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

Area of Science:

• Neurosurgery
• Biomedical Engineering
• Computational Neuroscience

Background:

• Deep Brain Stimulation (DBS) is a crucial therapy for neurological and psychiatric disorders.
• Optimizing DBS parameters is challenging, requiring patient-specific settings and precise targeting.
• Current methods may not fully account for individual patient anatomy and potential side effects.

Purpose of the Study:

• To develop and evaluate a systematic approach for optimizing DBS parameters.
• To identify patient-specific stimulation settings and optimal targets for various disorders.
• To address the challenge of personalized DBS therapy.

Main Methods:

• Introduction of TuneS, a novel pipeline for predicting optimal DBS contact configurations.
• Utilizing patient-specific models of stimulation spread and automated, model-based targeting of streamlines.
• Extending optimization beyond traditional neural structures to include streamline targeting.

Main Results:

• Consistent engagement of the STN motor subdivision and motor streamlines in clinical settings.
• Minimal stimulation of regions identified for avoidance.
• Demonstrated value of model-based contact predictions in ten Parkinson's disease patients.
• Predicted settings achieved higher target coverage and better side effect trade-offs.

Conclusions:

• TuneS shows promise as a research tool for assessing DBS target effectiveness.
• Facilitates constraint-aware optimization of DBS stimulation parameters.
• Offers a pathway to improve patient-specific DBS therapies and understanding of targeting strategies.