Adam Hantman, PhD
Group Leader
HHMI – Janelia Research Campus
March 26, 2019
Neural Circuits of Dexterity
To perform a movement task, such as reaching, the motor cortex (an area of the brain dedicated to the control of movement) must control activity across a variety of muscles. This is dependent on precisely timed patterns of activity. Previous research had not clarified whether these activity patterns were generated locally in motor cortex alone, or whether a movement such as grasping required input from other brain regions. Dr. Hartman discussed the work of his lab using a mouse model to determine whether multiple brain areas were required for grasping. Their results indicated that reaching and grasping were dependent on multiple brain regions, but that specific regions of motor cortex can influence motor learning.
Reaching, grasping, and object manipulation play a central role in the lives of mammals with prehensile forelimbs. The musculoskeletal complexity of the limb poses a challenging control problem for the central nervous system, which must coordinate precisely timed patterns of activity across many muscles to perform a wide diversity of tasks. The motor cortex is a brain region involved in the control of dexterous forelimb movement. In nonhuman primates, motor cortical lesions impair the coordination of the hand and fingers, and the activity of motor cortical neurons is closely linked to muscle activation, joint torques, and limb kinematics. In rodents, stimulation of motor cortex generates limb twitches, chronic lesions impair dexterity, and optogenetic inactivation blocks the initiation and execution of reaching.
We present our work to advance knowledge of how motor cortex is involved in dexterous motor control.
A large body of work in nonhuman primates has demonstrated that motor cortex provides flexible, time-varying activity patterns that control the arm during reaching and grasping. Previous studies have suggested that these patterns are generated by strong local recurrent dynamics operating autonomously from inputs during movement execution. An alternative possibility is that motor cortex requires coordination with upstream brain regions throughout the entire movement in order to yield these patterns. We developed an experimental preparation in the mouse to directly test these possibilities using optogenetics and electrophysiology during a skilled reach-to-grab-to-eat task. To validate this preparation, we first established that a specific, time- varying pattern of motor cortical activity was required to produce coordinated movement. Next, in order to disentangle the contribution of local recurrent motor cortical dynamics from external input, we optogenetically held the recurrent contribution constant, then observed how motor cortical activity recovered following the end of this perturbation. Both the neural responses and hand trajectory varied from trial to trial, and this variability reflected variability in external inputs. To directly probe the role of these inputs, we used optogenetics to perturb activity in the thalamus. Thalamic perturbation at the start of the trial prevented movement initiation, and perturbation at any stage of the movement prevented progression of the hand to the target; this demonstrates that input is required throughout the movement. By comparing motor cortical activity with and without thalamic perturbation, we were able to estimate the effects of external inputs on motor cortical population activity. Thus, unlike pattern-generating circuits that are local and autonomous, such as those in the spinal cord that generate left-right alternation during locomotion, the pattern generator for reaching and grasping is distributed across multiple, strongly interacting brain regions.
Having demonstrated the role of cortex in well-executed movements, we next asked how it might contribute to learning skilled behaviors. We investigated if the primary motor cortex (M1) has access to outcome related information, whether this information signals performance outcome or reward, what are the cell specific and network mechanisms for such representations, and how these signals can be used for correcting future movements. Using a dexterity task in mice, combined with calcium imaging, optogenetic perturbations, and behavioral manipulations, we find performance outcome signals, reported by “success” and “failure” neurons in layers 2-3 of M1, that distinguish if the prior attempt was successful or not. In contrast, pyramidal tract neurons hold historical performance information influencing network activity for future movements. Optogenetic experiments indicate that such inter- trial cortical activity was needed to learn new task requirements. Therefore, layer specific M1 subnetworks carry outcome signals that can ultimately support reinforcement motor learning.