Computer Modeling of the Influence of Surrounding Tissues on Electrical Current Delivered to the Median Nerve During Neuromodulation for Pain Relief

Abstract

Background: Peripheral nerve stimulation is an effective neuromodulation technique for pain management. However, the effect of surrounding tissue composition on electric field distribution remains not well understood. This study uses computational modeling to quantify the way different tissue types affect the electric field at the nerve surface. Materials and Methods: A finite element model was developed based on histologic analyses of the human median nerve and its surrounding tissues. The model included various tissue configurations, such as fat, muscle, tendon, blood vessels, and bone, to simulate six different electrode placement scenarios. The z-component of the electric field vector (aligned with the nerve axis) was calculated to evaluate stimulation efficacy. Results: The tissue between the electrode and the nerve strongly influenced the intensity of the electric field at the surface of the nerve. Fat, which possesses the lowest electrical conductivity, yielded the highest electric field values, whereas muscle and tendon significantly reduced field intensity, necessitating increased current for comparable stimulation. Moreover, the presence of nearby structures, such as blood vessels (which have high conductivity) or bone (which has low conductivity), modulated the field, either slightly decreasing or increasing its intensity, respectively. Conclusion: These findings underscore the critical role of tissue composition in the effectiveness of peripheral nerve stimulation. Optimizing lead placement by considering tissue conductivity can enhance therapeutic outcomes. Computational modeling may serve as a valuable tool for customizing surgical planning to the individual patient, improving electrode positioning and stimulation efficiency.