FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI

The success of large-scale deep learning models in neuro-science is fundamentally constrained by severe data heterogeneity. NativefMRI data aggregated from diverse sources exhibit substantial variationin both spatial and temporal resolutions. Consequently, most existingframeworks rely on lengthy, rigid preprocessing pipelines that enforceuniformity across datasets. This practice introduces two critical limita-tions: (1) potential degradation of subject-specix001Cc anatomical informa-tion; (2) signix001Ccant computational overhead, often requiring hours of pro-cessing per subject. Here, we propose FlexiBrain, a resolution-agnosticvoxel-level encoding framework for native fMRI based on Mamba-JEPA.FlexiBrain dex001Cnes patch sizes in real-world physical units and employs adynamic patch resizing, thereby bypassing destructive spatial standard-ization while enabling direct ingestion of data in native space. We instan-tiate the framework using an ex001Ecient Mamba-JEPA backbone to modelhigh-dimensional 4D fMRI signals. Across x001Cve diverse downstream neuro-science tasks, FlexiBrain consistently outperforms recent state-of-the-artmethods, achieving gains of up to 12 percentage points without exter-nal data augmentation. Importantly, FlexiBrain functions as a seamlessplug-in module, substantially reducing preprocessing costs and accelerat-ing the development of robust voxel-level fMRI foundation models. Codeis available at https://github.com/OneMore1/FlexiBrain.