Multimodal Sensory-Spatial Integration and Retrieval of Trained Motor Patterns for Body Coordination in Musicians and Dancers.

Aija Marie Ladda,Aija Marie Ladda,Martin Lotze,Sarah B Wallwork

doi:10.3389/fpsyg.2020.576120

Abstract

Dancers and musicians are experts in spatial and temporal processing, which allows them to coordinate movement with music. This high-level processing has been associated with structural and functional adaptation of the brain for high performance sensorimotor integration. For these integration processes, adaptation does not only take place in primary and secondary sensory and motor areas but also in tertiary brain areas, such as the lateral prefrontal cortex (lPFC) and the intraparietal sulcus (IPS), providing vital resources for highly specialized performance. Here, we review evidence for the role of these brain areas in multimodal training protocols and integrate these findings into a new model of sensorimotor processing in complex motor learning.

Highlights

We are often perplexed by the precision of spatiotemporal coordination of complex movement patterns in athletes, musicians, and dancers
While emotional aspects like arousal and motivation certainly play a central role in entraining motor action to rhythmic auditory stimulation (Bowling et al, 2019), we here would like to focus on the role of the lateral prefrontal cortex in storing movement patterns and the intraparietal sulcus (IPS) in generating movement trajectories that can be modified and updated online to enable amazing performances of dance and music
We describe the neurofunctional basis of the brain processes, which might represent highly specialized performance in dancers and musicians

Summary

Introduction

We are often perplexed by the precision of spatiotemporal coordination of complex movement patterns in athletes, musicians, and dancers. Athletes, dancers, and instrumentalist may form strong associations between movements, sounds, emotions, and cognitive processes and as such, the supporting neural networks adapt by integrating and connecting the respective networks.

Results

Conclusion