fMRI Foundation Models

fMRI: Functional Magnetic Resonance Imaging

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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders
Sep 29, 2026Giovanni Marraffini, Victoria Shevchenko, Carlo Alberto Barbano +1Kernel Ridge Regression Medical Imaging Foundation Models
UNITO · MIND
Natural Image Autoencoder-Based fMRI Representations for Trait and State Prediction
Sep 28, 2026Juhyeon Park, Yeonwoo Kim, Peter Yongho Kim +5Medical Imaging Foundation Models fMRI Foundation Models
IPAI, Seoul National University · Microsoft Research · ECE, Seoul National University +1
A Scaling Study for fMRI Foundation Models
Sep 23, 2026Wenhao Ye, Xuanye Pan, Junfeng Xia +3Medical Imaging Foundation Models fMRI Foundation Models
Southern University of Science and Technology · Shenzhen University · SLAI +1
BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models
Sep 9, 2026Junfeng Xia, Wenhao Ye, Junxiang Zhang +3Medical Imaging Foundation Models fMRI Foundation Models
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China · Department of Computer Science, University of Warwick, The UK · Omni-Intelligence, Shenzhen, China +1
RABBiT: Rapidly adaptive BOLD foundation model via brain-tuning for accurate zero-shot and few-shot prediction of speech-elicited responses in the brain
Jul 6, 2026Omer Moussa, Mariya TonevaMedical Imaging Foundation Models fMRI Foundation Models
Max Planck Institute for Software Systems · Saarbrücken, Germany
FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI
Jun 9, 2026Mo Wang, Wenhao Ye, Junfeng Xia +3Medical Imaging Foundation Models fMRI Foundation Models
Southern University of Science and Technology · University of Warwick
The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail
May 29, 2026Giovanni Marraffini, Gabriel Mahuas, Trinidad Borrell +2Medical Imaging Foundation Models Functional Connectivity
Inria Saclay Île-de-France, CEA, Université Paris-Saclay, Palaiseau, France · Sigma Nova · Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM +1
A foundation model of vision, audition, and language for in-silico neuroscience
May 5, 2026Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit +5Cognitive Modeling fMRI Foundation Models
FAIR at Meta · Laboratoire de Neurosciences Cognitives et Computationnelles, Ecole Normale Supérieure - PSL
Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
Apr 23, 2026Deepank Girish, Yi Hao Chan, Sukrit Gupta +2Medical Imaging Foundation Models Functional Connectivity
College of Computing and Data Science, Nanyang Technological University, Singapore
Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining
Apr 14, 2026Junfeng Xia, Wenhao Ye, Xuanye Pan +3Medical Imaging Foundation Models Diffusion Transformer
Department of Biomedical Engineering, Southern University of Science and Technology, China · School of Biomedical Engineering, Shenzhen University, China
Scaling Vision Transformers for Functional MRI with Flat Maps
Oct 15, 2025Connor Lane, Mihir Tripathy, Leema Krishna Murali +15Medical Imaging Foundation Models Masked Autoencoders
MedARC · Sophont · Baylor College of Medicine +3

Sep 29, 2026, cs.AIJ/K move · Enter open · S save

Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders

Giovanni Marraffini, Victoria Shevchenko, Carlo Alberto Barbano, Demian Wassermann

UNITO · MIND

Kernel Ridge Regression Medical Imaging Foundation Models Functional Connectivity Self-Supervised Pre-Training fMRI Foundation Models Spectral Methods

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Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learned from large unlabelled corpora should capture individual functional dynamics and generalise across cohorts. However, kernel ridge regression (KRR) fitted on functional connectivity (FC) matrices still predicts individual phenotypes more accurately than any BFM we tested. In this paper, we show that KRR is weighted by the eigenvalues of the FC which are miscalibrated for phenotype prediction. We apply an efficient spectral filter to recalibrate the eigenvalues of each subject's FC matrix, enabling the model to exploit more inter-individual variance. Across the 5 datasets, 11 parcellations and 6 prediction targets we tested, we match or exceed the KRR baseline. Based on this finding, we then pretrain a small encoder model on about 4,000 hours of fMRI from 162 open datasets, whereby we align the pairwise similarities between the embeddings of recording snippets with those between the recalibrated connectomes. Our model performs on par with the best of the 6 published BFMs we tested while having an order of magnitude fewer parameters. Our encoder performs better than FC on short scans and in smaller cohorts, especially in fingerprinting. We release the pretrained model weights, the code and the pretraining data, preprocessed and parcellated.

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