Compartmentalized dynamics within a common multi-area mesoscale manifold represent a repertoire of human hand movements

The human hand is unique in the animal kingdom for unparalleled dexterity, ranging from complex prehension to fine finger individuation. How does the brain represent such a diverse repertoire of movements? We evaluated mesoscale neural dynamics across the human grasp network,using electrocorticography and dimensionality reduction methods, for a repertoire of hand movements. Strikingly, we found that the grasp network represented both finger and grasping movements alike. Specifically, the manifold characterizing the multi-areal neural covariance structure was preserved during all movements across this distributed network. In contrast, latent neural dynamics within this manifold were surprisingly specific to movement type. Aligning latent activity to kinematics further uncovered distinct submanifolds despite similarities in synergistic coupling of joints between movements. We thus find that despite preserved neural covariance at the distributed network level, mesoscale dynamics are compartmentalized into movement-specific submanifolds; this mesoscale organization may allow flexible switching between a repertoire of hand movements.

Automated summary

This study evaluated the neural dynamics of the human grasp network during a repertoire of hand movements using electrocorticography and dimensionality reduction methods. The researchers found that the grasp network represented both finger and grasping movements. While the neural covariance at the distributed network level was preserved, the latent neural dynamics were specific to movement type, indicating compartmentalization into movement-specific submanifolds. This mesoscale organization allows for flexible switching between a repertoire of hand movements.