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30 Publications

Showing 1-10 of 30 results
03/11/22 | Motor cortical output for skilled forelimb movement is selectively distributed across projection neuron classes.
Park J, Phillips JW, Guo J, Martin KA, Hantman AW, Dudman JT
Science Advances. 2022 Mar 11;8(10):eabj5167. doi: 10.1126/sciadv.abj5167

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.

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12/07/21 | A genetically defined insula-brainstem circuit selectively controls motivational vigor.
Deng H, Xiao X, Yang T, Ritola K, Hantman A, Li Y, Huang ZJ, Li B
Cell. 2021 Dec 07:. doi: 10.1016/j.cell.2021.11.019

The anterior insular cortex (aIC) plays a critical role in cognitive and motivational control of behavior, but the underlying neural mechanism remains elusive. Here, we show that aIC neurons expressing Fezf2 (aIC), which are the pyramidal tract neurons, signal motivational vigor and invigorate need-seeking behavior through projections to the brainstem nucleus tractus solitarii (NTS). aIC neurons and their postsynaptic NTS neurons acquire anticipatory activity through learning, which encodes the perceived value and the vigor of actions to pursue homeostatic needs. Correspondingly, aIC → NTS circuit activity controls vigor, effort, and striatal dopamine release but only if the action is learned and the outcome is needed. Notably, aIC neurons do not represent taste or valence. Moreover, aIC → NTS activity neither drives reinforcement nor influences total consumption. These results pinpoint specific functions of aIC → NTS circuit for selectively controlling motivational vigor and suggest that motivation is subserved, in part, by aIC's top-down regulation of dopamine signaling.

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10/18/21 | Genetically identified amygdala-striatal circuits for valence-specific behaviors.
Zhang X, Guan W, Yang T, Furlan A, Xiao X, Yu K, An X, Galbavy W, Ramakrishnan C, Deisseroth K, Ritola K, Hantman A, He M, Josh Huang Z, Li B
Nature Neuroscience. 2021 Oct 18;24(11):1586-1600. doi: 10.1038/s41593-021-00927-0

The basolateral amygdala (BLA) plays essential roles in behaviors motivated by stimuli with either positive or negative valence, but how it processes motivationally opposing information and participates in establishing valence-specific behaviors remains unclear. Here, by targeting Fezf2-expressing neurons in the BLA, we identify and characterize two functionally distinct classes in behaving mice, the negative-valence neurons and positive-valence neurons, which innately represent aversive and rewarding stimuli, respectively, and through learning acquire predictive responses that are essential for punishment avoidance or reward seeking. Notably, these two classes of neurons receive inputs from separate sets of sensory and limbic areas, and convey punishment and reward information through projections to the nucleus accumbens and olfactory tubercle, respectively, to drive negative and positive reinforcement. Thus, valence-specific BLA neurons are wired with distinctive input-output structures, forming a circuit framework that supports the roles of the BLA in encoding, learning and executing valence-specific motivated behaviors.

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07/29/21 | Disrupting cortico-cerebellar communication impairs dexterity.
Guo J, Sauerbrei BA, Cohen JD, Mischiati M, Graves AR, Pisanello F, Branson KM, Hantman AW
eLife. 2021 Jul 29;10:. doi: 10.7554/eLife.65906

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.

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06/15/21 | A cerebellar-thalamocortical pathway drives behavioral context-dependent movement initiation.
Dacre J, Colligan M, Clarke T, Ammer JJ, Schiemann J, Chamosa-Pino V, Claudi F, Harston JA, Eleftheriou C, Pakan JM, Huang C, Hantman AW, Rochefort NL, Duguid I
Neuron. 2021 Jun 15;109(14):2326-2338. doi: 10.1016/j.neuron.2021.05.016

Executing learned motor behaviors often requires the transformation of sensory cues into patterns of motor commands that generate appropriately timed actions. The cerebellum and thalamus are two key areas involved in shaping cortical output and movement, but the contribution of a cerebellar-thalamocortical pathway to voluntary movement initiation remains poorly understood. Here, we investigated how an auditory "go cue" transforms thalamocortical activity patterns and how these changes relate to movement initiation. Population responses in dentate/interpositus-recipient regions of motor thalamus reflect a time-locked increase in activity immediately prior to movement initiation that is temporally uncoupled from the go cue, indicative of a fixed-latency feedforward motor timing signal. Blocking cerebellar or motor thalamic output suppresses movement initiation, while stimulation triggers movements in a behavioral context-dependent manner. Our findings show how cerebellar output, via the thalamus, shapes cortical activity patterns necessary for learned context-dependent movement initiation.

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04/16/21 | Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings.
Steinmetz NA, Aydın Ç, Lebedeva A, Okun M, Pachitariu M, Bauza M, Beau M, Bhagat J, Böhm C, Broux M, Chen S, Colonell J, Gardner RJ, Karsh B, Kloosterman F, Kostadinov D, Mora-Lopez C, O'Callaghan J, Park J, Putzeys J, Sauerbrei B, van Daal RJ, Vollan AZ, Wang S, Welkenhuysen M, Ye Z, Dudman JT, Dutta B, Hantman AW, Harris KD, Lee AK, Moser EI, O'Keefe J, Renart A, Svoboda K, Häusser M, Haesler S, Carandini M, Harris TD
Science. 2021 Apr 16;372(6539):. doi: 10.1126/science.abf4588

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

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10/01/20 | A genetically defined compartmentalized striatal direct pathway for negative reinforcement.
Xiao X, Deng H, Furlan A, Yang T, Zhang X, Hwang G, Tucciarone J, Wu P, He M, Palaniswamy R, Ramakrishnan C, Ritola K, Hantman A, Deisseroth K, Osten P, Huang ZJ, Li B
Cell. 2020 Oct 1;181(1):211. doi: 10.1016/j.cell.2020.08.032

The striosome compartment within the dorsal striatum has been implicated in reinforcement learning and regulation of motivation, but how striosomal neurons contribute to these functions remains elusive. Here, we show that a genetically identified striosomal population, which expresses the Teashirt family zinc finger 1 (Tshz1) and belongs to the direct pathway, drives negative reinforcement and is essential for aversive learning in mice. Contrasting a "conventional" striosomal direct pathway, the Tshz1 neurons cause aversion, movement suppression, and negative reinforcement once activated, and they receive a distinct set of synaptic inputs. These neurons are predominantly excited by punishment rather than reward and represent the anticipation of punishment or the motivation for avoidance. Furthermore, inhibiting these neurons impairs punishment-based learning without affecting reward learning or movement. These results establish a major role of striosomal neurons in behaviors reinforced by punishment and moreover uncover functions of the direct pathway unaccounted for in classic models.

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09/09/20 | Cell-type specific outcome representation in primary motor cortex.
Lavzin M, Levy S, Benisty H, Dubin U, Brosh Z, Aeed F, Mensh BD, Schiller Y, Meir R, Barak O, Talmon R, Hantman AW, Schiller J
Neuron. 2020 Sep 9;107(5):954-71. doi: 10.1016/j.neuron.2020.06.006

Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, “success”- and “failure” related neurons that develop with training and report end-result of trials. In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trial’s outcome. Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.

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08/30/20 | Parvalbumin+ and Npas1+ Pallidal neurons have distinct circuit topology and function.
Pamukcu A, Cui Q, Xenias HS, Berceau BL, Augustine EC, Fan I, Hantman AW, Lerner TN, Boca SM, Chan CS
Journal of Neuroscience. 2020 Aug 30:
05/14/20 | Detecting the Starting Frame of Actions in Video
Kwak IS, Guo J, Hantman A, Branson K, Kriegman D
2020 IEEE Winter Conference on Applications of Computer Vision (WACV). 2020 May 14:. doi: 10.1109/WACV45572.202010.1109/WACV45572.2020.9093405

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.

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