Transition from predictable to variable motor cortex and striatal ensemble patterning during behavioral exploration

Kondapavulur, Sravani; Lemke, Stefan; Darevsky, David; Guo, Ling; Khanna, Preeya; Ganguly, Karunesh

doi:10.1038/s41467-022-30069-1

Cited by 12 publications

(16 citation statements)

References 67 publications

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“…We found inter-individual variability within animals, and we found that LFO activity emerged in animals that were able to gain expertise in the task within five days. Previous reports indicate that slow improvements in accuracy can continue to occur with extended practice in rodent reach-to-grasp task (Lemke et al, 2019) and behavioral exploration phase can vary between animals when the target location is switched in this reaching task (Kondapavulur et al, 2022). Our paradigm involved training for contiguous five days and we observed emergent LFO trends in animals that gained proficiency in the task.…”

Section: Discussionmentioning

confidence: 67%

Emergent Low-Frequency Activity in Cortico-Cerebellar Networks with Motor Skill Learning

Fleischer

Abbasi

Fealy

et al. 2023

eNeuro

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The motor cortex controls skilled arm movement by recruiting a variety of targets in the nervous system, and it is important to understand the emergent activity in these regions as refinement of a motor skill occurs. One fundamental projection of the motor cortex (M1) is to the cerebellum. However, the emergent activity in the motor cortex and the cerebellum that appears as a dexterous motor skill is consolidated is incompletely understood. Here, we report on low-frequency oscillatory (LFO) activity that emerges in cortico-cerebellar networks with learning the reach-to-grasp motor skill. We chronically recorded the motor and the cerebellar cortices in rats which revealed the emergence of coordinated movement-related activity in the local-field potentials (LFPs) as the reaching skill consolidated. Interestingly, we found this emergent activity only in the rats that gained expertise in the task. We found that the local and cross-area spiking activity was coordinated with LFOs in proficient rats. Finally, we also found that these neural dynamics were more prominently expressed during accurate behavior in the M1. This work furthers our understanding on emergent dynamics in the cortico-cerebellar loop that underlie learning and execution of precise skilled movement.Significance StatementMovement execution involves parallel processing across brain regions, with the motor cortex (M1) being a key hub that recruits several subcortical nodes. The cerebellar cortex is a principal receiver of M1 projections via pons, but the emergent dynamics in these regions with motor skill learning is incompletely understood. We performed simultaneous recordings of M1 and cerebellum in a reach-to-grasp task. We found low frequency activity and coordinated neural dynamics emerged within and across regions with skillful task execution. Recent interest in modulating cortico-cerebellar networks for motor-recovery post-injury/stroke make this work an important precursor to assessing whether similar low-frequency activity in cortico-cerebellar networks can serve as a biomarker of motor recovery and help optimize modulation of these networks.

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Section: Discussionmentioning

confidence: 67%

Emergent Low-Frequency Activity in Cortico-Cerebellar Networks with Motor Skill Learning

Fleischer

Abbasi

Fealy

et al. 2023

eNeuro

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“…To directly compare the neural substrates of flexible and automatic motor sequence execution, we designed a discrete sequence production task for rats based on similar paradigms in humans and non-human primates (NHPs) 22,57,63 . Importantly, we wanted our paradigm to distinguish motor automaticity from habit formation 11,12 , two independent processes that can occur alongside each other and that may involve some of the same neural substrates 11,64,65 . Thus, we designed our task to ensure that rats achieve automaticity on a motor sequence without developing it into a habit.…”

Section: Resultsmentioning

confidence: 99%

“…Importantly, we wanted our paradigm to distinguish motor automaticity from habit formation 11,12 , two independent processes that can occur alongside each other and that may involve some of the same neural substrates 11,127,128 . Thus, we designed our task to ensure that rats achieve automaticity on a motor sequence without developing it into a habit.…”

Section: Methodsmentioning

confidence: 99%

Dissociating the contributions of sensorimotor striatum to automatic and visually-guided motor sequences

Mizes

Lindsey

Escola

et al. 2022

Preprint

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The ability to sequence discrete movements in new ways enables rich and adaptive behavior. Such flexibility can be computationally costly and result in halting performances. Practicing the same motor sequence repeatedly, however, can render its execution precise, fast, and effortless, i.e., ‘automatic’. The basal ganglia are thought to underlie both flexible and automatic motor sequences, yet how their contributions differ across these modes is unclear. We parse this in rats trained to perform the same motor sequence in both modes, focusing on the sensorimotor striatum. Neural recordings revealed a kinematic code independent of execution mode. While lesions affected the detailed kinematics across modes, they disrupted high-level sequential structure for automatic but not visually-guided flexible behaviors. These results suggest that while the basal ganglia contribute to learned movement kinematics and are essential for ‘automatic’ motor skills, they can be dispensable for the flexible sequencing of discrete motor elements.

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“…The dynamical system approach faces the same challenges as the representational model: rotations exhibit diverse shapes and occur during various movement types, contexts, and behaviors (see Introduction), which are difficult to explain by either model or their combination [16,17,29,51].…”

Section: Discussionmentioning

confidence: 99%

“…In humans, the following areas have been studied: primary motor cortex ('hand knob' area) [45,46] and left superior temporal gyrus (STG) [47] in individuals with movement impairment. In rodents, jPCA method has been applied to the data collected in the following areas: auditory and motor cortices of mice [48][49][50] and primary motor cortex and dorsolateral striatum (DLS) of rats [51]. Furthermore, jPCA method has been applied across different motor tasks and contexts.…”

mentioning

confidence: 99%

On the Rotational Structure in Neural Data

Kuzmina,

Kriukov,

Lebedev

2023

Preprint

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Spatiotemporal properties of the activity of neuronal populations in cortical motor areas have been the subject of many experimental and theoretical investigations, which generated numerous interpretations regarding the mechanisms of preparing and executing limb movements. Two competing models, namely representational and dynamical models, strive to explain the temporal course of neuronal activity and its relationship with different paramaters of movements. One proposed dynamical model employs the jPCA method, a dimensionality reduction technique, to holistically characterize oscillatory activity in a population of neurons by maximizing rotational dynamics that are present in the data. Different interpretations have been proposed for the rotational dynamics revealed with jPCA approach in various brain areas. Yet, the nature of such dynamics remains poorly understood. Here we conducted a comprehensive analysis of several neuronal-population datasets. We demonstrated that rotational dynamics were consistently accounted for by a travelling wave pattern. To quantify the rotation strength, we developed a complex-valued measure termed the gyration number. Additionally, we identify the parameters influencing the extent of rotation in the data. Overall, our findings suggest that rotational dynamics and travelling waves are the same phenomena, which requires reevaluation of the previous interpretations where they were considered as separate entities.

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Transition from predictable to variable motor cortex and striatal ensemble patterning during behavioral exploration

Cited by 12 publications

References 67 publications

Emergent Low-Frequency Activity in Cortico-Cerebellar Networks with Motor Skill Learning

Emergent Low-Frequency Activity in Cortico-Cerebellar Networks with Motor Skill Learning

Dissociating the contributions of sensorimotor striatum to automatic and visually-guided motor sequences

On the Rotational Structure in Neural Data

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