An optimization framework for targeted spinal cord stimulation.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Computational Modeling

Background:

• Spinal cord stimulation (SCS) is a prevalent therapy for chronic pain management.
• Technological advancements in SCS systems offer expanded capabilities but increase parameter complexity.
• Efficient exploration of SCS parameter space is challenging in clinical settings.

Purpose of the Study:

• To develop an optimization approach for determining optimal current amplitudes in SCS electrode arrays.
• To create a method for efficient exploration of the SCS parameter space.
• To enable selective SCS through multi-objective optimization.

Main Methods:

• Developed an analytic method using Lagrange multipliers and smoothing approximations.
• Employed a hybrid computational modeling approach combining finite element and multi-compartment models.
• Extended the framework to multi-objective optimization for balancing activation and avoidance regions.

Main Results:

• The framework identified optimized configurations, including novel non-bipolar settings for multi-objective scenarios.
• Multi-objective optimization yielded Pareto fronts for targeting multiple regions of interest (ROIs) or avoiding regions of avoidance (ROAs).
• Novel configurations demonstrated unique neural recruitment profiles not achievable with conventional SCS.

Conclusions:

• An analytic, fast, and optimized framework for targeted SCS has been developed.
• The first multi-objective approach for selective SCS was introduced, enabling novel stimulation configurations.
• The framework's integration with patient-specific computational models allows for tailored SCS settings.