Faculty Opinions recommendation of Beta band oscillations in motor cortex reflect neural population signals that delay movement onset.

Motor cortical beta oscillations have been reported for decades, yet their behavioral correlates remain unresolved. Some studies link beta oscillations to changes in underlying neural activity, but the specific behavioral manifestations of these reported changes remain elusive. To investigate how changes in population neural activity, beta oscillations, and behavior are linked, we recorded multi-scale neural activity from motor cortex while three macaques performed a novel neurofeedback task. Subjects volitionally brought their beta oscillatory power to an instructed state and subsequently executed an arm reach. Reaches preceded by a reduction in beta power exhibited significantly faster movement onset times than reaches preceded by an increase in beta power. Further, population neural activity was found to shift farther from a movement onset state during beta oscillations that were neurofeedback-induced or naturally occurring during reaching tasks. This finding establishes a population neural basis for slowed movement onset following periods of beta oscillatory activity.

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2190 Longitudinal changes in EEG power envelope connectivity are proportional to motor recovery in chronic stroke patients

Journal of Clinical and Translational Science ◽

10.1017/cts.2018.92 ◽

2018 ◽

Vol 2 (S1) ◽

pp. 17-17

Author(s):

Joseph B. Humphries ◽

David T. Bundy ◽

Eric C. Leuthardt ◽

Thy N. Huskey

Keyword(s):

Motor Cortex ◽

Motor Imagery ◽

Stroke Rehabilitation ◽

Brain Computer Interface ◽

Chronic Stroke ◽

Motor Recovery ◽

Stroke Recovery ◽

Computer Interface ◽

Beta Band ◽

Stroke Patients

OBJECTIVES/SPECIFIC AIMS: The objective of this study is to determine the degree to which the use of a contralesionally-controlled brain-computer interface for stroke rehabilitation drives change in interhemispheric motor cortical activity. METHODS/STUDY POPULATION: Ten chronic stroke patients were trained in the use of a brain-computer interface device for stroke recovery. Patients perform motor imagery to control the opening and closing of a motorized hand orthosis. This device was sent home with patients for 12 weeks, and patients were asked to use the device 1 hour per day, 5 days per week. The Action Research Arm Test (ARAT) was performed at 2-week intervals to assess motor function improvement. Before the active motor imagery task, patients were asked to quietly rest for 90 seconds before the task to calibrate recording equipment. EEG signals were acquired from 2 electrodes—one each centered over left and right primary motor cortex. Signals were preprocessed with a 60 Hz notch filter for environmental noise and referenced to the common average. Power envelopes for 1 Hz frequency bands (1–30 Hz) were calculated through Gabor wavelet convolution. Correlations between electrodes were then calculated for each frequency envelope on the first and last 5 runs, thus generating one correlation value per subject, per run. The chosen runs approximately correspond to the first and last week of device usage. These correlations were Fisher Z-transformed for comparison. The first and last 5 run correlations were averaged separately to estimate baseline and final correlation values. A difference was then calculated between these averages to determine correlation change for each frequency. The relationship between beta-band correlation changes (13–30 Hz) and the change in ARAT score was determined by calculating a Pearson correlation. RESULTS/ANTICIPATED RESULTS: Beta-band inter-electrode correlations tended to decrease more in patients achieving greater motor recovery (Pearson’s r=−0.68, p=0.031). A similar but less dramatic effect was observed with alpha-band (8–12 Hz) correlation changes (Pearson’s r=−0.42, p=0.22). DISCUSSION/SIGNIFICANCE OF IMPACT: The negative correlation between inter-electrode power envelope correlations in the beta frequency band and motor recovery indicates that activity in the motor cortex on each hemisphere may become more independent during recovery. The role of the unaffected hemisphere in stroke recovery is currently under debate; there is conflicting evidence regarding whether it supports or inhibits the lesioned hemisphere. These findings may support the notion of interhemispheric inhibition, as we observe less in common between activity in the 2 hemispheres in patients successfully achieving recovery. Future neuroimaging studies with greater spatial resolution than available with EEG will shed further light on changes in interhemispheric communication that occur during stroke rehabilitation.

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Latent factors and dynamics in motor cortex and their application to brain-machine interfaces

10.7287/peerj.preprints.27217 ◽

2018 ◽

Author(s):

Chethan Pandarinath ◽

K. Cora Ames ◽

Abigail A Russo ◽

Ali Farshchian ◽

Lee E Miller ◽

...

Keyword(s):

Motor Cortex ◽

Movement Planning ◽

Neural Population ◽

Great Effort ◽

Single Neurons ◽

Latent Factors ◽

Brain Machine Interfaces ◽

Population Activity ◽

Systems Perspective ◽

Behaving Monkeys

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study network-level phenomena is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective, and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

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Time Course of Determination of Movement Direction in the Reaction Time Task in Humans

Journal of Neurophysiology ◽

10.1152/jn.2001.86.3.1195 ◽

2001 ◽

Vol 86 (3) ◽

pp. 1195-1201 ◽

Cited By ~ 18

Author(s):

Martin Sommer ◽

Joseph Classen ◽

Leonardo G. Cohen ◽

Mark Hallett

Keyword(s):

Reaction Time ◽

Motor Cortex ◽

Time Course ◽

Primary Motor Cortex ◽

Cortical Excitability ◽

Motor Evoked Potentials ◽

Movement Direction ◽

Movement Onset ◽

Thumb Movement

The primary motor cortex produces motor commands that include encoding the direction of movement. Excitability of the motor cortex in the reaction time (RT) task can be assessed using transcranial magnetic stimulation (TMS). To elucidate the timing of the increase in cortical excitability and of the determination of movement direction before movement onset, we asked six right-handed, healthy subjects to either abduct or extend their right thumb after a go-signal indicated the appropriate direction. Between the go-signal and movement onset, single TMS pulses were delivered to the contralateral motor cortex. We recorded the direction of the TMS-induced thumb movement and the amplitude of motor-evoked potentials (MEPs) from the abductor pollicis brevis and extensor pollicis brevis muscles. Facilitation of MEPs from the prime mover, as early as 200 ms before the end of the reaction time, preceded facilitation of MEPs from the nonprime mover, and both preceded measurable directional change. Compared with a control condition in which no voluntary movement was required, the direction of the TMS-induced thumb movement started to change in the direction of the intended movement as early as 90 ms before the end of the RT, and maximum changes were seen shortly before the end of reaction time. Movement acceleration also increased with maxima shortly before the end of the RT. We conclude that in concentric movements a change of the movement direction encoded in the primary motor cortex occurs in the 200 ms prior to movement onset, which is as early as increased excitability itself can be detected.

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Primary motor cortex neurons classified in a postural task predict muscle activation patterns in a reaching task

Journal of Neurophysiology ◽

10.1152/jn.00971.2015 ◽

2016 ◽

Vol 115 (4) ◽

pp. 2021-2032 ◽

Cited By ~ 10

Author(s):

Ethan A. Heming ◽

Timothy P. Lillicrap ◽

Mohsen Omrani ◽

Troy M. Herter ◽

J. Andrew Pruszynski ◽

...

Keyword(s):

Motor Cortex ◽

Muscle Activation ◽

Primary Motor Cortex ◽

Network Connectivity ◽

Spatiotemporal Patterns ◽

Neural Population ◽

Activation Patterns ◽

Reaching Task ◽

Muscle Activation Patterns ◽

Muscle Groups

Primary motor cortex (M1) activity correlates with many motor variables, making it difficult to demonstrate how it participates in motor control. We developed a two-stage process to separate the process of classifying the motor field of M1 neurons from the process of predicting the spatiotemporal patterns of its motor field during reaching. We tested our approach with a neural network model that controlled a two-joint arm to show the statistical relationship between network connectivity and neural activity across different motor tasks. In rhesus monkeys, M1 neurons classified by this method showed preferred reaching directions similar to their associated muscle groups. Importantly, the neural population signals predicted the spatiotemporal dynamics of their associated muscle groups, although a subgroup of atypical neurons reversed their directional preference, suggesting a selective role in antagonist control. These results highlight that M1 provides important details on the spatiotemporal patterns of muscle activity during motor skills such as reaching.

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Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

10.1101/2021.02.01.429168 ◽

2021 ◽

Author(s):

Shreya Saxena ◽

Abigail A. Russo ◽

John P. Cunningham ◽

Mark M. Churchland

Keyword(s):

Motor Cortex ◽

Muscle Activity ◽

Single Neuron ◽

Activity Patterns ◽

Network Activity ◽

Movement Speed ◽

Neural Population ◽

Recurrent Networks ◽

Neural Signals ◽

Population Activity

AbstractLearned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling, and yielded quantitative and qualitative predictions. To evaluate predictions, we recorded motor cortex population activity during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

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Neural population dynamics in human motor cortex during movements in people with ALS

eLife ◽

10.7554/elife.07436 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 37

Author(s):

Chethan Pandarinath ◽

Vikash Gilja ◽

Christine H Blabe ◽

Paul Nuyujukian ◽

Anish A Sarma ◽

...

Keyword(s):

Dynamical System ◽

Clinical Trial ◽

Motor Cortex ◽

Neural Activity ◽

Temporal Structure ◽

Neural Population ◽

Dynamical Structure ◽

Human Motor Cortex ◽

Human Motor ◽

Movement Parameters

The prevailing view of motor cortex holds that motor cortical neural activity represents muscle or movement parameters. However, recent studies in non-human primates have shown that neural activity does not simply represent muscle or movement parameters; instead, its temporal structure is well-described by a dynamical system where activity during movement evolves lawfully from an initial pre-movement state. In this study, we analyze neuronal ensemble activity in motor cortex in two clinical trial participants diagnosed with Amyotrophic Lateral Sclerosis (ALS). We find that activity in human motor cortex has similar dynamical structure to that of non-human primates, indicating that human motor cortex contains a similar underlying dynamical system for movement generation.Clinical trial registration: NCT00912041.

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