abstract: Electromagnetic fields (EMFs) generated by biologically active neural tissue are critical in the diagnosis and treatment of neurological diseases. Biological EMFs are characterized by electromagnetic properties such as electrical conductivity, permittivity and magnetic susceptibility. The electrical conductivity of active tissue has been shown to serve as a biomarker for the direct detection of neural activity, and the diagnosis, staging and prognosis of disease states such as cancer. Magnetic resonance electrical impedance tomography (MREIT) was developed to map the cross-sectional conductivity distribution of electrically conductive objects using externally applied electrical currents. Simulation and in vitro studies of invertebrate neural tissue complexes demonstrated the correlation of membrane conductivity variations with neural activation levels using the MREIT technique, therefore laying the foundation for functional MREIT (fMREIT) to detect neural activity, and future in vivo fMREIT studies. The development of fMREIT for the direct detection of neural activity using conductivity contrast in in vivo settings has been the focus of the research work presented here. An in vivo animal model was developed to detect neural activity initiated changes in neuronal membrane conductivities under external electrical current stimulation. Neural activity was induced in somatosensory areas I (SAI) and II (SAII) by applying electrical currents between the second and fourth digits of the rodent forepaw. The in vivo animal model involved the use of forepaw stimulation to evoke somatosensory neural activations along with hippocampal fMREIT imaging currents contemporaneously applied under magnetic field strengths of 7 Tesla. Three distinct types of fMREIT current waveforms were applied as imaging currents under two inhalants – air and carbogen. Active regions in the somatosensory cortex showed significant apparent conductivity changes as variations in fMREIT phase (φ_d and ∇^2 φ_d) signals represented by fMREIT activation maps (F-tests, p <0.05). Consistent changes in the standard deviation of φ_d and ∇^2 φ_d in cortical voxels contralateral to forepaw stimulation were observed across imaging sessions. These preliminary findings show that fMREIT may have the potential to detect conductivity changes correlated with neural activity. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2020

Brain Machine Interfaces (BMI) provide a communication pathway between the electrical conducting units of the brain (neurons) and external devices. BMI technology may provide improved neurological and physiological functions to patients suffering from disabilities due to damaged nervous systems. Unfortunately, microelectrodes used in Intracortical Neural Interfaces (INI), a subset of the BMI device family, have yet to demonstrate long-term in vivo performance due to material, mechanical and electrical failures. Many state-of-the-art INI devices are constructed using stacks of multiple materials, such as silicon (Si), titanium (Ti), platinum (Pt), parylene C, and polyimide. Not only must each material tolerate the biological environment without exacerbating the inflammatory response, each of the materials used must physically withstand the environment as well as interact well with each other. One approach to address abiotic mechanisms has been optimizing the materials required to fabricate the INI devices. Silicon Carbide (SiC) is a physically robust, hemo and biocompatible, and chemically inert semiconductor. An ‘all-SiC’, or monolithic SiC, device may be the disruptive technology needed in the BMI field to finally achieve long-term and wide-spread use of this technology in humans. The all-SiC device concept is where SiC serves as all device layers: the base (substrate), the conducting traces (electrodes), and the surface conformal insulating layer. The monolithic SiC neural probe is realized by forming high-quality pn junctions of heavily doped SiC on a layer of the opposite polarity. Heavily doped semiconductors display semi-metallic electrical performance, which allow for efficient electrical conduction in the electrode without the deleterious effects of metal ions interacting with the neural environment. The conformal insulator is realized using amorphous-SiC (a-SiC) which can be patterned to open windows to allow electrical conduction to occur between the electrode tips and the brain. Several generations of monolithic SiC devices have been fabricated, tested and are reported in this dissertation. The devices were fabricated utilizing two polytypes of SiC (4H-SiC and 3C-SiC). The monolithic SiC microelectrodes were fabricated utilizing techniques used in the fabrication of Si based microelectrodes. Monolithic SiC devices fabricated include planar single-ended MEAs (with varying lengths and varying active recording area), 60-channel MEAs for in-vitro studies, and 16-electrode Michigan style neural probes for in-vivo studies. Electrical testing of the pn junction demonstrated that the 4H-SiC device can block a forward bias voltage of up to 2.3V and displays reverse bias leakage below 1 nArms well past -20V. Current leakage between adjacent electrodes was ~7.5 nArms over a voltage range of -50V to +50V. Furthermore, electrochemical results show that the 4H-SiC microelectrodes interact with an electrochemical environment primarily through capacitive mechanisms and has an impedance comparable to gold electrodes. Electrode impedance ranged from 675±130 kΩ (GSA = 496 µm2) to 46.5±4.80 kΩ (GSA = 500K µm2). However, the 4H-SiC devices cannot deliver charge as efficiently as other conventionally used microelectrode materials, such as iridium oxide (IrOx), but a larger water window compensates for this since larger stimulation voltages are supported compared to IrOx. All studies and data collected thus far indicate that the monolithic SiC neural device can aid in the advancement of chronic INI use in clinical settings. The all-SiC devices rely on the integration of only robust and highly compatible SiC material, they may offer a promising solution to probe delamination and biological rejection associated with the use of multiple materials used in many current INI devices. Follow-on work is planned to prove this assertion via in vivo studies.

Numerous applications in neuroscience research and neural prosthetics, such as retinal prostheses, spinal-cord surface stimulation for prosthetics, electrocorticogram (ECoG) recording for epilepsy detection, etc., involve electrical interaction with soft excitable tissues using a surface stimulation and/or recording approach. These applications require an interface that is able to set up electrical communications with a high throughput between electronics and the excitable tissue and that can dynamically conform to the shape of the soft tissue. Being a compliant and biocompatible material with mechanical impedance close to that of soft tissues, polydimethylsiloxane (PDMS) offers excellent potential as the substrate material for such neural interfaces. However, fabrication of electrical functionalities on PDMS has long been very challenging. This thesis work has successfully overcome many challenges associated with PDMS-based microfabrication and achieved an integrated technology platform for PDMS-based stretchable microelectrode arrays (sMEAs). This platform features a set of technological advances: (1) we have fabricated uniform current density profile microelectrodes as small as 10 microns in diameter; (2) we have patterned high-resolution (feature as small as 10 microns), high-density (pitch as small as 20 microns) thin-film gold interconnects on PDMS substrate; (3) we have developed a multilayer wiring interconnect technology within the PDMS substrate to further boost the achievable integration density of such sMEA; and (4) we have invented a bonding technology---via-bonding---to facilitate high-resolution, high-density integration of the sMEA with integrated circuits (ICs) to form a compact implant. Taken together, this platform provides a high-resolution, high-density integrated system solution for neural and muscular surface interfacing. sMEAs of example designs are evaluated through in vitro and in vivo experimentations on their biocompatibility, surface conformability, and surface recording/stimulation capabilities, with a focus on epimysial (i.e. on the surface of muscle) applications. Finally, as an example medical application, we investigate a prosthesis for unilateral vocal cord paralysis (UVCP) based on simultaneous multichannel epimysial recording and stimulation.

<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>

Dans ce projet nous nous intéresserons à la création d’un nouveau modèle permettant de simuler des enregistrements extracellulaires de l’activité électrique neurale dans le système nerveux périphérique. Ce modèle fut développé pour permettre une meilleure compréhension de l’impact des différentes propriétés des interfaces sur la qualité des signaux recueillis. Ce projet fut en particulier conduit pour répondre au contexte actuel qui voit le développement de nombreuses applications dans le domaine des neuro-prothèses et autres interfaces neurales à but biomédical. Nos intentions étaient de fournir un nouvel outil permettant de mieux comprendre les particularités des interfaces existantes ou d’aider à leur amélioration et à la planification de futures innovations. Ce modèle est construit comme la synthèse de la compréhension actuelle des différents rouages biophysiques impactant les enregistrements. Sa structure peut être perçue comme l’assemblage de différents sous-systèmes interconnectés et représentant chacun une dimension du processus. Il s’avère particulièrement efficace pour l’analyse comparative des performances entre diffèrent types/géométries d’électrodes invasives. Dans ce document, nous nous efforcerons d’expliquer en détail la structure et les paramètres de notre modèle. Nous décrirons ensuite les différents tests que nous avons entrepris pour sa validation expérimentale, ainsi que les différentes voies d’applications que nous avons commencé à explorer. Nous finirons par décrire les améliorations qui nous sont apparues comme nécessaires ou possibles et par une discussion sur les ouvertures futures offerte à ce domaine de recherche. / Neural interfaces are becoming a newly dynamic and promising field especially thanks to the numerous applications they could have in the biomedical domain. A great deal of these applications requires a monitoring of targeted neural activity. Among the different technologies available for such recording practice, chronic electrodes implanted in the peripheral nervous system offer a good compromise on the resolution versus invasiveness technological constraint. A large array of electrodes has been developed in this intention but there is still only a limited comprehension of their recording principles and weakness. This makes difficult any targeted improvement of the electrodes and led this field to be mainly dominated by a trial and error empirical approach simultaneously costly in funds, animal lives and time. In particular, intrafascicular electrodes, while providing exiting results for stimulation, have often failed in recordings. These electrodes typically show interesting recording performance right after implantation but have rapid decline of their efficacy up to the points that they often become useless after a few weeks. Such performance proves problematic as they drastically limit the transfer of experimental results to human applications. The extent of our work has been the development of a theoretical framework for the study of implantable electrodes. Our goal here has been to construct a model that could be used as a platform to better understand implanted electrode and compare their performance and possible improvement. We focused our work on intrafascicular electrode for the peripheral nervous system. However, our procedure could easily be applied to other type of interface. During this project we first constructed a detailed model of the recording biophysical process happening at the peripheral nerve electrical interface. This model encompasses all the mechanism known to influence the quality and shape of neural activity recordings. We have then recreated within our model specific controlled experiments and by comparing the properties of the simulated recording with their experimental counterparts demonstrated the potency of our approach to produce bio-plausible signals. This validated our model as an in silico alternative to compare and test electrodes. We then further developed this model to also simulate some of the changes happening in the nerve post implantation. In particular, we found that the growth of the fibrotic scar could already explain a large part of the signal degradation happening in the first weeks. Then to demonstrate the adaptability of this model we used it to compare the performance of the main type of electrodes implanted nowadays peripherally. Finally, as the main weakness of our model relied in its relative complexity and the related long computing time, we started to analyze how this model could be simplified without losing the precision necessary for the intended applications. In conclusion, this project led to the creation of a model which in its current form can be used as an in silico platform to test and compare electrodes. This will facilitate the planning and development of future peripheral neural interface by proving both more economical and informative that current strategies. Conjointly, we opened the way to future improvement of our model, leading to more practicality.