Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control.

Additional treatment options for temporal lobe epilepsy are needed, and potential interventions targeting the cerebellum are of interest. Previous animal work has shown strong inhibition of hippocampal seizures through on-demand optogenetic manipulation of the cerebellum. However, decades of work examining electrical stimulation-a more immediately translatable approach-targeting the cerebellum has produced very mixed results. We were therefore interested in exploring the impact that stimulation parameters may have on seizure outcomes. Using a mouse model of temporal lobe epilepsy, we conducted on-demand electrical stimulation of the cerebellar cortex, and varied stimulation charge, frequency and pulse width, resulting in over 1000 different potential combinations of settings. To explore this parameter space in an efficient, data-driven, manner, we utilized Bayesian optimization with Gaussian process regression, implemented in MATLAB with an Expected Improvement Plus acquisition function. We examined three different fitting conditions and two different electrode orientations. Following the optimization process, we conducted additional on-demand experiments to test the effectiveness of selected settings. Regardless of experimental setup, we found that Bayesian optimization allowed identification of effective intervention settings. Additionally, generally similar optimal settings were identified across animals, suggesting that personalized optimization may not always be necessary. While optimal settings were effective, stimulation with settings predicted from the Gaussian process regression to be ineffective failed to provide seizure control. Taken together, our results provide a blueprint for exploration of a large parameter space for seizure control and illustrate that robust inhibition of seizures can be achieved with electrical stimulation of the cerebellum, but only if the correct stimulation parameters are used.

Reliability of visual review of intracranial electroencephalogram in identifying the seizure onset zone: A systematic review and implications for the accuracy of automated methods.

Visual review of intracranial electroencephalography (iEEG) is often an essential component for defining the zone of resection for epilepsy surgery. Unsupervised approaches using machine and deep learning are being employed to identify seizure onset zones (SOZs). This prompts a more comprehensive understanding of the reliability of visual review as a reference standard. We sought to summarize existing evidence on the reliability of visual review of iEEG in defining the SOZ for patients undergoing surgical workup and understand its implications for algorithm accuracy for SOZ prediction. We performed a systematic literature review on the reliability of determining the SOZ by visual inspection of iEEG in accordance with best practices. Searches included MEDLINE, Embase, Cochrane Library, and Web of Science on May 8, 2022. We included studies with a quantitative reliability assessment within or between observers. Risk of bias assessment was performed with QUADAS-2. A model was developed to estimate the effect of Cohen kappa on the maximum possible accuracy for any algorithm detecting the SOZ. Two thousand three hundred thirty-eight articles were identified and evaluated, of which one met inclusion criteria. This study assessed reliability between two reviewers for 10 patients with temporal lobe epilepsy and found a kappa of .80. These limited data were used to model the maximum accuracy of automated methods. For a hypothetical algorithm that is 100% accurate to the ground truth, the maximum accuracy modeled with a Cohen kappa of .8 ranged from .60 to .85 (F-2). The reliability of reviewing iEEG to localize the SOZ has been evaluated only in a small sample of patients with methodologic limitations. The ability of any algorithm to estimate the SOZ is notably limited by the reliability of iEEG interpretation. We acknowledge practical limitations of rigorous reliability analysis, and we propose design characteristics and study questions to further investigate reliability.

Epidural stimulation restores muscle synergies by modulating neural drives in participants with sensorimotor complete spinal cord injuries.

Multiple studies have corroborated the restoration of volitional motor control after motor-complete spinal cord injury (SCI) through the use of epidural spinal cord stimulation (eSCS), but rigorous quantitative descriptions of muscle coordination have been lacking. Six participants with chronic, motor and sensory complete SCI underwent a brain motor control assessment (BMCA) consisting of a set of structured motor tasks with and without eSCS. We investigated how muscle activity complexity and muscle synergies changed with and without stimulation. We performed this analysis to better characterize the impact of stimulation on neuromuscular control. We also recorded data from nine healthy participants as controls. Competition exists between the task origin and neural origin hypotheses underlying muscle synergies. The ability to restore motor control with eSCS in participants with motor and sensory complete SCI allows us to test whether changes in muscle synergies reflect a neural basis in the same task. Muscle activity complexity was computed with Higuchi Fractal Dimensional (HFD) analysis, and muscle synergies were estimated using non-negative matrix factorization (NNMF) in six participants with American Spinal Injury Association (ASIA) Impairment Score (AIS) A. We found that the complexity of muscle activity was immediately reduced by eSCS in the SCI participants. We also found that over the follow-up sessions, the muscle synergy structure of the SCI participants became more defined, and the number of synergies decreased over time, indicating improved coordination between muscle groups. Lastly, we found that the muscle synergies were restored with eSCS, supporting the neural hypothesis of muscle synergies. We conclude that eSCS restores muscle movements and muscle synergies that are distinct from those of healthy, able-bodied controls.

Mapping Spinal Cord Stimulation-Evoked Muscle Responses in Patients With Chronic Spinal Cord Injury.

OBJECTIVES: Epidural spinal cord stimulation (eSCS) has shown promise for restoring some volitional motor control after spinal cord injury (SCI). Maximizing therapeutic response requires effective spatial stimulation generated through careful configuration of anodes and cathodes on the eSCS lead. By exploring the way the spatial distribution of low frequency stimulation affects muscle activation patterns, we investigated the spatial specificity of stimulation-evoked responses for targeted muscle groups for restoration after chronic SCI (cSCI) in participants in the Epidural Stimulation After Neurologic Damage (E-STAND) trial. MATERIALS AND METHODS: Fifteen participants with Abbreviated Injury Scale A cSCI from the E-STAND study were evaluated with a wide range of bipolar spatial patterns. Surface electromyography captured stimulation-evoked responses from the rectus abdominis (RA), intercostal, paraspinal, iliopsoas, rectus femoris (RF), tibialis anterior (TA), extensor hallucis longus (EHL), and gastrocnemius muscle groups bilaterally. Peak-to-peak amplitudes were analyzed for each pulse across muscles. Stimulation patterns with dipoles parallel (vertical configurations), perpendicular (horizontal configurations), and oblique (diagonal configurations) relative to the rostral-caudal axis were evaluated. RESULTS: Cathodic stimulation in the transverse plane indicated ipsilaterally biased activation in RA, intercostal, paraspinal, iliopsoas, RF, TA, EHL, and gastrocnemius muscles (p < 0.05). We found that caudal cathodic stimulation was significantly more activating only in the RF and EHL muscle groups than in the rostral (p < 0.037 and p < 0.006, respectively). Oblique stimulation was found to be more activating in the RA, intercostal, paraspinal, iliopsoas, and TA muscle groups than in the transverse (p < 0.05). CONCLUSIONS: Cathodic stimulation provides uniform specificity for targeting laterality. Few muscle groups responded specifically to variation in rostral/caudal stimulation, and oblique stimulation improved stimulation responses when compared with horizontal configurations. These relations may enable tailored targeting of muscle groups, but the surprising amount of variation observed suggests that monitoring these evoked muscle responses will play a key role in this tailoring process. CLINICAL TRIAL REGISTRATION: The Clinicaltrials.gov registration number for the study is NCT03026816.

Evaluation of functional MRI-based human brain parcellation: a review.

Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.

The safety of epidural spinal cord stimulation to restore function after spinal cord injury: post-surgical complications and incidence of cardiovascular events.

STUDY DESIGN: Cohort prospective study. OBJECTIVES: Epidural spinal cord stimulation (eSCS) improves volitional motor and autonomic function after spinal cord injury (SCI). While eSCS has an established history of safety for chronic pain, it remains unclear if eSCS in the SCI population presents the same risk profile. We aimed to assess safety and autonomic monitoring data for the first 14 participants in the E-STAND trial. SETTING: Hennepin County Medical Center, Minneapolis and Minneapolis Veterans Affairs Medical Center, Minnesota, USA. METHODS: Monthly follow-up visits assessed surgical and medical device-related safety outcomes as well as stimulation usage. Beat-by-beat blood pressure (BP) and continuous electrocardiogram data were collected during head-up tilt-table testing with and without eSCS. RESULTS: All participants had a motor-complete SCI. Mean (SD) age and time since injury were 38 (10) and 7 (5) years, respectively. There were no surgical complications but one device malfunction 4 months post implantation. Stimulation was applied for up to 23 h/day, across a broad range of parameters: frequency (18-700 Hz), pulse width (100-600 µs), and amplitude (0.4-17 mA), with no adverse events reported. Tilt-table testing with eSCS demonstrated no significant increases in the incidence of elevated systolic BP or a greater frequency of arrhythmias. CONCLUSIONS: eSCS to restore autonomic and volitional motor function following SCI has a similar safety profile as when used to treat chronic pain, despite the prevalence of significant comorbidities and the wide variety of stimulation parameters tested.

Closed-Loop neuromodulation for clustering neuronal populations.

Pathological synchronization of neurons is associated with symptoms of movement disorders, such as Parkinson's disease and essential tremor. High-frequency deep brain stimulation (DBS) suppresses symptoms, presumably through the desynchronization of neurons. Coordinated reset (CR) delivers trains of high-frequency stimuli to different regions in the brain through multiple electrodes and may have more persistent therapeutic effects than conventional DBS. As an alternative to CR, we present a closed-loop control setup that desynchronizes neurons in brain slices by inducing clusters using a single electrode. Our setup uses calcium fluorescence imaging to extract carbachol-induced neuronal oscillations in real time. To determine the appropriate stimulation waveform for inducing clusters in a population of neurons, we calculate the phase of the neuronal populations and then estimate the phase response curve (PRC) of those populations to electrical stimulation. The phase and PRC are then fed into a control algorithm called the input of maximal instantaneous efficiency (IMIE). By using IMIE, the synchrony across the slice is decreased by dividing the population of neurons into subpopulations without suppressing the oscillations locally. The desynchronization effect is persistent 10 s after stimulation is stopped. The IMIE control algorithm may be used as a novel closed-loop DBS approach to suppress the symptoms of Parkinson's disease and essential tremor by inducing clusters with a single electrode.NEW & NOTEWORTHY Here, we present a closed-loop controller to desynchronize neurons in brain slices by inducing clusters using a single electrode using calcium imaging feedback. Phase of neurons are estimated in real time, and from the phase response curve stimulation is applied to achieve target phase differences. This method is an alternative to coordinated reset and is a novel therapy that could be used to disrupt synchronous neuronal oscillations thought to be the mechanism underlying Parkinson's disease.

Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease.

BACKGROUND: Deep brain stimulation (DBS) is a treatment option for Parkinson's disease patients when medication does not sufficiently manage their symptoms. DBS can be a highly effect therapy, but only after a time-consuming trial-and-error stimulation parameter adjustment process that is susceptible to clinician bias. This trial-and-error process will be further prolonged with the introduction of segmented electrodes that are now commercially available. New approaches to optimizing a patient's stimulation parameters, that can also handle the increasing complexity of new electrode and stimulator designs, is needed. METHODS: To improve DBS parameter programming, we explored two semi-automated optimization approaches: a Bayesian optimization (BayesOpt) algorithm to efficiently determine a patient's optimal stimulation parameter for minimizing rigidity, and a probit Gaussian process (pGP) to assess patient's preference. Quantified rigidity measurements were obtained using a robotic manipulandum in two participants over two visits. Rigidity was measured, in 5Hz increments, between 10-185Hz (total 30-36 frequencies) on the first visit and at eight BayesOpt algorithm-selected frequencies on the second visit. The participant was also asked their preference between the current and previous stimulation frequency. First, we compared the optimal frequency between visits with the participant's preferred frequency. Next, we evaluated the efficiency of the BayesOpt algorithm, comparing it to random and equal interval selection of frequency. RESULTS: The BayesOpt algorithm estimated the optimal frequency to be the highest tolerable frequency, matching the optimal frequency found during the first visit. However, the participants' pGP models indicate a preference at frequencies between 70-110 Hz. Here the stimulation frequency is lowest that achieves nearly maximal suppression of rigidity. BayesOpt was efficient, estimating the rigidity response curve to stimulation that was almost indistinguishable when compared to the longer brute force method. CONCLUSIONS: These results provide preliminary evidence of the feasibility to use BayesOpt for determining the optimal frequency, while pGP patient's preferences include more difficult to measure outcomes. Both novel approaches can shorten DBS programming and can be expanded to include multiple symptoms and parameters.

Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson's disease.

In this paper, we present a novel Bayesian adaptive dual controller (ADC) for autonomously programming deep brain stimulation devices. We evaluated the Bayesian ADC's performance in the context of reducing beta power in a computational model of Parkinson's disease, in which it was tasked with finding the set of stimulation parameters which optimally reduced beta power as fast as possible. Here, the Bayesian ADC has dual goals: (a) to minimize beta power by exploiting the best parameters found so far, and (b) to explore the space to find better parameters, thus allowing for better control in the future. The Bayesian ADC is composed of two parts: an inner parameterized feedback stimulator and an outer parameter adjustment loop. The inner loop operates on a short time scale, delivering stimulus based upon the phase and power of the beta oscillation. The outer loop operates on a long time scale, observing the effects of the stimulation parameters and using Bayesian optimization to intelligently select new parameters to minimize the beta power. We show that the Bayesian ADC can efficiently optimize stimulation parameters, and is superior to other optimization algorithms. The Bayesian ADC provides a robust and general framework for tuning stimulation parameters, can be adapted to use any feedback signal, and is applicable across diseases and stimulator designs.

Responses of thalamic neurons to itch- and pain-producing stimuli in rats.

Understanding of processing and transmission of information related to itch and pain in the thalamus is incomplete. In fact, no single unit studies of pruriceptive transmission in the thalamus have yet appeared. In urethane-anesthetized rats, we examined responses of 66 thalamic neurons to itch- and pain- inducing stimuli including chloroquine, serotonin, ß-alanine, histamine, and capsaicin. Eighty percent of all cells were activated by intradermal injections of one or more pruritogens. Forty percent of tested neurons responded to injection of three, four, or even five agents. Almost half of the examined neurons had mechanically defined receptive fields that extended onto distant areas of the body. Pruriceptive neurons were located within what appeared to be a continuous cell column extending from the posterior triangular nucleus (PoT) caudally to the ventral posterior medial nucleus (VPM) rostrally. All neurons tested within PoT were found to be pruriceptive. In addition, neurons in this nucleus responded at higher frequencies than did those in VPM, an indication that PoT might prove to be a particularly interesting region for additional studies of itch transmission. NEW & NOTEWORTHY Processing of information related to itch within in the thalamus is not well understood, We show in this, the first single-unit electrophysiological study of responses of thalamic neurons to pruritogens, that itch-responsive neurons are concentrated in two nuclei within the rat thalamus, the posterior triangular, and the ventral posterior medial nuclei.

Phasic Burst Stimulation: A Closed-Loop Approach to Tuning Deep Brain Stimulation Parameters for Parkinson's Disease.

We propose a novel, closed-loop approach to tuning deep brain stimulation (DBS) for Parkinson's disease (PD). The approach, termed Phasic Burst Stimulation (PhaBS), applies a burst of stimulus pulses over a range of phases predicted to disrupt pathological oscillations seen in PD. Stimulation parameters are optimized based on phase response curves (PRCs), which would be measured from each patient. This approach is tested in a computational model of PD with an emergent population oscillation. We show that the stimulus phase can be optimized using the PRC, and that PhaBS is more effective at suppressing the pathological oscillation than a single phasic stimulus pulse. PhaBS provides a closed-loop approach to DBS that can be optimized for each patient.

Modulations in oscillatory frequency and coupling in globus pallidus with increasing parkinsonian severity.

While beta oscillations often occur within the parkinsonian basal ganglia, how these oscillations emerge from a naive state and change with disease severity is not clear. To address this question, a progressive, nonhuman primate model of Parkinson's disease was developed using staged injections of MPTP. Within each parkinsonian state (naive, mild, moderate, and severe), spontaneous local field potentials were recorded throughout the sensorimotor globus pallidus. In the naive state, beta oscillations (11-32 Hz) occurred in half of the recordings, indicating spontaneous beta oscillations in globus pallidus are not pathognomonic. Mild and moderate states were characterized by a narrower distribution of beta frequencies that shifted toward the 8-15 Hz range. Additionally, coupling between the phase of beta and the amplitude of high-frequency oscillations (256-362 Hz) emerged in the mild state and increased with severity. These findings provide a novel mechanistic framework to understand how progressive loss of dopamine translates into abnormal information processing in the pallidum through alterations in oscillatory activity. The results suggest that rather than the emergence of oscillatory activity in one frequency spectrum or the other, parkinsonian motor signs may relate more to the development of altered coupling across multiple frequency spectrums.

Origins and suppression of oscillations in a computational model of Parkinson's disease.

Efficacy of deep brain stimulation (DBS) for motor signs of Parkinson's disease (PD) depends in part on post-operative programming of stimulus parameters. There is a need for a systematic approach to tuning parameters based on patient physiology. We used a physiologically realistic computational model of the basal ganglia network to investigate the emergence of a 34 Hz oscillation in the PD state and its optimal suppression with DBS. Discrete time transfer functions were fit to post-stimulus time histograms (PSTHs) collected in open-loop, by simulating the pharmacological block of synaptic connections, to describe the behavior of the basal ganglia nuclei. These functions were then connected to create a mean-field model of the closed-loop system, which was analyzed to determine the origin of the emergent 34 Hz pathological oscillation. This analysis determined that the oscillation could emerge from the coupling between the globus pallidus external (GPe) and subthalamic nucleus (STN). When coupled, the two resonate with each other in the PD state but not in the healthy state. By characterizing how this oscillation is affected by subthreshold DBS pulses, we hypothesize that it is possible to predict stimulus frequencies capable of suppressing this oscillation. To characterize the response to the stimulus, we developed a new method for estimating phase response curves (PRCs) from population data. Using the population PRC we were able to predict frequencies that enhance and suppress the 34 Hz pathological oscillation. This provides a systematic approach to tuning DBS frequencies and could enable closed-loop tuning of stimulation parameters.

Neural signal data collection and analysis of Percept™ PC BrainSense recordings for thalamic stimulation in epilepsy.

Deep brain stimulation (DBS) using Medtronic's Percept™ PC implantable pulse generator is FDA-approved for treating Parkinson's disease (PD), essential tremor, dystonia, obsessive compulsive disorder, and epilepsy. Percept™ PC enables simultaneous recording of neural signals from the same lead used for stimulation. Many Percept™ PC sensing features were built with PD patients in mind, but these features are potentially useful to refine therapies for many different disease processes. When starting our ongoing epilepsy research study, we found it difficult to find detailed descriptions about these features and have compiled information from multiple sources to understand it as a tool, particularly for use in patients other than those with PD. Here we provide a tutorial for scientists and physicians interested in using Percept™ PC's features and provide examples of how neural time series data is often represented and saved. We address characteristics of the recorded signals and discuss Percept™ PC hardware and software capabilities in data pre-processing, signal filtering, and DBS lead performance. We explain the power spectrum of the data and how it is shaped by the filter response of Percept™ PC as well as the aliasing of the stimulation due to digitally sampling the data. We present Percept™ PC's ability to extract biomarkers that may be used to optimize stimulation therapy. We show how differences in lead type affects noise characteristics of the implanted leads from seven epilepsy patients enrolled in our clinical trial. Percept™ PC has sufficient signal-to-noise ratio, sampling capabilities, and stimulus artifact rejection for neural activity recording. Limitations in sampling rate, potential artifacts during stimulation, and shortening of battery life when monitoring neural activity at home were observed. Despite these limitations, Percept™ PC demonstrates potential as a useful tool for recording neural activity in order to optimize stimulation therapies to personalize treatment.

Impact of anesthesia on micromagnetic stimulation (µMS) of the vagus nerve.

To treat diseases associated with vagal nerve control of peripheral organs, it is necessary to selectively activate efferent and afferent fibers in the vagus. As a result of the nerve's complex anatomy, fiber-specific activation proves challenging. Spatially selective neuromodulation using micromagnetic stimulation(µMS) is showing incredible promise. This neuromodulation technique uses microcoils(µcoils) to generate magnetic fields by powering them with a time-varying current. Following the principles of Faraday's law of induction, a highly directional electric field is induced in the nerve from the magnetic field. In this study on rodent cervical vagus, a solenoidalµcoil was oriented at an angle to left and right branches of the nerve. The aim of this study was to measure changes in the mean arterial pressure (MAP) and heart rate (HR) followingµMS of the vagus. Theµcoils were powered by a single-cycle sinusoidal current varying in pulse widths(PW = 100, 500, and 1000µsec) at a frequency of 20 Hz. Under the influence of isoflurane,µMS of the left vagus at 1000µsec PW led to an average drop in MAP of 16.75 mmHg(n = 7). In contrast,µMS of the right vagus under isoflurane resulted in an average drop of 11.93 mmHg in the MAP(n = 7). Surprisingly, there were no changes in HR to either right or left vagalµMS suggesting the drop in MAP associated with vagusµMS was the result of stimulation of afferent, but not efferent fibers. In urethane anesthetized rats, no changes in either MAP or HR were observed uponµMS of the right or left vagus(n = 3). These findings suggest the choice of anesthesia plays a key role in determining the efficacy ofµMS on the vagal nerve. Absence of HR modulation uponµMS could offer alternative treatment options using VNS with fewer heart-related side-effects.

Parameterized phase response curves for characterizing neuronal behaviors under transient conditions.

Phase response curves (PRCs) are a simple model of how a neuron's spike time is affected by synaptic inputs. PRCs are useful in predicting how networks of neurons behave when connected. One challenge in estimating a neuron's PRCs experimentally is that many neurons do not have stationary firing rates. In this article we introduce a new method to estimate PRCs as a function of firing rate of the neuron. We call the resulting model a parameterized PRC (pPRC). Experimentally, we perturb the neuron applying a current with two parts: 1) a current held constant between spikes but changed at the onset of a spike, used to make the neuron fire at different rates, and 2) a pulse to emulate a synaptic input. A model of the applied constant current and the history is made to predict the interspike interval (ISI). A second model is then made to fit the modulation of the spike time from the expected ISI by the pulsatile stimulus. A polynomial with two independent variables, the stimulus phase and the expected ISI, is used to model the pPRC. The pPRC is validated in a computational model and applied to pyramidal neurons from the CA1 region of the hippocampal slices from rat. The pPRC can be used to model the effect of changing firing rates on network synchrony. It can also be used to characterize the effects of neuromodulators and genetic mutations (among other manipulations) on network synchrony. It can also easily be extended to account for more variables.

In silicodevelopment and validation of Bayesian methods for optimizing deep brain stimulation to enhance cognitive control.

Objective.deep brain stimulation (DBS) of the ventral internal capsule/striatum (VCVS) is a potentially effective treatment for several mental health disorders when conventional therapeutics fail. Its effectiveness, however, depends on correct programming to engage VCVS sub-circuits. VCVS programming is currently an iterative, time-consuming process, with weeks between setting changes and reliance on noisy, subjective self-reports. An objective measure of circuit engagement might allow individual settings to be tested in seconds to minutes, reducing the time to response and increasing patient and clinician confidence in the chosen settings. Here, we present an approach to measuring and optimizing that circuit engagement.Approach.we leverage prior results showing that effective VCVS DBS engages cognitive control circuitry and improves performance on the multi-source interference task, that this engagement depends primarily on which contact(s) are activated, and that circuit engagement can be tracked through a state space modeling framework. We develop a simulation framework based on those empirical results, then combine this framework with an adaptive optimizer to simulate a principled exploration of electrode contacts and identify the contacts that maximally improve cognitive control. We explore multiple optimization options (algorithms, number of inputs, speed of stimulation parameter changes) and compare them on problems of varying difficulty.Main results.we show that an upper confidence bound algorithm outperforms other optimizers, with roughly 80% probability of convergence to a global optimum when used in a majority-vote ensemble.Significance.we show that the optimization can converge even with lag between stimulation and effect, and that a complete optimization can be done in a clinically feasible timespan (a few hours). Further, the approach requires no specialized recording or imaging hardware, and thus could be a scalable path to expand the use of DBS in psychiatric and other non-motor applications.

Feasibility testing of a novel prosthetic socket sensor system.

PURPOSE: Poorly fitting prosthetic sockets contribute to decreased quality of life, health, and well-being for persons with amputations. Therefore, improved socket fit is a high clinical priority. METHODS: In this study, we describe the design and testing of a novel sensor system that can be incorporated into a prosthetic socket to measure distal end weight bearing in the socket and can alert a prosthesis user if poor socket fit is suspected. We present the results of testing this device with three Veterans who were new prosthesis users and three Veterans who were experienced prosthesis users. RESULTS AND CONCLUSIONS: We collected sensor data during walking trials while participants wore varying numbers of sock plies and qualitative feedback on the design of the socket fit sensor system. For analysis, peak sensor measurements during walking cycles were identified and combined with socket fit data (i.e., a clinician-determined level of "good," "too tight," or "too loose" and the number of sock ply worn each trial). We found consistent relationships between peak sensor measurements and socket fit in our sample. Also, all users expressed an interest in the device, highlighting its potential benefits during early prosthesis training.Implications for RehabilitationEnsuring socket fit is challenging for many prosthesis users.A novel wearable sensor system can be used to identify socket fit issues for some prosthesis users.This type of system could be most helpful for new prosthesis users and those with sensory and cognitive challenges.

Methods and system for recording human physiological signals from implantable leads during spinal cord stimulation.

Objectives: This article presents a method-including hardware configuration, sampling rate, filtering settings, and other data analysis techniques-to measure evoked compound action potentials (ECAPs) during spinal cord stimulation (SCS) in humans with externalized percutaneous electrodes. The goal is to provide a robust and standardized protocol for measuring ECAPs on the non-stimulation contacts and to demonstrate how measured signals depend on hardware and processing decisions. Methods: Two participants were implanted with percutaneous leads for the treatment of chronic pain with externalized leads during a trial period for stimulation and recording. The leads were connected to a Neuralynx ATLAS system allowing us to simultaneously stimulate and record through selected electrodes. We examined different hardware settings, such as online filters and sampling rate, as well as processing techniques, such as stimulation artifact removal and offline filters, and measured the effects on the ECAPs metrics: the first negative peak (N1) time and peak-valley amplitude. Results: For accurate measurements of ECAPs, the hardware sampling rate should be least at 8 kHz and should use a high pass filter with a low cutoff frequency, such as 0.1 Hz, to eliminate baseline drift and saturation (railing). Stimulation artifact removal can use a double exponential or a second-order polynomial. The polynomial fit is 6.4 times faster on average in computation time than the double exponential, while the resulting ECAPs' N1 time and peak-valley amplitude are similar between the two. If the baseline raw measurement drifts with stimulation, a median filter with a 100-ms window or a high pass filter with an 80-Hz cutoff frequency preserves the ECAPs. Conclusions: This work is the first comprehensive analysis of hardware and processing variations on the observed ECAPs from SCS leads. It sets recommendations to properly record and process ECAPs from the non-stimulation contacts on the implantable leads.

Neuromodulation Through Spinal Cord Stimulation Restores Ability to Voluntarily Cycle After Motor Complete Paraplegia.

Abstract Epidural spinal cord stimulation (eSCS) of the lower thoracic spinal cord has been shown to partially restore volitional movement in patients with complete chronic spinal cord injury (cSCI). Combining eSCS with intensive locomotor training improves motor function, including standing and stepping, but many patients with cSCI suffer from long-standing muscle atrophy and loss of bone mineral density, which may prohibit safe implementation. Safe, accessible, and effective avenues for pairing neuromodulation with activity-based therapy remain unexplored. Cycling is one such option that can be utilized as an eSCS therapy given its low-risk and low-weight-bearing requirement. We investigated the feasibility and kinematics of motor-assisted and passive cycle-based therapy for cSCI patients with epidural spinal cord stimulation. Seven participants who underwent spinal cord stimulation surgery in the Epidural Stimulation After Neurologic Damage (E-STAND) trial (NCT03026816) participated in a cycling task using the motor assist MOTOmed Muvi 300. A factorial design was used such that participants were asked to cycle with and without conscious effort with and without stimulation. We used mixed effects models assessing maximum power output and time pedaling unassisted to evaluate the interaction between stimulation and conscious effort. Cycling was well-tolerated and we observed no adverse events, including in participants up to 17 years post-initial injury and up to 58 years old. All participants were found to be able to pedal without motor assist, which primarily occurred when stimulation and effort were applied together (p = 0.001). Additionally, the combination of stimulation and intention was significantly associated with higher maximum power production (p < 0.0001) and distance pedaled (p = 0.0001). No association was found between volitional movement and participant factors: age, time since injury, and spinal cord atrophy. With stimulation and conscious effort, all participants were able to achieve active cycling without motor assistance. Thus, our stationary cycling factorial study design demonstrated volitional movement restoration with eSCS in a diverse study population of cSCI participants. Further, motor-assist cycling was well-tolerated without any adverse events. Cycling has the potential to be a safe research assessment and physical therapy modality for cSCI patients utilizing eSCS who have a high risk of injury with weight bearing exercise. The cycling modality in this study was demonstrated to be a straightforward assessment of motor function and safe for all participants regardless of age or time since initial injury.