Time-resolved connectivity reveals the “how” and “when” of brain networks reconfiguration during face processing

Abstract

Recent advances in the study of human brain networks suggest that efficient cognitive operations depend on dynamic changes in large-scale connectivity. In this study we used face processing as a probe to shed light into these dynamics, considering that it is relies on a set of well-studied brain regions, whose activity has been well detailed in terms of its timing. By modeling cortical connectivity from MEG recordings during the presentation of face and scrambled images, we show that the whole-brain network topology becomes more efficient and complex in response to a face than a scrambled image, in an early time-window with a peak at ~170 ms. We also observed that the core and the extended systems of the face processing network become topologically closer, in a dynamic readjustment of connectivity weights that maximize the efficiency of their communication. Furthermore, using time-resolved decoding we observed that face familiarity can be distinguished very early on from the functional connectivity. Altogether, these results represent a crucial advancement for understanding of how dynamic reshaping of cortical connectivity supports cognitive processing of complex visual stimuli, and provide critical insights on the dynamic subtending face processing.