FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI
The success of large-scale deep learning models in neuroscience is fundamentally constrained by severe data heterogeneity. Native fMRI data aggregated from diverse sources exhibit substantial variation in both spatial and temporal resolutions. Consequently, most existing frameworks rely on lengthy, rigid preprocessing pipelines that enforce uniformity across datasets. This practice introduces two critical limitations: (1) potential degradation of subject-specific anatomical information; (2) significant computational overhead, often requiring hours of processing per subject. Here, we propose FlexiBrain, a resolution-agnostic voxel-level encoding framework for native fMRI based on Mamba-JEP...
| Authors | Mo Wang, Wenhao Ye, Junfeng Xia et al. |
|---|---|
| Primary category | eess.IV |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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