Xuan Ma

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

Neural signals collected from motor cortex were quantified for identification of subjects 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.

Neurons in Primary Motor Cortex Encode Hand Orientation in a Reach-to-Grasp Task

It is disputed whether those neurons in the primary motor cortex (M1) that encode hand orientation constitute an independent channel for orientation control in reach-to-grasp behaviors. Here, we trained two monkeys to reach forward and grasp objects positioned in the frontal plane at different orientation angles, and simultaneously recorded the activity of M1 neurons. Among the 2235 neurons recorded in M1, we found that 18.7% had a high correlation exclusively with hand orientation, 15.9% with movement direction, and 29.5% with both movement direction and hand orientation. The distributions of neurons encoding hand orientation and those encoding movement direction were not uniform but coexisted in the same region. The trajectory of hand rotation was reproduced by the firing patterns of the orientation-related neurons independent of the hand reaching direction. These results suggest that hand orientation is an independent component for the control of reaching and grasping activity.

Predicting hand orientation in reach-to-grasp tasks using neural activities from primary motor cortex.

Hand orientation is an important control parameter during reach-to-grasp task. In this paper, we presented a study for predicting hand orientation of non-human primate by decoding neural activities from primary motor cortex (M1). A non-human primate subject was guided to do reaching and grasping tasks meanwhile neural activities were acquired by chronically implanted microelectrode arrays. A Support Vector Machines (SVMs) classifier has been trained for predicting three different hand orientations using these M1 neural activities. Different number of neurons were selected and analyzed; the classifying accuracy was 94.1% with 2 neurons and was 100% with 8 neurons. Data from highly event related neuron units contribute a lot to the accuracy of hand orientation prediction. These results indicate that three different hand orientations can be predicted accurately and effectively before the actual movements occurring with a small number of related neurons in M1.

Neurons in dorsal premotor cortex represent the switching of intended hand path in a delayed reaching task

Dorsal premotor cortex (PMd) is considered to play a crucial role in motor preparation, yet how the variation of neuronal activity affects the generation of different circumstances dependent movements remains unclear. Here we trained two monkeys to perform a delayed reaching task instructed by two sets of cues, one for indicating the target locations and another for indicating a conditionally presented virtual obstacle in the reaching path, which required the monkey to make a bypassing instead of straight reaching. We recorded the activity of PMd neurons and investigated how they responded to the switching of intended hand path induced by obstacle bypassing. Comparing the neuronal activity between hand bypassing trials and straight reaching trials, we found 30% of the total 687 set-related neurons showed different overall discharging level, and another 24% showed different onset time during the delay period. We also found 16% of the neurons were modulated only by target location and 14% were modulated by both target location and path switching. Our results demonstrate PMd neurons not only represent the planning of reaching to different target locations, as many previous studies have shown, but also represent the switching of intended reaching path induced by hand bypassing, suggesting how PMd neurons coordinate for such circumstances dependent motor planning.

The Neural Mechanism Exploration of Adaptive Motor Control: Dynamical Economic Cell Allocation in the Primary Motor Cortex

Adaptive flexibility is of significance for the smooth and efficient movements in goal attainment. However, the underlying work mechanism of the cerebral cortex in adaptive motor control still remains unclear. How does the cerebral cortex organize and coordinate the activity of a large population of cells in the implementation of various motor strategies? To explore this issue, single-unit activities from the M1 region and kinematic data were recorded simultaneously in monkeys performing 3D reach-to-grasp tasks with different perturbations. Varying motor control strategies were employed and achieved in different perturbed tasks, via the dynamic allocation of cells to modulate specific movement parameters. An economic principle was proposed for the first time to describe a basic rule for cell allocation in the primary motor cortex. This principle, defined as the Dynamic Economic Cell Allocation Mechanism (DECAM), guarantees benefit maximization in cell allocation under limited neuronal resources, and avoids committing resources to uneconomic investments for unreliable factors with no or little revenue. That is to say, the cells recruited are always preferentially allocated to those factors with reliable return; otherwise, the cells are dispatched to respond to other factors about task. The findings of this study might partially reveal the working mechanisms underlying the role of the cerebral cortex in adaptive motor control, wherein is also of significance for the design of future intelligent brain–machine interfaces and rehabilitation device.

A bidirectional peripheral neural interface for restoring sensorimotor function of non-human primates

Upper limb loss is a big disaster and markedly affects the quality of life. Bidirectional peripheral neural interface (PNI) is a feasible way to restore the sensorimotor functions of amputees. Bidirectional interface includes motor commands decoding and sensory feedback restoring. In this paper, we presented two neurobehavioral platforms for studying bidirectional PNI in the monkey. In the first platform, the monkey was guided to do reaching and grasping tasks meanwhile cortical and peripheral neural signals were recorded, motor commands can be decoded. In the other platform, the monkey was guided to express tactile sensation into motor action; sensory feedback restoring can be seen from the action of the monkey. We designed a bidirectional PNI and tried to achieve it, many substantial challenges still remains.