AUTHOR=Mikroulis Apostolos , Lasica Andrej , Filip Pavel , Bakstein Eduard , Novak Daniel 

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

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1661987

DOI=10.3389/fnins.2025.1661987

ISSN=1662-453X

ABSTRACT=BackgroundOptimisation 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.MethodsThe 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.ResultsBased 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.ConclusionThe 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.