fMRI Representation Learning

fMRI: Functional Magnetic Resonance Imaging

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13 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Alignment of visual brain encoding models with individual perceptual similarity judgments
Oct 15, 2026Daniel Chong, Haomiao Chen, Keith W Jamison +4Neural Encoding Visual Neuroscience
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
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

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Alignment of visual brain encoding models with individual perceptual similarity judgments

Daniel Chong, Haomiao Chen, Keith W Jamison, Raja Marjieh+3

Cornell University · Cornell Tech · Weill Cornell Medicine+2

Neural Encoding Visual Neuroscience fMRI Representation Learning

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Encoding models that predict brain activation responses to images have traditionally been trained to maximize accuracy when predicting observed brain data. However, their internal representations may not reflect individual human perception. In this work, we investigate whether we can fine-tune brain encoding models to better align their representations with individuals' similarity judgments. Using similarity judgments of 100 images from 8 participants in the Natural Scenes Dataset (NSD), we adapt a generic human similarity judgment model (DreamSim) separately for each participant to obtain personalized perceptual similarity models. These models are then used to obtain subject-specific similarity structure across images in their encoding model training sets. We fine-tune each participant's baseline encoder in two conditions, aligning its representations with either generic or personalized similarity judgments, with the same initialization and objective. We find that baseline brain encoders reflect within-subject perceptual similarity more strongly than measured fMRI responses and preserve intersubject correlations present in measured fMRI, but fail to reflect those of human perceptual similarity judgments. Fine-tuning with generic, population-level perceptual similarity information results in better alignment with human judgments than baseline encoders, but these models still fail to capture intersubject correlations of human judgments. However, fine-tuning with personalized perceptual similarity information results in models that are the most aligned with human perceptual similarity judgments and also reflect intersubject correlations of human judgments. Our framework provides an approach for bootstrapping human similarity judgment data into more individualized, human-aligned brain encoding models of vision.