Non-Penetrating Microelectrode Interfaces for Cortical Neuroprosthetic Applications with a Focus on Sensory Encoding: Feasibility and Chronic Performance in Striate Cortex

abstract: Growing understanding of the neural code and how to speak it has allowed for notable advancements in neural prosthetics. With commercially-available implantable systems with bi- directional neural communication on the horizon, there is an increasing imperative to develop high resolution interfaces that can survive the environment and be well tolerated by the nervous system under chronic use. The sensory encoding aspect optimally interfaces at a scale sufficient to evoke perception but focal in nature to maximize resolution and evoke more complex and nuanced sensations. Microelectrode arrays can maintain high spatial density, operating on the scale of cortical columns, and can be either penetrating or non-penetrating. The non-penetrating subset sits on the tissue surface without puncturing the parenchyma and is known to engender minimal tissue response and less damage than the penetrating counterpart, improving long term viability in vivo. Provided non-penetrating microelectrodes can consistently evoke perception and maintain a localized region of activation, non-penetrating micro-electrodes may provide an ideal platform for a high performing neural prosthesis; this dissertation explores their functional capacity. The scale at which non-penetrating electrode arrays can interface with cortex is evaluated in the context of extracting useful information. Articulate movements were decoded from surface microelectrode electrodes, and additional spatial analysis revealed unique signal content despite dense electrode spacing. With a basis for data extraction established, the focus shifts towards the information encoding half of neural interfaces. Finite element modeling was used to compare tissue recruitment under surface stimulation across electrode scales. Results indicated charge density-based metrics provide a reasonable approximation for current levels required to evoke a visual sensation and showed tissue recruitment increases exponentially with electrode diameter. Micro-scale electrodes (0.1 – 0.3 mm diameter) could sufficiently activate layers II/III in a model tuned to striate cortex while maintaining focal radii of activated tissue. In vivo testing proceeded in a nonhuman primate model. Stimulation consistently evoked visual percepts at safe current thresholds. Tracking perception thresholds across one year reflected stable values within minimal fluctuation. Modulating waveform parameters was found useful in reducing charge requirements to evoke perception. Pulse frequency and phase asymmetry were each used to reduce thresholds, improve charge efficiency, lower charge per phase – charge density metrics associated with tissue damage. No impairments to photic perception were observed during the course of the study, suggesting limited tissue damage from array implantation or electrically induced neurotoxicity. The subject consistently identified stimulation on closely spaced electrodes (2 mm center-to-center) as separate percepts, indicating sub-visual degree discrete resolution may be feasible with this platform. Although continued testing is necessary, preliminary results supports epicortical microelectrode arrays as a stable platform for interfacing with neural tissue and a viable option for bi-directional BCI applications.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Shifting gazes with visual prostheses: Long-term hand-camera coordination

Purpose: Prosthetic vision is young, and many aspects of its use remain unexplored. Hand-camera coordination, the prosthetic correlate of hand-eye coordination, relies heavily on how the camera is aligned with the eye. It is unknown whether users of prostheses can adapt to using misaligned cameras, or whether requirements for proper alignment remain constant over time. Methods: Four blind subjects implanted with Argus II retinal prostheses participated in this study. Each subject attempted to touch a single 4°–7° white target that was randomly located on an otherwise black touchscreen in a target localization task. Touch response accuracy was used to determine the necessary adjustment to eye-camera alignment, the optimal camera alignment position (OCAP). Subjects attended over 100 sessions across up to 5.3 years. S1–S3 were given misaligned cameras for over 1 year. Adaptation was measured through changes in localization errors. Outside that period of intentional misalignment, cameras were aligned to maximize localization accuracy. During the final year, localization tasks were performed in alternation with eye tracking. S2–S4 also participated in 1-day experiments with simultaneous eye tracking and target localization. Results: Subjects were not able to significantly reduce localization error when cameras were misaligned. When trying to maximize localization accuracy, necessary OCAPs changed significantly over time. OCAP trend directions within days and trial runs matched changes between the beginnings of days and runs. Changes between the end of a day or run and the beginning of the next tended to point in the opposite direction of the previous trend, indicating a reset of OCAP changes. Changes in eye orientations correlated significantly with changes in OCAPs. Eye-orientation trends displayed the same reset behavior between days and runs as OCAPs. Simultaneous eye tracking and localization showed agreement between eye-orientation and localization-error trend directions. Adjusting camera alignment with eye-tracking data slowed changes in localization errors. Conclusions: Users of current visual prostheses cannot passively adapt to camera misalignments. OCAPs are not constant with time. Prosthesis users who desire maximum pointing accuracy will require regular camera realignments. Camera alignments based on eye tracking can reduce both transient and long-term changes in localization that are related to eye movements

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Ultrahigh Field Functional Magnetic Resonance Electrical Impedance Tomography (fMREIT) in Neural Activity Imaging

abstract: A direct Magnetic Resonance (MR)-based neural activity mapping technique with high spatial and temporal resolution may accelerate studies of brain functional organization. The most widely used technique for brain functional imaging is functional Magnetic Resonance Image (fMRI). The spatial resolution of fMRI is high. However, fMRI signals are highly influenced by the vasculature in each voxel and can be affected by capillary orientation and vessel size. Functional MRI analysis may, therefore, produce misleading results when voxels are nearby large vessels. Another problem in fMRI is that hemodynamic responses are slower than the neuronal activity. Therefore, temporal resolution is limited in fMRI. Furthermore, the correlation between neural activity and the hemodynamic response is not fully understood. fMRI can only be considered an indirect method of functional brain imaging. Another MR-based method of functional brain mapping is neuronal current magnetic resonance imaging (ncMRI), which has been studied over several years. However, the amplitude of these neuronal current signals is an order of magnitude smaller than the physiological noise. Works on ncMRI include simulation, phantom experiments, and studies in tissue including isolated ganglia, optic nerves, and human brains. However, ncMRI development has been hampered due to the extremely small signal amplitude, as well as the presence of confounding signals from hemodynamic changes and other physiological noise. Magnetic Resonance Electrical Impedance Tomography (MREIT) methods could have the potential for the detection of neuronal activity. In this technique, small external currents are applied to a body during MR scans. This current flow produces a magnetic field as well as an electric field. The altered magnetic flux density along the main magnetic field direction caused by this current flow can be obtained from phase images. When there is neural activity, the conductivity of the neural cell membrane changes and the current paths around the neurons change consequently. Neural spiking activity during external current injection, therefore, causes differential phase accumulation in MR data. Statistical analysis methods can be used to identify neuronal-current-induced magnetic field changes.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Neural networks-on-chip for hybrid bio-electronic systems

PhD ThesisBy modelling the brains computation we can further our understanding of its function and develop novel treatments for neurological disorders. The brain is incredibly powerful and energy e cient, but its computation does not t well with the traditional computer architecture developed over the previous 70 years. Therefore, there is growing research focus in developing alternative computing technologies to enhance our neural modelling capability, with the expectation that the technology in itself will also bene t from increased awareness of neural computational paradigms. This thesis focuses upon developing a methodology to study the design of neural computing systems, with an emphasis on studying systems suitable for biomedical experiments. The methodology allows for the design to be optimized according to the application. For example, di erent case studies highlight how to reduce energy consumption, reduce silicon area, or to increase network throughput. High performance processing cores are presented for both Hodgkin-Huxley and Izhikevich neurons incorporating novel design features. Further, a complete energy/area model for a neural-network-on-chip is derived, which is used in two exemplar case-studies: a cortical neural circuit to benchmark typical system performance, illustrating how a 65,000 neuron network could be processed in real-time within a 100mW power budget; and a scalable highperformance processing platform for a cerebellar neural prosthesis. From these case-studies, the contribution of network granularity towards optimal neural-network-on-chip performance is explored

SYNAPTIC: Structural, Functional and Chemical Assessments of the Visual Pathway in Retinal Disease.

Retinal diseases including age-related macular degeneration (AMD) and retinitis pigmentosa (RP), have been associated with significant secondary structural changes to the posterior visual pathway. What has yet to be established is whether such cortical changes reflect atrophy (cortical shrinkage), demyelination (reduced axon myelination) and/or degeneration (cell death). Understanding the effects of retinal disease on the entire visual pathway and how this may differ with the type of retinal disease, will aid future techniques aimed to restore visual input and patient selection for such treatments In this thesis, novel magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) protocols were employed to quantify changes to the posterior visual pathway, specifically whether there is evidence of cortical atrophy, degeneration, or demyelination in retinal disease. Outcome measures from the anterior and posterior visual pathways were correlated to investigate potential biomarkers of disease progression. The penultimate chapter investigated how the Argus® II retinal prosthesis affects the structure and function of the visual cortex. Chapters two and three reveals that significant cortical atrophy of the entire occipital cortex is observed in long-term unilateral and bilateral AMD. Pilot data from a small cohort of long-term bilateral RP patients, suggest some patients show signs of atrophy whilst other do not, although a larger sample is needed to draw definitive conclusions. Moreover, there were no significant signs of cortical degeneration or demyelination were observed in either retinal disease. Chapters four and five reveal that reduced macular thickness, specifically the ganglion cell layer (GCL), is observed in both retinal diseases suggesting degeneration of the retina. Monitoring GCL thickness may be a sensitive biomarker of disease progression. This thesis also revealed that reduced cortical thickness in the occipital pole significantly predicts visual acuity performance in AMD, the first study to report such a finding. Finally, in an individual AMD patient implanted with the Argus® II, 13-months post-surgery there was a very modest increase in cortical thickness of the occipital cortex yet diminished stimulus-driven responses. The success of restoring visual input in this case may well have been limited by the substantial cortical atrophy observed pre-surgery

Characterization of concurrent stimulation in multi-electrode array for use in a vision prosthesis

Concurrent stimulation in a visual prosthesis is necessary in order to deliver sufficient phospenes (perceived spots of light) for effective vision. Major issues with concurrent stimulation are the effects of inter-electrode current distribution which lead to current leakage and issues with charge recovery which determines whether balanced charges being delivered are recovered at each electrode. This thesis investigates concurrent stimulation in multi-electrode arrays of different electrode configurations and orientations using platinum electrodes immersed in physiological saline bath along with results from computational modelling. Current waveforms were recorded to determine current interactions and charge recovery in each electrode. Current interaction was found to be highest when imbalanced stimuli were delivered. Current interaction and charge recovery were found to be minimal for combined current source and sink stimulation mode, especially in the tripolar configuration of the hexagonal orientation, indicating that the combination of appropriate electrode configuration, orientation and stimulus mode will aid in current focusing and avoiding current interaction. Simplified models were developed to mimic the experimental setup and used to fit multiple experimental current waveforms, based on the stimulus currents and electrode-electrolyte properties. The models were optimized to predict the electrode-electrolyte properties of electrode arrays as well as the current interactions and voltage distributions in different electrode configurations and orientations. The resistance and capacitance of the electrode-electrolyte interface were found to decrease and increase with stimulus current, respectively. The optimized models were able to reproduce current results from the experimental recordings in terms of the dynamic waveforms , with a goodness of fit between 53 % - 90 %, where the percentage reduces in more complex model with multi-electrode. To validate that the optimized model is appropriate to present concurrent stimulation of multi-electrode array, 3D models of the multi-electrode array were constructed to mimic and reproduce the experimental results and to validate the optimized model. The model was able to predict current and voltage distribution as well as the electrode-electrolyte interface voltages and voltages at each individual electrodes which is not possible to measure experimentally. Improved version of the model with additional parameters will be useful to predict the performance of the implant in various numerical settings which may not be possible to be conducted experimentally. The new methodology developed in this thesis shows a strong link between experimental and computational modeling of concurrent stimulation in multi-electrode arrays, allowing for prediction for specific electrode design simulations and also as a platform for neural prosthesis evaluation using multi-electrode arrays

Complexity, the auditory system, and perceptual learning in naïve users of a visual-to-auditory sensory substitution device.

PhDSensory substitution devices are a non-invasive visual prostheses that use sound or touch to aid functioning in the blind. Algorithms informed by natural crossmodal correspondences convert and transmit sensory information attributed to an impaired modality back to the user via an unimpaired modality and utilise multisensory networks to activate visual areas of cortex. While behavioural success has been demonstrated in non-visual tasks suing SSDs how they utilise a metamodal brain, organised for function is still a question in research. While imaging studies have shown activation of visual cortex in trained users it is likely that naïve users rely on auditory characteristics of the output signal for functionality and that it is perceptual learning that facilitates crossmodal plasticity. In this thesis I investigated visual-to-auditory sensory substitution in naïve sighted users to assess whether signal complexity and processing in the auditory system facilitates and limits simple recognition tasks. In four experiments evaluating; signal complexity, object resolution, harmonic interference and information load I demonstrate above chance performance in naïve users in all tasks, an increase in generalized learning, limitations in recognition due to principles of auditory scene analysis and capacity limits that hinder performance. Results are looked at from both theoretical and applied perspectives with solutions designed to further inform theory on a multisensory perceptual brain and provide effective training to aid visual rehabilitation.Queen Mary University of Londo

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems