@article {67962, title = {Motor cortical output for skilled forelimb movement is selectively distributed across projection neuron classes.}, journal = {Science Advances}, volume = {8}, year = {2022}, month = {2022 Mar 11}, pages = {eabj5167}, abstract = {

The interaction of descending neocortical outputs and subcortical premotor circuits is critical for shaping skilled movements. Two broad classes of motor cortical output projection neurons provide input to many subcortical motor areas: pyramidal tract (PT) neurons, which project throughout the neuraxis, and intratelencephalic (IT) neurons, which project within the cortex and subcortical striatum. It is unclear whether these classes are functionally in series or whether each class carries distinct components of descending motor control signals. Here, we combine large-scale neural recordings across all layers of motor cortex with cell type-specific perturbations to study cortically dependent mouse motor behaviors: kinematically variable manipulation of a joystick and a kinematically precise reach-to-grasp. We find that striatum-projecting IT neuron activity preferentially represents amplitude, whereas pons-projecting PT neurons preferentially represent the variable direction of forelimb movements. Thus, separable components of descending motor cortical commands are distributed across motor cortical projection cell classes.

}, issn = {2375-2548}, doi = {10.1126/sciadv.abj5167}, author = {Park, Junchol and Phillips, James W and Guo, Jian-Zhong and Martin, Kathleen A and Hantman, Adam W and Dudman, Joshua T} } @article {67657, title = {Disrupting cortico-cerebellar communication impairs dexterity.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Jul 29}, abstract = {

To control reaching, the nervous system must generate large changes in muscle activation to drive the limb toward the target, and must also make smaller adjustments for precise and accurate behavior. Motor cortex controls the arm through projections to diverse targets across the central nervous system, but it has been challenging to identify the roles of cortical projections to specific targets. Here, we selectively disrupt cortico-cerebellar communication in the mouse by optogenetically stimulating the pontine nuclei in a cued reaching task. This perturbation did not typically block movement initiation, but degraded the precision, accuracy, duration, or success rate of the movement. Correspondingly, cerebellar and cortical activity during movement were largely preserved, but differences in hand velocity between control and stimulation conditions predicted from neural activity were correlated with observed velocity differences. These results suggest that while the total output of motor cortex drives reaching, the cortico-cerebellar loop makes small adjustments that contribute to the successful execution of this dexterous movement.

}, issn = {2050-084X}, doi = {10.7554/eLife.65906}, author = {Guo, Jian-Zhong and Sauerbrei, Britton A and Cohen, Jeremy D and Mischiati, Matteo and Graves, Austin R and Pisanello, Ferruccio and Branson, Kristin M and Hantman, Adam W} } @article {65259, title = {Cortical pattern generation during dexterous movement is input-driven.}, journal = {Nature}, volume = {577}, year = {2020}, month = {2020 Jan 16}, pages = {386-91}, abstract = {

The motor cortex controls skilled arm movement by sending temporal patterns of activity to lower motor centres. Local cortical dynamics are thought to shape these patterns throughout movement execution. External inputs have been implicated in setting the initial state of the motor cortex, but they may also have a pattern-generating role. Here we dissect the contribution of local dynamics and inputs to cortical pattern generation during a prehension task in mice. Perturbing cortex to an aberrant state prevented movement initiation, but after the perturbation was released, cortex either bypassed the normal initial state and immediately generated the pattern that controls reaching or failed to generate this pattern. The difference in these two outcomes was probably a result of external inputs. We directly investigated the role of inputs by inactivating the thalamus; this perturbed cortical activity and disrupted limb kinematics at any stage of the movement. Activation of thalamocortical axon terminals at different frequencies disrupted cortical activity and arm movement in a graded manner. Simultaneous recordings revealed that both thalamic activity and the current state of cortex predicted changes in cortical activity. Thus, the pattern generator for dexterous arm movement is distributed across multiple, strongly interacting brain regions.

}, issn = {1476-4687}, doi = {10.1038/s41586-019-1869-9}, author = {Sauerbrei, Britton A and Guo, Jian-Zhong and Cohen, Jeremy D and Mischiati, Matteo and Guo, Wendy and Kabra, Mayank and Verma, Nakul and Mensh, Brett and Branson, Kristin and Hantman, Adam W} } @conference {65612, title = {Detecting the Starting Frame of Actions in Video}, booktitle = {2020 IEEE Winter Conference on Applications of Computer Vision (WACV)}, year = {2020}, month = {2020 May 14}, publisher = {IEEE}, organization = {IEEE}, address = {Snowmass Village, CO, USA}, abstract = {

In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a crowd begins to applaud. Key frame localization is a largely overlooked and important action-recognition problem, for example in the field of neuroscience, in which we would like to understand the neural activity that produces the start of a bout of an action. To address this problem, we introduce a novel structured loss function that properly weights the types of errors that matter in such applications: it more heavily penalizes extra and missed action start detections over small misalignments. Our structured loss is based on the best matching between predicted and labeled action starts. We train recurrent neural networks (RNNs) to minimize differentiable approximations of this loss. To evaluate these methods, we introduce the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was collected and labeled by experts for the purpose of neuroscience research. On this dataset, we demonstrate that our method outperforms related approaches and baseline methods using an unstructured loss.

}, doi = {10.1109/WACV45572.202010.1109/WACV45572.2020.9093405}, url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9087828}, author = {Kwak, Iljung S. and Guo, Jian-Zhong and Hantman, Adam and Branson, Kristin and Kriegman, David} } @article {49585, title = {Motor cortex is an input-driven dynamical system controlling dexterous movement.}, journal = {bioRxiv}, year = {2018}, month = {2018-12-14}, pages = {266320}, abstract = {

Skillful control of movement is central to our ability to sense and manipulate the world. 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. Here, 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.

}, doi = {10.1101/266320}, author = {Sauerbrei, Britton and Guo, Jian-Zhong and Mischiati, Matteo and Guo, Wendy and Kabra, Mayank and Verma, Nakul and Branson, Kristin M and Hantman, Adam W} } @article {47277, title = {Cortex commands the performance of skilled movement.}, journal = {eLife}, volume = {4}, year = {2015}, month = {2015 Dec 3}, abstract = {

Mammalian cerebral cortex is accepted as being critical for voluntary motor control, but what functions depend on cortex is still unclear. Here we used rapid, reversible optogenetic inhibition to test the role of cortex during a head-fixed task in which mice reach, grab, and eat a food pellet. Sudden cortical inhibition blocked initiation or froze execution of this skilled prehension behavior, but left untrained forelimb movements unaffected. Unexpectedly, kinematically normal prehension occurred immediately after cortical inhibition even during rest periods lacking cue and pellet. This {\textquoteright}rebound{\textquoteright} prehension was only evoked in trained and food-deprived animals, suggesting that a motivation-gated motor engram sufficient to evoke prehension is activated at inhibition{\textquoteright}s end. These results demonstrate the necessity and sufficiency of cortical activity for enacting a learned skill.

}, issn = {2050-084X}, doi = {10.7554/eLife.10774}, author = {Guo, Jian-Zhong and Graves, Austin R and Guo, Wendy W and Zheng, Jihong and Lee, Allen and Rodr{\'\i}guez-Gonz{\'a}lez, Juan and Li, Nuo and Macklin, John J and Phillips, James W and Mensh, Brett D and Branson, Kristin and Hantman, Adam W} }