Time-resolved functional connectivity during visuomotor graph learning

Humans naturally attend to patterns that emerge in our perceptual environments, building mental models that allow future experiences to be processed more effectively and efficiently. Perceptual events and statistical relations can be represented as nodes and edges in a graph. Recent work in graph learning has shown that human behavior is sensitive to graph topology, but little is known about how that topology might elicit distinct neural responses during learning. Here, we address this knowledge gap by applying time-resolved network analyses to fMRI data collected during a visuomotor graph learning task. We assess neural signatures of learning on two types of structures: modular and non-modular lattice graphs. We find that task performance is supported by a highly flexible visual system, relatively stable brain-wide community structure, cohesiveness within the dorsal attention, limbic, default mode, and subcortical systems, and an increasing degree of integration between the visual and ventral attention systems. Additionally, we find that the time-resolved connectivity of the limbic, default mode, temporoparietal, and subcortical systems is associated with enhanced performance on modular graphs but not on lattice-like graphs. These findings provide evidence for the differential processing of statistical patterns with distinct underlying graph topologies. Our work highlights the similarities between the neural correlates of graph learning and those of statistical learning.