Learning-induced Dependence of Neuronal Activity in Primary Motor Cortex on Motor Task Condition

Two-way ANOVA to identify impacts of multiple interactive behavioral factors on the neuronal population dependency during the reaching motion

Brain neural activities interact with behaviors. The contribution of this paper is that we investigate the significance of the impacts of multiple behavioral factors: target direction (left or right) and obstacle (presence or absence), and their interaction on the dependency among a neuronal ensemble in the dorsal premotor (PMd) area of a monkey during a reaching motion. We first use bootstrapping to extract multiple resamples from successful trials. Then we use redundancy to measure dependency in each resample. We use ANOVA to investigate the significance of the impacts of these two behavioral factors and their interaction on the redundancy. The ANOVA shows that these two behavioral factors and their interaction have significant impacts on the single feature of neural activity. The same significance of impacts is identified in another neuronal ensemble assessed in another experimental session. Its biological explanation is given.

A cluster analysis of neuronal activity in the dorsal premotor cortical area for neuroprosthetic control

With the use of the neuronal data acquisition technology, millisecond-level multi-electrode data from several regions of the premotor area were obtained from two rhesus monkeys trained to perform arm-reach tasks with visual cues in virtual reality. In each trial, animals were required to select and perform one of the four possible arm reaching movements to the target on the top-left or top-right of the virtual reality space. They were also required to decide whether they would move their arms straight to the target or curve them in order to avoid the obstacle that was presented. After the acquired neuronal signals were processed, unsupervised Hierarchical clustering and K-means clustering were performed to uncover the similarity and difference in the average firing rate of spike train data between neurons and phases for each experiment condition. The clustering results indicate the similarity of neuronal data in the movement planning and actual movement phases, and the difference of such data from the data in information processing phases. Furthermore, the clustering results show that when the target location is on the right, the move planning started earlier. The analysis of variance (ANOVA) on the neuronal data confirms the results from the hierarchical clustering.