Long-Term Reductions in Opioid Medication Use After Spinal Stimulation: A Claims Analysis Among Commercially-Insured Population.

Purpose: Chronic, non-cancer pain significantly and negatively impacts patient quality of life. Neuromodulation is a major component of multi-modal interdisciplinary approaches to chronic pain management, which includes opioid and nonopioid medications. In randomized controlled trials, spinal cord stimulation (SCS) has been shown to reduce pain and decrease short-term opioid use for patients. This study sought to evaluate the effect of SCS on longer term opioid and non-opioid pain medication usage among patients over ≥3 years of follow-up. Patients and Methods: Claims analysis was conducted using the Merative™ MarketScan® Commercial Database. Patients aged ≥18 who initiated SCS between 1/1/2010 and 3/31/2021 with ≥1 year of baseline data and ≥3 years of follow-up data were included. Opioid discontinuation, daily dose (DD) reduction, proportion of days covered (PDC), concomitant co-medication with benzodiazepines and/or gabapentinoids, and polypharmacy were evaluated during the baseline and follow-up periods. Adjusted logistic regression was used to evaluate the impact of baseline dosages on discontinuation and dose reduction. Results: During follow-up, 60% of 2,669 SCS patients either discontinued opioid use or reduced opioid DD by at least 20% from baseline; another 15% reduced DD by 1-19%. Logistic regression showed patients with higher baseline dosages were less likely to discontinue opioids completely (odds ratio[OR] 95% confidence intervals[CI]: 0.31[0.18,0.54]) but more likely to reduce their daily dose (OR[CI]: 7.14[4.00,12.73], p<0.001). Mean PDC with opioids decreased from 0.58 (210 of 365 days) at baseline to 0.51 at year 3 (p<0.001). With SCS, co-medication with benzodiazepines decreased from 47.3% at baseline to 30.3% at year 3, co-medication with gabapentinoids reduced from 58.6% to 42.2%, and polypharmacy dropped from 15.6% to 9.6% (all p<0.001). Conclusion: Approximately three-quarters of patients who received SCS therapy either discontinued or reduced systemic opioid use over the study period. SCS could assist in reducing long-term reliance on opioids and other pain medications to treat chronic non-cancer pain.

Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods.

Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks. Teaser: A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.

Effect of Anisotropic Brain Conductivity on Patient-Specific Volume of Tissue Activation in Deep Brain Stimulation for Parkinson Disease.

OBJECTIVE: Deep brain stimulation (DBS) modeling can improve surgical targeting by quantifying the spatial extent of stimulation relative to subcortical structures of interest. A certain degree of model complexity is required to obtain accurate predictions, particularly complexity regarding electrical properties of the tissue around DBS electrodes. In this study, the effect of anisotropy on the volume of tissue activation (VTA) was evaluated in an individualized manner. METHODS: Tissue activation models incorporating patient-specific tissue conductivity were built for 40 Parkinson disease patients who had received bilateral subthalamic nucleus (STN) DBS. To assess the impact of local changes in tissue anisotropy, one VTA was computed at each electrode contact using identical stimulation parameters. For comparison, VTAs were also computed assuming isotropic tissue conductivity. Stimulation location was considered by classifying the anisotropic VTAs relative to the STN. VTAs were characterized based on volume, spread in three directions, sphericity, and Dice coefficient. RESULTS: Incorporating anisotropy generated significantly larger and less spherical VTAs overall. However, its effect on VTA size and shape was variable and more nuanced at the individual patient and implantation levels. Dorsal VTAs had significantly higher sphericity than ventral VTAs, suggesting more isotropic behavior. Contrastingly, lateral and posterior VTAs had significantly larger and smaller lateral-medial spreads, respectively. Volume and spread correlated negatively with sphericity. CONCLUSION: The influence of anisotropy on VTA predictions is important to consider, and varies across patients and stimulation location. SIGNIFICANCE: This study highlights the importance of considering individualized factors in DBS modeling to accurately characterize the VTA.

Electrical Stimulation of Regenerative Peripheral Nerve Interfaces (RPNIs) Induces Referred Sensations in People With Upper Limb Loss.

Individuals with upper limb loss lack sensation of the missing hand, which can negatively impact their daily function. Several groups have attempted to restore this sensation through electrical stimulation of residual nerves. The purpose of this study was to explore the utility of regenerative peripheral nerve interfaces (RPNIs) in eliciting referred sensation. In four participants with upper limb loss, we characterized the quality and location of sensation elicited through electrical stimulation of RPNIs over time. We also measured functional stimulation ranges (sensory perception and discomfort thresholds), sensitivity to changes in stimulation amplitude, and ability to differentiate objects of different stiffness and sizes. Over a period of up to 54 months, stimulation of RPNIs elicited sensations that were consistent in quality (e.g. tingling, kinesthesia) and were perceived in the missing hand and forearm. The location of elicited sensation was partially-stable to stable in 13 of 14 RPNIs. For 5 of 7 RPNIs tested, participants demonstrated a sensitivity to changes in stimulation amplitude, with an average just noticeable difference of 45 nC. In a case study, one participant was provided RPNI stimulation proportional to prosthetic grip force. She identified four objects of different sizes and stiffness with 56% accuracy with stimulation alone and 100% accuracy when stimulation was combined with visual feedback of hand position. Collectively, these experiments suggest that RPNIs have the potential to be used in future bi-directional prosthetic systems.