Adaptive Parameter Modulation of Deep Brain Stimulation Based on Improved Supervisory Algorithm.

Journal Article (Journal Article)

Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson's disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and suboptimal therapy. The objective of this manuscript is to establish, through simulation in a computational model, a closed-loop control system that can automatically adjust the stimulation parameters to recover normal activity in model neurons. Exaggerated beta band activity is recognized as a hallmark of Parkinson's disease and beta band activity in model neurons of the globus pallidus internus (GPi) was used as the feedback signal to control DBS of the GPi. Traditional proportional controller and proportional-integral controller were not effective in eliminating the error between the target level of beta power and the beta power under Parkinsonian conditions. To overcome the difficulties in tuning the controller parameters and improve tracking performance in the case of changes in the plant, a supervisory control algorithm was implemented by introducing a Radial Basis Function (RBF) network to build the inverse model of the plant. Simulation results show the successful tracking of target beta power in the presence of changes in Parkinsonian state as well as during dynamic changes in the target level of beta power. Our computational study suggests the feasibility of the RBF network-driven supervisory control algorithm for real-time modulation of DBS parameters for the treatment of Parkinson's disease.

Full Text

Duke Authors

Cited Authors

  • Zhu, Y; Wang, J; Li, H; Liu, C; Grill, WM

Published Date

  • January 2021

Published In

Volume / Issue

  • 15 /

Start / End Page

  • 750806 -

PubMed ID

  • 34602976

Pubmed Central ID

  • PMC8481598

Electronic International Standard Serial Number (EISSN)

  • 1662-453X

International Standard Serial Number (ISSN)

  • 1662-4548

Digital Object Identifier (DOI)

  • 10.3389/fnins.2021.750806

Language

  • eng