A method for investigating cortical control of stand and squat in conscious behavioral monkeys

Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements

Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed algorithms. Our findings provide new insights for extending current BMI design concepts and techniques on upper limbs to lower limb circumstances. Brain controlled exoskeleton, prostheses or neuromuscular electrical stimulators for lower limbs are expected to be developed, which enables the subject to manipulate complex biomechatronic devices with mind in more harmonized manner.

Neuronal representation of stand and squat in the primary motor cortex of monkeys

BACKGROUND

Determining neuronal topographical information in the cerebral cortex is of fundamental importance for developing neuroprosthetics. Significant progress has been achieved in decoding hand voluntary movement with cortical neuronal activity in nonhuman primates. However, there are few successful reports in scientific literature for decoding lower limb voluntary movement with the cortical neuronal firing. We once reported an experimental system, which consists of a specially designed chair, a visually guided stand and squat task training paradigm and an acute neuron recording setup. With this system, we can record high quality cortical neuron activity to investigate the correlation between these neuronal signals and stand/squat movement.

METHODS/RESULTS

In this research, we train two monkeys to perform the visually guided stand and squat task, and record neuronal activity in the vast areas targeted to M1 hind-limb region, at a distance of 1 mm. We find that 76.9% of recorded neurons (1230 out of 1598 neurons) showing task-firing modulation, including 294 (18.4%) during the pre-response window; 310 (19.4%) for standing up; 104 (6.5%) for the holding stand phase; and 205 (12.8%) during the sitting down. The distributions of different type neurons have a high degree of overlap. They are mainly ranged from +7.0 to 13 mm in the Posterior-Anterior dimension, and from +0.5 to 4.0 mm in Dosal-lateral dimension, very close to the midline, and just anterior of the central sulcus.

CONCLUSIONS/SIGNIFICANCE

The present study examines the neuronal activity related to lower limb voluntary movements in M1 and find topographical information of various neurons tuned to different stages of the stand and squat task. This work may contribute to understanding the fundamental principles of neural control of lower limb movements. Especially, the topographical information suggests us where to implant the chronic microelectrode arrays to harvest the most quantity and highest quality neurons related to lower limb movements, which may accelerate to develop cortically controlled lower limb neuroprosthetics for spinal cord injury subjects.

Properties of primary motor cortex output to hindlimb muscles in the macaque monkey

The cortical control of forelimb motor function has been studied extensively, especially in the primate. In contrast, cortical control of the hindlimb has been relatively neglected. This study assessed the output properties of the primary motor cortex (M1) hindlimb representation in terms of the sign, latency, magnitude, and distribution of effects in stimulus-triggered averages (StTAs) of electromyography (EMG) activity recorded from 19 muscles, including hip, knee, ankle, digit, and intrinsic foot muscles, during a push-pull task compared with data reported previously on the forelimb. StTAs (15, 30, and 60 μA at 15 Hz) of EMG activity were computed at 317 putative layer V sites in two rhesus macaques. Poststimulus facilitation (PStF) was distributed equally between distal and proximal muscles, whereas poststimulus suppression (PStS) was more common in distal muscles than proximal muscles (51/49%, respectively, for PStF; 72/28%, respectively, for PStS) at 30 μA. Mean PStF and PStS onset latency generally increased the more distal the joint of a muscle's action. Most significantly, the average magnitude of hindlimb poststimulus effects was considerably weaker than the average magnitude of effects from forelimb M1. In addition, forelimb PStF magnitude increased consistently from proximal to distal joints, whereas hindlimb PStF magnitude was similar at all joints except the intrinsic foot muscles, which had a magnitude of approximately double that of all of the other muscles. The results suggest a greater monosynaptic input to forelimb compared with hindlimb motoneurons, as well as a more direct synaptic linkage for the intrinsic foot muscles compared with the other hindlimb muscles.

Selection of cortical neurons for identifying movement transitions in stand and squat

Neural signals collected from motor cortex were quantified for identification of subject's specific movement intentions in a Brain Machine Interface (BMI). Neuron selection serves as an important procedure in this decoding process. In this study, we proposed a neuron selection method for identifying movement transitions in standing and squatting tasks by analyzing cortical neuron spike train patterns. A nonparametric analysis of variation, Kruskal-Wallis test, was introduced to evaluate whether the average discharging rate of each neuron changed significantly among different motion stages, and thereby categorize the neurons according to their active periods. Selection was performed based on neuron categorizing information. Finally, the average firing rates of selected neurons were assembled as feature vectors and a classifier based on support vector machines (SVM) was employed to discriminate different movement stages and identify transitions. The results indicate that our neuron selection method is accurate and efficient for finding neurons correlated with movement transitions in standing and squatting tasks.

Physiological, morphological and neurochemical characterization of neurons modulated by movement

The role of individual neurons and their function in neuronal circuits is fundamental to understanding the neuronal mechanisms of sensory and motor functions. Most investigations of sensorimotor mechanisms rely on either examination of neurons while an animal is static or record extracellular neuronal activity during a movement. While these studies have provided the fundamental background for sensorimotor function, they either do not evaluate functional information which occurs during a movement or are limited in their ability to fully characterize the anatomy, physiology and neurochemical phenotype of the neuron. A technique is shown here which allows extensive characterization of individual neurons during an in vivo movement. This technique can be used not only to study primary afferent neurons but also to characterize motoneurons and sensorimotor interneurons. Initially the response of a single neuron is recorded using electrophysiological methods during various movements of the mandible followed by determination of the receptive field for the neuron. A neuronal tracer is then intracellularly injected into the neuron and the brain is processed so that the neuron can be visualized with light, electron or confocal microscopy (Fig. 1). The detailed morphology of the characterized neuron is then reconstructed so that neuronal morphology can be correlated with the physiological response of the neuron (Figs. 2,3). In this communication important key details and tips for successful implementation of this technique are provided. Valuable additional information can be determined for the neuron under study by combining this method with other techniques. Retrograde neuronal labeling can be used to determine neurons with which the labeled neuron synapses; thus allowing detailed determination of neuronal circuitry. Immunocytochemistry can be combined with this method to examine neurotransmitters within the labeled neuron and to determine the chemical phenotypes of neurons with which the labeled neuron synapses. The labeled neuron can also be processed for electron microscopy to determine the ultrastructural features and microcircuitry of the labeled neuron. Overall this technique is a powerful method to thoroughly characterize neurons during in vivo movement thus allowing substantial insight into the role of the neuron in sensorimotor function.