Technologies to Study Action Potential Propagation With a Focus on HD-MEAs

Axons convey information in neuronal circuits via reliable conduction of action potentials (APs) from the axon initial segment (AIS) to the presynaptic terminals. Recent experimental findings increasingly evidence that the axonal function is not limited to the simple transmission of APs. Advances in subcellular-resolution recording techniques have shown that axons display activity-dependent modulation in spike shape and conduction velocity, which influence synaptic strength and latency. We briefly review here, how recent methodological developments facilitate the understanding of the axon physiology. We included the three most common methods, i.e., genetically encoded voltage imaging (GEVI), subcellular patch-clamp and high-density microelectrode arrays (HD-MEAs). We then describe the potential of using HD-MEAs in studying axonal physiology in more detail. Due to their robustness, amenability to high-throughput and high spatiotemporal resolution, HD-MEAs can provide a direct functional electrical readout of single cells and cellular ensembles at subcellular resolution. HD-MEAs can, therefore, be employed in investigating axonal pathologies, the effects of large-scale genomic interventions (e.g., with RNAi or CRISPR) or in compound screenings. A combination of extracellular microelectrode arrays (MEAs), intracellular microelectrodes and optical imaging may potentially reveal yet unexplored repertoires of axonal functions

Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces

High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes in a single chip with an area of few square millimeters. The obtained electrode density is comparable and even higher than the typical density of neuronal cells in cortical cultures. Commercially available HDMEA-based acquisition systems are able to record the neural activity from the whole array at the same time with submillisecond resolution. These devices are a very promising tool and are increasingly used in neuroscience to tackle fundamental questions regarding the complex dynamics of neural networks. Even if electrical or optical stimulation is generally an available feature of such systems, they lack the capability of creating a closed-loop between the biological neural activity and the artificial system. Stimuli are usually sent in an open-loop manner, thus violating the inherent working basis of neural circuits that in nature are constantly reacting to the external environment. This forbids to unravel the real mechanisms behind the behavior of neural networks. The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable processing system capable of providing real-time feedback to the ongoing neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable System on Chip (APSoC) has been used. The device features reconfigurable logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of these components allows to achieve high elaboration performances while maintaining a high level of flexibility and adaptivity. The developed system has been embedded in an acquisition and stimulation setup featuring the following platforms: \u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of recording in parallel from 4096 electrodes at 18 kHz per electrode. \u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with custom waveforms to 16 different output channels. \u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting 608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical stimulation. All the features of the system, such as band-pass filtering and spike detection of all the recorded channels, have been validated by means of ex vivo experiments. Very low-latency has been achieved while processing the whole input data stream in real-time. In the case of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed, instead, the total latency is a little higher, being 21 ms in the worst case. The final setup is ready to be used to infer cellular properties by means of closed-loop experiments. As a proof of this concept, it has been successfully used for the clustering and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells. In addition to the closed-loop system, a denoising prototype has been developed. The main idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased acquisition systems. The prototype is capable of processing in real-time all the input signals from the BioCam X, and it is currently being tested to evaluate the performance in terms of signal-to-noise-ratio improvement

Design, characterization and testing of a thin-film microelectrode array and signal conditioning microchip for high spatial resolution surface laplacian measurement.

Cardiac mapping has become an important area of research for understanding the mechanisms responsible for cardiac arrhythmias and the associated diseases. Current technologies for measuring electrical potentials on the surface of the heart are limited due to poor spatial resolution, localization issues, signal distortion due to noise, tissue damage, etc. Therefore, the purpose of this study is to design, develop, characterize and investigate a custom-made microfabricated, polyimide-based, flexible Thin-Film MicroElectrode Array (TFMEA) that is directly interfaced to an integrated Signal Conditioning Microchip (SCM) to record cardiac surface potentials on the cellular level to obtain high spatial resolution Surface Laplacian (SL) measurement. TFMEAs consisting of five fingers (Cover area = 4 mm2 and 16 mm2), which contained five individual microelectrodes placed in orthogonal directions (25-µm in diameter, 75-µm interelectrode spacing) to one another, were fabricated within a flexible polyimide substrate and capable of recording electrical activities of the heart on the order of individual cardiomyocytes. A custom designed SCM consisting of 25 channels of preamplification stages and second order band-pass filters was interfaced directly with the TFMEA in order to improve the signal-to-noise ratio (SNR) characteristics of the high spatial resolution recording data. Metrology characterization using surface profilometry and high resolution Scanning Electron Microscope (SEM) indicated the geometry of fabricated TFMEAs closely matched the design parameters \u3c 0.4%). The DC resistances of the 25 individual micro electrodes were consistent (1.050 ± 0.026 kO). The simulation and testing results of the SCM verified the pre-amplification and filter stages met the designed gain and frequency parameters within 2.96%. The functionality of the TFMEA-SCM system was further characterized on a TX 151 conductive gel. The characterization results revealed that the system functionality was sufficient for high spatial cardiac mapping. In vivo testing results clearly demonstrated feasibility of using the TFMEA-SCM system to obtain cellular level SL measurements with significantly improved the SNRs during normal sinus rhythm and Ventricular Fibrillation (VF). Local activation times were detected via evaluating the zero crossing of the SL electro grams, which coincided with the gold standard (dV/dt)min of unipolar electro grams within ± 1%. The in vivo transmembrane current densities calculated from the high spatial resolution SLs were found to be significantly higher than the transmembrane current densities computed using electrodes with higher interelectrode spacings. In conclusion, the custom-made TFMEASCM systems demonstrated feasibility as a tool for measuring cardiac potentials and to perform high resolution cardiac mapping experiments

CMOS MULTI-MODAL INTEGRATED SYSTEMS FOR FUTURE BIOELECTRONICS AND BIOSENSORS

Cells are the basic structural biological units of all known living organisms. They are highly sophisticated system with thousands of molecules operating in hundreds of pathways to maintain their proper functions, phenotypes, and physiological behaviors. With this scale of complexity, cells often exhibit multi-physiological properties as their cellular fingerprints from external stimulations. In order to further advance the frontiers in bioscience and biotechnologies such as stem cell manufacturing, synthetic biology, and regenerative medicine, it is required to comprehend complex cell physiology of living cells. Therefore, a comprehensive set of technologies is needed to harvest quantitative biological data from given cell samples. Such demands have stimulated extensive research on new bioelectronics and biosensors to characterize their functional information by converting their biological activities to electrical signals. As a result, various bioelectronics and biosensors are reported and employed in many in vivo and in vitro applications. Since sensing electrodes of the devices are physically in touch with biological/chemical samples and record their signals, long-term biocompatibility and chemical/mechanical stability is of paramount importance in numerous biological applications. Furthermore, the devices should achieve high sensitivity/resolution/linearity, large field-of-view (FoV), multi-modal sensing, and real-time monitoring, while maintaining small feature size of devices to use small volume of biological/chemical samples and reduce cost. As a result, My Ph.D research aims to study interfacial electrochemical impedance spectroscopy (EIS) of electrodes with different combination of materials/sizes and to design novel multi-modal sensing/actuation array architectures with CMOS compatible in-house post-processing to address the design challenges of the bioelectronics and biosensors.Ph.D

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

Compressive Sensing and Multichannel Spike Detection for Neuro-Recording Systems

RÉSUMÉ Les interfaces cerveau-machines (ICM) sont de plus en plus importantes dans la recherche biomédicale et ses applications, tels que les tests et analyses médicaux en laboratoire, la cérébrologie et le traitement des dysfonctions neuromusculaires. Les ICM en général et les dispositifs d'enregistrement neuronaux, en particulier, dépendent fortement des méthodes de traitement de signaux utilisées pour fournir aux utilisateurs des renseignements sur l’état de diverses fonctions du cerveau. Les dispositifs d'enregistrement neuronaux courants intègrent de nombreux canaux parallèles produisant ainsi une énorme quantité de données. Celles-ci sont difficiles à transmettre, peuvent manquer une information précieuse des signaux enregistrés et limitent la capacité de traitement sur puce. Une amélioration de fonctions de traitement du signal est nécessaire pour s’assurer que les dispositifs d'enregistrements neuronaux peuvent faire face à l'augmentation rapide des exigences de taille de données et de précision requise de traitement. Cette thèse regroupe deux approches principales de traitement du signal - la compression et la réduction de données - pour les dispositifs d'enregistrement neuronaux. Tout d'abord, l’échantillonnage comprimé (AC) pour la compression du signal neuronal a été utilisé. Ceci implique l’usage d’une matrice de mesure déterministe basée sur un partitionnement selon le minimum de la distance Euclidienne ou celle de la distance de Manhattan (MDC). Nous avons comprimé les signaux neuronaux clairsemmés (Sparse) et non-clairsemmés et les avons reconstruit avec une marge d'erreur minimale en utilisant la matrice MDC construite plutôt. La réduction de données provenant de signaux neuronaux requiert la détection et le classement de potentiels d’actions (PA, ou spikes) lesquelles étaient réalisées en se servant de la méthode d’appariement de formes (templates) avec l'inférence bayésienne (Bayesian inference based template matching - BBTM). Par comparaison avec les méthodes fondées sur l'amplitude, sur le niveau d’énergie ou sur l’appariement de formes, la BBTM a une haute précision de détection, en particulier pour les signaux à faible rapport signal-bruit et peut séparer les potentiels d’actions reçus à partir des différents neurones et qui chevauchent. Ainsi, la BBTM peut automatiquement produire les appariements de formes nécessaires avec une complexité de calculs relativement faible.----------ABSTRACT Brain-Machine Interfaces (BMIs) are increasingly important in biomedical research and health care applications, such as medical laboratory tests and analyses, cerebrology, and complementary treatment of neuromuscular disorders. BMIs, and neural recording devices in particular, rely heavily on signal processing methods to provide users with nformation. Current neural recording devices integrate many parallel channels, which produce a huge amount of data that is difficult to transmit, cannot guarantee the quality of the recorded signals and may limit on-chip signal processing capabilities. An improved signal processing system is needed to ensure that neural recording devices can cope with rapidly increasing data size and accuracy requirements. This thesis focused on two signal processing approaches – signal compression and reduction – for neural recording devices. First, compressed sensing (CS) was employed for neural signal compression, using a minimum Euclidean or Manhattan distance cluster-based (MDC) deterministic sensing matrix. Sparse and non-sparse neural signals were substantially compressed and later reconstructed with minimal error using the built MDC matrix. Neural signal reduction required spike detection and sorting, which was conducted using a Bayesian inference-based template matching (BBTM) method. Compared with amplitude-based, energy-based, and some other template matching methods, BBTM has high detection accuracy, especially for low signal-to-noise ratio signals, and can separate overlapping spikes acquired from different neurons. In addition, BBTM can automatically generate the needed templates with relatively low system complexity. Finally, a digital online adaptive neural signal processing system, including spike detector and CS-based compressor, was designed. Both single and multi-channel solutions were implemented and evaluated. Compared with the signal processing systems in current use, the proposed signal processing system can efficiently compress a large number of sampled data and recover original signals with a small reconstruction error; also it has low power consumption and a small silicon area. The completed prototype shows considerable promise for application in a wide range of neural recording interfaces

Development of a Dual-Mode CMOS Microelectrode Array for the Simultaneous Study of Electrochemical and Electrophysiological Activities of the Brain

Medical diagnostic devices are in high demand due to increasing cases of neurodegenerative diseases in the aging population and pandemic outbreaks in our increasingly connected global community. Devices capable of detecting the presence of a disease in its early stages can have dramatic impacts on how it can be treated or eliminated. High cost and limited accessibility to diagnostic tools are the main barriers preventing potential patients from receiving a timely disease diagnosis. This dissertation presents several devices that are aimed at providing higher quality medical diagnostics at a low cost. Brain function is commonly studied with systems detecting the action potentials that are formed when neurons fire. CMOS technology enables extremely high-density electrode arrays to be produced with integrated amplifiers for high-throughput action potential measurement systems while greatly reducing the cost per measurement compared to traditional tools. Recently, CMOS technology has also been used to develop high-throughput electrochemical measurement systems. While action potentials are important, communication between neurons occurs by the flow of neurotransmitters at the synapses, so measurement of action potentials alone is incapable of fully studying neurotransmission. In many neurodegenerative diseases the breakdown in neurotransmission begins well before the disease manifests itself. The development of a dual-mode CMOS device that is capable of simultaneous high-throughput measurement of both action potentials and neurotransmitter flow via an on-chip electrode array is presented in this dissertation. This dual-mode technology is useful to those studying the dynamic decay of the neurotransmission process seen in many neurodegenerative diseases using a low-cost CMOS chip. This dissertation also discusses the development of more traditional diagnostic devices relying on PCR, a method commonly used only in centralized laboratories and not readily available at the point-of-care. These technologies will enable faster, cheaper, more accurate, and more accessible diagnostics to be performed closer to the patient

Fabricación y caracterización de electrodos nanoestructurados para interfaces neurales no invasivas de eficiencia mejorada

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 07-04-2022Neurological disorders produce serious cognitive and motor disabilities, and they account for 7% of total global disease burden, measured in disability‐adjusted life years. In addition, the life expectancy has been continuously growing during the last decades and, as a consequence, neurodegenerative disorders are becoming more prevalent representing a larger part of the healthcare efforts and expenses. Every year, treating brain conditions accounts for 35% of Europe’s disease burden with a yearly cost of €798.000 million. Despite the efforts performed in the medical and science fields, the cure for most of the neurological disorders is far from being achieved. One of the most efficient strategies to treat neurological disorders is the use of implanted electrodes to produce neural electrical stimulation. Furthermore, electrodes are one of the main neural interfaces used in diagnostics techniques, and for the study of the neural activity in basic investigation, in vitro and in vivo. Despite their enormous potential, these electrodes face nowadays limitations. Generally, they are too big, producing unspecific stimulation that can lead to secondary effects. Their size reduction is limited by the associated impedance increase, which restricts their charge‐injection to the tissue...Los trastornos neurológicos producen graves discapacidades cognitivas y motoras y suponen actualmente el 7% de la carga global de morbilidad, medida en años de vida ajustados por discapacidad. Además, el aumento progresivo en la esperanza de vida de las últimas décadas ha incrementado la prevalencia de las enfermedades neurodegenerativas, produciéndose una mayor demanda de recursos médicos y un incremento del coste económico asociado. Cada año, el tratamiento de enfermedades cerebrales supone el 35% del gasto total médico europeo, con un coste de 798.000millones de euros. A pesar de los esfuerzos realizados en medicina y otras ciencias, actualmente no existe una cura para la mayoría de las enfermedades neurológicas. Una de las terapias más utilizadas en clínica es la estimulación eléctrica neuronal con electrodos implantados. Los electrodos se usan extensamente en técnicas de diagnóstico y en el estudio de la actividad neuronal, in vitro e in vivo. Sin embargo, a pesar de su enorme potencial, su uso lleva asociadas algunas limitaciones. En general son grandes, por lo que la estimulación neuronal no es localizada, pudiendo producir efectos secundarios, y la reducción de su tamaño lleva asociada un incremento de su impedancia, reduciéndose su capacidad de inyección de carga...Fac. de Ciencias FísicasTRUEunpu

Fabrication and characterization of nanostructured electrodes for more efficient low‐invasiveness neural interfaces

Neurological disorders produce serious cognitive and motor disabilities, and they account for 7% of total global disease burden, measured in disability‐adjusted life years. In addition, the life expectancy has been continuously growing during the last decades and, as a consequence, neurodegenerative disorders are becoming more prevalent representing a larger part of the healthcare efforts and expenses. Every year, treating brain conditions accounts for 35% of Europe’s disease burden with a yearly cost of €798.000 million. Despite the efforts performed in the medical and science fields, the cure for most of the neurological disorders is far from being achieved. One of the most efficient strategies to treat neurological disorders is the use of implanted electrodes to produce neural electrical stimulation. Furthermore, electrodes are one of the main neural interfaces used in diagnostics techniques, and for the study of the neural activity in basic investigation, in vitro and in vivo. Despite their enormous potential, these electrodes face nowadays limitations. Generally, they are too big, producing unspecific stimulation that can lead to secondary effects. Their size reduction is limited by the associated impedance increase, which restricts their charge‐injection to the tissue..

Energy Efficient Computing with Time-Based Digital Circuits

University of Minnesota Ph.D. dissertation. May 2019. Major: Electrical Engineering. Advisor: Chris Kim. 1 computer file (PDF); xv, 150 pages.Advancements in semiconductor technology have given the world economical, abundant, and reliable computing resources which have enabled countless breakthroughs in science, medicine, and agriculture which have improved the lives of many. Due to physics, the rate of these advancements is slowing, while the demand for the increasing computing horsepower ever grows. Novel computer architectures that leverage the foundation of conventional systems must become mainstream to continue providing the improved hardware required by engineers, scientists, and governments to innovate. This thesis provides a path forward by introducing multiple time-based computing architectures for a diverse range of applications. Simply put, time-based computing encodes the output of the computation in the time it takes to generate the result. Conventional systems encode this information in voltages across multiple signals; the performance of these systems is tightly coupled to improvements in semiconductor technology. Time-based computing elegantly uses the simplest of components from conventional systems to efficiently compute complex results. Two time-based neuromorphic computing platforms, based on a ring oscillator and a digital delay line, are described. An analog-to-digital converter is designed in the time domain using a beat frequency circuit which is used to record brain activity. A novel path planning architecture, with designs for 2D and 3D routes, is implemented in the time domain. Finally, a machine learning application using time domain inputs enables improved performance of heart rate prediction, biometric identification, and introduces a new method for using machine learning to predict temporal signal sequences. As these innovative architectures are presented, it will become clear the way forward will be increasingly enabled with time-based designs