Neuroimaging

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22 papers in the last four weeks, up 267% on the four weeks before. 0.2% of all new papers.

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Latest papers 166

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Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation
Jun 4, 2026Yohann Benchetrit, Marlène Careil, Simon Dahan +3Synthetic Data Augmentation Neural Decoding
Meta AI
Tractogram foundation model
Jun 3, 2026Guikun Chen, Yuqian Chen, Yijie Li +5Medical Imaging Foundation Models Representation Learning
The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310058, Zhejiang, China · Department of Radiology, Brigham and Women’s Hospital, Mass General Brigham, Boston 02115, Massachusetts, USA · Harvard Medical School, Boston 02115, Massachusetts, USA +4
Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
Jun 3, 2026Hoang-Son Vo, Van-Hung Bui, Minh-Huy Mai-Duc +2Hierarchical Representation Learning Visual SSMs
Chonnam National University, Gwangju, Republic of Korea · Vietnam National University - Ho Chi Minh City, University of Science, Vietnam · Institute for Cybersecurity and Digital Technologies, Russia
SC-TauPath: A Structural Connectivity Attribution Framework for Mapping Tau Propagation Pathways in Alzheimer's Disease
Jun 2, 2026Jing Zhang, Norman Scheel, Minheng Chen +5Alzheimer's Disease Neuroimaging
University of Texas at Arlington, Arlington, TX, USA · Michigan State University, East Lansing, MI, USA · University of Texas Southwestern Medical Center, Dallas, TX, USA
Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis
Jun 2, 2026Jaeyoon Sim, Soojin Hwang, Seunghun Baek +2Neuroimaging Hypergraph Learning
Pohang University of Science and Technology (POSTECH), Pohang, South Korea · University of North Carolina at Chapel Hill, Chapel Hill, USA
NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis
May 31, 2026Xiongri Shen, Zhenxi Song, Jiaqi wang +13Feature Attribution Functional Connectivity
Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China · School of Intelligence Science and Engineering, College of Artificial Intelligence, Harbin Institute of Technology, Shenzhen, 518055, China · School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China +5
Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes
May 31, 2026Xiongri Shen, Jiaqi Wang, Zhenxi Song +5Alzheimer's Disease Counterfactual Explanations
Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China · School of Intelligence Science and Engineering, College of Artificial Intelligence, Harbin Institute of Technology, Shenzhen, 518055, China · School of Biomedical Engineering, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical, Measurements and Ultrasound Imaging, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
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
MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding
May 28, 2026Abdulkadir Gokce, Badr AlKhamissi, Martin SchrimpfNeuroimaging Neural Encoding
NeuroAI Lab, EPFL
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
May 28, 2026Hwa Hui Tew, Junn Yong Loo, Fang Yu Leong +6Flow Matching Time Series Generation
School of Information Technology, Monash University Malaysia · Statistics Program, King Abdullah University of Science and Technology · School of Engineering, Monash University Malaysia
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
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 Siamese Neural Networks
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
A Two-Scan Deep Learning Model for Predicting Dementia in Mild Cognitive Impairment
May 27, 2026Sara Fin, Alireza Moayedikia, David J. White +2Hybrid CNN-Transformer Architectures Longitudinal Medical Image Analysis
The Maersk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
vSV-ViT: Variable-size SuperVertex Vision Transformer for Cortical Surface Learning in Alzheimer's Disease
May 26, 2026Geonwoo Baek, Ikbeom JangAlzheimer's Disease 3D ViTs
Department of Computer Science and Engineering, Hankuk University of Foreign Studies, Seoul, Republic of Korea
ULF-Synth: Physics-Guided Ultra-Low-Field MRI Enhancement for Pediatric Neuroimaging
May 23, 2026Toufiq Musah, Salvatore Calcagno, Federica Proietto Salanitri +3Medical Image Super-Resolution Magnetic Resonance Imaging
Kwame Nkrumah University of Science and Technology, Kumasi, Ghana · University of Catania, Catania, Italy · The Hong Kong University of Science and Technology, Hong Kong SAR, China +1
From Activation to Causality: Discovery of Causal Visual Representations in the Human Brain
May 22, 2026Yuval Golbari, Navve Wasserman, Matias Cosarinsky +5Causal Representation Learning Causal Discovery
Weizmann Institute of Science · Massachusetts Institute of Technology
Distance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis
May 22, 2026Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1Functional Connectivity Depression Detection
School of Information Technology and Communication, Griffith University, Australia
Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces
May 21, 2026Mario Severino, Manuela Moretto, Robert A. McCutcheon +1Functional Connectivity Neuroimaging
Department of Information Engineering, University of Padova, Padova, Italy · Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom · Department of Psychiatry, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom +2
Learning fMRI activations dictionaries across individual geometries via optimal transport
May 20, 2026Sonia Mazelet, Rémi Flamary, Bertrand ThirionLearning to Optimize Geometric Representation Learning
CMAP, Ecole Polytechnique · Palaiseau, France · Mind, Inria-Saclay
Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry
May 19, 2026Pablo Marcos-Manchón, Rishi Jha, Lluís FuentemillaNeural Representation Geometry Unsupervised Representation Learning
Department of Cognition, Development and Education Psychology, University of Barcelona · Institute of Neurosciences, University of Barcelona · Department of Computer Science, Cornell University +1
Nonlocal operator learning for fMRI encoding and decoding tasks
May 19, 2026Andreas Kramer, Saugat Acharya, Alice Giola +1Neural Decoding Neuroimaging
Department of Computer Science, Idaho State University 921 S. 8th Ave Mail Stop 8060, Pocatello, ID 83209-8023 · Department of Mathematics and Statistics, Idaho State University Physical Science Complex, 921 S. 8th Ave., Stop 8085, Pocatello, ID 83209
BrainDyn: A Sheaf Neural ODE for Generative Brain Dynamics
May 19, 2026Siddharth Viswanath, Panayiotis Ketonis, Chen Liu +3Graph Neural Networks Sheaf Neural Networks
1Yale University · 2Boise State University · University of Wisconsin–Madison
FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding
May 19, 2026Yudan Ren, Pengcheng Shi, Zihan Ma +2Neural Decoding Mixture-of-Experts Inference
School of Electronic Information (School of Artificial Intelligence), Northwest University, Xi’an 710127, China
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 Transfer Learning
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
Diffeomorphic Cortical Alignment via Direct Warping of Streamline Endpoints
May 16, 2026Yang Xiang, Martin Cole, Zhengwu ZhangMedical Image Registration Neuroimaging
Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill · Department of Psychiatry, University of Rochester
Isolating Nonlinear Independent Sources in fMRI with $β$ -TCVAE Models
May 15, 2026Qiang Li, Shujian Yu, Jesus Malo +3Variational Autoencoders Disentangled Representation Learning
1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA · Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands · 3Image Processing Laboratory, University of Valencia, Valencia, Spain +2
Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex
May 15, 2026Idan Daniel Grosbard, Mor Geva, Galit YovelMechanistic Interpretability Visual Neuroscience
1Sagol School of Neuroscience · 2Blavatnik School of Computer Science and AI · School of Psychological Sciences
Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction
May 14, 2026Yujie Wei, Chenglong Ma, Jianxiong Gao +6Neural Decoding Neuroimaging
1Fudan University · 2Alibaba Group · 3Ant Group
Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
May 13, 2026Leo Milecki, Qingyu Hu, Bahram Jafrasteh +2Representation Learning Masked Autoencoders
Department of Radiology, Weill Cornell Medicine, New York, NY, USA. · School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA.
Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling
May 13, 2026Gaurav Rudravaram, Lianrui Zuo, Karthik Ramadass +17Variational Autoencoders Latent Variable Models
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA

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Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation

Yohann Benchetrit, Marlène Careil, Simon Dahan, Hubert Banville+2

Meta AI

Synthetic Data Augmentation Neural Decoding Neuroimaging

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Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain decoding can be boosted by augmenting small fMRI datasets with synthetic data generated by a pretrained model of fMRI responses to stimuli. We use TRIBE v2, a large encoding model pretrained on more than 1000 hours of fMRI responses to video, audio and language. For each dataset, we evaluate systematic grids that show how the performance of image decoders varies with the amount of synthetic data used for training. Our results, based on two datasets (the 7T fMRI Natural Scenes Dataset and 3T fMRI BOLD5000), show up to 68% improvement in Top-10 image-retrieval accuracy compared to decoders trained only on real data. Importantly, the proportion of augmented data required to reach a given image decoding performance needs to be adjusted depending on the data source. Surprisingly, image decoders trained exclusively on synthetic fMRI can perform above chance in some settings, suggesting that TRIBE v2 can support zero-shot brain-to-image decoding. Together, these results show how large-scale models of the fMRI responses to sight, sound and language may provide a foundation to improve the data efficiency for image decoding.

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