FIELD

This present disclosure provides systems and methods relating to neuromodulation. In particular, the present disclosure provides systems and methods for identifying optimized temporal patterns of spinal cord simulation (SCS) for minimizing variability in patient responses to SCS. The systems and methods of neuromodulation disclosed herein facilitate the treatment of neuropathic pain associated with various disease states and clinical indications.

BACKGROUND

Spinal cord stimulation (SCS) involves the therapeutic application of electrical pulses using a surgically implanted device to treat chronic pain. The device is typically implanted near a targeted area of the spinal cord. Unfortunately, patient responses to SCS-based therapy are highly variable and depend on a number of factors, including the anatomy of the spine and spinal cord, electrode position, and the origin, location and characteristics of pain.

These factors, among others, contribute to the variance in patient responses to SCS (e.g., degree of pain relief). Changing the amplitude, pulse duration, and timing of the applied electrical pulses modulates the effectiveness of SCS in relieving pain, but the breadth of this parameter space makes optimizing programming for specific patients a significant clinical challenge. Furthermore, evaluating SCS performance is difficult because patients have a broad range of parameter settings and different device programmers use different protocols to select parameters. Additionally, the location and characteristics of pain as well as the effectiveness of any particular set of stimulation parameters vary over time, and these factors also contribute to the variance of patient responses to SCS.

SUMMARY

Embodiments of the present disclosure include a method of identifying an optimized spinal cord simulation (SCS) pattern for pain reduction. In accordance with these embodiments, the method includes generating a plurality of SCS patterns using an optimization algorithm based on predetermined performance criteria, evaluating the plurality of SCS patterns for pain reduction using a computational model of a neuronal network, and identifying at least one candidate SCS pattern having an optimized temporal pattern of stimulation capable of reducing pain.

In some embodiments, the plurality of SCS patterns are generated for at least one of efficiency optimization, efficacy optimization, and variance optimization. In some embodiments, the optimized SCS pattern reduces pain in a plurality of subjects with different pain states. In some embodiments, the optimized SCS pattern comprises a temporal pattern of electrical stimulation pulses.

In some embodiments, the temporal pattern of electrical stimulation pulses comprises a non-regular temporal pattern with one or more varying inter-pulse intervals. In some embodiments, the optimized SCS pattern comprises a stimulation frequency ranging from about 1 Hz to about 200 Hz. In some embodiments, the optimization algorithm comprises at least one of a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm, an ant colony algorithm, an estimation of distribution algorithm, a gradient descent algorithm, and any combinations and derivations thereof.

In some embodiments, the predetermined performance criteria are incorporated into a fitness function used to evaluate the fitness of the plurality of SCS patterns. In some embodiments, the predetermined performance criteria comprise at least one of: i) reduction in pain score; ii) SCS pattern efficiency; and iii) variance of pain score reduction across different pain states.

In some embodiments, the reduction in the pain score comprises a change in firing rate and/or firing pattern of one or more neurons in the computational model. In some embodiments, the SCS pattern efficiency is proportional to the average frequency of stimulation. In some embodiments, the variance of pain score reduction across different pain states corresponds to variance of the response to SCS across a population of computational models of a neuronal network. In some embodiments, the response to SCS comprises a change in firing rate and/or firing pattern of one or more neurons in the computational model. In some embodiments, the computational model of the neuronal network is coupled to the optimization algorithm by the predetermined performance criteria. In some embodiments, the computational model of the neuronal network simulates activity of a wide dynamic range (WDR) neuron. In some embodiments, the activity of the WDR neuron in the computational model is a proxy for pain. In some embodiments, the computational model of the neural network comprises three network zones comprising heterogeneous inhibitory and excitatory neural connections. In some embodiments, the computational model of the neural network simulates a network state by varying at least one of: a biophysical input parameter, a stimulation input parameter, and a mechanism input parameter.

In some embodiments, the computational model simulates a pain state of a subject by varying the at least one biophysical input parameter, and wherein the at least one biophysical input parameter comprises: i) reversal potential of inhibitory synapses within each network zone; ii) maximum conductance of GABAergic synapses within each network zone; iii) maximum conductance of AMPA synapses onto inhibitory neurons within each network zone; and iv) number of C fibers activated in each surround zone.

In some embodiments, the reversal potential of inhibitory synapses ranges from about −50 mV to about −100 mV. In some embodiments, the maximum conductance of GABAergic synapses ranges from about 50% to about 100%. In some embodiments, the maximum conductance of AMPA synapses onto inhibitory neurons ranges from about 50% to about 100%. In some embodiments, the number of C fibers activated in each surround zone ranges from about 0% to about 50%. In some embodiments, the computational model simulates a response to an SCS pattern by varying the at least one stimulation input parameter, and wherein the at least one stimulation input parameter comprises: i) number of fibers activated within each network zone by an SCS pattern; and ii) stimulation frequency of an SCS pattern within each network zone. In some embodiments, the number of fibers activated within each network zone by the SCS pattern ranges from about 0% to about 100% in a first network zone, from about 0% to about 100% in a second network zone, and from about 0% to about 100% in a third network zone. In some embodiments, the stimulation frequency within each network zone ranges from about 1 Hz to about 200 Hz.

In some embodiments, the computational model simulates a response to an SCS pattern by varying the at least one mechanism input parameter, and wherein the at least one mechanism input parameter comprises: i) maximum sodium conductance; and ii) maximum potassium conductance. In some embodiments, the maximum sodium conductance ranges from about 50% to about 150% within a network zone. In some embodiments, the maximum potassium conductance ranges from about 50% to about 150% within a network zone.

Embodiments of the present disclosure also include a system for delivering spinal cord stimulation (SCS) to reduce pain. In accordance with these embodiments, the system includes an electrode sized and configured for implantation in proximity to neural tissue, and a pulse generator coupled to the electrode. In some embodiments, the pulse generator includes a power source comprising a battery and a microprocessor coupled to the battery. In some embodiments, the pulse generator is configured to generate electrical signals for delivering an SCS pattern having an optimized temporal pattern of electrical stimulation capable of reducing pain.

In some embodiments, the optimized SCS pattern reduces pain in a plurality of subjects with different pain states. In some embodiments, the optimized SCS pattern comprises non-regular temporal patterns with one or more varying inter-pulse intervals. In some embodiments, delivering an SCS pattern having an optimized temporal pattern of electrical stimulation comprises delivering one or more SCS patterns to one or more electrodes.

Embodiments of the present disclosure also include a method for delivering spinal cord stimulation (SCS) to reduce pain using the systems described above. In accordance with these embodiments, the method includes programming the pulse generator to output the optimized SCS pattern, and delivering the SCS pattern to a subject to reduce pain.

In some embodiments, the optimized SCS pattern reduces pain in a plurality of subjects with different pain states. In some embodiments, the optimized SCS pattern comprises non-regular temporal patterns with one or more varying inter-pulse intervals. In some embodiments, delivering an optimized SCS pattern to a subject comprises delivering one or more different SCS patterns to one or more different neuronal populations.

