Functional Magnetic Resonance Imaging

Also known as fMRI

Latest papers

19 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

MCDA: Multi-View Graph Contrastive Learning with Dynamic Topology Augmentation for Functional MRI Analysis
Oct 19, 2026Hao Zhang, Xiaoyun Liu, Shuo Huang +4Graph Contrastive Learning Functional Magnetic Resonance Imaging
College of Biomedical Engineering, Fudan University, Shanghai 200433, China · College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China · Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing 210009, China +3
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
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
Bayesian Uncertainty Quantification for fMRI Functional Connectivity via Simulation-Based Inference
Sep 24, 2026Simon Carter, Zeming Kuang, Lilianne R. Mujica-Parodi +1Functional Magnetic Resonance Imaging Functional Connectivity
Applied Mathematics and Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook NY 11794-5281, USA · Biomedical Engineering Department and Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook NY 11794-5281, USA · Santa Fe Institute, Santa Fe, NM, USA
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
Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis
Jul 24, 2026Laura M. Montaldo, Ricardo A. Borsoi, Sebastian Miron +1Latent Dynamics Modeling Functional Magnetic Resonance Imaging
CRAN, Université de Lorraine, CNRS, Vandoeuvre-l`es-Nancy, France. · Department of CSEE, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
Unsupervised Susceptibility Distortion Correction of EPI without Calibration Scans via Image Translation-Based Registration
Jun 19, 2026Wooseung Kim, Sung-Hong ParkFunctional Magnetic Resonance Imaging Medical Image Registration
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics
Jun 16, 2026Junfeng Xia, Wenhao Ye, Junxiang Zhang +3Medical Image Synthesis Neuroimaging
Department of Biomedical Engineering, Southern University of Science and Technology, China · School of Biomedical Engineering, Shenzhen University, China
DD-INR: Dynamics-Driven Implicit Neural Representation for Accelerated Whole-Brain Functional MRI Reconstruction
Jun 9, 2026Qiaoxin Li, Caini Pan, Pierre-Antoine Comby +2Implicit Neural Representations Accelerated MRI Reconstruction
MIND · MIND, Inria, Palaiseau, France · Neurospin, CEA Paris Saclay, France +3
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
Brain-IT-VQA: From Brain Signals to Answers
May 28, 2026Roman Beliy, Matias Cosarinsky, Oliver Heinimann +2Visual Question Answering Neural Decoding
Weizmann Institute of Science
FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis
May 26, 2026Peiyu Duan, Jiyao Wang, Nicha C. Dvornek +4Flow Matching Functional Magnetic Resonance Imaging
Department of Biomedical Engineering, Yale University, New Haven, CT, USA · Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA · Department of Electrical Engineering, Yale University, New Haven, CT, USA
Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG
May 19, 2026Aditya R. Vaidya, Richard J. Antonello, Alexander G. HuthCross-Modal Learning Functional Magnetic Resonance Imaging
Department of Computer Science The University of Texas at Austin, USA · Zuckerman Mind Brain Behavior Institute Columbia University, USA · Departments of Neuroscience and Statistics University of California, Berkeley, USA
A Universal Space of Brain Dynamics for Unveiling Cognitive Transitions and Individual Differences
May 1, 2026Ronghua Zheng, Chengyuan Qian, Weiyang DingLatent Dynamics Modeling Functional Magnetic Resonance Imaging
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. · Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.
Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching
Apr 13, 2026Nicole Rogalla, Yuzhen Qin, Mario Senden +2Functional Magnetic Resonance Imaging Probabilistic Time Series Forecasting
Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands · Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands · Maastricht Brain Imaging Center, Maastricht University, Maastricht, the Netherlands
BrainCast: A Spatio-Temporal Forecasting Model for Whole-Brain fMRI Time Series Prediction
Mar 9, 2026Yunlong Gao, Jinbo Yang, Li Xiao +5Time Series Forecasting Spatiotemporal Forecasting
Institute of Advanced Technology, University of Science and Technology of China, Hefei 230052, China. · MoE Key Laboratory of Brain-Inspired Intelligence Perception and Cognition, University of Science and Technology of China, Hefei 230052, China. · School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China. +3
Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEG
Feb 26, 2026Hanning Guo, Hanwen Bi, Farah Abdellatif +4Functional Magnetic Resonance Imaging Multimodal Foundation Models
INM-4, Forschungszentrum Jülich, Germany · Department of Computer Science 3 - Software Engineering, RWTH Aachen University, Germany · INM-7, Forschungszentrum Jülich, Germany +6
Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation Using 7 Tesla Functional Magnetic Resonance Imaging
Feb 13, 2026Puneet Kumar, Winson F. Z. Yang, Alakhsimar Singh +2Functional Magnetic Resonance Imaging Neural Decoding
School of AI and Data Engineering, Indian Institute of Technology Ropar 140001, India · Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA · Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA +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

Oct 19, 2026J/K move · Enter open · S save

MCDA: Multi-View Graph Contrastive Learning with Dynamic Topology Augmentation for Functional MRI Analysis

Hao Zhang, Xiaoyun Liu, Shuo Huang, Yue Ma+3

College of Biomedical Engineering, Fudan University, Shanghai 200433, China · College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China · Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Southeast University, Nanjing 210009, China+3

Graph Contrastive Learning Functional Magnetic Resonance Imaging Graph Data Augmentation Functional Connectivity

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Resting-state fMRI (rs-fMRI) is a vital non-invasive tool for diagnosing brain disorders and uncovering pathological mechanisms. However, existing graph contrastive learning (GCL) methods often underexploit augmentation histories across training epochs and the relationships among different augmented views. In this paper, we introduce a topology-guided Multi-view graph Contrastive learning framework with Dynamic graph topology Augmentation (MCDA) for fMRI analysis. Unlike traditional GCL that uses fixed noise or edge-dropping, MCDA employs a Dynamic Augmentation Update (DAU) module that iteratively refines the augmented topology. Specifically, we incorporate self-aware and cross-sample augmentation strategies to simultaneously capture individual-specific functional signatures and inter-subject heterogeneity. We then design a topology-guided multi-view graph contrastive learning scheme that explores interrelationships not only between the original network and its augmented views, but also across different augmented views to maximize structural information preservation. We characterize the score-level optimization directions and establish a lower bound for the contrastive objective. The framework combines classification and contrastive losses for brain disorder diagnosis and examines discriminative functional connectivity patterns as potential biomarkers. Experiments on two public rs-fMRI datasets comprising 1,236 subjects demonstrate that MCDA outperforms the state-of-the-art methods. The code has been made publicly available at: https://github.com/goodcodingagoodboy/MCDA.