The representation of finger movement and force in human motor and premotor cortices

Abstract

The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force in rapid succession. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we asked the question of how movement and force were represented cortically, when executed sequentially with the same finger. We recorded high-density electrocorticography (ECoG) from the motor and premotor cortices of seven human subjects performing a movement-force motor task. We decoded finger movement (0.7±0.3 fractional variance accounted for; FVAF) and force (0.7±0.2 FVAF) with high accuracy, yet found different spatial representations. In addition, we used a state-of-the-art deep learning method to uncover smooth, repeatable trajectories through ECoG state space during the movement-force task. We also summarized ECoG across trials and participants by developing a new metric, the neural vector angle. Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90±6%). Thus, finger movement and force appear to have distinct representations in motor/premotor cortices. These results inform our understanding of the neural control of movement, as well as the design of grasp brain-machine interfaces.

Significance Statement The human ability to manipulate objects is central to our daily lives and requires control of both grasping movement and force. Here, we explored how these motor activities are represented at the level of the cortex. Understanding these representations will influence the design of brain-machine interfaces (BMIs) to restore function after paralysis. We recorded electrocorticography (ECoG) from seven human subjects who performed a sequential movement-force motor task. We found differences between the cortical representations of movement and force using decoding methods, deep learning, and a new neural ensemble metric. Thus, ECoG could be used in a BMI to control both movement and force behaviors. These results can potentially accelerate the translation of BMIs for individuals with paralysis.