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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2506.11167 |
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| _version_ | 1866917359915106304 |
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| author | Wang, Cheng Jiang, Yu Peng, Zhihao Li, Chenxin Bang, Changbae Zhao, Lin Fu, Wanyi Lv, Jinglei Sepulcre, Jorge Yang, Carl He, Lifang Liu, Tianming Kong, Xue-Jun Li, Quanzheng Barron, Daniel S. Qiu, Anqi Hirschtick, Randy Kim, Byung-Hoon Han, Hongbin Li, Xiang Yuan, Yixuan |
| author_facet | Wang, Cheng Jiang, Yu Peng, Zhihao Li, Chenxin Bang, Changbae Zhao, Lin Fu, Wanyi Lv, Jinglei Sepulcre, Jorge Yang, Carl He, Lifang Liu, Tianming Kong, Xue-Jun Li, Quanzheng Barron, Daniel S. Qiu, Anqi Hirschtick, Randy Kim, Byung-Hoon Han, Hongbin Li, Xiang Yuan, Yixuan |
| contents | Functional MRI (fMRI) is crucial for studying brain function and diagnosing neurological disorders. However, existing analysis methods suffer from reproducibility and transferability challenges due to complex preprocessing pipelines and task-specific model designs. In this work, we introduce NeuroSTORM (Neuroimaging Foundation Model with Spatial-Temporal Optimized Representation Modeling) that learns generalizable representations directly from 4D fMRI volumes and enables efficient transfer to diverse downstream applications. Specifically, NeuroSTORM is pre-trained on 28.65 million fMRI frames from over 50,000 subjects, spanning multiple centers and ages 5 to 100. It combines an efficient spatiotemporal modeling design and lightweight task adaptation to enable scalable pre-training and fast transfer to downstream applications. Here we show that NeuroSTORM consistently outperforms existing methods across five downstream tasks, including demographic prediction, phenotype prediction, disease diagnosis, re-identification, and state classification. On two multi-hospital clinical cohorts with 17 diagnoses, NeuroSTORM achieves the best diagnosis performance while remaining predictive of psychological and cognitive phenotypes. These results suggest that NeuroSTORM could become a standardized foundation model for reproducible and transferable fMRI analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards a general-purpose foundation model for fMRI analysis Wang, Cheng Jiang, Yu Peng, Zhihao Li, Chenxin Bang, Changbae Zhao, Lin Fu, Wanyi Lv, Jinglei Sepulcre, Jorge Yang, Carl He, Lifang Liu, Tianming Kong, Xue-Jun Li, Quanzheng Barron, Daniel S. Qiu, Anqi Hirschtick, Randy Kim, Byung-Hoon Han, Hongbin Li, Xiang Yuan, Yixuan Computer Vision and Pattern Recognition Machine Learning Functional MRI (fMRI) is crucial for studying brain function and diagnosing neurological disorders. However, existing analysis methods suffer from reproducibility and transferability challenges due to complex preprocessing pipelines and task-specific model designs. In this work, we introduce NeuroSTORM (Neuroimaging Foundation Model with Spatial-Temporal Optimized Representation Modeling) that learns generalizable representations directly from 4D fMRI volumes and enables efficient transfer to diverse downstream applications. Specifically, NeuroSTORM is pre-trained on 28.65 million fMRI frames from over 50,000 subjects, spanning multiple centers and ages 5 to 100. It combines an efficient spatiotemporal modeling design and lightweight task adaptation to enable scalable pre-training and fast transfer to downstream applications. Here we show that NeuroSTORM consistently outperforms existing methods across five downstream tasks, including demographic prediction, phenotype prediction, disease diagnosis, re-identification, and state classification. On two multi-hospital clinical cohorts with 17 diagnoses, NeuroSTORM achieves the best diagnosis performance while remaining predictive of psychological and cognitive phenotypes. These results suggest that NeuroSTORM could become a standardized foundation model for reproducible and transferable fMRI analysis. |
| title | Towards a general-purpose foundation model for fMRI analysis |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2506.11167 |