Ricerc@Sapienza

Towering models of brain functioning propose that the brain is a predictive machine aiming at updating internal models of the environmental causes of sensory inflow in order to minimize perceptuo-motor errors (Friston, 2010). Predictive models are fundamental when dealing with a dynamic environment such as when interacting with other individuals by predicting their behavior.

On one side, fronto-parietal cortical and cerebellar systems are thought to support predictions about the consequences of one's own as well as others' movements (Kilner et al., 2007; Sokolov et al., 2017). Crucially, given the cerebellar function in monitoring and predicting sensorimotor events (Leggio et al., 2011; Peterburs & Desmond, 2016), the role of cortico-cerebellar connections in supporting interactive behaviors is of great interest but still largely unknown (Van Overwalle et al., 2014).

On the other side, interacting with others also depends on the reactivity of the autonomic system which modulates arousal and readiness to control one's behavior. Among the available measures of autonomic functioning, heart rate variability (HRV) indexes the degree to which the cardiac activity can be modulated to meet changing situational and emotional demands (Thayer & Lane, 2009). Since neuroimaging studies point to an HRV-related cortico-cerebellar network (Kumral et al., 2019), we plan to study for the first time in healthy individuals and cerebellar patients whether HRV mediates the reactivity to prediction errors during interpersonal interactions.

The current project aims at advancing the understanding of cortico-cerebellar networks supporting interpersonal interactions by integrating motion kinematics, autonomic monitoring, functional brain imaging and brain lesion approaches in healthy individuals and cerebellar patients engaged in realistic interpersonal interactions in virtual reality.