This dissertation describes the electrochemical behavior of nickel and iron that was studied in different acid solutions via linear sweep voltammetry, cyclic voltammetry, and potentiostatic measurements over a range of temperatures at specific potential ranges. The presented work displays novel experiments where a nickel electrode was heated locally with an inductive heating system, and a platinum (Pt) electrode was used to change the proton concentration at iron and nickel electrode surfaces to control the periodic oscillations (frequency and amplitude) produced and to gain a greater understanding of the systems (kinetics), oscillatory processes, and corrosion processes. Temperature pulse voltammetry, linear sweep voltammetry, and cyclic voltammetry were used for temperature calibration at different heating conditions. Several other metal systems (bismuth, lead, zinc, and silver) also produce periodic oscillations as corrosion occurs; however, creating these with pure metal electrodes is very expensive. In this work, metal systems were created via electrodeposition by using inexpensive, efficient, coupled microelectrode array sensors (CMASs) as a substrate. CMASs are integrated devices with multiple electrodes that are connected externally in a circuit in which all of the electrodes have the same amount of potential applied or current passing through them. CMASs have been used for many years to study different forms of corrosion (crevice corrosion, pitting corrosion, intergranular corrosion, and galvanic corrosion), and they are beneficial because they can simulate single electrodes of the same size. The presented work also demonstrates how to construct CMASs and shows that the unique phenomena of periodic oscillations that can be created and studied by using coated and bare copper CMASs. Furthermore, these systems can be controlled by implementing external forcing with a Pt electrode at the CMAS surface. The data from the single Ni electrode experiments and CMAS experiments were analyzed by using the Nonlinear Time-Series Analysis approach.

<p>In this dissertation, we show three approaches towards selective vagus nerve stimulation (VNS). First, we investigated VNS using microelectrode with circle and Vicsek fractal shape. Our rat study shows that fractal microelectrode can activate C-fibers in cervical vagus nerve with higher energy efficiency compared to circle microelectrode. Secondly, we developed stretchable and adhesive cuff device for a compliant neural interface for a long-term stability. We designed Y-shaped kirigami thin-film device for stretchable neural interface and applied a tissue-adhesive hydrogel to enable tough adhesion of the cuff electrode, which can be potentially used to fix the position of microelectrode for a reliable selective stimulation with minimal mechanical mismatch. Lastly, we developed a microchannel electrode array device to potentially measure high-quality of single fiber action potential (SFAP) from the abdominal vagal trunk of rat to explore natural patterns selective organ activities which can be used for a fine-tuned selective VNS. Our results show the potential of measuring C-fiber activities evoked by cervical VNS.</p>

No description available.

In this work, the problem of detecting neuronal spikes or action potentials (AP) in noisy recordings from a Microelectrode Array (MEA) is investigated. In particular, the spike detection algorithms should be less complex and with low computational complexity so as to be amenable for real time applications. The use of the MEA is that it allows collection of extracellular signals from either a single unit or multiple (45) units within a small area. The noisy MEA recordings then undergo basic filtering, digitization and are presented to a computer for further processing. The challenge lies in using this data for detection of spikes from neuronal firings and extracting spatiotemporal patterns from the spike train which may allow control of a robotic limb or other neuroprosthetic device directly from the brain. The aim is to understand the spiking action of the neurons, and use this knowledge to devise efficient algorithms for Brain Machine Interfaces (BMIs). An effective BMI will require a realtime, computationally efficient implementation which can be carried out on a DSP board or FPGA system. The aim is to devise algorithms which can detect spikes and underlying spatio-temporal correlations having computational and time complexities to make a real time implementation feasible on a specialized DSP chip or an FPGA device. The time-frequency localization, multiresolution representation and analysis properties of wavelets make them suitable for analysing sharp transients and spikes in signals and distinguish them from noise resembling a transient or the spike. Three algorithms for the detection of spikes in low SNR MEA neuronal recordings are proposed: 1. A wavelet denoising method based on the Discrete Wavelet Transform (DWT) to suppress the noise power in the MEA signal or improve the SNR followed by standard thresholding techniques to detect the spikes from the denoised signal. 2. Directly thresholding the coefficients of the Stationary (Undecimated) Wavelet Transform (SWT) to detect the spikes. 3. Thresholding the output of a Teager Energy Operator (TEO) applied to the signal on the discrete wavelet decomposed signal resulting in a multiresolution TEO framework. The performance of the proposed three wavelet based algorithms in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families in the presence of colored AR(5) (autoregressive model with order 5) and additive white Gaussian noise (AWGN) is evaluated. The performance is further evaluated for the wavelet family chosen under different levels of SNR in the presence of the colored AR(5) and AWGN noise. Chapter 1 gives an introduction to the concept behind Brain Machine Interfaces (BMIs), an overview of their history, the current state-of-the-art and the trends for the future. It also describes the working of the Microelectrode Arrays (MEAs). The generation of a spike in a neuron, the proposed mechanism behind it and its modeling as an electrical circuit based on the Hodgkin-Huxley model is described. An overview of some of the algorithms that have been suggested for spike detection purposes whether in MEA recordings or Electroencephalographic (EEG) signals is given. Chapter 2 describes in brief the underlying ideas that lead us to the Wavelet Transform paradigm. An introduction to the Fourier Transform, the Short Time Fourier Transform (STFT) and the Time-Frequency Uncertainty Principle is provided. This is followed by a brief description of the Continuous Wavelet Transform and the Multiresolution Analysis (MRA) property of wavelets. The Discrete Wavelet Transform (DWT) and its filter bank implementation are described next. It is proposed to apply the wavelet denoising algorithm pioneered by Donoho, to first denoise the MEA recordings followed by standard thresholding technique for spike detection. Chapter 3 deals with the use of the Stationary or Undecimated Wavelet Transform (SWT) for spike detection. It brings out the differences between the DWT and the SWT. A brief discussion of the analysis of non-stationary time series using the SWT is presented. An algorithm for spike detection based on directly thresholding the SWT coefficients without any need for reconstructing the denoised signal followed by thresholding technique as in the first method is presented. In chapter 4 a spike detection method based on multiresolution Teager Energy Operator is discussed. The Teager Energy Operator (TEO) picks up localized spikes in signal energy and thus is directly used for spike detection in many applications including R wave detection in ECG and various (alpha, beta) rhythms in EEG. Some basic properties of the TEO are discussed followed by the need for a multiresolution approach to TEO and the methods existing in literature. The wavelet decomposition and the subsampled signal involved at each level naturally lends it to a multiresolution TEO framework at the same time significantly reducing the computational complexity due the subsampled signal at each level. A wavelet-TEO algorithm for spike detection with similar accuracies as the previous two algorithms is proposed. The method proposed here differs significantly from that in literature since wavelets are used instead of time domain processing. Chapter 5 describes the method of evaluation of the three algorithms proposed in the previous chapters. The spike templates are obtained from MEA recordings, resampled and normalized for use in spike trains simulated as Poisson processes. The noise is modeled as colored autoregressive (AR) of order 5, i.e AR(5), as well as Additive White Gaussian Noise (AWGN). The noise in most human and animal MEA recordings conforms to the autoregressive model with orders of around 5. The AWGN Noise model is used in most spike detection methods in the literature. The performance of the proposed three wavelet based algorithms is measured in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families. The optimal wavelet for this purpose is then chosen from the wavelet family which gives the best results. Also, optimal levels of decomposition and threshold factors are chosen while maintaining a balance between accuracy and false positives. The algorithms are then tested for performance under different levels of SNR with the noise modeled as AR(5) or AWGN. The proposed wavelet based algorithms exhibit a detection accuracy of approximately 90% at a low SNR of 2.35 dB with the false positives below 5%. This constitutes a significant improvement over the results in existing literature which claim an accuracy of 80% with false positives of nearly 10%. As the SNR increases, the detection accuracy increases to close to 100% and the false alarm rate falls to 0. Chapter 6 summarizes the work. A comparison is made between the three proposed algorithms in terms of detection accuracy and false positives. Directions in which future work may be carried out are suggested.

The entorhinal cortex (EC) in the medial temporal lobe plays a critical role in memory formation and is implicated in several neurological diseases including temporal lobe epilepsy and Alzheimer’s disease. Despite the known importance of this brain region, little is known about the normal bioelectrical activity patterns of the EC in awake, behaving primates. In order to develop effective therapies for diseases affecting the EC, we must first understand its normal properties. To contribute to our understanding of the EC, I monitored the activity of individual neurons and populations of neurons in the EC of rhesus macaque monkeys during free-viewing of photographs using electrophysiological techniques. The results of these experiments help to explain how primates can form memories of, and navigate through, the visual world. These experiments revealed neurons in the EC that represent visual space with triangular grid receptive fields and other neurons that prefer to fire near image borders. These properties are similar to those previously described in the rodent EC, but here the neuronal responses relate to viewing of remote space as opposed to representing the physical location of the animal. The representation of visual space may be aided by another EC neuron type that was discovered, free-viewing saccade direction cells, neurons that signaled the direction of upcoming saccades. Such a signal could be used by other cells to prepare to fire according to the future gaze location. Many of these spatially-responsive neurons also represented memory for images, suggesting that they may be useful for associating items with their locations. I also examined the neuronal circuitry of recognition memory for visual stimuli in the EC, and I found that population synchronization within the gamma-band (30-140 Hz) in superficial layers of the EC was modulated by stimulus novelty, while the strength of memory formation modulated gamma-band synchronization in the deep layers and in layer III. Furthermore, the strength of connectivity in the gamma-band between different layers was correlated with the strength of memory formation, with deep to superficial power transfer being correlated with stronger memory formation and superficial to deep transfer correlated with weaker memory formation. These findings support several previous investigations of hippocampal-entorhinal connectivity in the rodent and advance our understanding of the functional circuitry of the medial temporal lobe memory system. Finally, I explored the design of a device that could be used to investigate properties of brain tissue in vitro, potentially aiding in the development of treatments for disorders of the EC and other brain structures. We designed, fabricated, and validated a novel device for long-term maintenance of thick brain slices and 3-dimensional dissociated cell cultures on a perforated multi-electrode array. To date, most electrical recordings of thick tissue preparations have been performed by manually inserting electrode arrays. This work demonstrates a simple and effective solution to this problem by building a culture perfusion chamber around a planar perforated multi-electrode array. By making use of interstitial perfusion, the device maintained the thickness of tissue constructs and improved cellular survival as demonstrated by increased firing rates of perfused slices and 3-D cultures, compared to unperfused controls. To the best of our knowledge, this is the first thick tissue culture device to combine forced interstitial perfusion for long-term tissue maintenance and an integrated multi-electrode array for electrical recording and stimulation.

Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-10-05T18:08:34Z No. of bitstreams: 1 TeseERLR.pdf: 4461748 bytes, checksum: 2fe540767de5ff5f23af02775508026b (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:11:13Z (GMT) No. of bitstreams: 1 TeseERLR.pdf: 4461748 bytes, checksum: 2fe540767de5ff5f23af02775508026b (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:11:22Z (GMT) No. of bitstreams: 1 TeseERLR.pdf: 4461748 bytes, checksum: 2fe540767de5ff5f23af02775508026b (MD5) / Made available in DSpace on 2016-10-14T14:11:41Z (GMT). No. of bitstreams: 1 TeseERLR.pdf: 4461748 bytes, checksum: 2fe540767de5ff5f23af02775508026b (MD5) Previous issue date: 2015-12-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / This thesis combines image and signal processing to obtain virtual neuron distribution maps in a Microelectrode Array (MEA), which are devices designed for non-invasive electrophysiological signal recording for in vitro cultures of neuron cells. In the electrophysiological signal analysis, it is of interest the knowledge of the topological distribution of the cells along the MEA microelectrodes, but, usually the photographic images of the cell culture are not available. This doctoral work presents an approach to obtain the statistical topologic distribution of the neurons of an in vitro cell culture, denoted virtual distribution of neurons, from the electrophysiological signals. To certify that the statistical computation of the neuron counting is associated to each MEA microelectrode, it is used the ICA (Independent component Analysis) technique, for the separation of the neuron signals distributed throughout the MEA area, to obtain for each microelectrode, only the signals from its adjacent neurons. Assuming the hypothesis that the spontaneous neuron activities, spikes and bursts, are directly proportional to the neuron counting, it is realized the spike counting and burst counting, and it is assigned for each microelectrode, a number of neurons proportional to that numbers of activities. For the validation of the proposal, as well as for calibration of the system, to obtain the estimated number of neurons, it was used an experiment denoted 371, realized in Genoa University, Italy, in which it was recorded electrophysiological signals in 46 DIVs (Days In- Vitro), obtaining 20 minutes of recording in 25, 29, 32, 36, 39, 43, and 46 DIVs, and a set of photographic images in 38 DIV. Assuming that microelectrode neuron counting in the 38 DIV photographic image is proportional to the 39 DIV spontaneous electrophysiological activity signal recording, one day after the imaging, if was determined the neuron counting as function of the spontaneous electrophysiological activities recording, in a process denoted as calibration of the virtual number of neurons. The distance error from the neuron activities as function of the neuron counting in photographic image and in function of the recorded electrophysiological signals was calculated and compared for validation. In this way, it was possible to construct virtual topologic maps of neurons, proportional to the electrophysiological activities measured in 39 DIV, as a function of the spike and the burst countings. Comparing these two virtual maps, the spike counting virtual map was more close to the real neuron distributions viewed at the photographic image of 38 DIV. Also, the variance of the spike and burst counting along the 20 min of electrophysiological recording in a DIV, was calculated, and noted that the spike counting is more stable than burst counting. / Esta tese combina processamento de imagens e sinais, para a obtenção de uma distribuição virtual de neurônios em Matrizes de microeletrodos (Microelectrode Array, MEA), dispositivos projetados para o registro de sinais eletrofisiológicos de culturas de células neuronais, in-vitro, de forma não-invasiva. Na análise dos sinais eletrofisiológicos é de interesse o conhecimento da distribuição topológica das células ao longo dos microeletrodos, porém, nem sempre as imagens fotográficas das culturas são disponíveis. O presente trabalho apresenta uma metodologia de obtenção da distribuição topológica estatística dos neurônios numa cultura in-vitro, a partir dos sinais eletrofisiológicos. Para o cálculo estatístico do número de neurônios nessa distribuição topológica, é feito o uso da técnica de ICA (Independent Component Analysis), para obter os sinais relativos aos neurônios mais próximos para cada microeletrodo. Assumindo-se a hipótese de que as atividades eletrofisiológicas espontâneas dos neurônios, spikes e bursts, sejam diretamente proporcionais ao número de neurônios, realiza-se a contagem do número de spikes ou o número de bursts, e atribui-se o número de neurônios para cada microeletrodo, proporcionalmente à quantidade dessas atividades. Para a validação da proposta, foi utilizado um experimento, Experimento 371, realizado na Universidade de Gênova, Itália, em que foram registrados os sinais eletrofisiológicos ao longo de 46 DIVs (Dias In-Vitro), obtendo amostras de 20 minutos de registros para os 25, 29, 32, 36, 39, 43 e 46 DIVs, e um conjunto de imagens fotográficas da cultura no 38 DIV. Considerando-se que o número de neurônios associados a cada microeletrodo na imagem fotográfica no 38 DIV é proporcional à atividade eletrofisiológica espontânea dos neurônios, num registro realizado no 39 DIV, um dia após as fotos, foi feita uma regra de determinação do número virtual de neurônios em função das atividades eletrofisiológicas espontâneas medidas, denominada de calibração. O erro relativo à distância da atividade dos neurônios em relação à quantidade de neurônios na imagem fotográfica, e a atividade dos neurônios em função do registro de sinais eletrofisiológicos é calculado para comparação e validação. Dessa forma são construídos os mapas topológicos virtuais de neurônios proporcionais às atividades eletrofisiológicas medidas no 39 DIV, em função da quantidade de spikes e de bursts. O mapa obtido pela contagem de spikes se aproxima mais da distribuição real de neurônios vista na imagem fotográfica, do que o mapa obtido em função da contagem de bursts. No estudo de variância de atividades em função da contagem de spikes e bursts durante os 20 minutos de medidas num DIV, e constata-se que as atividades em contagem spikes é mais estável que em contagem de bursts.

This PhD research relates to the design, fabrication, characterization, and optimization of on-chip electrochemical microelectrode arrays (MEAs) for measurement of transient concentrations and gradients, focusing on fast transients and physiological applications. In particular, this work presents the determination of kinetic mechanisms taking place at an active interface (either physiological or non-physiological) in contact with a liquid phase using the MEA device to simultaneously estimate the concentration and gradient of the analyte of interest at the surface of the active interface. The design approach of the MEA device and the corresponding measurement methodology to acquire reliable concentration information is discussed. The ability of the MEA device to measure fast (i.e., in sub-second time scale) transient gradients is demonstrated experimentally using a controllable diffusion-reaction system which mimics the consumption of hydrogen peroxide by a 2D cell culture. The proposed MEA device and measurement methodology meet effectively most of the requirements for physiological applications and as a demonstration of this, two physiological applications are presented. In one application, the MEA device was tailored to measure the hydrogen peroxide uptake rate of human astrocytes and glioblastoma multiforme cells in 2D cell culture as a function of hydrogen peroxide concentration at the cell surface; the results allowed to quantitatively determine the uptake kinetics mechanisms which are well-described by linear and Michaelis-Menten expressions, in agreement with the literature. In the other application, further customization of the MEA device was realized to study the glucose uptake kinetics of human bronchial epithelial and small cell lung cancer cells, these latter with and without DDX5 gene knockdown; the results allowed to distinguish mechanistic differences in the glucose uptake kinetics among the three cell lines. These results were complemented with measurements of glycolytic and respiration rates to obtain a bigger picture of the glucose metabolism of the three cell lines. Finally, additional applications, both physiological and non-physiological, are proposed for the developed MEA device.

Type I diabetes (T1D) is an autoimmune disorder affecting the insulin-producing beta cells of the islets of Langerhans, disrupting the glucose homeostasis regulatory system. Nowadays, islet transplantation is one of the anticipated treatments to revive the endocrinal function by injecting isolated pancreatic islets from a deceased donor into the patient’s liver’s portal vein. Regardless of the promising aspect, the main issue prior to transplantation is the inconsistent quality and low percentage of functioning islets post-transplantation. Therefore, a rapid islet functionality test with minimal complicated operation becomes necessary to tackle the pre-transplantation issue. This project revolves around the end-to-end development of an electrophysiology recording platform to monitor extracellular activities in murine pancreatic islets. A microfluidic perfusion system with hydrodynamic trapping is integrated with planar gold microelectrode arrays (MEA) as the preliminary device directly interfacing the islets. The design and fabrication of both the microfluidics and electrode devices, as well as in-house front-end electronics with analog filters and amplifiers tailored to capture the microvolt-scale signals, covered most of the project. Offline digital processing was performed in Python to analyse the recorded signals further. As a result, the complete platform and recording setup have been fully integrated, with successful islet trapping on top of electrodes and front-end electronics with 220x voltage gain and 0.1-3000 Hz bandwidth to record extracellular electrophysiology signals from intact pancreatic islets. While the current preliminary electrophysiology recordings are still quite inconclusive and require further validation, the project serves as a starting point in developing devices for extracellular electrophysiology measurement, which has not commonly been investigated specifically in pancreatic islets, and enables further exploration in the field. / Typ I-diabetes (T1D) är en autoimmun sjukdom som påverkar de insulinproducerande betacellerna på de Langerhanska öarna och stör det reglerande systemet för glukoshomeostas. Nuförtiden är ötransplantation en av de förväntade behandlingarna för att återuppliva den endokrina funktionen genom att injicera isolerade pankreasöar från en avliden donator i patientens levers portven. Oavsett den lovande aspekten är huvudfrågan före transplantation den inkonsekventa kvaliteten och låga andelen fungerande öar efter transplantationen. Därför blir ett snabbt funktionstest av öar med minimalt komplicerad operation nödvändigt för att ta itu med problemet före transplantation. Detta projekt kretsar kring end-to-end utveckling av en elektrofysiologisk inspelningsplattform för att övervaka extracellulära aktiviteter i murina pankreatiska öar. Ett mikrofluidiskt perfusionssystem med hydrodynamisk infångning är integrerat med plana guldmikroelektrodarrayer (MEA) som den preliminära enheten som direkt gränsar till öarna. Designen och tillverkningen av både mikrofluidik och elektrodenheter, såväl som intern front-end-elektronik med analoga filter och förstärkare skräddarsydda för att fånga signalerna i mikrovoltskala, täckte större delen av projektet. Offline digital bearbetning utfördes i Python för att analysera de inspelade signalerna ytterligare. Som ett resultat har den kompletta plattformen och inspelningsuppsättningen integrerats helt, med lyckad ö-infångning ovanpå elektroder och front-end-elektronik med 220x spänningsförstärkning och 0,1-3000 Hz för att registrera extracellulära elektrofysiologiska signaler från intakta pankreatiska öar. Medan de nuvarande preliminära elektrofysiologiska inspelningarna fortfarande är ganska ofullständiga och kräver ytterligare validering, fungerar projektet som en utgångspunkt för att utveckla enheter för extracellulär elektrofysiologisk mätning, som inte vanligtvis har undersökts specifikt i pankreasöar, och möjliggör ytterligare utforskning inom området.

This thesis addresses simulation of electrochemical experiments, with an emphasis on processes of diffusional mass transport to electrode surface. Following system has been studied: &bull; Applying theoretical modeling and experimentation is shown that even significant surface roughness produced by deliberate polishing or scratching is not sufficient to be distinguished in cyclic voltammetry experiments conducted under the usual conditions. In stripping voltammetry experiment the shape of the voltammograms strongly depends on the model of the electron transfer but is not always sensitive to the precise model of the electrode surface; the conditions under which this is the case are identified, and generic roughness effects on stripping voltammetry are quantified. Electrode roughness can have a significant effect on the stripping of the metals from the solid electrode especially in respect of the voltammetric waveshape. &bull; We first consider two different models of electrodes covered with electroinactive layers: the electrode is covered with a uniform layer and the layer contains pinholes. Both models are simulated and then compared to identify conditions under which they can be distinguished. Next we propose generic model to predict the influence of electroactive layer on the cyclic voltammetric. The conditions under which deviation from the behavior of a planar electrode are predicted. &bull; We first consider one electron, one proton and next two electron, two proton reduction of surface bound species. Two mechanisms of reaction are considered: stepwise and concerted. Voltammetry studied under the three regimes of protons mass transport: infinitely fast (fully buffered solution), infinitely slow (infinitely high surface coverage of electrode) and intermediate case of finite rate of diffusional mass transport to electrode surface. Types of voltammograms observed in each case are presented and discussed. &bull; Theory of chronoamperometry on disc and ring-recessed microelectrodes and their arrays is reported. Three and four different regimes of transient current versus time can be observed at microelectrode arrays of disc and ring electrodes, accordingly. A generic, accurate and easy to use method of experimental chronoamperometric data analysis is proposed. It is shown that the method can be applied to the simultaneous measurement of D and nC in solution. &bull; The fabrication, characterization, and use of arrays of ring-recessed disk generator-colector microelectrodes are reported. Experiments and simulations relating to time- of-flight experiments in which material electrogenerated at a disk is diffusionally transported to the ring are reported. We further study voltammetry of electrochemically active species which undergoes first and second order chemical reactions. Current transients are found to be sensitive to the diffusion coefficient of both the reduced and oxidised species as well as to the rate of the chemical reaction and its mechanism.