fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study whether measured learning relations can organize both stages without modifying...
Jun-Feng Xia, Wen-Hao Ye, Jun-Xiang Zhang et al.· 1 citation
Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models are efficient but coarse, whereas voxel-level models preserve fine-grained spatial structure but require specialized 3D/4D architectures and costly fMRI-specific pretraining...
Mo Wang, Wen-Hao Ye, Zi-Han Ning et al.· 0 citations
Scaling laws have guided large-model development in computer vision and natural language processing, but the relationships among data, model size, and compute remain unclear for functional magnetic resonance imaging (fMRI) foundation models. Here, we conduct a controlled empirical study using pretraining data from more...
Wen-Hao Ye, Xuan-Ye Pan, Jun-Feng Xia et al.· 0 citations
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