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A model of evoked potentials in spinal cord stimulation

James H Laird, John L Parker

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed

    Abstract:

    Electrical stimulation of the spinal cord is used for pain relief, and is in use for hundreds of thousands of cases of chronic neuropathic pain. In spinal cord stimulation (SCS), an array of electrodes is implanted in the epidural space of the cord, and electrical currents are used to stimulate nearby nerve fibers, believed to be in the dorsal columns of the cord. Despite the long history of SCS for pain, stretching over 30 years, its underlying mechanisms are poorly understood, and the therapy has evolved very little in this time. Recent work has resulted in the ability to record complex compound action potential waveforms during therapy. These waveforms reflect the neural activity evoked by the therapeutic stimulation, and reveal information about the underlying physiological processes. We aim to simulate these processes to the point of reproducing these recordings. We establish a hybrid model of SCS, composed of a three dimensional electrical model and a neural model. The 3D model describes the geometry of the spinal regions under consideration, and the electric fields that result from any flow of current within them. The neural model simulates the behaviour of spinal nerve fibers, which are the target tissues of the therapy. The combination of these two models is used to predict which fibers may be recruited by a given stimulus, as well as to predict the ensuing recorded waveforms. The model is shown to reproduce major features of spinal compound action potentials, such as threshold and propagation behaviour, which have been observed in experiments. The model's coverage of processes from stimulation to recording allows it to be compared side-by-side with actual experimental data, and will permit its refinement to a substantial level of accuracy.

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