Intrinsic functional architecture of the human speech processing network

Abstract Speech engages distributed temporo-fronto-parietal brain regions, however a comprehensive understanding of its intrinsic functional network architecture is lacking. Here we investigate the human speech processing network using the largest sample to date, high temporal resolution resting-state fMRI data, network stability analysis, and theoretically informed models. Network consensus analysis revealed three stable functional modules encompassing: (1) superior temporal plane (STP) and Area Spt, (2) superior temporal sulcus (STS) + ventral frontoparietal cortex, and (3) dorsal frontoparietal cortex. The STS + ventral frontoparietal cortex module showed the highest participation coefficient, and a hub-like organization linking STP with frontoparietal cortical nodes. Node-wise analysis revealed key connectivity features underlying this modular architecture, including a leftward asymmetric connectivity profile, and differential connectivity of STS and STP, with frontoparietal cortex. Our findings, replicated across cohorts, reveal a tripartite functional network architecture supporting speech processing and provide a novel template for future studies.