# US 11,298,546 B2 · Systems and methods for minimizing response variability of spinal cord stimulation

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- Number: US 11,298,546 B2
- Document ID: US11298546B2
- Status: Granted
- Kind: B2
- Application number: 16/217,787
- Filed: 2018-12-12
- Published: 2019-06-13
- Granted: 2022-04-12
- Earliest claimed: 2017-12-12
- Assignees: Duke University
- Applicants: Duke University
- Inventors: Warren Grill; John Gilbert
- Web page: https://patentgrep.com/patent/US11298546B2

## CPC

- A61N 1/36185: {Selection of the electrode configuration}
- A61N 1/0551: {Spinal or peripheral nerve electrodes}
- A61N 1/36071: {Pain}
- A61N 1/36135: {using physiological parameters}
- A61N 1/36146: {specified by the stimulation parameters}
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- A61N 1/37241: {providing test stimulations}
- A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

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.

## Claims

### Claim 1

A method of identifying an optimized spinal cord simulation (SCS) pattern for use in delivering electrical stimulation to a subject for pain reduction, the method comprising:
generating a plurality of SCS patterns using an optimization algorithm based on predetermined performance criteria, wherein the predetermined performance criteria comprise variance of pain score reduction across different pain states;
evaluating the plurality of SCS patterns for pain reduction using a computational model of a neuronal network;
identifying at least one candidate SCS pattern having an optimized temporal pattern of stimulation capable of reducing pain; and
delivering the at least one candidate SCS pattern to the subject using a neuromodulation device.

### Claim 2 (depends on 1)

The method of claim 1, wherein the plurality of SCS patterns are generated for at least one of efficiency optimization, efficacy optimization, and variance optimization.

### Claim 3 (depends on 1)

The method of claim 1, wherein the optimized SCS pattern reduces pain in a plurality of subjects with different pain states.

### Claim 4 (depends on 1)

The method of claim 1, wherein the optimized SCS pattern comprises a temporal pattern of electrical stimulation pulses.

### Claim 5 (depends on 4)

The method of claim 4, wherein the temporal pattern of electrical stimulation pulses comprises a non-regular temporal pattern with one or more varying inter-pulse intervals.

### Claim 6 (depends on 4)

The method of claim 4, wherein the optimized SCS pattern comprises a stimulation frequency ranging from about 1 Hz to about 200 Hz.

### Claim 7 (depends on 1)

The method of claim 1, wherein 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.

### Claim 8 (depends on 1)

The method of claim 1, wherein the predetermined performance criteria are incorporated into a fitness function used to evaluate the fitness of the plurality of SCS patterns.

### Claim 9 (depends on 1)

The method of claim 1, wherein the predetermined performance criteria further comprise at least one of: i) reduction in pain score and/or ii) SCS pattern efficiency.

### Claim 10 (depends on 9)

The method of claim 9, wherein 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.

### Claim 11 (depends on 9)

The method of claim 9, wherein the SCS pattern efficiency is proportional to average frequency of stimulation.

### Claim 12 (depends on 1)

The method of claim 1, wherein the variance of pain score reduction across different pain states corresponds to variance of a response to SCS across a population of computational models of a neuronal network.

### Claim 13 (depends on 12)

The method of claim 12, wherein the response to SCS comprises a change in firing rate and/or firing pattern of one or more neurons in the computational model.

### Claim 14 (depends on 1)

The method of claim 1, wherein the computational model of the neuronal network is coupled to the optimization algorithm by the predetermined performance criteria.

### Claim 15 (depends on 1)

The method of claim 1, wherein the computational model of the neuronal network simulates activity of a wide dynamic range (WDR) neuron.

### Claim 16 (depends on 15)

The method of claim 15, wherein the activity of the WDR neuron in the computational model is a proxy for pain.

### Claim 17 (depends on 1)

The method of claim 1, wherein the computational model of the neural network comprises three network zones comprising heterogeneous inhibitory and excitatory neural connections.

### Claim 18 (depends on 1)

The method of claim 1, wherein 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.

### Claim 19 (depends on 18)

The method of claim 18, wherein 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 a network zone; ii) maximum conductance of GABAergic synapses within a network zone; iii) maximum conductance of AMPA synapses onto inhibitory neurons within a network zone; and iv) number of C fibers activated in a surround zone.

### Claim 20 (depends on 19)

The method of claim 19, wherein the reversal potential of inhibitory synapses ranges from about −50 mV to about −100 mV.

### Claim 21 (depends on 19)

The method of claim 19, wherein the maximum conductance of GABAergic synapses ranges from about 50% to about 100%.

### Claim 22 (depends on 19)

The method of claim 19, wherein the maximum conductance of AMPA synapses onto inhibitory neurons ranges from about 50% to about 100%.

### Claim 23 (depends on 19)

The method of claim 19, wherein the number of C fibers activated in each surround zone ranges from about 0% to about 50%.

### Claim 24 (depends on 18)

The method of claim 18, wherein 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 a network zone by an SCS pattern; and ii) stimulation frequency of an SCS pattern within a network zone.

### Claim 25 (depends on 24)

The method of claim 24, wherein the number of fibers activated within a network zone by the SCS pattern (i) 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.

### Claim 26 (depends on 24)

The method of claim 24, wherein the stimulation frequency within a network zone (ii) ranges from about 1 Hz to about 200 Hz.

### Claim 27 (depends on 18)

The method of claim 18, wherein 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.

### Claim 28 (depends on 27)

The method of claim 27, wherein the maximum sodium conductance ranges from about 50% to about 150% within a network zone.

### Claim 29 (depends on 27)

The method of claim 27, wherein the maximum potassium conductance ranges from about 50% to about 150% within a network zone.