Resting network architecture of theta oscillations reflects hyper-learning of sensorimotor information in Gilles de la Tourette syndrome

Gilles de la Tourette syndrome (GTS) is a neurodevelopmental disorder characterized by motor and vocal tics. GTS is associated with enhanced processing of stimulus-response (S-R) associations, including a higher propensity to learn probabilistic S-R contingencies (i.e., statistical learning), the nature of which is still elusive. In this study, we investigated the hypothesis that resting-state theta network organization is key for the understanding of superior statistical learning in these patients. We investigated the graph-theoretical network architecture of theta oscillations in adult patients with GTS and healthy controls (HC) during a statistical learning task, and in resting states both before and after learning. We found that patients with GTS showed a higher statistical learning score than healthy controls, as well as a more optimal (small-world-like) theta network before the task. Thus, patients with GTS had a superior facility to integrate and evaluate novel information as a trait-like characteristic. Additionally, the theta network architecture in GTS adapted more to the statistical information during the task than in HC. We suggest that hyper-learning in patients with GTS is likely a consequence of increased sensitivity to perceive and integrate sensorimotor information leveraged through theta-oscillation-based resting state dynamics. The study delineates the neural basis of a higher propensity in patients with GTS to pick up statistical contingencies in their environment. Moreover, the study emphasizes pathophysiologically endowed abilities in patients with GTS, which are often not taken into account in the perception of this common disorder but could play an important role in destigmatization.