A systematic review of computational models for the design of spinal cord stimulation therapies: from neural circuits to patient-specific simulations

Abstract

Seventy years ago, Hodgkin and Huxley published the first mathematical model to describe action potential generation, laying the foundation for modern computational neuroscience. Since then, the field has evolved enormously, with studies spanning from basic neuroscience to clinical applications for neuromodulation. Computer models of neuromodulation have evolved in complexity and personalization, advancing clinical practice and novel neurostimulation therapies, such as spinal cord stimulation. Spinal cord stimulation is a therapy widely used to treat chronic pain, with rapidly expanding indications, such as restoring motor function. In general, simulations contributed dramatically to improve lead designs, stimulation configurations, waveform parameters and programming procedures and provided insight into potential mechanisms of action of electrical stimulation. Although the implementation of neural models are relentlessly increasing in number and complexity, it is reasonable to ask whether this observed increase in complexity is necessary for improved accuracy and, ultimately, for clinical efficacy. With this aim, we performed a systematic literature review and a qualitative meta-synthesis of the evolution of computational models, with a focus on complexity, personalization and the use of medical imaging to capture realistic anatomy. Our review showed that increased model complexity and personalization improved both mechanistic and translational studies. More specifically, the use of medical imaging enabled the development of patient-specific models that can help to transform clinical practice in spinal cord stimulation. Finally, we combined our results to provide clear guidelines for standardization and expansion of computational models for spinal cord stimulation.

Keywords: chronic pain; computational models; computer simulation; electrical stimulation; medical imaging; movement restoration; personalization; spinal cord stimulation.

Figure 1.

SCS computational modeling components. (A) Example of human spinal cord segments morphology, and their general applications in SCS (movement: movement restoration, pain: chronic pain treatment, autonomic: autonomic/lower urinary tract function restoration). Differences in root angle and segment shape for each spine area are highlighted with thickened lines. (B) Steps for building the volume conductor model that can be used to simulate the electromagnetic fields generated by SCS: 1) tissue contours are derived from anatomical sources, i.e., measurements, atlas, and/or medical images; 2) these segmentations are then used for the 3D volume reconstruction; 3) proper conductivity values are assigned to each tissue and the mesh is generated to simulate electromagnetic field distributions. (C) Potential components for the biophysical neuron model. Neuron fiber activation can be simulated based on assigned fiber diameter, neuron population, or neural trajectory, which with parameter tuning based on experimental and/or clinical data, can be translated into experimental and/or clinical predictions, such as perception and discomfort thresholds, and measurements of motor restoration. Subfigures in panel C include modified figures from Greiner et al. 2021 (Greiner et al., 2021) and de Freitas et al. 2022 (de Freitas et al., 2022) with permission.

Figure 2.

Flow diagram of study selection results.

Figure 3.

Pie charts of objective- and derived-variable distributions amongst the studies included in our analysis.

Figure 4.

SCS model applications and purposes. (A) The top plot shows the different modeling studies based on their corresponding application (abscissa) and publication year (ordinate). The pie charts show the percentages of the anatomical sources used to create models for each application. For this analysis, we only considered models published after the introduction of MRI to SCS models in 2010. (B) Bubble charts illustrating the distributions of FEM and biophysical complexity scores for different applications. Autonomic was not included due to the low number of studies (n=3). Bold outlines indicate the most frequent complexity score for each application. (C) The bottom plot shows the different modeling studies based on their corresponding purpose (abscissa) and publication year (ordinate). The pie charts show the percentages of the anatomical sources used to create the models for each purpose. For this analysis, we only considered models published after the introduction of MRI to SCS models in 2010. (D) Bubble charts illustrating the distributions of FEM and biophysical complexity scores for different purposes. Bold outlines indicate the most frequent complexity score for each purpose.

Figure 5.

FEM model complexity (A), biophysical model complexity (B) and personalization (C) scores of SCS models as a function of publication year. We have indicated the anatomical sources of the model and the corresponding SCS technique by marker color and shape, respectively. The pie charts in A and C indicate the corresponding percentage of anatomical sources for each complexity and personalization score: 0 (bottom), 1 (middle), and 2 (top). For this analysis, we only considered models published after the introduction of MRI to SCS models in 2010. (D) Bar plots representing the mode of complexity scores for the FEM and biophysical models for each personalization level. (E) Bar plots representing, for each level of personalization, the percentage of papers with no validation, a quantitative validation or a qualitative validation.