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To bridge the gap between computationally expensive 4D voxel architectures and overly coarse ROI representations, the authors project cortical surface activations into geometry-aware 2D flatmap sequences evaluated through a frozen SigLIP2 vision encoder. Evaluated across resting-state and visual-fMRI benchmarks, this approach consistently outperforms standard ROI-level models on HCP and ADNI, with spatial perturbation controls verifying that preserved cortical topology is essential for downstream readout. The work establishes that standard 2D vision backbones can successfully exploit macroscale brain geometry, providing a highly efficient baseline that narrows the gap to full voxel-level foundation models.
Off-the-shelf 2D vision transformers can effectively decode macroscale brain dynamics without any specialized 3D fMRI pretraining, simply by projecting cortical geometry into flatmap video sequences.
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. We ask how effectively an image-pretrained encoder can reuse the spatial organization of cortical activity. Motivated by evidence that macroscale brain activity is strongly constrained by brain geometry, we introduce FlatClip, a frozen-encoder surface-level baseline that renders cortical activity as geometry-aware flatmap sequences and reuses a frozen SigLIP2 image encoder with only a lightweight downstream probe. Across resting-state benchmarks, FlatClip serves as a competitive middle-ground representation, outperforming ROI-level baselines on HCP and ADNI tasks while remaining weaker on PPMI and below the strongest voxel-level models overall. On visual-fMRI decoding, restricting the input to visual or NSD-provided task-active cortex improves performance, highlighting the value of task-relevant cortical coverage. Spatial perturbation controls reduce the predictive performance of flatmap features under both retrained and fixed readouts, and anatomy-linked arrangements consistently outperform vertex permutations across three colormaps. Together, these results position surface-level flatmap sequences as a practical middle-ground baseline between ROI and voxel models, and support the utility of anatomy-linked spatial organization for reusing image-pretrained features. Code is available at https://github.com/OneMore1/FlatClip.