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Enhancing the efficacy of spinal cord stimulation (SCS) is needed to alleviate the burden of chronic pain and dependence on opioids. Present SCS therapies are characterized by the delivery of constant stimulation in the form of trains of tonic pulses (TPs). We tested the hypothesis that modulated SCS using novel time-dynamic pulses (TDPs) leads to improved analgesia and compared the effects of SCS using conventional TPs and a collection of TDPs in a rat model of neuropathic pain according to a longitudinal, double-blind, and crossover design. We tested the effects of the following SCS patterns on paw withdrawal threshold and resting state EEG theta power as a biomarker of spontaneous pain: Tonic (conventional), amplitude modulation, pulse width modulation, sinusoidal rate modulation, and stochastic rate modulation. Results demonstrated that under the parameter settings tested in this study, all tested patterns except pulse width modulation, significantly reversed mechanical hypersensitivity, with stochastic rate modulation achieving the highest efficacy, followed by the sinusoidal rate modulation. The anti-nociceptive effects of sinusoidal rate modulation on EEG outlasted SCS duration on the behavioral and EEG levels. These results suggest that TDP modulation may improve clinical outcomes by reducing pain intensity and possibly improving the sensory experience.

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

Spinal cord stimulation (SCS) utilizing time-dynamic pulses (TDPs) is an emergent field of neuromodulation that continuously and automatically modulates pulse parameters. We previously demonstrated that TDPs delivered for 60 min at sub-paresthesia amplitudes significantly reversed allodynia in a rat model of neuropathic pain. Because we observed these anti-allodynic effects persisted post-cessation, we investigated the extended temporal dynamics of SCS-induced analgesia. We hypothesized that TDPs achieve a longer duration of analgesia than tonic stimulation. Both TDPs and tonic stimulation reversed PWT to near pre-chronificiation levels within 30 minutes. Most TDPs exhibited significantly slower ramp-up slope (analgesia wash-in rates) compared to tonic stimulation (amplitude modulation: 0.16±0.03 min-1, pulse width modulation: 0.18±0.05 min-1, stochastic modulation: 0.17±0.04 min-1, tonic: 0.31±0.06 min-1). All TDPs showed slower wind-down slopes (analgesia wash-out rates) compared to tonic (-0.29±0.07 min-1), with pulse width modulation (-0.11±0.02 min-1) reaching significance. Extending SCS from 60 to 90 minutes revealed all TDPs maintain analgesic efficacy longer than tonic stimulation, which decreased significantly at both 75 and 90 minutes (from 13.8±0.5 g to 12.3±0.9 g and to 11.0±0.5 g, respectively). Although TDPs and tonic stimulation comparably mitigated allodynia, TDPs generally exhibited slower temporal dynamics, suggesting longer-lasting analgesic effects and potentially different mechanisms of action.Competing Interest StatementThe authors have declared no competing interest.

Spinal cord stimulation (SCS) evokes fast epidural Evoked Compound Action Potential (ECAPs) that represent activity of dorsal column axons, but not necessarily a spinal circuit response. Using a multimodal approach, we identified and characterized a delayed and slower potential evoked by SCS that reflects synaptic activity within the spinal cord. Anesthetized female Sprague Dawley rats were implanted with an epidural SCS lead, epidural motor cortex stimulation electrodes, an epidural spinal cord recoding lead, an intraspinal penetrating recording electrode array, and intramuscular electromyography (EMG) electrodes in the hindlimb and back. We stimulated the motor cortex or the epidural spinal cord and recorded epidural, intraspinal, and EMG responses. SCS pulses produced characteristic propagating ECAPs (composed of P1, N1, and P2 waves with latencies <2 ms) and an additional wave (S1) starting after the N2. We verified the S1-wave was not a stimulation artifact and was not a reflection of hindlimb/back EMG. The S1-wave has a distinct stimulation-intensity dose response and spatial profile compared to ECAPs. CNQX (a selective competitive antagonist of AMPA receptors) significantly diminished the S1-wave, but not ECAPs. Furthermore, cortical stimulation, which did not evoke ECAPs, produced epidurally detectable and CNQX-sensitive responses at the same spinal sites, confirming epidural recording of an evoked synaptic response. Finally, applying 50 Hz SCS resulted in dampening of ESAPs, but not ECAPs. Therefore, we hypothesize that the S1-wave is synaptic in origin, and we term the S1-wave type responses: Evoked Synaptic Activity Potentials (ESAPs). The identification and characterization of epidurally recorded ESAPs from the dorsal horn may elucidate SCS mechanisms.Competing Interest StatementTZ and RE are employees of Boston Scientific Neuromodulation. TZ Owns Boston Scientific Stock. The City University of New York holds patents on brain stimulation with MB as inventor. MB has equity in Soterix Medical Inc. MB consults, received grants, assigned inventions, and/or serves on the SAB of SafeToddles, Boston Scientific (and MS), GlaxoSmithKline, Biovisics, Mecta, Lumenis, Halo Neuroscience, Google-X, i-Lumen, Humm (and NG), Allergan (Abbvie), Apple, Ybrain, Ceragem, Remz.

Individuals with epilepsy experience seizure disabilities, injuries, impairment of productivity, and disabling side effects from medications. The goal of this research is to detect seizure onset as early as possible with maximal accuracy and with a minimum number of false negatives and false positives, ultimately developing an automatic system allowing patients to take appropriate precautions. The system designed is based on a pattern recognition approach that encompasses the stages of preprocessing, processing, classification, and validation. The extraction and selection of features was performed within a designed methodological environment that also allowed a parallel investigation of seizure prediction. For the classification stage, a probabilistic neural network was chosen as the decision block. A crossvalidation scheme was used to validate the results. The overall detector was evaluated for 119 one-hour records from all the patients with a three-dimensional feature vector. The average delay for detecting the seizure electrographic onset with the system developed was 1.76 seconds with zero false negatives and an average of 1.02 false positives per hour, resulting in an average clinical onset prediction time of 11.26 seconds. In addition, from the features investigated, the accumulated energy was found as a promising indicator for seizure electrographic onset prediction, yielding 85.19% of accuracy with an average prediction time of 18.49 ± 13.42 minutes. The main contribution of this research is the development of a systematic methodology for tackling the seizure onset detection problem on a patient basis, as well as the actual software implementation of the overall seizure detection system. Within this methodology, the most relevant steps toward advancing the field are: the development of an original technique to determine an “optimal” window length for every feature on each patient; the establishment of the best feature vector according to a measure of class-separability; a comparison for the first time of fractal dimension algorithms which demonstrated that factors like the window length used, noise level in the data set, and fractal dimension range of the data, can greatly affect the accuracy and performance of the algorithm used; the finding of the accumulated energy as a very promising feature for forecasting seizures; and the design of an original linear performance metric to evaluate the classification results adapted to this particular application.

Spinal cord stimulation (SCS) reduces chronic pain. Conventional (40-60 Hz) SCS engages spinal inhibitory mechanisms by activating low-threshold mechanoreceptive afferents with axons in the dorsal columns (DCs). But activating DC axons typically causes a buzzing sensation (paresthesia) that can be uncomfortable. Kilohertz-frequency (1-10 kHz) SCS produces analgesia without paresthesia and is thought, therefore, not to activate DC axons, leaving its mechanism unclear. Here we show in rats that kilohertz-frequency SCS activates DC axons but causes them to spike less synchronously than conventional SCS. Spikes desynchronize because axons entrain irregularly when stimulated at intervals shorter than their refractory period, a phenomenon we call overdrive desynchronization. Effects of overdrive desynchronization on evoked compound action potentials were verified in simulations, rats, pigs, and a chronic pain patient. Whereas synchronous spiking in DC axons is necessary for paresthesia, asynchronous spiking is sufficient to produce analgesia. Asynchronous activation of DC axons thus produces paresthesia-free analgesia.Competing Interest StatementSAP has received grant funding from Boston Scientific. SAP has received compensation from Boston Scientific and Presidio Medical as a member of their Scientific Advisory Boards. TZ and RE are paid employees of Boston Scientific and own stock in Boston Scientific. TZ has received royalty payments from Boston Scientific for licensed IP. RE has stocks in NeuroPace.

This chapter describes the results of a multicenter study of a closed-loop neurostimulation system called the responsive neurostimulator (RNS) system undertaken by the NeuroPace, Inc. The institution performed nine implants and all cases involved more than one year follow-up. One of these nine (the only case without preoperative invasive monitoring) cases was an insulin dependent juvenile diabetic who was subsequently found to have anti-GAD antibody. This patient never responded to the neurostimulator system and her initial IPG was not replaced when the battery depleted. Follow-up on the other eight cases ranged from 19 to 32 months. All of these eight cases underwent preimplant invasive monitoring with discrete seizure focus localization. The median seizure frequency reduction was 56% and the mean reduction was 65%. The range in seizure frequency reduction was 43–100%. Seven cases required replacement of IPGs due to battery depletion and the time to IPG replacement ranged from 12 to 26 months with a median of 22 months and a mean of 21 months. There has been only one infection requiring explantation of the system. This infection occurred 16 months after implantation of a new IPG and 28 months after the original implantation. No adverse neurological events were reported in these cases. Observation of this study also support the ability of this automated seizure detection/therapeutic stimulation device to positively influence electrographic seizure activity. However, the study is still in a preliminary phase and more data is required to define optimal stimulation parameters as well as patient candidacy for seizure control.