An optimization framework for targeted spinal cord stimulation

Abstract

Objective. Spinal cord stimulation (SCS) is a common neurostimulation therapy to manage chronic pain. Technological advances have produced new neurostimulation systems with expanded capabilities in an attempt to improve the clinical outcomes associated with SCS. However, these expanded capabilities have dramatically increased the number of possible stimulation parameters and made it intractable to efficiently explore this large parameter space within the context of standard clinical programming procedures. Therefore, in this study, we developed an optimization approach to define the optimal current amplitudes or fractions across individual contacts in an SCS electrode array(s). Approach. We developed an analytic method using the Lagrange multiplier method along with smoothing approximations. To test our optimization framework, we used a hybrid computational modeling approach that consisted of a finite element method model and multi-compartment models of axons and cells within the spinal cord. Moreover, we extended our approach to multi-objective optimization to explore the trade-off between activating regions of interest (ROIs) and regions of avoidance (ROAs). Main results. For simple ROIs, our framework suggested optimized configurations that resembled simple bipolar configurations. However, when we considered multi-objective optimization, our framework suggested nontrivial stimulation configurations that could be selected from Pareto fronts to target multiple ROIs or avoid ROAs. Significance. We developed an optimization framework for targeted SCS. Our method is analytic, which allows for the fast calculation of optimal solutions. For the first time, we provided a multi-objective approach for selective SCS. Through this approach, we were able to show that novel configurations can provide neural recruitment profiles that are not possible with conventional stimulation configurations (e.g. bipolar stimulation). Most importantly, once integrated with computational models that account for sources of interpatient variability (e.g. anatomy, electrode placement), our optimization framework can be utilized to provide stimulation settings tailored to the needs of individual patients.

Neuromodulation and Habituation: A Literature Review and Conceptional Analysis of Sustaining Therapeutic Efficacy and Mitigating Habituation

“…They developed computer modeling, which allowed for customization of stimulation configurations for targeted SCS. Furthermore, the computational model can be integrated into individual patient variables and allow for more efficient and personalized therapy [77].…”

Neuromodulation and Habituation: A Literature Review and Conceptional Analysis of Sustaining Therapeutic Efficacy and Mitigating Habituation

“…They developed computer modeling which allowed for customization of stimulation configurations for targeted SCS. Furthermore, the computational model can be integrated to individual patient variables and allow for more efficient and personalized therapy [76].…”

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling

“…We note here that the AF has been used as a fast predictor for SCS optimization in prior studies, e.g. in [35], where it was used to facilitate multiobjective optimization. In previous work, we extended on the classical AF formulation by introducing analytical Green's functions to consider leakage currents, axial conductance, and temporal stimulation dynamics-the so-called generalized activating function (GAF) [37].…”