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Updated: Sep 19, 2025

Corticospinal Excitability Modulation During Action Observation
Published on: December 31, 2013
Cerebellar control over inter-regional excitatory/inhibitory dynamics discriminates execution from observation of an
Roberta Maria Lorenzi1, Gökçe Korkmaz1, Adnan A S Alahmadi2,3
1Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
Abstract:
The motor learning theory anticipates that cerebro-cerebellar loops perform sensorimotor prediction, thereby regulating motor control during action execution (AE) and observation (AO), but the causal interaction between the cerebellum and cerebral cortex remains unclear. Therefore, our aim was to understand what triggers neuronal activity between brain areas engaged in a visuo-motor task that involves cortico-cerebellar interactions, organised in loops. We used Dynamic Causal Modelling (DCM) to study functional MRI (fMRI) data obtained in healthy participants during a squeeze-ball task in either execution or observation conditions. In both cases, active regions included bilateral primary visual cortex (V1), left primary motor cortex (M1), supplementary motor and premotor cortex (SMAPMC), cingulate cortex (CC), superior parietal lobule (SPL), and right cerebellum (CRBL). Networks supporting executing or observing an action showed the same effective connectivity, with pathways between regions wired in closed loops. However, the cerebellar communication towards the cerebral cortex switched from excitatory in execution to inhibitory in observation. Moreover, when executing the action signal modulation was non-linear from SMAPMC to CRBL and within the CRBL self-connection, supporting that the CRBL elaborates motor plans received from SMAPMC. Thus, the need for motor planning and the presence of a sensorimotor feedback in action execution discriminate the modality of forward control operated by the CRBL. Interestingly, this study also showed that the CRBL differentially controls the excitatory/inhibitory dynamics of inter-regional effective connectivity, depending on its functional engagement. These findings are fundamental for understanding brain dynamics in health and disease and for designing artificial sensorimotor controllers.
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