Analyzing the Effects of Parameters for Tremor Modulation via Phase-Locked Electrical Stimulation on a Peripheral Nerve

(1) Background: Non-invasive neuromodulation is a promising alternative to medication or deep-brain stimulation treatment for Parkinson’s Disease or essential tremor. In previous work, we developed and tested a wearable system that modulates tremor via the non-invasive, electrical stimulation of peripheral nerves. In this article, we examine the proper range and the effects of various stimulation parameters for phase-locked stimulation. (2) Methods: We recruited nine participants with essential tremor. The subjects performed a bean-transfer task that mimics an eating activity to elicit kinetic tremor while using the wearable stimulation system. We examined the effects of stimulation with a fixed duty cycle, at different stimulation amplitudes and frequencies. The epochs of stimulation were locked to one of four phase positions of ongoing tremor, as measured with an accelerometer. We analyzed stimulation-evoked changes of the frequency and amplitude of tremor. (3) Results: We found that the higher tremor amplitude group experienced a higher rate of tremor power reduction (up to 65%) with a higher amplitude of stimulation when the stimulation was applied at the ±peak of tremor phase. (4) Conclusions: The stimulation parameter can be adjusted to optimize tremor reduction, and this study lays the foundation for future large-scale parameter optimization experiments for personalized peripheral nerve stimulation.

Embedding Digital Chronotherapy into Bioelectronic Medicines

Biological rhythms permeate all living organisms at a variety of timescales. These rhythms are fundamental to physiological homeostasis, and their disruption is thought to play a key role in the initiation, progression, and expression of disease. In the last two decades, neuromodulation has been established as an effective adjunct therapy for medically refractory neurological disorders. To date, however, due to the limited sensing and algorithm capabilities of neuromodulation devices, exploring the influence of biological rhythms on therapy efficacy has not been feasible. However, with the development of new bioelectronic devices capable of long-term data recording and adaptive stimulation parameter adjustments, clinical neuroscience researchers are now gaining unprecedented insight into patient physiology across a variety of neurological diseases, including longitudinal rhythmic behavior. In this perspective, we propose that future bioelectronic devices should integrate chronobiological considerations in their physiological control structure to maximize the benefits of therapy. We specifically highlight this need for deep brain stimulation (DBS) chronotherapy, where the DBS therapeutic dosage would be titrated based on the time-of-day and synchronized to each patient’s individual chronotype/sleep-wake cycle. This is motivated by preliminary longitudinal data recorded from both patients with Parkinson’s disease (PD) and epilepsy, which show periodic symptom biomarkers synchronized to sub-daily (ultradian), daily (circadian), and longer time scale (infradian) rhythms. In addition, considering side effects, tonic stimulation can undermine diurnal patterns and cause fragmentation of sleep-wake rhythms. Based on these observations, we suggest a control structure for future bioelectronic devices which incorporates anticipatory, time-based adaptation of stimulation control, locked to patient-specific biological rhythms, as an adjunct to classical feedforward and feedback control methods. Initial results from three case studies using chronotherapy-enabled prototypes will illustrate the concept. The proposed control architecture for a future bioelectronic implant mimics more closely the classical integration of adaptive, feedforward, and feedback control methods found in physiology, and could be useful as a general method for personalized therapy refinement.

Impact of stimulation location relative to grey and white matter on single pulse electrical stimulation responses in the human brain

Background: Electrical neuromodulation is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, possibly a result of our limited understanding of the mechanisms and the massive stimulation parameter space. Objective/Hypothesis: To better understand the role different parameters play in inducing a response, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration and location in the brain and relative to grey and white matter. Methods: We measured voltage peak amplitudes and area under the curve of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N=52, n=719 stimulation sites). Results: Increasing stimulation pulse width increased response values near the stimulation location. Increasing stimulation amplitude (current) increased responses nonlinearly. Locally (<15 mm from the stimulation site), stimulation closer to the grey matter-white matter boundary induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. These relationships were different between cingulate, lateral prefrontal, and lateral temporal cortical stimulation. Conclusion: These results demonstrate the importance of location and stimulation parameters in inducing a specific output and indicate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter may be needed for network activation, suggesting that stimulation location can be tailored for a specific outcome, key to informed neuromodulatory therapy.

8-Channel Biphasic Current Stimulator Optimized for Retinal Prostheses

Retinal prostheses substitute the functionality of damaged photoreceptors by electrically stimulating retinal ganglion cells (RGCs). RGCs, densely packed in a small region, needs a high spatial resolution of the microelectrode, which in turn raises its impedance. Therefore, the high output impedance circuit and the high compliance output voltage are the key characteristics of the current-source-based stimulator. Also, as the system is intended to implant in the retina, the stimulation parameter should be optimized for efficiency and safety. Here we designed 8-channel neural stimulator customized to the retinal ganglion cell. Designed IC is fabricated in the TSMC 0.18 μm 1P6M RF CMOS process with 3.3 V supply voltage, occupying the 1060 μm×950 μm area.

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.

Selective Surface Electrostimulation of the Denervated Zygomaticus Muscle

This article describes a first attempt to generate a standardized and safe selective surface electrostimulation (SES) protocol, including detailed instructions on electrode placement and stimulation parameter choice to obtain a selective stimulation of the denervated zygomaticus muscle (ZYG), without unwanted simultaneous activation of other ipsilateral or contralateral facial muscles. Methods: Single pulse stimulation with biphasic triangular and rectangular waveforms and pulse widths (PW) of 1000, 500, 250, 100, 50, 25, 15, 10, 5, 2, 1 ms, at increasing amplitudes between 0.1 and 20 mA was performed. Stimulations delivered in trains were assessed at a PW of 50 ms only. The stimulation was considered successful exclusively if it drew the ipsilateral corner of the mouth upwards and outwards, without the simultaneous activation of other ipsilateral or contralateral facial muscles. I/t curves, accommodation quotient, rheobase, and chronaxie were regularly assessed over 1-year follow-up. Results: 5 facial paralysis patients were assessed. Selective ZYG response in absence of discomfort and unselective contraction of other facial muscle was reproducibly obtained for all the assessed patients. The most effective results with single pulses were observed with PW ≥ 50 ms. The required amplitude was remarkably lower (≤5 mA vs. up to 15 mA) in freshly diagnosed (≤3 months) than in long-term facial paralysis patients (>5 years). Triangular was more effective than rectangular waveform, mostly because of the lower discomfort threshold of the latter. Delivery of trains of stimulation showed similar results to the single pulse setting, though lower amplitudes were necessary to achieve the selective ZYG response. Initial reinnervation signs could be detected effectively by needle-electromyography (n-EMG). Conclusion: It is possible to define stimulation parameters able to elicit an effective selective stimulation of a specific facial muscle, in our case, of the ZYG, without causing discomfort to the patient and without causing unwanted unspecific reactions of other ipsilateral and/or contralateral facial muscles. We observed that the SES success is strongly conditioned by the correct electrode placement, which ideally should exclusively interest the area of the target muscles and its immediate proximity.

DyNeuMo Mk-1: A Fully-Implantable, Motion-Adaptive Neurostimulator with Configurable Response Algorithms

There is growing interest in using adaptive neuro-modulation to provide a more personalized therapy experience that might improve patient outcomes. This paper describes the design of the ‘DyNeuMo Mk-1’, a fully-implantable, motion-adaptive research stimulator that titrates stimulation based on the patient’s movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability and modest cost. We used a three-axis accelerometer and its embedded digital motion processor to enable real-time stimulation adaption based on configurable motion parameters. The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. The implantable system was designed, prototyped and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. With the design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms. The design highlights how consumer electronics technology can be leveraged for efficient and reliable medical device development. The implantable system automatically provides activity- and posture-based responsive stimulation which can be configured by the clinician to optimize therapy. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as shocks for urinary incontinence.