Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs.

BACKGROUND: Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. METHODS: We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. RESULTS: First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. CONCLUSIONS: We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

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.

REPORT-SCS: minimum reporting standards for spinal cord stimulation studies in spinal cord injury.

Objective.Electrical spinal cord stimulation (SCS) has emerged as a promising therapy for recovery of motor and autonomic dysfunctions following spinal cord injury (SCI). Despite the rise in studies using SCS for SCI complications, there are no standard guidelines for reporting SCS parameters in research publications, making it challenging to compare, interpret or reproduce reported effects across experimental studies.Approach.To develop guidelines for minimum reporting standards for SCS parameters in pre-clinical and clinical SCI research, we gathered an international panel of expert clinicians and scientists. Using a Delphi approach, we developed guideline items and surveyed the panel on their level of agreement for each item.Main results.There was strong agreement on 26 of the 29 items identified for establishing minimum reporting standards for SCS studies. The guidelines encompass three major SCS categories: hardware, configuration and current parameters, and the intervention.Significance.Standardized reporting of stimulation parameters will ensure that SCS studies can be easily analyzed, replicated, and interpreted by the scientific community, thereby expanding the SCS knowledge base and fostering transparency in reporting.

Intraoperative neurophysiological monitoring and spinal cord stimulator implantation.

INTRODUCTION: Spinal cord injury (SCI) is one of the most dreaded complications after spinal cord stimulation (SCS) implantation surgery. As a result, intraoperative neurophysiological monitoring (IONM) has been proposed to avoid accidental damage to nervous structures under anesthesia and confirm positioning for optimal stimulation. Our study uses a large administrative claims database to determine the 30-day risk of SCI after SCS implantation. METHODS: This retrospective cohort study used the IBM MarketScan Commercial and Medicare Supplemental Databases from 2016 to 2019. Adult patients undergoing SCS surgical procedures with at least 90 days of follow-up, IONM use, the type of sedation used during the procedure, and subsequent SCI were identified using administrative codes. In addition, logistic regression was used to examine the relationship between various risk factors and subsequent SCI. RESULTS: A total of 9676 patients underwent SCS surgery (64.7% percutaneous implants) during the study period. Nine hundred and forty-four (9.75%) patients underwent SCS implantation with IONM. Conscious sedation, Monitored Anesthesia Care anesthesia, and general anesthesia were used in patients with 0.9%, 60.2%, and 28.6%, respectively. Eighty-one (0.8%) patients developed SCI within 30 days after SCS implant surgery. The SCI rate was higher in the group that underwent IONM (2% vs 0.7%, p value <0.001) during the implantation procedure, reflecting the underlying risk. After adjustment for other factors, the OR of SCI is 2.39 (95% CI: 1.33 to 4.14, p value=0.002) times higher for those with IONM than those without IONM. CONCLUSIONS: Increased SCI risk among patients with IONM likely reflects higher baseline risk, and further research is needed for risk mitigation.

Magnetic Resonance Imaging-Aided SmartFlow Convection Delivery of DNX-2401: A Pilot, Prospective Case Series.

BACKGROUND: The Combination Adenovirus + Pembrolizumab to Trigger Immune Virus Effects (CAPTIVE) study is a phase II clinical trial testing the efficacy of a recombinant adenovirus DNX-2401 combined with the immune checkpoint inhibitor pembrolizumab. Here, we report the first patients in this study who underwent viral delivery through real-time magnetic resonance imaging (MRI) stereotaxis-guided SmartFlow convection delivery of DNX-2401. METHODS: Patients who underwent real-time MRI-guided DNX-2401 delivery through the SmartFlow convection catheter were prospectively followed. RESULTS: Precise catheter placement was achieved in all patients treated, and no adverse events were noted. Average radial error from target was 0.9 mm. Average procedural time was 3 hours 16 minutes and was comparable to other convection-enhanced delivery techniques. In 2 patients, delivery of DNX-2401 was visualized as >1 cm maximal diameter of T1 hypointensity infusate on MRI obtained immediately after completion of viral infusion. These patients exhibited partial response based on Response Assessment in Neuro-Oncology assessment. The remaining patient showed <1 cm maximal diameter of infusate on immediate postinfusion MRI and showed disease progression on subsequent MRI. CONCLUSIONS: Our pilot case series supports compatibility of the SmartFlow system with oncolytic adenovirus delivery and provides the basis for future validation studies.

Learning signatures of decision making from many individuals playing the same game.

Human behavior is incredibly complex and the factors that drive decision making-from instinct, to strategy, to biases between individuals-often vary over multiple timescales. In this paper, we design a predictive framework that learns representations to encode an individual's 'behavioral style', i.e. long-term behavioral trends, while simultaneously predicting future actions and choices. The model explicitly separates representations into three latent spaces: the recent past space, the short-term space, and the long-term space where we hope to capture individual differences. To simultaneously extract both global and local variables from complex human behavior, our method combines a multi-scale temporal convolutional network with latent prediction tasks, where we encourage embeddings across the entire sequence, as well as subsets of the sequence, to be mapped to similar points in the latent space. We develop and apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task, and analyze what our model's resulting embeddings reveal about the human decision making process. In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.

Low-frequency deep brain stimulation reveals resonant beta-band evoked oscillations in the pallidum of Parkinson's Disease patients.

Introduction: Evidence suggests that spontaneous beta band (11-35 Hz) oscillations in the basal ganglia thalamocortical (BGTC) circuit are linked to Parkinson's disease (PD) pathophysiology. Previous studies on neural responses in the motor cortex evoked by electrical stimulation in the subthalamic nucleus have suggested that circuit resonance may underlie the generation of spontaneous and stimulation-evoked beta oscillations in PD. Whether these stimulation-evoked, resonant oscillations are present across PD patients in the internal segment of the globus pallidus (GPi), a primary output nucleus in the BGTC circuit, is yet to be determined. Methods: We characterized spontaneous and stimulation-evoked local field potentials (LFPs) in the GPi of four PD patients (five hemispheres) using deep brain stimulation (DBS) leads externalized after DBS implantation surgery. Results: Our analyses show that low-frequency (2-4 Hz) stimulation in the GPi evoked long-latency (>50 ms) beta-band neural responses in the GPi in 4/5 hemispheres. We demonstrated that neural sources generating both stimulation-evoked and spontaneous beta oscillations were correlated in their frequency content and spatial localization. Discussion: Our results support the hypothesis that the same neuronal population and resonance phenomenon in the BGTC circuit generates both spontaneous and evoked pallidal beta oscillations. These data also support the development of closed-loop control systems that modulate the GPi spontaneous oscillations across PD patients using beta band stimulation-evoked responses.

Cardiovascular Effects of Spinal Cord Stimulation: The Highs, the Lows, and the Don't Knows.

BACKGROUND AND OBJECTIVES: There are many potential etiologies of impaired cardiovascular control, from chronic stress to neurodegenerative conditions or central nervous system lesions. Since 1959, spinal cord stimulation (SCS) has been reported to modulate blood pressure (BP), heart rate (HR), and HR variability (HRV), yet the specific stimulation sites and parameters to induce a targeted cardiovascular (CV) change for mitigating abnormal hemodynamics remain unclear. To investigate the ability and parameters of SCS to modulate the CV, we reviewed clinical studies using SCS with reported HR, BP, or HRV findings. MATERIALS AND METHODS: A keyword-based electronic search was conducted through MEDLINE, Embase, and PubMed data bases, last searched on February 3, 2023. Inclusion criteria were studies with human participants receiving SCS with comparison with SCS turned off, with reporting of either HR, HRV, or BP findings. Non-English studies, conference abstracts, and studies not reporting standalone effects of SCS when comparing SCS with non-SCS interventions were excluded. Results were plotted for visual analysis. When available, participant-specific stimulation parameters and effects were extracted and quantitatively analyzed using ordinary least squares regression. RESULTS: A total of 59 studies were included in this review; 51 studies delivered SCS invasively through implanted/percutaneous leads. Eight studies used noninvasive, transcutaneous electrodes. We found numerous reports of cervical, high thoracic, and mid-to-low thoracolumbar SCS increasing resting BP, and cervical/mid-to-low thoracolumbar SCS decreasing BP. The effect of SCS location on HR and HRV was equivocal. We were unable to analyze stimulation parameters owing to inadequate parameter reporting in many publications. CONCLUSIONS: Our findings suggest CV neuromodulation, particularly BP modulation, with SCS to be a promising frontier. Further research with larger randomized controlled trials and detailed reporting of SCS parameters will be necessary for appropriate evaluation of SCS as a CV therapy.

A low dimensional manifold of human exploratory behavior reveals opposing roles for apathy and anxiety.

Exploration-exploitation decision-making is a feature of daily life that is altered in a number of neuropsychiatric conditions. Humans display a range of exploration and exploitation behaviors, which can be affected by apathy and anxiety. It remains unknown how factors underlying decision-making generate the spectrum of observed exploration-exploitation behavior and how they relate to states of anxiety and apathy. Here, we report a latent structure underlying sequential exploration and exploitation decisions that explains variation in anxiety and apathy. 1001 participants in a gender-balanced sample completed a three-armed restless bandit task along with psychiatric symptom surveys. Using dimensionality reduction methods, we found that decision sequences reduced to a low-dimensional manifold. The axes of this manifold explained individual differences in the balance between states of exploration and exploitation and the stability of those states, as determined by a statistical mechanics model of decision-making. Position along the balance axis was correlated with opposing symptoms of behavioral apathy and anxiety, while position along the stability axis correlated with the level of emotional apathy. This result resolves a paradox over how these symptoms can be correlated in samples but have opposite effects on behavior. Furthermore, this work provides a basis for using behavioral manifolds to reveal relationships between behavioral dynamics and affective states, with important implications for behavioral measurement approaches to neuropsychiatric conditions.

Probabilistic tractography of the extracranial branches of the trigeminal nerve using diffusion tensor imaging.

PURPOSE: The peripheral course of the trigeminal nerves is complex and spans multiple bony foramen and tissue compartments throughout the face. Diffusion tensor imaging of these nerves is difficult due to the complex tissue interfaces and relatively low MR signal. The purpose of this work is to develop a method for reliable diffusion tensor imaging-based fiber tracking of the peripheral branches of the trigeminal nerve. METHODS: We prospectively acquired imaging data from six healthy adult participants with a 3.0-Tesla system, including T2-weighted short tau inversion recovery with variable flip angle (T2-STIR-SPACE) and readout segmented echo planar diffusion weighted imaging sequences. Probabilistic tractography of the ophthalmic, infraorbital, lingual, and inferior alveolar nerves was performed manually and assessed by two observers who determined whether the fiber tracts reached defined anatomical landmarks using the T2-STIR-SPACE volume. RESULTS: All nerves in all subjects were tracked beyond the trigeminal ganglion. Tracts in the inferior alveolar and ophthalmic nerve exhibited the strongest signal and most consistently reached the most distal landmark (58% and 67%, respectively). All tracts of the inferior alveolar and ophthalmic nerve extended beyond their respective third benchmarks. Tracts of the infraorbital nerve and lingual nerve were comparably lower-signal and did not consistently reach the furthest benchmarks (9% and 17%, respectively). CONCLUSION: This work demonstrates a method for consistently identifying and tracking the major nerve branches of the trigeminal nerve with diffusion tensor imaging.

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.

Effect of epidural spinal cord stimulation on female sexual function after spinal cord injury.

Sexual dysfunction is a common consequence for women with spinal cord injury (SCI); however, current treatments are ineffective, especially in the under-prioritized population of women with SCI. This case-series, a secondary analysis of the Epidural Stimulation After Neurologic Damage (E-STAND) clinical trial aimed to investigate the effect of epidural spinal cord stimulation (ESCS) on sexual function and distress in women with SCI. Three females, with chronic, thoracic, sensorimotor complete SCI received daily (24 h/day) tonic ESCS for 13 months. Questionnaires, including the Female Sexual Function Index (FSFI) and Female Sexual Distress Scale (FSDS) were collected monthly. There was a 3.2-point (13.2%) mean increase in total FSFI from baseline (24.5 ± 4.1) to post-intervention (27.8 ± 6.6), with a 4.8-50% improvement in the sub-domains of desire, arousal, orgasm and satisfaction. Sexual distress was reduced by 55%, with a mean decrease of 12 points (55.4%) from baseline (21.7 ± 17.2) to post-intervention (9.7 ± 10.8). There was a clinically meaningful change of 14 points in the International Standards for Neurological Classification of Spinal Cord Injury total sensory score from baseline (102 ± 10.5) to post-intervention (116 ± 17.4), without aggravating dyspareunia. ESCS is a promising treatment for sexual dysfunction and distress in women with severe SCI. Developing therapeutic interventions for sexual function is one of the most meaningful recovery targets for people with SCI. Additional large-scale investigations are needed to understand the long-term safety and feasibility of ESCS as a viable therapy for sexual dysfunction. Clinical Trial Registration:https://clinicaltrials.gov/ct2/show/NCT03026816, NCT03026816.

Flexible Multi-Step Hypothesis Testing of Human ECoG Data using Cluster-based Permutation Tests with GLMEs.

Background: Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. Methods: We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. Results: First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. Conclusions: We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power and accuracy leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Prolonged Physiological Stress Is Associated With a Lower Rate of Exploratory Learning That Is Compounded by Depression.

BACKGROUND: Stress is a major risk factor for depression, and both are associated with important changes in decision-making patterns. However, decades of research have only weakly connected physiological measurements of stress to the subjective experience of depression. Here, we examined the relationship between prolonged physiological stress, mood, and explore-exploit decision making in a population navigating a dynamic environment under stress: health care workers during the COVID-19 pandemic. METHODS: We measured hair cortisol levels in health care workers who completed symptom surveys and performed an explore-exploit restless-bandit decision-making task; 32 participants were included in the final analysis. Hidden Markov and reinforcement learning models assessed task behavior. RESULTS: Participants with higher hair cortisol exhibited less exploration (r = -0.36, p = .046). Higher cortisol levels predicted less learning during exploration (ß = -0.42, false discovery rate [FDR]-corrected p [pFDR] = .022). Importantly, mood did not independently correlate with cortisol concentration, but rather explained additional variance (ß = 0.46, pFDR = .022) and strengthened the relationship between higher cortisol and lower levels of exploratory learning (ß = -0.47, pFDR = .022) in a joint model. These results were corroborated by a reinforcement learning model, which revealed less learning with higher hair cortisol and low mood (ß = -0.67, pFDR = .002). CONCLUSIONS: These results imply that prolonged physiological stress may limit learning from new information and lead to cognitive rigidity, potentially contributing to burnout. Decision-making measures link subjective mood states to measured physiological stress, suggesting that they should be incorporated into future biomarker studies of mood and stress conditions.

Learning signatures of decision making from many individuals playing the same game.

Human behavior is incredibly complex and the factors that drive decision making--from instinct, to strategy, to biases between individuals--often vary over multiple timescales. In this paper, we design a predictive framework that learns representations to encode an individual's 'behavioral style', i.e. long-term behavioral trends, while simultaneously predicting future actions and choices. The model explicitly separates representations into three latent spaces: the recent past space, the short-term space, and the long-term space where we hope to capture individual differences. To simultaneously extract both global and local variables from complex human behavior, our method combines a multi-scale temporal convolutional network with latent prediction tasks, where we encourage embeddings across the entire sequence, as well as subsets of the sequence, to be mapped to similar points in the latent space. We develop and apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task, and analyze what our model's resulting embeddings reveal about the human decision making process. In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.

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.

Effect of lesion temperature on the durability of percutaneous radiofrequency rhizotomies to treat trigeminal neuralgia.

OBJECTIVE: Percutaneous radiofrequency rhizotomy is a common procedure for trigeminal neuralgia (TN) that creates thermocoagulative lesions in the trigeminal ganglion. Lesioning parameters for the procedure are left to the individual surgeon's discretion, and published guidance is primarily anecdotal. The purpose of this work was to assess the role of lesioning temperature on long-term surgical outcomes. METHODS: This was a retrospective analysis of patients who underwent percutaneous radiofrequency rhizotomy from 2009 to 2020. Patient data, including demographics, disease presentation, surgical treatment, and outcomes, were collected from medical records. The primary endpoint was the recurrence of TN pain. Univariate and multivariate Cox proportional hazards regressions were used to assess the impact of chosen covariates on pain-free survival. RESULTS: A total of 280 patients who had undergone 464 procedures were included in the analysis. Overall, roughly 80% of patients who underwent rhizotomy would have a recurrence within 10 years. Lower lesion temperature was predictive of longer periods without pain recurrence (HR 1.05, p < 0.001). The inclusion of lesion time, postoperative numbness, prior history of radiofrequency rhizotomy, surgeon, and multiple sclerosis as confounding variables did not affect the hazard ratio or the statistical significance of this finding. Postoperative numbness and the absence of multiple sclerosis were significant protective factors in the model. CONCLUSIONS: The study findings suggest that lower lesion temperatures and, separately, postoperative numbness result in improved long-term outcomes for patients with TN who undergo percutaneous radiofrequency rhizotomies. Given the limitations of retrospective analysis, the authors suggest that a prospective multisite clinical trial testing lesion temperatures would provide definitive guidance on this issue with specific recommendations about the number needed to treat and trial design.