[2506.11167] Towards a general-purpose foundation model for fMRI analysis

Computer Science > Computer Vision and Pattern Recognition arXiv:2506.11167 (cs) [Submitted on 11 Jun 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Towards a general-purpose foundation model for fMRI analysis Authors:Cheng Wang, Yu Jiang, Zhihao Peng, Chenxin Li, Changbae Bang, Lin Zhao, Wanyi Fu, Jinglei Lv, Jorge Sepulcre, Carl Yang, Lifang He, Tianming Liu, Xue-Jun Kong, Quanzheng Li, Daniel S. Barron, Anqi Qiu, Randy Hirschtick, Byung-Hoon Kim, Hongbin Han, Xiang Li, Yixuan Yuan View a PDF of the paper titled Towards a general-purpose foundation model for fMRI analysis, by Cheng Wang and 20 other authors View PDF HTML (experimental) Abstract: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 app...