fMRI Representation Learning

fMRI: Functional Magnetic Resonance Imaging

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18 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Latent Prediction Yields Cross-Subject Alignable Representations in Sensory-Evoked fMRI
Oct 29, 2026Serwan Jassim, Victoria Bosch, Daniel Anthes +2fMRI Representation Learning fMRI-Based Visual Decoding
Institute of Cognitive Science University of Osnabrück
ACAM-J-MARS: Multi-Atlas Riemannian Spectral Representations for Decoding Advanced Meditation States from fMRI
Oct 29, 2026Jian Wang, Winson F. Z. Yang, Wugeng Zheng +1Riemannian Representation Learning Neural Decoding
Meditation Research Program, Psychiatry, MGH and Harvard Medical School, Boston, MA, USA · Martinos Center for Biomedical Imaging, Radiology, MGH and Harvard Medical School, Boston, MA, USA · College of Engineering, Northeastern University, Boston, MA, USA
Beyond Phenotypic Similarity: Relation Refinement and Selective Population Graph Learning for Multimodal Brain Disorder Diagnosis
Oct 22, 2026Yinyu LanBrain Disorder Classification Multimodal Graph Learning
School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
Alignment of visual brain encoding models with individual perceptual similarity judgments
Oct 15, 2026Daniel Chong, Haomiao Chen, Keith W Jamison +4fMRI Encoding Models Neural Encoding
Cornell University · Cornell Tech · Weill Cornell Medicine +2
CoFlu-Brain: An fMRI Connectivity Foundation Model with Cofluctuation-Based Pretraining
Oct 11, 2026Ziteng Sui, Junfeng Xia, Wenhao Ye +2fMRI Foundation Models fMRI Representation Learning
Department of Biomedical Engineering, Southern University of Science and Technology, China · School of Biomedical Engineering, Shenzhen University, China · Department of Computer Science, University of Warwick, UK
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
FlatClip: Reusing Image Foundation Models for fMRI Representation Learning via Cortical Flatmaps
Sep 25, 2026Mo Wang, Wenhao Ye, Zihan Ning +4fMRI Representation Learning Neuroimaging
Department of Biomedical Engineering, Southern University of Science and Technology, China · Department of Computer Science, University of Warwick, The UK · School of Biomedical Engineering, Shenzhen University, Shenzhen, China
MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences
Aug 25, 2026Sourav Pal, Viet Luong, Hoseok Lee +5fMRI Foundation Models Neuroimaging
University of Wisconsin–Madison, Madison, United States · Pohang University of Science and Technology (POSTECH), Pohang, South Korea · University of North Carolina at Chapel Hill, Chapel Hill, United States
GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning
Aug 12, 2026Vishnu M. Bashyam, Guray Erus, Junhao Wen +29Medical Imaging Foundation Models fMRI Representation Learning
Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania · Department of Electrical and Systems Engineering, University of Pennsylvania · Florey Institute of Neuroscience and Mental Health, University of Melbourne +16
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
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
Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
May 27, 2026Jiyao Wang, Peiyu Duan, Nicha C. Dvornek +4Self-Supervised Learning fMRI Representation Learning
Department of Biomedical Engineering, Yale University, New Haven, CT, USA · Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA · Electrical Engineering, Yale University, New Haven, CT, USA +1
Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?
Apr 4, 2026Peter Yongho Kim, Juhyeon Park, Jungwoo Park +4fMRI Representation Learning Long-Context Modeling
ECE, Seoul National University · IPAI, Seoul National University · BCS, Seoul National University +2
Scaling Vision Transformers for Functional MRI with Flat Maps
Oct 15, 2025Connor Lane, Mihir Tripathy, Leema Krishna Murali +15fMRI Foundation Models Masked Autoencoders
MedARC · Sophont · Baylor College of Medicine +3
Align, Then Adapt: Few-Shot fMRI Visual Reconstruction For New Subjects
May 3, 2025Christos Zangos, Danish Ebadulla, Thomas Christopher Sprague +1fMRI-Based Visual Decoding fMRI Representation Learning
Department of Computer Science, University of California, Santa Barbara, USA · Department of Psychological and Brain Sciences, University of California, Santa Barbara, USA
Stochastic Optimal Control for Continuous-Time fMRI Representation Learning
Feb 7, 2025Joonhyeong Park, Byoungwoo Park, Chang-Bae Bang +4Latent Dynamics Modeling Self-Supervised Time Series Representation Learning
KAIST · Yonsei University · EverEx +1
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Mar 17, 2024Paul S. Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva +8Image Reconstruction fMRI-Based Visual Decoding
Stability AI · Medical AI Research Center (MedARC) · Princeton Neuroscience Institute +3

Oct 29, 2026J/K move · Enter open · S save

Latent Prediction Yields Cross-Subject Alignable Representations in Sensory-Evoked fMRI

Serwan Jassim, Victoria Bosch, Daniel Anthes, Elia Bruni+1

Institute of Cognitive Science University of Osnabrück

fMRI Representation Learning fMRI-Based Visual Decoding Neural Decoding Latent Space Alignment Joint-Embedding Predictive Architecture

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Decoding sensory-evoked functional magnetic resonance imaging (fMRI) data is a central analytical tool in cognitive neuroscience. Here we ask whether self-supervised learning (SSL), which has been scarcely explored in this domain, can yield representations that support strong downstream performance. To do so, we adapt a joint embedding predictive architecture (JEPA) to train participant-specific encoders on brain data alone. The encoders never see the stimulus images or any representation derived from them. Applied to the Natural Scenes Dataset (NSD), we evaluate our learned brain embeddings on the downstream tasks of brain captioning and image reconstruction, as well as cross-participant generalization. Our self-supervised approach achieves competitive performance with parameter-rich supervised pipelines that are trained end-to-end with access to image and language data. We further find that encoders trained independently for different participants can be aligned fully unsupervised, using neither images nor stimuli shared between them. After alignment, a neural decoding head trained on one participant can be applied to another without fine-tuning and retains most of its within-subject performance on image reconstruction and brain captioning. By removing the need for images and shared stimuli, our approach is a step towards pooling fMRI data across participants and, eventually, across datasets.