A Point Process Approach to Identifying and Tracking Transitions in Neural Spiking Dynamics in the Subthalamic Nucleus of Parkinson's Patients

• Activity Number: 466
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Xinyi Deng*+ and Uri T. Eden and Emad Eskandar
• Companies: Boston University and Boston University and Harvard Medical School
• Keywords: point processes ; state-space methods ; hypothesis testing ; adaptive estimation ; neurodynamics ; Parkinson's disease

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 presents an additional challenge, because standard approaches for continuous-valued neural recordings 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.