Application of Reinforcement Learning to Deep Brain Stimulation in a Computational Model of Parkinson's Disease

Meili Lu, Xile Wei, Yanqiu Che, Jiang Wang, Kenneth A. Loparo

Abstract

Deep brain stimulation (DBS) has been proven to be an effective treatment to deal with the symptoms of Parkinson's disease (PD). Currently, the DBS is in an open-loop pattern with which the stimulation parameters remain constant regardless of fluctuations in the disease state, and adjustments of parameters rely mostly on trial and error of experienced clinicians. This could bring adverse effects to patients due to possible overstimulation. Thus closed-loop DBS of which stimulation parameters are automatically adjusted based on variations in the ongoing neurophysiological signals is desired. In this paper, we present a closed-loop DBS method based on reinforcement learning (RL) to regulate stimulation parameters based on a computational model. The network model consists of interconnected biophysically-based spiking neurons, and the PD state is described as distorted relay reliability of thalamus (TH). Results show that the RL-based closed-loop control strategy can effectively restore the distorted relay reliability of the TH but with less DBS energy expenditure.

Original languageEnglish (US)
Article number8895773
Pages (from-to)339-349
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume28
Issue number1
DOIs
Publication statusPublished - Jan 2020

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All Science Journal Classification (ASJC) codes

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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