Prediction of the Parkinsonian subthalamic nucleus spike activity from local field potentials using nonlinear dynamic models

Kyriaki Kostoglou, Kostis P. Michmizos, Pantelis Stathis, Damianos Sakas, Konstantina S. Nikita, Georgios D. Mitsis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Extracellular recordings in the area of the subthalamic nucleus (STN) of Parkinson's disease patients undergoing deep brain stimulation comprise fast events, Action Potentials and slower events, known as Local Field Potentials (LFP). The LFP is believed to represent the synchronized input into the observed area, as opposed to the spike data, which represents the output. We have shown before that there is an input-output relationship between these two components in the STN. In the present paper, we extend these observations by using LFP-driven Volterra models and the Laguerre expansion technique to estimate nonlinear dynamic models which are able to predict the recorded spiking activity. To this end, we rigorously examine the optimal model order. The improved performance of the second-order Volterra models indicates that there is a nonlinear relationship between the LFP and the spiking activity. To obtain a more compact and readily interpretable model, the most significant dynamic components of the identified Volterra models are extracted using principal dynamic mode analysis.

Original languageEnglish (US)
Title of host publicationIEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012
Pages298-302
Number of pages5
DOIs
StatePublished - 2012
Event12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012 - Larnaca, Cyprus
Duration: Nov 11 2012Nov 13 2012

Publication series

NameIEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012

Other

Other12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012
Country/TerritoryCyprus
CityLarnaca
Period11/11/1211/13/12

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biomedical Engineering

Keywords

  • Deep brain stimulation
  • Extracellular recordings
  • Laguerre expansion
  • Local field potentials
  • Nonlinear modeling
  • Spikes
  • Subthalamic nucleus
  • Volterra kernels

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