Carbon Fiber Electrode Arrays for Cortical and Peripheral Neural Interfaces

Neural interfaces create a connection between neural structures in the body and external electronic devices. Brain-machine interfaces and bioelectric medicine therapies rely on the seamless integration of neural interfaces with the brain, nerves, or spinal cord. However, conventional neural interfaces cannot meet the demands of high channel count, signal fidelity, and signal longevity that these applications require. I investigated the damage resulting from conventional Utah arrays after multiple years of implantation in the cortex of a non-human primate as a possible explanation for these limitations. The neuron density around the electrode shanks was compared to the neuron density of nearby healthy tissue, finding a 73% loss in density around the electrodes. The explanted arrays were imaged and characterized for degradation. Coating cracks, tip breakage, and parylene cracks were the most common degradation type. A significantly higher number of tip breakage and coating crack occurrences were found on the edges of the arrays as compared to the middle. In this work, I made clear the need for a minimally damaging alternative to the Utah electrode array. Neural interfaces composed of carbon fiber electrodes, with a diameter of 6.8 microns, could enable a seamless integration with the body. Previous work resulted in an array of individuated carbon fiber electrodes that reliably recorded high signal-to-noise ratio neural signals from the brain for months. However, the carbon fiber arrays were limited by only 30% of the electrodes recording neural signals, despite inducing minimal inflammation. Additionally, it was relatively unknown if carbon fibers would make suitable long-term peripheral neural interfaces. Here, I illustrate the potential of carbon fiber electrodes to meet the needs of a variety of neural applications. First, I optimized state-of-the-art carbon fiber electrodes to reliably record single unit electrophysiology from the brain. By analyzing the previous manufacturing process, the cause of the low recording yield of the carbon fiber arrays was identified as the consistency of the electrode tip. A novel laser cutting technique was developed to produce a consistent carbon fiber tip geometry, resulting in a near tripling of recording yield of high amplitude chronic neural signals. The longevity of the carbon fiber arrays was also addressed. The conventional polymer coating was compared against platinum iridium coating and an oxygen plasma treatment, both of which outperformed the polymer coating. In this work, I customized carbon fiber electrodes for reliable, long-term neural recording. Secondly, I translated the carbon fiber technology from the brain to the periphery in an architecture appropriate for chronic implantation. The insertion of carbon fibers into the stiffer structures in the periphery is enabled by sharpening the carbon fibers. The sharpening process combines a butane flame to sharpen the fibers with a water bath to protect the base of the array. Sharpened carbon fiber arrays recorded electrophysiology from the rat vagus nerve and feline dorsal root ganglia, both structures being important targets for bioelectric medicine therapies. The durability of carbon fibers was also displayed when partially embedded carbon fibers in medical-grade silicone withstood thousands of repeated bends without fracture. This work showed that carbon fibers have the electrical and structural properties necessary for chronic application. Overall, this work highlights the vast potential of carbon fiber electrodes. Through this thesis, future brain-machine interfaces and bioelectric medicine therapies may utilize arrays of sub-cellular electrodes such as carbon fibers in medical applications.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169982/1/elissajw_1.pd

Modulating and Monitoring Autonomic Nerves for Glycemic Control

Diabetic patients suffer from a long-term condition that results in high blood glucose levels (hyperglycemia). Many medications for diabetes lose their glycemic control effectiveness over time and patient compliance to these medications is a major challenge. Glycemic control is a vital continuous process and is innately regulated by the endocrine and autonomic nervous systems. There is an opportunity for developing an implantable and automated treatment for diabetic patients by accurately detecting and altering neural activity in autonomic nerves. Renal nerves provide neural control for glucose reabsorption in the kidneys, and the vagus nerve conveys important glucose regulation signals to and from the liver and pancreas. This dissertation investigated stimulation of renal nerves for glycemic control, assembled an implantation procedure for neural interface arrays designed for autonomic nerves, and recorded physiological action potential signals in the vagus nerve. In a first study, stimulation of renal nerves in anesthetized, normal rats at kilohertz frequency (33 kHz) showed a notable average increase in urine glucose excretion (+24.5%). In contrast, low frequency (5 Hz) stimulation of renal nerves showed a substantial decrease in urine glucose excretion (−40.4%). However, these responses may be associated with urine flow rate. In a second study, kilohertz frequency stimulation (50 kHz) of renal nerves in anesthetized, diabetic rats showed a significant average decrease (-168.4%) in blood glucose concentration rate, and an increase (+18.9%) in the overall average area under the curve for urine glucose concentration, with respect to values before stimulation. In a third study, an innovative procedure was assembled for the chronic implantation of novel intraneural MIcroneedle Nerve Arrays (MINAs) in rat vagus nerves. Two array attachment approaches (fibrin sealant and rose-bengal bonding) were investigated to secure non-wired MINAs in nerves. The fibrin sealant approach was unsuccessful in securing the MINA-nerve interface for 4- and 8-week implant durations. The rose-bengal coated MINAs were in close proximity to axons (≤ 50 μm) in 75% of 1-week and 14% of 6-week implants with no significant harm to the implanted nerves or the overall health of the rats. In a fourth study, physiological neural activity in the vagus nerve of anesthetized rats was recorded using Carbon Fiber Microelectrode Arrays (CFMAs). Neural activity was observed on 51% of inserted functional carbon fibers, and 1-2 neural clusters were sorted on each carbon fiber with activity. The mean peak-to-peak amplitudes of the sorted clusters were 15.1-91.7 µV with SNR of 2.0-7.0. Conducting signals were detected in the afferent direction (0.7-1.0 m/sec conduction velocities) and efferent direction (0.7-8.8 m/sec). These conduction velocities are within the conduction velocity range of unmyelinated and myelinated vagus fibers. Furthermore, changes in vagal nerve activity were monitored in breathing and blood glucose modulated conditions. This dissertation, to our knowledge, was the first to demonstrate glucose regulation benefits by stimulation of renal nerves, chronically implant intraneural arrays in rat vagus nerves, and record physiological action potential in vagus nerves using multi-channel intraneural electrodes. Future work is needed to evaluate the long-term glucose regulation benefits of stimulation of renal nerves, and assess the tissue reactivity and recording integrity of implanted intraneural electrodes in autonomic nerves. This work supports the potential development of an alternative implantable treatment modality for diabetic patients by modulating and monitoring neural activity in autonomic nerves.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155245/1/ajiman_1.pd

Carbon Nanomaterials Embedded in Conductive Polymers: A State of the Art

Carbon nanomaterials are at the forefront of the newest technologies of the third millennium, and together with conductive polymers, represent a vast area of indispensable knowledge for developing the devices of tomorrow. This review focusses on the most recent advances in the field of conductive nanotechnology, which combines the properties of carbon nanomaterials with conjugated polymers. Hybrid materials resulting from the embedding of carbon nanotubes, carbon dots and graphene derivatives are taken into consideration and fully explored, with discussion of the most recent literature. An introduction into the three most widely used conductive polymers and a final section about the most recent biological results obtained using carbon nanotube hybrids will complete this overview of these innovative and beyond belief materials.The European Union is acknowledged for funding this research through Horizon 2020 MSCA-IF-2018 No 838171 (TEXTHIOL). IMDEA Nanociencia acknowledges support from the “Severo Ochoa” Programme for Centres of Excellence in R&D (MINECO, Grant SEV- 2016-0686). European Regional Development fund Project “MSCAfellow4 @ MUNI” supported by MEYS CR (No. CZ.02.2.69/0.0/0.0/20_079/0017045) is acknowledged. N.A. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 753293, acronym NanoBEAT

Low-dimensional representations of neural time-series data with applications to peripheral nerve decoding

Bioelectronic medicines, implanted devices that influence physiological states by peripheral neuromodulation, have promise as a new way of treating diverse conditions from rheumatism to diabetes. We here explore ways of creating nerve-based feedback for the implanted systems to act in a dynamically adapting closed loop. In a first empirical component, we carried out decoding studies on in vivo recordings of cat and rat bladder afferents. In a low-resolution data-set, we selected informative frequency bands of the neural activity using information theory to then relate to bladder pressure. In a second high-resolution dataset, we analysed the population code for bladder pressure, again using information theory, and proposed an informed decoding approach that promises enhanced robustness and automatic re-calibration by creating a low-dimensional population vector. Coming from a different direction of more general time-series analysis, we embedded a set of peripheral nerve recordings in a space of main firing characteristics by dimensionality reduction in a high-dimensional feature-space and automatically proposed single efficiently implementable estimators for each identified characteristic. For bioelectronic medicines, this feature-based pre-processing method enables an online signal characterisation of low-resolution data where spike sorting is impossible but simple power-measures discard informative structure. Analyses were based on surrogate data from a self-developed and flexibly adaptable computer model that we made publicly available. The wider utility of two feature-based analysis methods developed in this work was demonstrated on a variety of datasets from across science and industry. (1) Our feature-based generation of interpretable low-dimensional embeddings for unknown time-series datasets answers a need for simplifying and harvesting the growing body of sequential data that characterises modern science. (2) We propose an additional, supervised pipeline to tailor feature subsets to collections of classification problems. On a literature standard library of time-series classification tasks, we distilled 22 generically useful estimators and made them easily accessible.Open Acces

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine