Network model of the effects of spinal cord stimulation

Abstract

Spinal cord stimulation (SCS) is an established treatment for chronic pain. However, SCS only provides 50% or greater pain relief to approximately 60% of patients, and the efficacy of SCS tends to decline over time. Efforts to optimize SCS have focused on the spatial aspects of SCS such as electrode design and electrode geometry while neglecting stimulation parameter selection and have assumed that the dorsal horn pain processing network is static. We developed a biophysical network model of the dorsal horn circuit capable of reproducing key electrophysiological data relevant to pain processing. Using this model, we simulated the effects of SCS applied at between 5 Hz and 150 Hz on the activity of projection neurons responsible for relaying pain signals to the brain. SCS at 30-100 Hz produced maximal inhibition of projection neuron activity. Furthermore, we quantified responses to SCS with diminished levels of inhibition in the dorsal horn to simulate the effect of disease progression. The degree to which projection neurons were inhibited by SCS declined as the strength of inhibitory mechanisms was reduced, and the optimal SCS frequency decreased to 15 Hz. Our simulation results suggest that the efficacy of SCS is dependent on stimulation frequency and that the loss of dorsal horn inhibition during chronic pain may explain the decline in SCS efficacy over time. These findings provide insights regarding the mechanisms of SCS and pave the way for improved SCS stimulation parameter selection.