A point process approach to identifying and tracking transitions in neural spiking dynamics in the subthalamic nucleus of Parkinson's patients

Abstract

Understanding the role of rhythmic dynamics in normal and diseased brain function is an important area of research in neural electrophysiology. Identifying and tracking changes in rhythms associated with spike trains present an additional challenge, because standard approaches for continuous-valued neural recordings--such as local field potential, magnetoencephalography, and electroencephalography data--require assumptions that do not typically hold for point process data. Additionally, subtle changes in the history dependent structure of a spike train have been shown to lead to robust changes in rhythmic firing patterns. Here, we propose a point process modeling framework to characterize the rhythmic spiking dynamics in spike trains, test for statistically significant changes to those dynamics, and track the temporal evolution of such changes. We first construct a two-state point process model incorporating spiking history and develop a likelihood ratio test to detect changes in the firing structure. We then apply adaptive state-space filters and smoothers to track these changes through time. We illustrate our approach with a simulation study as well as with experimental data recorded in the subthalamic nucleus of Parkinson's patients performing an arm movement task. Our analyses show that during the arm movement task, neurons underwent a complex pattern of modulation of spiking intensity characterized initially by a release of inhibitory control at 20-40 ms after a spike, followed by a decrease in excitatory influence at 40-60 ms after a spike.

Comparing open-source toolboxes for processing and analysis of spike and local field potentials data

“…Point-process spectrograms are usually used to illustrate rhythmic properties of otherwise stochastic spiking patterns rather than for statistical inference (Deng et al, 2013). We refer to (Hurtado et al, 2004, 2005) regarding methods to evaluate statistical significance of point-process spectral estimators and to (Jarvis and Mitra, 2001; Rivlin-Etzion et al, 2006) for a critical discussion.…”

Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems

“…SS have provided powerful tools in analysis of neuroscience data [12][13][14]. For instance, state-space poinprocess (SSPP) has been successfully applied to estimate a rat position in 2-D spaces from hippocampus place cells' spiking activity [15], tracking oscillatory activities in the subthalamic nucleus of Parkinson's patients [16], and decoding arm movement and grasp actions from neural activity of motor cortex area [13]. In parallel, deep neural networks (DNNs) have been utilized in neuroscience data analysis to address a similar class of decoding and inference problems.…”

Inferring oscillatory modulation in neural spike trains

“…Prominent slow oscillations are observed as patients undergo anesthesia [ 10 ], and exhibit changes in dynamics during transitions in unconscious brain state [ 14 ]. Furthermore, strong oscillatory signals are prominent in neurological disorders such as Parkinson’s disease, and can be used to characterize pathophysiology and its relation to behavior [ 12 , 18 , 19 ]. The oscillatory structure of individual neurons can thus provide insight into the dynamics of cognitive processing and motor planning, and their implementation in health and disease.…”

“…Point process models employing the GLM framework [ 21 , 27 ] have been employed to explain the spiking behavior by taking into account spiking refractoriness, behavioral and stimulus-induced non-stationarities, and spiking of neighboring neurons [ 20 ]. A series of investigations [ 12 , 18 , 19 ] have added long-term history effects to capture oscillation in the history dependence and have also used a state-space smoothing algorithm to track changes in the oscillatory dynamics over time. Eden et al used a long history to capture both, but as we shall see, LOST can better capture the irregularities present in realistic biological oscillations.…”