Nonmotor regions encode path-related information during movements

Abstract

Sensorimotor control and the involvement of motor brain regions has been extensively studied, but the role nonmotor brain regions play during movements has been overlooked. This is particularly due to the difficulty of recording from multiple regions in the brain during motor control. In this study, we utilize stereoelectroencephalography (SEEG) recording techniques to explore the role nonmotor brain areas have on the way we move. Nine humans were implanted with SEEG depth electrodes for clinical purposes, which rendered access to local field potential (LFP) activity in deep and peripheral nonmotor structures. Participants performed fast and slow arm reaching movements using a robotic manipulandum. In this study, we explored whether neural activity in a given nonmotor brain structure correlated to movement path metrics including: path length, path deviation, and path speed. Statistical analysis revealed correlations between averaged neural activity in middle temporal gyrus, supramarginal gyrus, and fusiform gyrus and our path metrics both within and across the subjects. Furthermore, we split trials across subjects into two groups: one group consisted of trials with high values of each path metric and the other with low values. We then found significant differences in LFP power in specific frequency bands (e.g. beta) during movement between each group. These results suggest that nonmotor regions may dynamically encode path-related information during movement.

An exploratory data analysis method for identifying brain regions and frequencies of interest from large-scale neural recordings

“…Spectral analysis on neural data traditionally partition frequencies into predefined frequency bands (Breault et al 2017;Canolty and Knight 2010;Crone et al 1998a, b;Kerr et al 2014;Sacré et al 2016;Ward 2003). However, this may result in losing key features where frequency bins are split.…”

“…We recently collected a data set consisting of neural activity from nonmotor brain regions during a movement task without any predefined hypothesis (Breault et al 2017;Kerr et al 2014Kerr et al , 2017. Due to the inherent complexity of human data, we were uncertain as to whether any neural correlates existed between the neural data and path-related behavioral information because no regions within the motor circuit were recorded.…”

“…The subject performed a speed and goal directed reaching movements that have been previously described (Breault et al 2017;Johnson et al 2014;Kerr et al 2014). The interface of the task was prepared in MATLAB (Mathworks, Natick, MA) using MonkeyLogic (Asaad and Eskandar 2008;Asaad et al 2013) and displayed on a computer screen attached to the manipulandum.…”

“…We conducted a temporal confirmatory analysis by averaging the alpha band (8-15 Hz) activity in both brain regions over all right trials using their common FOIs band alpha. The temporal signal of each brain region was then compared using a nonparametric cluster statistic to locate times where the signals statistically differed (Breault et al 2017;Kerr et al 2017;Sacré et al 2016). A time window of 2 s after movement onset ensured we were capturing the neural activity for the entirety of all right trials.…”

“…To reduce its dimensionality, investigators typically limit the scope of their analysis to a specific subset of brain regions and frequency bands in recorded brain signals, thus ignoring a large chunk of their data (Crone et al 1998a, b). Another common trimming approach is to average neural activity across multiple trials, eliminating temporal variation that could have provided interesting insight (Breault et al 2017;Cunningham and Yu 2014;Kerr et al 2014). An ideal data mining approach would extract possible underlying trends in the neural data related to behavioral measurements without predefining constraints such as regions or frequencies.…”