PMID- 29291546
OWN - NLM
STAT- MEDLINE
DCOM- 20180710
LR  - 20191210
IS  - 1879-2782 (Electronic)
IS  - 0893-6080 (Linking)
VI  - 98
DP  - 2018 Feb
TI  - Nonlinear predictive control for adaptive adjustments of deep brain stimulation 
      parameters in basal ganglia-thalamic network.
PG  - 283-295
LID - S0893-6080(17)30285-X [pii]
LID - 10.1016/j.neunet.2017.12.001 [doi]
AB  - 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.
CI  - Copyright (c) 2017 Elsevier Ltd. All rights reserved.
FAU - Su, Fei
AU  - Su F
AD  - School of Electrical and Information Engineering, Tianjin University, 300072, 
      Tianjin, China. Electronic address: sufei@tju.edu.cn.
FAU - Wang, Jiang
AU  - Wang J
AD  - School of Electrical and Information Engineering, Tianjin University, 300072, 
      Tianjin, China. Electronic address: jiangwang@tju.edu.cn.
FAU - Niu, Shuangxia
AU  - Niu S
AD  - School of Electrical Engineering, The Hong Kong Polytechnic University, 999077, 
      Hong Kong, China. Electronic address: eesxniu@polyu.edu.hk.
FAU - Li, Huiyan
AU  - Li H
AD  - School of Automation and Electrical Engineering, Tianjin University of Technology 
      and Education, 300222, Tianjin, China. Electronic address: lhy2740@126.com.
FAU - Deng, Bin
AU  - Deng B
AD  - School of Electrical and Information Engineering, Tianjin University, 300072, 
      Tianjin, China. Electronic address: dengbin@tju.edu.cn.
FAU - Liu, Chen
AU  - Liu C
AD  - School of Electrical and Information Engineering, Tianjin University, 300072, 
      Tianjin, China. Electronic address: liuchen715@tju.edu.cn.
FAU - Wei, Xile
AU  - Wei X
AD  - School of Electrical and Information Engineering, Tianjin University, 300072, 
      Tianjin, China. Electronic address: xilewei@tju.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20171207
PL  - United States
TA  - Neural Netw
JT  - Neural networks : the official journal of the International Neural Network 
      Society
JID - 8805018
SB  - IM
MH  - *Basal Ganglia/physiology
MH  - Computer Simulation
MH  - Deep Brain Stimulation/*trends
MH  - Forecasting
MH  - Humans
MH  - *Neural Networks, Computer
MH  - Neurons/physiology
MH  - *Nonlinear Dynamics
MH  - Parkinson Disease/physiopathology/therapy
MH  - Reproducibility of Results
MH  - *Thalamus/physiology
OTO - NOTNLM
OT  - Closed-loop stimulation
OT  - Nonlinear predictive control
OT  - Parameter adjustment
OT  - Parkinson's disease
OT  - Volterra model
EDAT- 2018/01/02 06:00
MHDA- 2018/07/11 06:00
CRDT- 2018/01/02 06:00
PHST- 2017/02/14 00:00 [received]
PHST- 2017/09/05 00:00 [revised]
PHST- 2017/12/01 00:00 [accepted]
PHST- 2018/01/02 06:00 [pubmed]
PHST- 2018/07/11 06:00 [medline]
PHST- 2018/01/02 06:00 [entrez]
AID - S0893-6080(17)30285-X [pii]
AID - 10.1016/j.neunet.2017.12.001 [doi]
PST - ppublish
SO  - Neural Netw. 2018 Feb;98:283-295. doi: 10.1016/j.neunet.2017.12.001. Epub 2017 
      Dec 7.