Assessing the effective connectivity of premotor areas during real vs imagined grasping: a DCM-PEB approach

Federica Bencivenga, Valentina Sulpizio, Maria Giulia Tullo, Gaspare Galati

Research output: Contribution to journalArticlepeer-review

Abstract

The parieto-frontal circuit underlying grasping, which requires the serial involvement of the anterior intraparietal area (aIPs) and the ventral premotor cortex (PMv), has been recently extended enlightening the role of the dorsal premotor cortex (PMd). The supplementary motor area (SMA) has been also suggested to encode grip force for grasping actions; furthermore, both PMd and SMA are known to play a crucial role in motor imagery. Here, we aimed at assessing the dynamic couplings between left aIPs, PMv, PMd, SMA and primary motor cortex (M1) by comparing executed and imagined right-hand grasping, using Dynamic Causal Modelling (DCM) and Parametrical Empirical Bayes (PEB) analyses. 24 subjects underwent an fMRI exam (3T) during which they were asked to perform or imagine a grasping movement visually cued by photographs of commonly used objects. We tested whether the two conditions a) exert a modulatory effect on both forward and feedback couplings among our areas of interest, and b) differ in terms of strength and sign of these parameters. Results of the real condition confirmed the serial involvement of aIPs, PMv and M1. PMv also exerted a positive influence on PMd and SMA, but received an inhibitory feedback only from PMd. Our results suggest that a general motor program for grasping is planned by the aIPs-PMv circuit; then, PMd and SMA encode high-level features of the movement. During imagery, the connection strength from aIPs to PMv was weaker and the information flow stopped in PMv; thus, a less complex motor program was planned. Moreover, results suggest that SMA and PMd cooperate to prevent motor execution. In conclusion, the comparison between execution and imagery reveals that during grasping premotor areas dynamically interplay in different ways, depending on task demands.

Original languageEnglish
Article number117806
JournalNeuroImage
Volume230
DOIs
Publication statusPublished - Apr 15 2021

Keywords

  • Dynamic causal modelling
  • fMRI
  • Grasping network
  • Motor imagery
  • Parametrical empirical bayes
  • Supplementary motor area

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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