Frontoparietal Hub Connectivity Integrates Information from Multiple Sources

Frontoparietal connector hubs are thought to support information integration across the brain, but this role has largely been inferred from static connectivity, leaving unclear how computational processes shape inter-regional connectivity during behavior. Here, we address this question using a model-based functional connectivity approach in human fMRI data. Thirty-Eight participants (males and females) performed a task requiring the integration of sensory evidence with an internally maintained state belief to guide behavior. We developed a computational model that combines these information sources into an integrated representation and generates distinct variables at successive stages of integration: uncertainty before choice (entropy), the inferred task representation guiding action (task belief), and feedback-driven updating (task inference error). We then tested how these variables modulate the connectivity of frontoparietal connector hubs. Entropy increased coupling between hubs and regions encoding task-relevant inputs and outputs during cue processing, suggesting enhanced communication under uncertainty. During task selection, task belief selectively modulated hub connectivity with motor regions according to the selected task. During feedback, task inference errors increased coupling with regions supporting task-relevant inputs and internal state, while reducing coupling with motor regions, consistent with updating internal representations. Together, our findings show that frontoparietal connector hubs implement integrative control by using an integrated representation to generate distinct computational signals that selectively and dynamically reconfigure inter-regional communication.