Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats

Khorasani, Abed; Beni, Nargess Heydari; Shalchyan, Vahid; Daliri, Mohammad Reza

doi:10.1038/srep35238

Cited by 38 publications

(34 citation statements)

References 33 publications

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“…Previous studies have demonstrated task-related modulation of activity during reaching tasks including center-out movements of a lever, push/pull movements of a lever, and pellet retrieval tasks [1, 7, 11, 24–26]. Importantly, these previous studies found that neural activity could be used to decode movement parameters, such as kinematics and kinetics [11, 25]. Here we examined the neural correlates of reaching movements using a novel automated behavior box that combined a gross reaching task (lever press) and a fine reaching task (skilled pellet retrieval) in close temporal succession.…”

Section: Discussionmentioning

confidence: 99%

Chronic Stability of Single-Channel Neurophysiological Correlates of Gross and Fine Reaching Movements in the Rat

Bundy

Guggenmos

Murphy

et al. 2019

Preprint

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17Following injury to motor cortex, reorganization occurs throughout spared brain regions and is 18 thought to underlie motor recovery. Unfortunately, the standard neurophysiological and 19 neuroanatomical measures of post-lesion plasticity are only indirectly related to observed 20 changes in motor execution. While substantial task-related neural activity has been observed 21 during motor tasks in rodent primary motor cortex and premotor cortex, the long-term stability of 22 these responses in healthy rats is uncertain, limiting the interpretability of longitudinal changes in 23 the specific patterns of neural activity during motor recovery following injury. This study 24 examined the stability of task-related neural activity associated with execution of reaching 25 movements in healthy rodents. Rats were trained to perform a novel reaching task combining a 26 'gross' lever press and a 'fine' pellet retrieval. In each animal, two chronic microelectrode arrays 27 were implanted in motor cortex spanning the caudal forelimb area (rodent primary motor cortex) 28 and the rostral forelimb area (rodent premotor cortex). We recorded multiunit spiking and local 29 field potential activity from 10 days to 7-10 weeks post-implantation to characterize the patterns 30 of neural activity observed during each task component and analyzed the consistency of channel-31 specific task-related neural activity. Task-related changes in neural activity were observed on the 32 majority of channels. While the task-related changes in multi-unit spiking and local field 33 potential spectral power were consistent over several weeks, spectral power changes were more 34 stable, despite the trade-off of decreased spatial and temporal resolution. These results show that 35 rodent primary and premotor cortex are both involved in reaching movements with stable 36 patterns of task-related activity across time, establishing the relevance of the rodent for future 37 studies designed to examine changes in task-related neural activity during recovery from focal 38 cortical lesions. 39 Introduction 40An important challenge in neuroscience is determining how the brain controls skilled 41 forelimb movements, a topic that has important implications for motor recovery following brain 42 injuries as well as the development of neuroprosthetic systems. Along with non-human primates, 43 rodents are valuable models for examining the neurophysiological basis of motor control. In 44 particular, rodents can learn to perform a wide variety of motor tasks, including: lever press/pull 45 movements with complex timing [1], 2D center-out joystick movements [2], single-pellet reach-46 to-grasp food retrievals [3, 4], and even control of brain-computer interface systems with neural 47 substantial task-related neural activity during the performance of a reaching task [7]. In part, this 63 may be due to the dense reciprocal connectivity between the two regions [13], which depending 64 on the relative timing of excitation in each area, allows RFA and CFA to modify the outpu...

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Section: Discussionmentioning

confidence: 99%

Chronic Stability of Single-Channel Neurophysiological Correlates of Gross and Fine Reaching Movements in the Rat

Bundy

Guggenmos

Murphy

et al. 2019

Preprint

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“…Previously reported procedures for animal training, microarray implantation, and brain data acquisition were used [33].…”

Section: Methodsmentioning

confidence: 99%

State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

et al. 2020

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The functional use of brain-machine interfaces (BMIs) in everyday tasks requires the accurate decoding of both movement and force information. In real-word tasks such as reach-to-grasp movements, a prosthetic hand should be switched between reaching and grasping modes, depending on the detection of the user intents in the decoder part of the BMI. Therefore, it is important to detect the rest or active states of different actions in the decoder to produce the corresponding continuous command output during the estimated state. In this study, we demonstrated that the resting and force-generating time-segments in a key pressing task could be accurately detected from local field potentials (LFPs) in rat's primary motor cortex. Common spatial pattern (CSP) algorithm was applied on different spectral LFP sub-bands to maximize the difference between the two classes of force and rest. We also showed that combining a discrete state decoder with linear or non-linear continuous force variable decoders could lead to a higher force decoding performance compared with the case we use a continuous variable decoder only. Moreover, the results suggest that gamma LFP signals (50-100 Hz) could be used successfully for decoding the discrete rest/force states as well as continuous values of the force variable. The results of this study can offer substantial benefits for the implementation of a self-paced, force-related command generator in BMI experiments without the need for manual external signals to select the state of the decoder.

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“…All the components of experimental setup including force sensor, mechanical arm and water pump were monitored or controlled using the Arduino micro controller based on a C++ script. The details of surgery and recording procedures have been previously published in (Khorasani et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Each component is identified in order to maximize the covariance between the input and output variables. The details of this method is described in Khorasani et al (2016).…”

Section: Methodsmentioning

confidence: 99%

“…Ideal control of an external device based on the BMI techniques depends on accurate decoding of both kinematic such as hand trajectories and kinetic information such as force and torque values. In Khorasani et al (2016) we showed that force information can be decoded from the low number of LFP channels of motor cortex area. In the current study, we decode continuous force signal from the combination of LFP and MUA signals and compared this decoder with MUA-only and LFP- only decoders.…”

Section: Introductionmentioning

confidence: 97%

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Adaptive Artifact Removal From Intracortical Channels for Accurate Decoding of a Force Signal in Freely Moving Rats

2019

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Intracortical data recorded with multi-electrode arrays provide rich information about kinematic and kinetic states of movement in the brain–machine interface (BMI) systems. Direct estimation of kinetic information such as the force from cortical data has the same importance as kinematic information to make a functional BMI system. Various types of the information including single unit activity (SUA), multiunit activity (MUA) and local field potential (LFP) can be used as an input information to extract motor commands for control of the external devices in BMI. Here we combine LFP and MUA information to improve decoding accuracy of the force signal from the multi-channel intracortical data of freely moving rats. We suggest a weighted common average referencing (CAR) algorithm in order to valid interpretation of the force decoding from different data types. The proposed spatial filter adaptively identifies contribution of the common noise on the channels employing Kalman filter method. We evaluated the efficacy of the proposed artifact algorithm on both simulation and real data. In the simulation study, the average R 2 between the original and reconstructed signal of all channels after applying the proposed artifact removal method was computed for input SNRs in the range of −45 to 0 dB. Weighted CAR method can effectively reconstruct the original signal with average R 2 higher than 0.5 for input SNRs higher than −s10 dB in case of adding simulated outlier and motion artifacts. We also show that the proposed artifact removal algorithm 33% improves the accuracy of force decoding in terms of R 2 value compared to standard CAR filters.

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Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats

Cited by 38 publications

References 33 publications

Chronic Stability of Single-Channel Neurophysiological Correlates of Gross and Fine Reaching Movements in the Rat

Chronic Stability of Single-Channel Neurophysiological Correlates of Gross and Fine Reaching Movements in the Rat

State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

Adaptive Artifact Removal From Intracortical Channels for Accurate Decoding of a Force Signal in Freely Moving Rats

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