A Hierarchical Tokenization Framework for Voxel-Level fMRI Representation Learning

We introduce HBAR (Hierarchical Brain Activity Representation), a hierarchical tokenization framework for learning compact, multi-scale representations of neuroimaging data.HBAR organizes voxel-level resting-state fMRI into a hierarchy spanning sub-parcel voxel groups, parcels, and large-scale functional networks, enabling approximately 415x compression while preserving fine-grained spatial information beyond conventional parcellation. The framework supports fixed atlas-based, fully learned, and softly prior-guided hierarchies, allowing us to systematically study the role of structured priors in brain representation learning. Evaluated on large-scale fMRI data, HBAR substantially improves reconstruction over parcellation-based and single-scale tokenization baselines, while supporting downstream modeling of brain dynamics and demographic prediction tasks such as sex classification. Across reconstruction and downstream evaluation, atlas-guided hierarchies provide the strongest overall performance, suggesting that established functional atlases encode organizational structure that remains difficult to recover from reconstruction alone. By combining voxel-level fidelity, compact discrete tokens, and multiscale brain structure, HBAR offers a practical framework for representation learning in high-dimensional neuroimaging data.