scholarly journals Reward Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

2018 ◽  
Author(s):  
Junmo An ◽  
Taruna Yadav ◽  
John P. Hessburg ◽  
Joseph T. Francis
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Brain Machine Interface ◽  
Field Potentials ◽  
Alpha Oscillations ◽  
Firing Rates ◽  
Neural Spiking ◽  
Machine Interface ◽  
Field Coherence ◽  
Spike Firing

ABSTRACTReward modulation of the primary motor cortex (M1) could be exploited in developing an autonomously updating brain-machine interface (BMI) based on a reinforcement learning architecture. In order to understand the multifaceted effects of reward on M1 activity, we investigated how neural spiking, oscillatory activities and their functional interactions are modulated by conditioned stimuli related reward expectation. To do so, local field potentials (LFPs) and singleunit/multi-unit activities were recorded simultaneously and bilaterally from M1 cortices while five non-human primates performed cued center-out reaching or grip force tasks either manually using their right arm/hand or observed passively. We found that reward expectation influenced the strength of alpha (8-14 Hz) power, alpha-gamma comodulation, alpha spike-field coherence, and firing rates in general in M1. Furthermore, we found that an increase in alpha-band power was correlated with a decrease in neural spiking activity, that firing rates were highest at the trough of the alpha-band cycle and lowest at the peak of its cycle. These findings imply that alpha oscillations modulated by reward expectation have an influence on spike firing rate and spike timing during both reaching and grasping tasks in M1. These LFP, spike, and spike-field interactions could be used to follow the M1 neural state in order to enhance BMI decoding (An et al., 2018; Zhao et al., 2018).Significance StatementKnowing the subjective value of performed or observed actions is valuable feedback that can be used to improve the performance of an autonomously updating brain-machine interface (BMI). Reward-related information in the primary motor cortex (M1) may be crucial for more stable and robust BMI decoding (Zhao et al., 2018). Here, we present how expectation of reward during motor tasks, or simple observation, is represented by increased spike firing rates in conjunction with decreased alpha (8-14 Hz) oscillatory power, alpha-gamma comodulation, and alpha spike-field coherence, as compared to non-rewarding trials. Moreover, a phasic relation between alpha oscillations and firing rates was observed where firing rates were found to be lowest and highest at the peak and trough of alpha oscillations, respectively.

scholarly journals Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

eNeuro ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. ENEURO.0178-19.2019 ◽  
Author(s):  
Junmo An ◽  
Taruna Yadav ◽  
John P. Hessburg ◽  
Joseph T. Francis
Keyword(s):  
Motor Cortex ◽  
Local Field ◽  
Primary Motor Cortex ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Field Coherence ◽  
Spiking Activity

scholarly journals Population subspaces reflect movement intention for arm and brain-machine interface control

2019 ◽  
Author(s):  
H. Lalazar ◽  
J.M. Murray ◽  
L.F. Abbott ◽  
E. Vaadia
Keyword(s):  
Motor Cortex ◽  
Neural Activity ◽  
Primary Motor Cortex ◽  
Action Observation ◽  
Neuronal Population ◽  
Brain Machine Interface ◽  
Population Activity ◽  
Interface Control ◽  
Machine Interface ◽  
Movement Intention

Motor cortex is active during covert motor acts, such as action observation and mental rehearsal, when muscles are quiescent. Such neuronal activity, which is thought to be similar to the activity underlying overt movement, is exploited by neural prosthetics to afford subjects control of an external effector. We compared neural activity in primary motor cortex of monkeys who controlled a cursor using either their arm or a brain-machine interface (BMI) to identify what features of neural activity are similar or dissimilar in these two control contexts. Neuronal population activity parcellates into orthogonal subspaces, with some representations that are unique to arm movements and others that are shared between arm and BMI control. The shared subspace is invariant to the effector used and to biomechanical details of the movement, revealing a representation that reflects movement intention. This intention representation is likely the signal extracted by BMI algorithms for cursor control, and subspace orthogonality accounts for how neurons involved in arm control can drive a BMI while the arm remains at rest. These results provide a resolution to the long-standing debate of whether motor cortex represents muscle activity or abstract movement variables, and it clarifies various puzzling aspects of neural prosthetic research.

Force decoding using local field potentials in primary motor cortex: PLS or Kalman filter regression?

2020 ◽  
Vol 43 (1) ◽  
pp. 175-186 ◽  
Author(s):  
Nargess Heydari Beni ◽  
Reza Foodeh ◽  
Vahid Shalchyan ◽  
Mohammad Reza Daliri
Keyword(s):  
Kalman Filter ◽  
Motor Cortex ◽  
Local Field ◽  
Primary Motor Cortex ◽  
Local Field Potentials ◽  
Field Potentials

scholarly journals Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics

2015 ◽  
Vol 114 (3) ◽  
pp. 1500-1512 ◽  
Author(s):  
Sagi Perel ◽  
Patrick T. Sadtler ◽  
Emily R. Oby ◽  
Stephen I. Ryu ◽  
Elizabeth C. Tyler-Kabara ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Local Field ◽  
Unit Activity ◽  
Single Unit ◽  
Primary Motor Cortex ◽  
Spatial Scales ◽  
Local Field Potentials ◽  
Single Unit Activity ◽  
Field Potentials ◽  
Threshold Crossing

A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals. We examined the similarities and differences in the information contained in four signal types recorded simultaneously from multielectrode arrays implanted in primary motor cortex: well-isolated action potentials from putative single units, multiunit threshold crossings, and local field potentials (LFPs) at two distinct frequency bands. We quantified the tuning of these signal types to kinematic parameters of reaching movements. We found 1) threshold crossing activity is not a proxy for single-unit activity; 2) when examined on individual electrodes, threshold crossing activity more closely resembles LFP activity at frequencies between 100 and 300 Hz than it does single-unit activity; 3) when examined across multiple electrodes, threshold crossing activity and LFP integrate neural activity at different spatial scales; and 4) LFP power in the “beta band” (between 10 and 40 Hz) is a reliable indicator of movement onset but does not encode kinematic features on an instant-by-instant basis. These results show that the diverse signals recorded from extracellular electrodes provide somewhat distinct and complementary information. It may be that these signal types arise from biological phenomena that are partially distinct. These results also have practical implications for harnessing richer signals to improve brain-machine interface control.

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