Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia–thalamic network

Fei Su, Jiang Wang, Shuangxia Niu, Huiyan Li, Bin Deng, Chen Liu, Xile Wei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

The efficacy of deep brain stimulation (DBS) for Parkinson's disease (PD) depends in part on the post-operative programming of stimulation parameters. Closed-loop stimulation is one method to realize the frequent adjustment of stimulation parameters. This paper introduced the nonlinear predictive control method into the online adjustment of DBS amplitude and frequency. This approach was tested in a computational model of basal ganglia–thalamic network. The autoregressive Volterra model was used to identify the process model based on physiological data. Simulation results illustrated the efficiency of closed-loop stimulation methods (amplitude adjustment and frequency adjustment) in improving the relay reliability of thalamic neurons compared with the PD state. Besides, compared with the 130Hz constant DBS the closed-loop stimulation methods can significantly reduce the energy consumption. Through the analysis of inter-spike-intervals (ISIs) distribution of basal ganglia neurons, the evoked network activity by the closed-loop frequency adjustment stimulation was closer to the normal state.

Original languageEnglish
Pages (from-to)283-295
Number of pages13
JournalNeural Networks
Volume98
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • Closed-loop stimulation
  • Nonlinear predictive control
  • Parameter adjustment
  • Parkinson's disease
  • Volterra model

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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