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arXiv · 2601.23090

Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model

Mo Wang·Wenhao Ye·Junfeng Xia·Junxiang Zhang·Xuanye Pan·Minghao Xu·Haotian Deng·Hongkai Wen·Quanying Liu
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Abstract

Self-supervised fMRI foundation models have shown promising transfer performance, yet most rely on predefined region-level parcellations that discard fine-grained voxel information and introduce atlas-dependent biases. We propose Omni-fMRI, an atlas-free foundation model that operates directly on voxel-level signals. To enable scalable pretraining on 49,497 fMRI sessions across nine datasets, Omni-fMRI introduces a dynamic patching mechanism that substantially reduces computational cost while preserving informative spatial structure. To support reproducibility and fair comparison, we establish a comprehensive benchmark suite spanning 11 datasets and a diverse set of resting-state and task-based fMRI tasks. Experimental results demonstrate that Omni-fMRI consistently outperforms existing foundation models, providing a scalable and reproducible framework for atlas-free brain representation learning. Code and logs are available.

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Mo Wang, Wenhao Ye, Junfeng Xia, Junxiang Zhang, Xuanye Pan, Minghao Xu, Haotian Deng, Hongkai Wen, Quanying Liu. 2026-01-30. Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model. https://arxiv.org/abs/2601.23090

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