Prospective Connectomic-Based Deep Brain Stimulation Programming for Parkinson's Disease.

ABSTRACT:

Background

Efficacy of deep brain stimulation (DBS) relies on accurate lead placement as well as optimization of the stimulation parameters. Although clinical software tools are now available, programming still largely relies on a monopolar review, a tedious process for both patients and programmers.

Objective

This study investigates the safety and feasibility of prospective automated connectomic DBS programming (automated connectomic programming [ACP]), focusing on the recruitment of specific white matter pathways.

Methods

After DBS implantation, a detailed connectomic DBS model in patient-specific space was developed for each study participant. A driving-force model was used to quantify pathway recruitment across 2400 different DBS settings. Optimization algorithms

SUBMITTER: Hines K 

PROVIDER: S-EPMC11659031 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature