Modulations in Oscillatory Activity of Globus Pallidus Internus Neurons During a Directed Hand Movement Task—A Primary Mechanism for Motor Planning

Abstract

Globus pallidus internus (GPi) neurons in the basal ganglia are traditionally thought to play a significant role in the promotion and suppression of movement via a change in firing rates. Here, we hypothesize that a primary mechanism of movement control by GPi neurons is through specific modulations in their oscillatory patterns. We analyzed neuronal spiking activity of 83 GPi neurons recorded from two healthy nonhuman primates executing a radial center-out motor task. We found that, in directionally tuned neurons, the power in the gamma band is significantly ( p < 0.05) greater than that in the beta band (a “cross-over” effect), during the planning stages of movements in their preferred direction. This cross-over effect is not observed in the non-directionally tuned neurons. These data suggest that, during movement planning, information encoding by GPi neurons may be governed by a sudden emergence and suppression of oscillatory activities, rather than simply by a change in average firing rates.

Detecting rhythmic spiking through the power spectra of point process model residuals

“…We further split the time domain methods into two subcategories. The first subcategory encompasses all Poisson GLM-based PPMs that may capture both recovery period and oscillation effects (e.g., [25, 26]). As described in the Materials and Methods (“Clarifications and Relationship to Pre-existing PPM Approaches”), these PPMs typically model spike history effects with a series of indicator functions of varying width.…”

“…Stevenson [47] noted that such biases might be reduced through introduction of variables that may capture suspected unmodeled effects (here, oscillations of unknown frequency), but also acknowledged that such strategies can be assumption-intensive and nontrivial. Such variables are present in those PPMs that estimate spike rhythms entirely in the time domain (e.g., [24][25][26]). We recap the pros and cons of these time domain methods in the concluding section below ("Conclusions: Tools for Spike Oscillation Analysis.…”

“…The specific method we describe should be seen as one instantiation of a general, mixed time-and frequency-domain framework that can be adapted flexibly to accommodate analysis goals that are more complex than those prioritized here. We also note that the method represents the combination of a number of existing ideas and techniques from prior work that has sought to estimate spike rhythms exclusively in the time domain (e.g., [24][25][26]).…”

Detecting rhythmic spiking through the power spectra of point process model residuals

“…The second category consists of autoregressive PPMs which predict spike intensity as a function of the spike counts recorded for each of a series of historical time bins (e.g. [25,26]). Specifically, these models consider a long historical time interval (up to t−150 ms in [25]), divide this interval into time bins of interest, and use the counts of spikes recorded in each bin as individual predictor variables in the PPM.…”

“…We also note that the method represents the combination of a number of existing ideas and techniques from prior work that has sought to estimate spike rhythms exclusively in the time domain (e.g. [24][25][26]).…”