature Reliability: Repeat-Informed Multifractal Curve Regression for Brain-Age Prediction
Sep 24, 2026Yu Chang, Anzhe Cheng, Jiahao Chen +8Functional Data Analysis Brain Age Prediction
University of Southern California, USA · University of Toronto, Canada
Brain-PACE: A Deep Siamese MRI Framework for Modelling Longitudinal Brain Acceleration
Sep 10, 2026Samuel Maddox, Jacob Newman, Saber Sami +4Alzheimer's Disease Brain Age Prediction
School of Computing Sciences, University of East Anglia · Norwich Medical School, University of East Anglia
Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs
Sep 9, 2026Saurabh Sihag, Andrea Cavallo, Elvin Isufi +2Graph Neural Networks Brain Age Prediction
Department of Electrical and Computer Engineering at the University at Albany, SUNY, Albany, NY · Delft University of Technology, Delft, The Netherlands · Department of Electrical and Computer Engineering at the University of Rochester, Rochester, NY +1
XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction
Sep 8, 2026Yang Qiao, Junjie Wu, Deqiang Qiu +2Explainable Artificial Intelligence Functional Connectivity
Department of Computer Science Emory University Atlanta, Georgia, USA · Department of Radiology and Imaging Sciences Computational NeuroImaging & Neuroscience Lab, Emory University Atlanta, Georgia, USA · Department of Neurology Emory University School of Medicine Atlanta, Georgia, USA
Attributing Cohen's d: Training Data Attribution for Disease-Related Effects in Normative Age Biomarkers
Sep 7, 2026Jakob Snel, Marc-Andre SchulzBrain Age Prediction Influence Functions
Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany · Tübingen AI Center, University of Tübingen, Tübingen, Germany · Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany +1
Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction
Aug 31, 2026Ce Ju, Antoine Collas, Florent Bouchard +1Functional Connectivity Brain Age Prediction
Inria, CEA, Universit´e Paris-Saclay, Palaiseau, France · Universit´e Paris-Saclay, CNRS, CentraleSup´elec, L2S, France
BrainNorm: A Foundation Model that knows Normal via Semantic Atlas Pretraining
Aug 18, 2026Madhumitha Venkatesh, Shanawaj S Madarkar, Konda Reddy MopuriMedical Imaging Foundation Models Medical Image Representation Learning
Indian Institute of Technology Hyderabad
Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI
Jul 12, 2026Fabian Mager, Lars Kai HansenContrastive Learning Brain Age Prediction
Department of Applied Mathematics and Computer Science Technical University of Denmark
STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning
Jul 7, 2026Roy Segal, Yoni Svechinsky, Tomer FeketeBrain Age Prediction Self-Supervised Time Series Representation Learning
Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI
Jun 25, 2026Minh Duc Do, Tillmann Rheude, Noel Kronenberg +23D ViTs Brain Age Prediction
1Berlin Institute of Health at Charité - Universitätsmedizin Berlin · 2Health Data Science Unit, Heidelberg University Hospital and BioQuant · 3Intelligent Medicine Institute, Fudan University +1
Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms
May 16, 2026Anni Zhao, Alex Bateh, Tyler Baldridge +2Brain Age Prediction
Center for Data Science Nell Hodgson Woodruff School of Nursing Emory University Atlanta, GA 30322, USA · Division of Nephrology Department of Medicine University of Alabama at Birmingham Birmingham, AL 35294, USA · Department of Neurology School of Medicine University of Kansas Medical Center Kansas City, KS 66103, USA
A Two-Stage Multi-Modal MRI Framework for Lifespan Brain Age Prediction
Apr 17, 2026Dingyi Zhang, Ruiying Liu, Yun WangBrain Age Prediction Neuroimaging
Department of Computer Science, Emory University, Atlanta, GA, USA · Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
BrainRotViT: Transformer-ResNet Hybrid for Explainable Modeling of Brain Aging from 3D sMRI
Nov 19, 2025Wasif Jalal, Md Nafiu Rahman, Atif Hasan Rahman +1Brain Age Prediction Brain MRI
Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology
BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Aug 9, 2025Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends +1Functional Connectivity Temporal GNNs
New Jersey Institute of Technology · University of Oxford
Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction
Jun 18, 2025Vincent Roca, Marc Tommasi, Paul Andrey +8Brain Age Prediction Clinical Outcome Prediction
Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France · Univ. Lille, Inria, F-59000 Lille, France · Univ. Montpellier, Inria, F-34090 Montpellier, France +3
Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps
Dec 1, 2024Jordan Jomsky, Zongyu Li, Kay C. Igwe +8Alzheimer's Disease Brain Age Prediction
Department of Biomedical Engineering, Columbia University, New York, NY 10027-7041, USA · Zuckerman Institute, Columbia University, New York, NY 10027-7041, USA · Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA +5

Oct 26, 2026J/K move · Enter open · S save

Interpretable Brain Age Prediction Using White Matter Fiber Orientation Distributions with Rotation-Equivariant Deep Learning

Chenyang Fan, Robert D. Sanders, Elie Matar, Yixuan Yuan+4

School of Biomedical Engineering and Brain and Mind Centre at the University of Sydney, Sydney, NSW 2050 Australia · Brain and Mind Centre and Central Clinical School at the University of Sydney, Sydney, NSW, 2050 Australia · Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR 999077, China+5

Brain Age Prediction Explainable Medical Image Analysis Diffusion MRI Equivariant Neural Networks

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Diffusion MRI (dMRI) provides information about white matter microstructure that can support brain age prediction. However, many existing prediction models use diffusion tensor imaging (DTI) scalar metrics that omit orientation information. Deep learning is increasingly used for brain age prediction, but conventional CNNs do not explicitly respect the geometric structure of orientation-dependent features. We present an interpretable brain age prediction framework that combines white matter fiber orientation distributions (WM-FODs) with an SE(3) (Special Euclidean group in 3 dimensions) equivariant CNN. The network operates directly on spherical harmonic (SH) coefficients of the FOD, preserving their transformation properties under rotation. Evaluation in 692 participants aged 36-90 years from the Human Connectome Project Aging dataset showed that the combined framework outperformed the evaluated DTI-based models and conventional CNNs. Averaging predictions from five cross-validation models yielded a mean absolute error of 3.54 years and Pearson's correlation of 0.958. Our results indicated that the performance advantage depended on combining WM-FODs with equivariant processing. Prediction accuracy was highest at a maximum SH order of Lmax = 2 and declined at higher orders. Tract-level occlusion, where each segmented tract was masked in turn, revealed age-dependent patterns of model sensitivity, with importance peaks in frontal projection pathways at 36-44 years, the fornix at 45-54 years, the callosal isthmus at 55-64 years, the middle cerebellar peduncle at 65-74 years, and the callosal genu at 75-90 years. These findings support rotation-equivariant processing of WM-FODs for accurate brain age prediction with anatomically interpretable model sensitivity.