Patient-specific and interpretable deep brain stimulation optimisation using MRI and clinical review data

TL;DR

This paper introduces an automated, MRI-based tool for optimizing deep brain stimulation settings in Parkinson's patients, showing better targeting and fewer side effects than expert settings.

Contribution

A novel geometry-based optimization method for DBS using MRI and clinical data, integrated into a cross-platform tool for clinical workflows.

Findings

Algorithm-selected DBS contacts better cover target structures and reduce electric field leakage compared to expert settings.

Retrospective analysis suggests algorithm settings may achieve similar motor outcomes to expert settings.

The method is shown to be effective without requiring iterative optimization.

Abstract

Optimisation of Deep Brain Stimulation (DBS) settings is a key aspect in achieving clinical efficacy in movement disorders, such as the Parkinson’s disease. Modern techniques attempt to solve the problem through data-intensive statistical and machine learning approaches, adding significant overhead to the existing clinical workflows. Here, we present a geometry-based optimisation approach for DBS electrode contact and current selection, grounded in routinely collected MRI data, well-established tools (Lead-DBS) and optionally, clinical review records.

The pipeline, packaged in a cross-platform tool, uses lead reconstruction data and simulation of Volume of Tissue Activated (VTA) to estimate the contacts in optimal position relative to the target structure, and suggests optimal stimulation current. The tool then allows further interactive user optimisation of the current settings. Existing electrode contact evaluations can be optionally included in the calculation process for further fine-tuning and adverse effect avoidance.

Based on a sample of 174 implanted electrode reconstructions from 87 Parkinson’s disease patients, we demonstrate that our algorithm’s DBS parameter settings are more effective in covering the target structure (Wilcoxon p < 5e-13, Hedges’ g > 0.94) and minimising electric field leakage to neighbouring regions (p < 2e-10, g > 0.46) compared to expert parameter settings. Retrospective analysis of a limited subset (n = 50) predicts comparable improved motor outcomes with expert settings (g = 0.05–0.08, p = 0.09–1), suggesting potential for similar clinical efficacy, pending prospective validation.

The proposed automated method for optimisation of the DBS electrode contact and current selection shows promising results and is readily applicable to existing clinical workflows. We demonstrate that the algorithmically selected contacts perform better than manual selections according to electric field calculations, without the iterative optimisation procedure.

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