Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866910006235889664 |
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| author | Wang, Mo Ye, Wenhao Xia, Junfeng Zhang, Junxiang Pan, Xuanye Xu, Minghao Deng, Haotian Wen, Hongkai Liu, Quanying |
| author_facet | Wang, Mo Ye, Wenhao Xia, Junfeng Zhang, Junxiang Pan, Xuanye Xu, Minghao Deng, Haotian Wen, Hongkai Liu, Quanying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_23090 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model Wang, Mo Ye, Wenhao Xia, Junfeng Zhang, Junxiang Pan, Xuanye Xu, Minghao Deng, Haotian Wen, Hongkai Liu, Quanying Computational Engineering, Finance, and Science Quantitative Methods 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. |
| title | Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model |
| topic | Computational Engineering, Finance, and Science Quantitative Methods |
| url | https://arxiv.org/abs/2601.23090 |