Proximo-distal muscle modulation as a function of hand orientation in a reach-and-grasp task

The aim of this study was to improve our understanding of muscle contractions in the arm as a function of hand orientation for grasp. While there have been several reports on arm kinematics for reach and grasp movements, little has been done at the muscular level. To this end, we analyzed the modulation of shoulder, elbow and hand muscles for a reach and grasp task involving a target in either horizontal or vertical orientation. We hypothesized that unlike what has been observed for kinematics, at the muscular level we would see less correlation between the three muscle groups. A decoding approach with Machine Learning revealed adaptation patterns that were not visible using classical methods. Reach-and-grasp has traditionally been treated as being made of two components - the reach and the grasp components. Our dynamic decoding approach revealed a more complex picture with very different dynamics in the shoulder and elbow muscle groups during reach. All muscle groups showed peak capacity for predicting hand orientation before the start of grasp and followed the ubiquitous proximo-distal organization. The patterns of muscular modulation for hand orientation were strongly perturbed by the eyes closed and slow movement conditions, potentially decreasing the available degrees of freedom for adaptation.