Doctor of Philosophy

dissertationThe development of devices to electrically interact with the brain is a challenging task that could potentially restore motion to paralyzed patients and sight to those with profound blindness. Neural engineers have designed many types of microelectrode arrays (MEAs) with this challenge in mind. These MEAs can be implanted into brain tissue to both record neural signals and electrically stimulate neurons with high selectivity and spatial resolution. Implanted MEAs have allowed patients to control of a variety of prosthetic devices in clinical trials, but the longevity of such motor prostheses is limited to a few years. Performance decreases over time as MEAs lose the ability to record neuronal signals, preventing their widespread clinical use. Microstimulation via intracortical MEAs has also not achieved broad clinical implementation. While microstimulation for the restoration of vision is promising, human clinical trials are needed. Chronic in vivo functionality assays in model systems will provide key insight to facilitate such trials. There are three goals that may help address insufficient MEA longevity, as well as provide insight on microstimulation functionality. First, thorough characterizations of how performance decreases over time, both with and without stimulation, will be needed. Next, factors that affect the chronic performance of microstimulating MEAs must be further investigated. Finally, intervention strategies can be designed to mitigate these factors and improve long term MEA performance. This dissertation takes steps towards meeting these goals by means of three studies. First, the chronic performance of intracortically implanted recording and stimulating MEAs is examined. It is found that while performance of implanted MEAs in feline cortex is dynamic, catastrophic device failure does not occur with microstimulation. Next, a variety of factors that affect microstimulation studies are investigated. It is found that many factors, including device iv damage, anesthesia depth, the application of microstimulation, and the use of impedance as a reporter play a role in observations of performance variability. Finally, a promising intervention strategy, a carbon nanotube coating, is chronically tested in vivo, indicating that carbon nanotubes do not cause catastrophic device failure and may impart benefits to future generations of MEAs

Long-Term Activity-Dependent Plasticity of Action Potential Propagation Delay and Amplitude in Cortical Networks

Background: The precise temporal control of neuronal action potentials is essential for regulating many brain functions. From the viewpoint of a neuron, the specific timings of afferent input from the action potentials of its synaptic partners determines whether or not and when that neuron will fire its own action potential. Tuning such input would provide a powerful mechanism to adjust neuron function and in turn, that of the brain. However, axonal plasticity of action potential timing is counter to conventional notions of stable propagation and to the dominant theories of activity-dependent plasticity focusing on synaptic efficacies. Methodology/Principal Findings: Here we show the occurrence of activity-dependent plasticity of action potentia

Evaluating the impact of intracortical microstimulation on distant cortical brain regions for neuroprosthetic applications

Enhancing functional motor recovery after localized brain injury is a widely recognized priority in healthcare as disorders of the nervous system that cause motor impairment, such as stroke, are among the most common causes of adult-onset disability. Restoring physiological function in a dysfunctional brain to improve quality of life is a primary challenge in scientific and clinical research and could be driven by innovative therapeutic approaches. Recently, techniques using brain stimulation methodologies have been employed to promote post-injury neuroplasticity for the restitution of motor function. One type of closed-loop stimulation, i.e., activity-dependent stimulation (ADS), has been shown to modify existing functional connectivity within either healthy or injured cerebral cortices and used to increase behavioral recovery following cortical injury. The aim of this PhD thesis is to characterize the electrophysiological correlates of such behavioral recovery in both healthy and injured cortical networks using in vivo animal models. We tested the ability of two different intracortical micro-stimulation protocols, i.e., ADS and its randomized open-loop version (RS), to potentiate cortico-cortical connections between two distant cortical locations in both anaesthetized and awake behaving rats. Thus, this dissertation has the following three main goals: 1) to investigate the ability of ADS to induce changes in intra-cortical activity in healthy anesthetized rats, 2) to characterize the electrophysiological signs of brain injury and evaluate the capability of ADS to promote electrophysiological changes in the damaged network, and 3) to investigate the long-term effects of stimulation by repeating the treatment for 21 consecutive days in healthy awake behaving animals. The results of this study indicate that closed-loop activity-dependent stimulation induced greater changes than open-loop random stimulation, further strengthening the idea that Hebbian-inspired protocols might potentiate cortico-cortical connections between distant brain areas. The implications of these results have the potential to lead to novel treatments for various neurological diseases and disorders and inspire new neurorehabilitation therapies

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

The Effects of Intracortical Microstimulation Parameters on Neural Responses

RÉSUMÉ Les microstimulations de tissues nerveux du cerveau sont utilisés dans un grand nombre de prothèses sensorielles, de thérapies cliniques et autres activités de recherche se servant de la stimulation électrique. Actuellement, les paramètres de stimulation sont adaptés à chaque application via des tests itératifs. Les méthodes d'optimisation cherchent à améliorer les stimuli développés pour des objectifs spécifiques de stimulation, mais la compréhension fondamentale de la façon dont les paramètres de stimulation influencent les circuits neuronaux qu’ils activent reste largement incomplète. Ce déficit retarde l'optimisation de protocoles existants et rend le développement de nouvelles applications de stimulation difficile. À ce jour, un certain nombre de dispositifs prothétiques validés dès les années 1970 restent en développement, principalement en raison de l'incapacité de ces dispositifs à communiquer efficacement avec le cerveau. Pour utiliser la stimulation électrique afin de transmettre des messages au système nerveux central, une meilleure conception du patron du signal de stimulation est nécessaire. Dans cette thèse, nous étudions l'influence que chaque paramètre du signal (un courant constant, symétrique carré biphasique) exerce sur les réponses qu'il évoquées au travers des microstimulations de la zone intracorticale caudale du membre antérieur dans le cortex moteur chez le rat. Les paramètres de ce signal sont l'amplitude du courant, la fréquence et la durée d'impulsion, l’intervalle d'interphase et la durée du train. Leurs effets ont été évalués par un examen des réponses électromyographiques évoquées dans les muscles des membres antérieurs du rat en réponse à chaque stimulus. Les principaux résultats décrivent comment chaque paramètre de stimulation influence l'amplitude, la latence d’apparition et la durée de la réponse. Une composante jusque-là inexplorée du signal de la réponse (que nous appelons 'activation résiduelle') est aussi analysée pour la première fois. Les théories quant à l'origine et le mécanisme neuronal sous-jacent de ce phénomène sont proposés et les paramètres de stimulation touchant son apparition, la prévalence et la durée sont décrits. La fiabilité des signaux de stimulation pour évoquer des réponses cohérentes est également évaluée par rapport aux variations de paramètres. Une méthodologie pour la conception optimisée des signaux de stimulation est proposée en utilisant un modèle de calcul simple, représentant les relations d'entrée-sortie entre les paramètres de stimulation et les réponses qu'ils évoquent. Ce modèle utilise une approche de réseau neuronal artificiel et peut être utilisé pour prédire les propriétés de la réponse lorsque les paramètres du stimulus sont connus. Compte tenu de la prévalence de la stimulation cérébrale dans les applications cliniques, de recherche et thérapeutiques, les procédures méthodologiques et de modélisation proposées ont des implications importantes dans l'optimisation des paradigmes de stimulation actuels et le développement de protocoles de stimulation pour de nouvelles applications. ----------ABSTRACT Microstimulation of brain tissue plays a key role in a variety of sensory prosthetics, clinical therapies and research applications. At present, stimulus parameters are tailored to each application via iterative testing. Computational optimization methods seek to improve tried and tested waveforms developed for specific purposes, however the fundamental understanding of how stimulation parameters influence the neural circuits they activate remains widely unknown. This deficit hinders both the optimization of existing protocols and the development of new stimulation applications. To date, a number of prosthetic devices validated as early as the 1970’s linger in the development stages largely due to the inability to effectively interface these devices with the brain. In order to use electrical stimulation to convey messages to the central nervous system, a better understanding of stimulus signal design is required. In this thesis, I investigate the influence that each parameter of the constant-current, symmetric, biphasic square waveform exerts on the responses it evokes through intracortical microstimulation of the caudal forelimb area of the rat motor cortex. The parameters under investigation include the current amplitude, pulse frequency, pulse duration, interphase interval and train duration of the stimulus and effects were assessed by examining the electromyographic responses evoked in the rat forelimb muscles in response to each stimulus. The major findings describe how each parameter of the stimulus signal influences the magnitude, onset latency, and duration of the response. A previously unexplored component of the response signal (which we called ‘residual activation’) is analyzed for the first time. Hypotheses as to the origin and underlying neural mechanism of this phenomenon are proposed and the stimulus parameters affecting its occurrence, prevalence and duration are described. The reliability of stimulation signals for evoking consistent responses is also assessed with respect to parameter variations. A methodology for the informed design of stimulation signals is proposed and aided by the development of a simple computational model representing the input-output relationships between stimulation parameters and the responses they evoke. This model uses an artificial neural network approach and can be used to predict the properties of the response when the parameters of the stimulus are known. Given the prevalence of brain stimulation in clinical, research and therapeutic applications the proposed methodological and modeling procedures have important implications in the optimization of current stimulation paradigms and the development of stimulation protocols for new applications

Decoding Distributed Neuronal Activity in Extrastriate Cortical Areas for the Visual Prosthetic Applications

Les prothèses visuelles corticales sont planifiées pour restaurer la vision chez les individus non-voyants en appliquant du courant électrique à des sites discrets sur le cortex visuel. À ce jour, la qualité de la vision rapportée dans la littérature est celle d'un petit nombre de phosphènes (percept de spots lumineux spatialement localisés) sans organisation pour générer un percept significatif. Le principal défi consiste à développer des méthodes pour transférer les informations d'une scène visuelle dans un schéma de stimulation compréhensible pour le cerveau. Une connaissance clé pour résoudre ce défi est de comprendre comment les caractéristiques du phosphène (ou en général, les caractéristiques visuelles) sont représentées dans le modèle distribué d'activité neuronale. Une approche pour obtenir ces connaissances consiste à déterminer dans quelle mesure les réponses neuronales bien réparties peuvent détecter les changements dans une caractéristique visuelle spécifique des stimuli. Pour atteindre cet objectif, nous avons étudié la capacité de discrimination des zones corticales extrastriées V4 chez les singes macaques. Ces zones extrastriées ont de petites régions rétinotopiques qui offrent la possibilité d'échantillonner une grande région de l'espace visuel à l'aide de réseaux de microelectrodes standard telles que celles de l'Université d'Utah. Cela aide à construire des prothèses mini-invasives. Notre contribution concerne la résolution spatiale des potentiels de champs locaux (LFP) dans la zone V4 pour déterminer les limites de la capacité des prothèses visuelles à induire des phosphènes à plusieurs positions. Les LFP ont été utilisés car ils représentent une activité neuronale sur une échelle de 400 microns, ce qui est comparable à la propagation de l'effet de microstimulation dans le cortex. La zone visuelle extrastriée V4 contient également une carte rétinotopique de l'espace visuel et offre la possibilité de récupérer l'emplacement des stimuli statiques. Nous avons appliqué la méthode «Support vector machine» (SVM) pour déterminer la capacité des LFP (par rapport aux réponses à plusieurs unités - MUA) à discriminer les réponses (phosphènes) aux stimuli à différentes séparations spatiales. Nous avons constaté que malgré les grandes tailles de champs récepteurs dans V4, les réponses combinées de plusieurs sites étaient capables de discrimination fine et grossière des positions. Nous avons proposé une stratégie de sélection des électrodes basée sur les poids linéaires des décodeurs (en utilisant les valeurs de poids les plus élevées) qui a considérablement réduit le nombre d'électrodes requis pour la discrimination avec une augmentation des performances. L'application de cette stratégie présente l'avantage potentiel de réduire les dommages tissulaires dans les applications réelles. Nous avons conclu que pour un fonctionnement correct des prothèses, la microstimulation électrique devrait générer un schéma d'activité neuronale similaire à l'activité évoquée correspondant à un percept attendu. De plus, lors de la conception d'une prothèse visuelle, les limites de la capacité de discrimination des zones cérébrales implantées doivent être prises en compte. Ces limites peuvent différer pour MUA et LFP.----------ABSTRACT Cortical visual prostheses are intended to restore vision to blind individuals by applying a pattern of electrical currents at discrete sites on the visual cortex. To date, the quality of vision reported in the literature is that of a small number of phosphenes (percept of spatially localized spots of light) with no organization to generate a meaningful percept. The main challenge consists of developing methods to transfer information of a visual scene into a pattern of stimulation that is understandable to the brain. The key to solving this challenge is understanding how phosphene characteristics (or in general, visual characteristics) are represented in a distributed pattern of neural activity. One approach is to determine how well neural responses can detect changes in a specific characteristic of stimuli. To this end, we have studied the discrimination capability of V4 extrastriate cortical area in macaque monkeys. Extrastriate cortical areas have small retinotopic maps that can provide an opportunity to sample a large region of visual space using standard devices such as Utah arrays. Thus, this helps to build minimally invasive prosthetic devices. Our contribution relates to the spatial resolution of local field potentials (LFPs) in area V4 to determine the limits in the capability of visual prosthetic devices in generation of phosphenes in multiple positions. LFPs were used because they represent neural activity over a scale of 400 microns, which is comparable to the spread of microstimulation effects in the cortex. Extrastriate visual area V4 also contains a retinotopic map of visual space and offers an opportunity to recover the location of static stimuli. We applied support vector machines (SVM) to determine the capability of LFPs (compared to multi-unit responses) in discriminating responses to phosphene-like stimuli (probes) located with different spatial separations. We found that despite large receptive field sizes in V4, combined responses from multiple sites were capable of fine and coarse discrimination of positions. We proposed an electrode selection strategy based on the linear weights of the decoder (using the highest weight values) that significantly reduced the number of electrodes required for discrimination, while at the same time, increased performance. Applying this strategy has the potential to reduce tissue damages in real applications. We concluded that for the correct operation of prosthetic devices, electrical microstimulation should generate a pattern of neural activity similar to the evoked activity corresponding to an expected percept. Moreover, in the design of visual prosthesis, limits in the discrimination capability of the implanted brain areas should be taken into account. These limits may differ for MUA and LFP

Utilizing microstimulation and local field potentials in the primary somatosensory and motor cortex

Brain-computer interfaces (BCIs) have advanced considerably from simple target detection by recording from a single neuron, to accomplishments like controlling a computer cursor accurately with neural activity from hundreds of neurons or providing instruction directly to the brain via microstimulation. However as BCIs continue to evolve, so do the challenges they face. Most BCIs rely on visual feedback, requiring sustained visual attention to use the device. As the role of BCIs expands beyond cursors moving on a computer screen to robotic hands picking up objects, there is increased need for an effective way to provide quick feedback independent of vision. Another challenge is utilizing all the signals available to produce the best decoding of movement possible. Local field potentials (LFPs) can be recorded at the same time as multi-unit activity (MUA) from multielectrode arrays but little is known in the area of what kind of information it possess, especially in relation to MUA. To tackle these issues, we preformed the following experiments. First, we examined the effectiveness of alternative forms of feedback applicable to BCIs, tactile stimuli delivered on the skin surface and microstimulation applied directly to the brain via the somatosensory cortex. To gauge effectiveness, we used a paradigm that captured a fundamental element of feedback: the ability to react to a stimulus while already in action. By measuring the response time to that stimulus, we were able to compare how well each modality could perform as a feedback stimulus. Second, we use regression and mutual information analyses to study how MUA, low-frequency LFP (15-40Hz, LFPL ), and high-frequency LFP (100-300Hz, LFPH) encoded reaching movements. The representation of kinematic parameters for direction, speed, velocity, and position were quantified and compared across these signals to be better applied in decoding models. Lastly, the results from these experiments could not have been accurately obtained without keeping careful account of the mechanical lags involved. Each of the stimuli affecting behavior had onset lags, which in some cases, varied greatly from trial to trial and could easily distorted timing effects if not accounted for. Special adaptations were constructed to precisely pinpoint display, system, and device onset lags

Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately

Optogenetic interrogation of primary visual cortex and its impact on neural coding and behavior

Understanding the mechanism by which the brain transforms simple sensory inputs into rich perceptual experiences is one of the great mysteries of systems neuroscience. Undoubtedly this involves the activity of large populations of interconnected neurons, but while the responses of individual neurons to a variety of sensory stimuli have been well-characterized, how populations of such neurons organize their activity to create our sensory perceptions is almost entirely unknown. To investigate this complex circuitry requires the ability to causally manipulate the activity of neural populations and monitor the resultant effects. Here we focus on primary visual cortex (V1), which has been shown to be crucial for visual perception, and utilize optogenetic tools to render the activity of genetically- defined neural populations sensitive to light. By simultaneously recording and modulating (either driving or silencing) the activity of excitatory (glutamatergic) neurons, we are able to causally examine their role in visual perception. Here we report 3 major findings. First, we show that activating subpopulations of excitatory neurons can improve visual perception under certain conditions and that information in V1 used for perceptual decisions is integrated across spatially-limited populations of neurons. Further, we show that a key signature of this information integration is a reduction in correlated variability between neurons. Correlated variability has been implicated as a major source of behavioral choice related activity in the cortex, and theorized to be a major factor limiting information in cortical populations. However, until now, there has not been a way to manipulate correlations without altering firing rates or other task related variables. Here we demonstrate a novel method using optogenetic stimulation to causally manipulate correlated variability between cortical neurons without altering their firing rates. Lastly, with the goal of expanding the currently limited repertoire of optogenetic tools for non-human primates, we establish the viability of a novel optogenetic construct capable of dramatically silencing neural populations using a recently discovered anion conducting channelrhodopsin

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost