Deep-learning segmentation of fascicles from microCT of the human vagus nerve

IntroductionMicroCT of the three-dimensional fascicular organization of the human vagus nerve provides essential data to inform basic anatomy as well as the development and optimization of neuromodulation therapies. To process the images into usable formats for subsequent analysis and computational modeling, the fascicles must be segmented. Prior segmentations were completed manually due to the complex nature of the images, including variable contrast between tissue types and staining artifacts.MethodsHere, we developed a U-Net convolutional neural network (CNN) to automate segmentation of fascicles in microCT of human vagus nerve.ResultsThe U-Net segmentation of ~500 images spanning one cervical vagus nerve was completed in 24 s, versus ~40 h for manual segmentation, i.e., nearly four orders of magnitude faster. The automated segmentations had a Dice coefficient of 0.87, a measure of pixel-wise accuracy, thus suggesting a rapid and accurate segmentation. While Dice coefficients are a commonly used metric to assess segmentation performance, we also adapted a metric to assess fascicle-wise detection accuracy, which showed that our network accurately detects the majority of fascicles, but may under-detect smaller fascicles.DiscussionThis network and the associated performance metrics set a benchmark, using a standard U-Net CNN, for the application of deep-learning algorithms to segment fascicles from microCT images. The process may be further optimized by refining tissue staining methods, modifying network architecture, and expanding the ground-truth training data. The resulting three-dimensional segmentations of the human vagus nerve will provide unprecedented accuracy to define nerve morphology in computational models for the analysis and design of neuromodulation therapies

Next-Generation Diamond Electrodes for Neurochemical Sensing: Challenges and Opportunities

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Carbon-based electrodes combined with fast-scan cyclic voltammetry (FSCV) enable neurochemical sensing with high spatiotemporal resolution and sensitivity. While their attractive electrochemical and conductive properties have established a long history of use in the detection of neurotransmitters both in vitro and in vivo, carbon fiber microelectrodes (CFMEs) also have limitations in their fabrication, flexibility, and chronic stability. Diamond is a form of carbon with a more rigid bonding structure (sp3-hybridized) which can become conductive when boron-doped. Boron-doped diamond (BDD) is characterized by an extremely wide potential window, low background current, and good biocompatibility. Additionally, methods for processing and patterning diamond allow for high-throughput batch fabrication and customization of electrode arrays with unique architectures. While tradeoffs in sensitivity can undermine the advantages of BDD as a neurochemical sensor, there are numerous untapped opportunities to further improve performance, including anodic pretreatment, or optimization of the FSCV waveform, instrumentation, sp2 /sp3 character, doping, surface characteristics, and signal processing. Here, we review the state-of-the-art in diamond electrodes for neurochemical sensing and discuss potential opportunities for future advancements of the technology. We highlight our team’s progress with the development of an all-diamond fiber ultramicroelectrode as a novel approach to advance the performance and applications of diamond-based neurochemical sensors

Neuroprosthetic devices: Inputs and outputs.

Prior studies have demonstrated that the firing rate of cortical neurons can be volitionally modulated by a subject to generate a controllable output signal; this neural output signal can then be manipulated to direct a robotic arm, a cursor on a computer screen, or other interface device. The burgeoning field of neural control has led to a number of innovative applications, known more commonly as neuroprosthetic devices. Neuroprosthetic devices have the potential to return some degree of functionality to the over 250,000 Americans with incapacitating spinal cord injuries, or allow healthy subjects to control electronic devices in their everyday lives. The research presented here consists of three studies focused on improving the current generation of neuroprosthetic devices. In the first study, we introduced and evaluated a Bayesian maximum-likelihood estimation (bMLE) strategy to identify optimized training data for neuroprosthetic devices. By limiting initial decoding assumptions and training only on relevant neural data, accurate neural-control was possible with as few as two neurons, using minimal training data and no a-priori movement measurements for calibration. Moreover, implanted subjects obtained useful prosthetic control using local field potentials and neurons from cingulate cortex as input. In the second study, we refined a method to electrochemically deposit surfactant-templated ordered poly(3,4-ethylenedioxythiophene) (PEDOT) films on the recording sites of standard Michigan probes, and evaluated the in vivo efficacy of these modified sites in recording chronic neural activity. PEDOT sites were found to outperform control sites in terms of signal-to-noise ratio and number of viable unit potentials---thereby improving the quality of neural input sources to the neuroprosthetic device. In the third study, we evaluated a technique known as common average referencing (CAR) to generate a more ideal reference electrode for microelectrode recordings. CAR was found to drastically outperform standard types of electrical referencing, reducing noise by more than 30 percent. As a result of the reduced noise floor, arrays referenced to a CAR yielded almost 60 percent more discernible neural units than traditional methods of electrical referencing---again improving the quality of neural input sources to a neuroprosthetic device.Ph.D.Applied SciencesBiological SciencesBiomedical engineeringNeurosciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/127110/2/3354316.pd

Non-clinical and Pre-clinical Testing to Demonstrate Safety of the Barostim Neo Electrode for Activation of Carotid Baroreceptors in Chronic Human Implants

The Barostim neo™ electrode was developed by CVRx, Inc.to deliver baroreflex activation therapy (BAT)™ to treat hypertension and heart failure. The neo electrode concept was designed to deliver electrical stimulation to the baroreceptors within the carotid sinus bulb, while minimizing invasiveness of the implant procedure. This device is currently CE marked in Europe, and in a Pivotal (akin to Phase III) Trial in the United States. Here we present the in vitro and in vivo safety testing that was completed in order to obtain necessary regulatory approval prior to conducting human studies in Europe, as well as an FDA Investigational Device Exemption (IDE) to conduct a Pivotal Trial in the United States. Stimulated electrodes (10 mA, 500 μs, 100 Hz) were compared to unstimulated electrodes using optical microscopy and several electrochemical techniques over the course of 27 weeks. Electrode dissolution was evaluated by analyzing trace metal content of solutions in which electrodes were stimulated. Lastly, safety testing under Good Laboratory Practice guidelines was conducted in an ovine animal model over a 12 and 24 week time period, with results processed and evaluated by an independent histopathologist. Long-term stimulation testing indicated that the neo electrode with a sputtered iridium oxide coating can be stimulated at maximal levels for the lifetime of the implant without clinically significant dissolution of platinum or iridium, and without increasing the potential at the electrode interface to cause hydrolysis or significant tissue damage. Histological examination of tissue that was adjacent to the neo electrodes indicated no clinically significant signs of increased inflammation and no arterial stenosis as a result of 6 months of continuous stimulation. The work presented here involved rigorous characterization and evaluation testing of the neo electrode, which was used to support its safety for chronic implantation. The testing strategies discussed provide a starting point and proven framework for testing new neuromodulation electrode concepts to support regulatory approval for clinical studies

High Charge Storage Capacity Micro Electrode Array on a Wire for Implantable Neuromuscular Applications

In this paper, we demonstrate fabrication of high charge storage capacity microelectrode arrays (MEAs) on a thin gold wire, targeted towards implantable neuromuscular applications. The patterned wire is flexible and can be brought in contact with the targeted area with ease unlike traditional MEAs. The key contribution here is the demonstration of wire patterning, followed by electro chemical deposition of PEDOT (Poly 3,4-Ethylene Dioxy Thiophene)/Functionalized Multi walled Carbon Nanotubes (FMWCNT) hybrid nano-composite material in selected areas that resulted in biocompatible, high charge storage capacity (CSC) and lower electrode interface impedance MEA on wire

Using a Common Average Reference to Improve Cortical Neuron Recordings From Microelectrode Arrays

In this study, we propose and evaluate a technique known as common average referencing (CAR) to generate a more ideal reference electrode for microelectrode recordings. CAR is a computationally simple technique, and therefore amenable to both on-chip and real-time applications. CAR is commonly used in EEG, where it is necessary to identify small signal sources in very noisy recordings. To study the efficacy of common average referencing, we compared CAR to both referencing with a stainless steel bone-screw and a single microelectrode site. Data consisted of in vivo chronic recordings in anesthetized Sprague-Dawley rats drawn from prior studies, as well as previously unpublished data. By combining the data from multiple studies, we generated and analyzed one of the more comprehensive chronic neural recording datasets to date. Reference types were compared in terms of noise level, signal-to-noise ratio, and number of neurons recorded across days. Common average referencing was found to drastically outperform standard types of electrical referencing, reducing noise by >30%. As a result of the reduced noise floor, arrays referenced to a CAR yielded almost 60% more discernible neural units than traditional methods of electrical referencing. CAR should impart similar benefits to other microelectrode recording technologies—for example, chemical sensing—where similar differential recording concepts apply. In addition, we provide a mathematical justification for CAR using Gauss-Markov theorem and therefore help place the application of CAR into a theoretical context