TuneS: Patient-specific model-based optimization of contact configuration in deep brain stimulation

The objective of this study is to develop and evaluate a systematic approach to optimize Deep Brain Stimulation (DBS) parameters, addressing the challenge of identifying patient-specific settings and optimal stimulation targets for various neurological and psychiatric disorders.

TuneS, a novel pipeline to predict clinically optimal DBS contact configurations based on predefined targets and constraints, is introduced. The method relies upon patient-specific models of stimulation spread and extends optimization beyond traditional neural structures to include automated, model-based targeting of streamlines.

Initial findings show that both the STN motor subdivision and STN motor streamlines are consistently engaged under clinical settings, while regions of avoidance receive minimal stimulation. Given these findings, the value of model-based contact predictions for assessing stimulation targets while observing anatomical constraints is demonstrated at the example of ten of Parkinson's disease patients. The predicted settings were generally found to achieve higher target coverages while providing a better trade-off between maximizing target coverage and minimizing stimulation of regions associated with side effects.

TuneS shows promise as a research tool, enabling systematic assessment of DBS target effectiveness and facilitating constraint-aware optimization of stimulation parameters.

The presented pipeline offers a pathway to improve patient-specific DBS therapies and contributes to the broader understanding of effective DBS targeting strategies.