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

doi:10.1016/j.neunet.2017.12.001

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

Neural Netw. 2018 Feb:98:283-295. doi: 10.1016/j.neunet.2017.12.001. Epub 2017 Dec 7.

Authors

Fei Su¹, Jiang Wang², Shuangxia Niu³, Huiyan Li⁴, Bin Deng⁵, Chen Liu⁶, Xile Wei⁷

Affiliations

¹ School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China. Electronic address: sufei@tju.edu.cn.
² School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China. Electronic address: jiangwang@tju.edu.cn.
³ School of Electrical Engineering, The Hong Kong Polytechnic University, 999077, Hong Kong, China. Electronic address: eesxniu@polyu.edu.hk.
⁴ School of Automation and Electrical Engineering, Tianjin University of Technology and Education, 300222, Tianjin, China. Electronic address: lhy2740@126.com.
⁵ School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China. Electronic address: dengbin@tju.edu.cn.
⁶ School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China. Electronic address: liuchen715@tju.edu.cn.
⁷ School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China. Electronic address: xilewei@tju.edu.cn.

PMID: 29291546
DOI: 10.1016/j.neunet.2017.12.001

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.

Keywords: Closed-loop stimulation; Nonlinear predictive control; Parameter adjustment; Parkinson’s disease; Volterra model.

MeSH terms

Basal Ganglia* / physiology
Computer Simulation
Deep Brain Stimulation / trends*
Forecasting
Humans
Neural Networks, Computer*
Neurons / physiology
Nonlinear Dynamics*
Parkinson Disease / physiopathology
Parkinson Disease / therapy
Reproducibility of Results
Thalamus* / physiology