Cortically-Resolved Recurrent Architecture for fMRI

Decoding cognitive functions and brain diseases from functional magnetic resonance imaging (fMRI) is a foundational problem in neuroscience advanced by recent deep learning across task and resting-state (rs) settings. Existing region-level decoders capture spatial structure through region-of-interest (ROI)-aware graph or Transformer architectures, but apply a uniform temporal operator across all regions. Consequently, they do not explicitly model how cortical regions integrate information over heterogeneous timescales. We propose CoRTeX, a region-resolved recurrent architecture for fMRI in which each cortical region carries its own learnable parameters and evolves on its own learned timescale. At its core, per-region differentiable temporal integration windows allow each ROI to learn how far back in time to integrate information through a soft mask over a private buffer. Cross-region information is restricted to a single identity-aware attention channel, additionally biased by a co-activation-driven memory whose learning and forgetting rates are themselves region-specific. Experiments on stimulus classification using task-fMRI (NSD) and on two disease diagnosis tasks using rs-fMRI (ABIDE, COBRE) demonstrate that CoRTeX achieves competitive performance with strong baselines while enabling region-level analyses that are not well-defined for models with exchangeable units: the learned per-region timescales exhibit interpretable spatial organization, and class-specific regional attribution aligns with category-selective cortex. Code is available at: https://anonymous.4open.science/r/CoRTeX-F.