Mixed Integer Linear Programming for Active Contact Selection in Deep Brain Stimulation

TL;DR

This paper introduces a MILP-based optimization framework for selecting active contacts in deep brain stimulation, aiming to improve programming efficiency and accuracy by mathematically modeling stimulation profiles.

Contribution

The study presents a novel MILP approach for DBS contact selection, comparing its performance to LP methods and analyzing its advantages and limitations.

Findings

MILP better matches predefined activation profiles.

LP solutions resemble clinical settings more closely.

MILP has higher computational demands and sensitivity to target definitions.

Abstract

Deep brain stimulation (DBS) programming remains a complex and time-consuming process, requiring manual selection of stimulation parameters to achieve therapeutic effects while minimizing adverse side-effects. This study explores mathematical optimization for DBS programming, using functional subdivisions of the subthalamic nucleus (STN) to define the desired activation profile. A Mixed Integer Linear Programming (MILP) framework is presented allowing for dissimilar current distribution across active contacts. MILP is compared to a Linear Programming (LP) approach in terms of computational efficiency and activation accuracy. Results from ten Parkinson's disease patients treated with DBS show that while MILP better matches the predefined stimulation target activation profile, LP solutions more closely resemble clinically applied settings, suggesting the profile may not fully capture clinically relevant patterns. Additionally, MILP's limitations are discussed, including its reliance on precisely defined target regions and its computational burden for larger target sets.