Objective Our goal is to develop an interface that integrates chronic monitoring of lower urinary tract (LUT) activity with stimulation of peripheral pathways. Approach Penetrating microelectrodes were implanted in sacral dorsal root ganglia (DRG) of adult male felines. Peripheral electrodes were placed on or in the pudendal nerve, bladder neck and near the external urethral sphincter. Supra-pubic bladder catheters were implanted for saline infusion and pressure monitoring. Electrode and catheter leads were enclosed in an external housing on the back. Neural signals from microelectrodes and bladder pressure of sedated or awake-behaving felines were recorded under various test conditions in weekly sessions. Electrodes were also stimulated to drive activity. Main results LUT single- and multi-unit activity was recorded for 4 to 11 weeks in four felines. As many as 18 unique bladder pressure single-units were identified in each experiment. Some channels consistently recorded bladder afferent activity for up to 41 days, and we tracked individual single-units for up to 23 days continuously. Distension-evoked and stimulation-driven (DRG and pudendal) bladder emptying was observed, during which LUT sensory activity was recorded. Significance This chronic implant animal model allows for behavioral studies of LUT neurophysiology and will allow for continued development of a closed-loop neuroprosthesis for bladder control.

IMPORTANCE Chronic pain is debilitating and profoundly affects health-related quality of life. Spinal cord stimulation (SCS) is a well-established therapy for chronic pain; however, SCS has been limited by the inability to directly measure the elicited neural response, precluding confirmation of neural activation and continuous therapy. A novel SCS system measures the evoked compound action potentials (ECAPs) to produce a real-time physiological closed-loop control system. OBJECTIVE To determine whether ECAP-controlled, closed-loop SCS is associated with better outcomes compared with fixed-output, open-loop SCS at 24 months following implant. DESIGN, SETTING, AND PARTICIPANTSThe Evoke study was a double-blind, randomized, controlled, parallel arm clinical trial with 36 months of follow-up. Participants were enrolled from February 2017 to 2018, and the study was conducted at 13 US investigation sites. SCS candidates with chronic, intractable back and leg pain refractory to conservative therapy, who consented, were screened. Key eligibility criteria included overall, back, and leg pain visual analog scale score of 60 mm or more; Oswestry Disability Index score of 41 to 80; stable pain medications; and no previous SCS. Analysis took place from October 2020 to April 2021. INTERVENTIONS ECAP-controlled, closed-loop SCS was compared with fixed-output, open-loop SCS.MAIN OUTCOMES AND MEASURES Reported here are the 24-month outcomes of the trial, which include all randomized patients in the primary and safety analyses. The primary outcome was a reduction of 50% or more in overall back and leg pain assessed at 3 and 12 months (previously published). RESULTSOf 134 randomized patients, 65 (48.5%) were female and the mean (SD) age was 55.2 (10.6) years. At 24 months, significantly more closed-loop than open-loop patients were responders (Ն50% reduction) in overall pain (53 of 67 [79.1%] in the closed-loop group; 36 of 67 [53.7%] in the open-loop group; difference, 25.4% [95% CI, 10.0%-40.8%]; P = .001). There was no difference in safety profiles between groups (difference in rate of study-related adverse events: 6.0 [95% CI, −7.8 to 19.7]). Improvements were also observed in health-related quality of life, physical and emotional functioning, and sleep, in parallel with opioid reduction or elimination. Objective neurophysiological measurements substantiated the clinical outcomes and provided evidence of activation of inhibitory pain mechanisms.CONCLUSIONS AND RELEVANCE ECAP-controlled, closed-loop SCS, which elicited a more consistent neural response, was associated with sustained superior pain relief at 24 months, consistent with the 3-and 12-month outcomes.

Overactive bladder (OAB) patients suffer from a frequent urge to urinate, which can lead to a poor quality of life. Current neurostimulation therapy uses open-loop electrical stimulation to alleviate symptoms. Continuous stimulation facilitates habituation of neural pathways and consumes battery power. Sensory feedback-based closed-loop stimulation may offer greater clinical benefit by driving bladder relaxation only when bladder contractions are detected, leading to increased bladder capacity. Effective delivery of such sensory feedback, particularly in real-time, is necessary to accomplish this goal. We implemented a Kalman filter-based model to estimate bladder pressure in real-time using unsorted neural recordings from sacral-level dorsal root ganglia, achieving a 0.88 ± 0.16 correlation coefficient fit across thirty-six normal and simulated OAB bladder fills in five experiments. We also demonstrated closed-loop neuromodulation using the estimated pressure to trigger pudendal nerve stimulation, which increased bladder capacity by 40% in two trials. An offline analysis indicated that unsorted neural signals had a similar stability over time as compared to sorted single units, which would require a higher computational load. We believe this study demonstrates the utility of decoding bladder pressure from neural activity for closed-loop control; however, real-time validation during behavioral studies is necessary prior to clinical translation.

A closed-loop device for bladder control may offer greater clinical benefit compared to current open-loop stimulation devices. Previous studies have demonstrated the feasibility of using single-unit recordings from sacral-level dorsal root ganglia (DRG) for decoding bladder pressure. Automatic online sorting, to differentiate single units, can be computationally heavy and unreliable, in contrast to simple multi-unit thresholded activity. In this study, the feasibility of using DRG multi-unit recordings to decode bladder pressure was examined. A broad range of feature selection methods and three algorithms (multivariate linear regression, basic Kalman filter, and a nonlinear autoregressive moving average model) were used to create training models and provide validation fits to bladder pressure for data collected in seven anesthetized feline experiments. A non-linear autoregressive moving average (NARMA) model with regularization provided the most accurate bladder pressure estimate, based on normalized root-mean-squared error, NRMSE, (17 ± 7%). A basic Kalman filter yielded the highest similarity to the bladder pressure with an average correlation coefficient, CC, of 0.81 ± 0.13. The best algorithm set (based on NRMSE) was further evaluated on data obtained from a chronic feline experiment. Testing results yielded a NRMSE and CC of 10.7% and 0.61, respectively from a model that was trained on data recorded 2 weeks prior. From offline analysis, implementation of NARMA in a closed-loop scheme for detecting bladder contractions would provide a robust control signal. Ultimate integration of closed-loop algorithms in bladder neuroprostheses will require evaluations of parameter and signal stability over time.