Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson's disease.

ABSTRACT: Objective.Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects.Approach.Cortical and motor evoked potential data collected from PD patients duri

SUBMITTER: Connolly MJ 

PROVIDER: S-EPMC9638882 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature