Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation

Alongside stereotactic magnetic resonance imaging, microelectrode recording (MER) is frequently used during the deep brain stimulation (DBS) surgery for optimal target localization. The aim of this study is to optimize subthalamic nucleus (STN) mapping using MER analytical patterns. 16 patients underwent bilateral STN-DBS. MER was performed simultaneously for 5 microelectrodes in a setting of Ben's-gun pattern in awake patients. Using spikes and background activity several different parameters and their spectral estimates in various frequency bands including low frequency (2-7 Hz), Alpha (8-12 Hz), Beta (sub-divided as Low_Beta (13-20 Hz) and High_Beta (21-30 Hz)) and Gamma (31 to 49 Hz) were computed. The optimal STN lead placement with the most optimal clinical effect/ side-effect ratio accorded to the maximum spike rate in 85% of the implantation. Mean amplitude of background activity in the low beta frequency range was corresponding to right depth in 85% and right location in 94% of the implantation respectively. MER can be used for STN mapping and intraoperative decisions for the implantation of DBS electrode leads with a high accuracy. Spiking and background activity in the beta range are the most promising independent parameters for the delimitation of the proper anatomical site

Unsupervised neural spike identification for large-scale, high-density micro-electrode arrays

This work deals with the development and evaluation of algorithms that extract sequences of single neuron action potentials from extracellular recordings of superimposed neural activity - a task commonly referred to as spike sorting. Large ($>10^3$ electrodes) and dense (subcellular spatial sampling) CMOS-based micro-electrode-arrays allow to record from hundreds of neurons simultaneously. State of the art algorithms for up to a few hundred sensors are not directly applicable to this type of data. Promising modern spike sorting algorithms that seek the statistically optimal solution or focus on real-time capabilities need to be initialized with a preceding sorting. Therefore, this work focused on unsupervised solutions, in order to learn the number of neurons and their spike trains with proper resolution of both temporally and spatiotemporally overlapping activity from the extracellular data alone. Chapter (1) informs about the nature of the data, a model based view and how this relates to spike sorting in order to understand the design decisions of this thesis. The main materials and methods chapter (2) bundles the infrastructural work that is independent of but mandatory for the development and evaluation of any spike sorting method. The main problem was split in two parts. Chapter (3) assesses the problem of analyzing data from thousands of densely integrated channels in a divide-and-conquer fashion. Making use of the spatial information of dense 2D arrays, regions of interest (ROIs) with boundaries adapted to the electrical image of single or multiple neurons were automatically constructed. All ROIs could then be processed in parallel. Within each region of interest the maximum number of neurons could be estimated from the local data matrix alone. An independent component analysis (ICA) based sorting was used to identify units within ROIs. This stage can be replaced by another suitable spike sorting algorithm to solve the local problem. Redundantly identified units across different ROIs were automatically fused into a global solution. The framework was evaluated on both real as well as simulated recordings with ground truth. For the latter it was shown that a major fraction of units could be extracted without any error. The high-dimensional data can be visualized after automatic sorting for convenient verification. Means of rapidly separating well from poorly isolated neurons were proposed and evaluated. Chapter (4) presents a more sophisticated algorithm that was developed to solve the local problem of densely arranged sensors. ICA assumes the data to be instantaneously mixed, thereby reducing spatial redundancy only and ignoring the temporal structure of extracellular data. The widely accepted generative model describes the intracellular spike trains to be convolved with their extracellular spatiotemporal kernels. To account for the latter it was assessed thoroughly whether convolutive ICA (cICA) could increase sorting performance over instantaneous ICA. The high computational complexity of cICA was dealt with by automatically identifying relevant subspaces that can be unmixed in parallel. Although convolutive ICA is suggested by the data model, the sorting results were dominated by the post-processing for realistic scenarios and did not outperform ICA based sorting. Potential alternatives are discussed thoroughly and bounded from above by a supervised sorting. This work provides a completely unsupervised spike sorting solution that enables the extraction of a major fraction of neurons with high accuracy and thereby helps to overcome current limitations of analyzing the high-dimensional datasets obtained from simultaneously imaging the extracellular activity from hundreds of neurons with thousands of electrodes

Scalable software and models for large-scale extracellular recordings

The brain represents information about the world through the electrical activity of populations of neurons. By placing an electrode near a neuron that is firing (spiking), it is possible to detect the resulting extracellular action potential (EAP) that is transmitted down an axon to other neurons. In this way, it is possible to monitor the communication of a group of neurons to uncover how they encode and transmit information. As the number of recorded neurons continues to increase, however, so do the data processing and analysis challenges. It is crucial that scalable software and analysis tools are developed and made available to the neuroscience community to keep up with the large amounts of data that are already being gathered. This thesis is composed of three pieces of work which I develop in order to better process and analyze large-scale extracellular recordings. My work spans all stages of extracellular analysis from the processing of raw electrical recordings to the development of statistical models to reveal underlying structure in neural population activity. In the first work, I focus on developing software to improve the comparison and adoption of different computational approaches for spike sorting. When analyzing neural recordings, most researchers are interested in the spiking activity of individual neurons, which must be extracted from the raw electrical traces through a process called spike sorting. Much development has been directed towards improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, I develop SpikeInterface, an open-source, Python framework designed to unify preexisting spike sorting technologies into a single toolkit and to facilitate straightforward benchmarking of different approaches. With this framework, I demonstrate that modern, automated spike sorters have low agreement when analyzing the same dataset, i.e. they find different numbers of neurons with different activity profiles; This result holds true for a variety of simulated and real datasets. Also, I demonstrate that utilizing a consensus-based approach to spike sorting, where the outputs of multiple spike sorters are combined, can dramatically reduce the number of falsely detected neurons. In the second work, I focus on developing an unsupervised machine learning approach for determining the source location of individually detected spikes that are recorded by high-density, microelectrode arrays. By localizing the source of individual spikes, my method is able to determine the approximate position of the recorded neuriii ons in relation to the microelectrode array. To allow my model to work with large-scale datasets, I utilize deep neural networks, a family of machine learning algorithms that can be trained to approximate complicated functions in a scalable fashion. I evaluate my method on both simulated and real extracellular datasets, demonstrating that it is more accurate than other commonly used methods. Also, I show that location estimates for individual spikes can be utilized to improve the efficiency and accuracy of spike sorting. After training, my method allows for localization of one million spikes in approximately 37 seconds on a TITAN X GPU, enabling real-time analysis of massive extracellular datasets. In my third and final presented work, I focus on developing an unsupervised machine learning model that can uncover patterns of activity from neural populations associated with a behaviour being performed. Specifically, I introduce Targeted Neural Dynamical Modelling (TNDM), a statistical model that jointly models the neural activity and any external behavioural variables. TNDM decomposes neural dynamics (i.e. temporal activity patterns) into behaviourally relevant and behaviourally irrelevant dynamics; the behaviourally relevant dynamics constitute all activity patterns required to generate the behaviour of interest while behaviourally irrelevant dynamics may be completely unrelated (e.g. other behavioural or brain states), or even related to behaviour execution (e.g. dynamics that are associated with behaviour generally but are not task specific). Again, I implement TNDM using a deep neural network to improve its scalability and expressivity. On synthetic data and on real recordings from the premotor (PMd) and primary motor cortex (M1) of a monkey performing a center-out reaching task, I show that TNDM is able to extract low-dimensional neural dynamics that are highly predictive of behaviour without sacrificing its fit to the neural data

Development of Advanced Closed-Loop Brain Electrophysiology Systems for Freely Behaving Rodents

[ES] La electrofisiología extracelular es una técnica ampliamente usada en investigación neurocientífica, la cual estudia el funcionamiento del cerebro mediante la medición de campos eléctricos generados por la actividad neuronal. Esto se realiza a través de electrodos implantados en el cerebro y conectados a dispositivos electrónicos para amplificación y digitalización de las señales. De los muchos modelos animales usados en experimentación, las ratas y los ratones se encuentran entre las especies más comúnmente utilizadas. Actualmente, la experimentación electrofisiológica busca condiciones cada vez más complejas, limitadas por la tecnología de los dispositivos de adquisición. Dos aspectos son de particular interés: Realimentación de lazo cerrado y comportamiento en condiciones naturales. En esta tesis se presentan desarrollos con el objetivo de mejorar diferentes facetas de estos dos problemas. La realimentación en lazo cerrado se refiere a todas las técnicas en las que los estímulos son producidos en respuesta a un evento generado por el animal. La latencia debe ajustarse a las escalas temporales bajo estudio. Los sistemas modernos de adquisición presentan latencias en el orden de los 10ms. Sin embargo, para responder a eventos rápidos, como pueden ser los potenciales de acción, se requieren latencias por debajo de 1ms. Además, los algoritmos para detectar los eventos o generar los estímulos pueden ser complejos, integrando varias entradas de datos en tiempo real. Integrar el desarrollo de dichos algoritmos en las herramientas de adquisición forma parte del diseño experimental. Para estudiar comportamientos naturales, los animales deben ser capaces de moverse libremente en entornos emulando condiciones naturales. Experimentos de este tipo se ven dificultados por la naturaleza cableada de los sistemas de adquisición. Otras restricciones físicas, como el peso de los implantes o limitaciones en el consumo de energía, pueden también afectar a la duración de los experimentos, limitándola. La experimentación puede verse enriquecida cuando los datos electrofisiológicos se ven complementados con múltiples fuentes distintas. Por ejemplo, seguimiento de los animales o miscroscopía. Herramientas capaces de integrar datos independientemente de su origen abren la puerta a nuevas posibilidades. Los avances tecnológicos presentados abordan estas limitaciones. Se han diseñado dispositivos con latencias de lazo cerrado inferiores a 200us que permiten combinar cientos de canales electrofisiológicos con otras fuentes de datos, como vídeo o seguimiento. El software de control para estos dispositivos se ha diseñado manteniendo la flexibilidad como objetivo. Se han desarrollado interfaces y estándares de naturaleza abierta para incentivar el desarrollo de herramientas compatibles entre ellas. Para resolver los problemas de cableado se siguieron dos métodos distintos. Uno fue el desarrollo de headstages ligeros combinados con cables coaxiales ultra finos y conmutadores activos, gracias al seguimiento de animales. Este desarrollo permite reducir el esfuerzo impuesto a los animales, permitiendo espacios amplios y experimentos de larga duración, al tiempo que permite el uso de headstages con características avanzadas. Paralelamente se desarrolló un tipo diferente de headstage, con tecnología inalámbrica. Se creó un algoritmo de compresión digital especializado capaz de reducir el ancho de banda a menos del 65% de su tamaño original, ahorrando energía. Esta reducción permite baterías más ligeras y mayores tiempos de operación. El algoritmo fue diseñado para ser capaz de ser implementado en una gran variedad de dispositivos. Los desarrollos presentados abren la puerta a nuevas posibilidades experimentales para la neurociencia, combinando adquisición elextrofisiológica con estudios conductuales en condiciones naturales y estímulos complejos en tiempo real.[CA] L'electrofisiologia extracel·lular és una tècnica àmpliament utilitzada en la investigació neurocientífica, la qual permet estudiar el funcionament del cervell mitjançant el mesurament de camps elèctrics generats per l'activitat neuronal. Això es realitza a través d'elèctrodes implantats al cervell, connectats a dispositius electrònics per a l'amplificació i digitalització dels senyals. Dels molts models animals utilitzats en experimentació electrofisiològica, les rates i els ratolins es troben entre les espècies més utilitzades. Actualment, l'experimentació electrofisiològica busca condicions cada vegada més complexes, limitades per la tecnologia dels dispositius d'adquisició. Dos aspectes són d'especial interès: La realimentació de sistemes de llaç tancat i el comportament en condicions naturals. En aquesta tesi es presenten desenvolupaments amb l'objectiu de millorar diferents aspectes d'aquestos dos problemes. La realimentació de sistemes de llaç tancat es refereix a totes aquestes tècniques on els estímuls es produeixen en resposta a un esdeveniment generat per l'animal. La latència ha d'ajustar-se a les escales temporals sota estudi. Els sistemes moderns d'adquisició presenten latències en l'ordre dels 10ms. No obstant això, per a respondre a esdeveniments ràpids, com poden ser els potencials d'acció, es requereixen latències per davall de 1ms. A més a més, els algoritmes per a detectar els esdeveniments o generar els estímuls poden ser complexos, integrant varies entrades de dades a temps real. Integrar el desenvolupament d'aquests algoritmes en les eines d'adquisició forma part del disseny dels experiments. Per a estudiar comportaments naturals, els animals han de ser capaços de moure's lliurement en ambients emulant condicions naturals. Aquestos experiments es veuen limitats per la natura cablejada dels sistemes d'adquisició. Altres restriccions físiques, com el pes dels implants o el consum d'energia, poden també limitar la duració dels experiments. L'experimentació es pot enriquir quan les dades electrofisiològiques es complementen amb dades de múltiples fonts. Per exemple, el seguiment d'animals o microscòpia. Eines capaces d'integrar dades independentment del seu origen obrin la porta a noves possibilitats. Els avanços tecnològics presentats tracten aquestes limitacions. S'han dissenyat dispositius amb latències de llaç tancat inferiors a 200us que permeten combinar centenars de canals electrofisiològics amb altres fonts de dades, com vídeo o seguiment. El software de control per a aquests dispositius s'ha dissenyat mantenint la flexibilitat com a objectiu. S'han desenvolupat interfícies i estàndards de naturalesa oberta per a incentivar el desenvolupament d'eines compatibles entre elles. Per a resoldre els problemes de cablejat es van seguir dos mètodes diferents. Un va ser el desenvolupament de headstages lleugers combinats amb cables coaxials ultra fins i commutadors actius, gràcies al seguiment d'animals. Aquest desenvolupament permet reduir al mínim l'esforç imposat als animals, permetent espais amplis i experiments de llarga durada, al mateix temps que permet l'ús de headstages amb característiques avançades. Paral·lelament es va desenvolupar un tipus diferent de headstage, amb tecnologia sense fil. Es va crear un algorisme de compressió digital especialitzat capaç de reduir l'amplada de banda a menys del 65% de la seua grandària original, estalviant energia. Aquesta reducció permet bateries més lleugeres i majors temps d'operació. L'algorisme va ser dissenyat per a ser capaç de ser implementat a una gran varietat de dispositius. Els desenvolupaments presentats obrin la porta a noves possibilitats experimentals per a la neurociència, combinant l'adquisició electrofisiològica amb estudis conductuals en condicions naturals i estímuls complexos en temps real.[EN] Extracellular electrophysiology is a technique widely used in neuroscience research. It can offer insights on how the brain works by measuring the electrical fields generated by neural activity. This is done through electrodes implanted in the brain and connected to amplification and digitization electronic circuitry. Of the many animal models used in electrophysiology experimentation, rodents such as rats and mice are among the most popular species. Modern electrophysiology experiments seek increasingly complex conditions that are limited by acquisition hardware technology. Two particular aspects are of special interest: Closed-loop feedback and naturalistic behavior. In this thesis, we present developments aiming to improve on different facets of these two problems. Closed-loop feedback encompasses all techniques in which stimuli is produced in response of an event generated by the animal. Latency, the time between trigger event and stimuli generation, must adjust to the biological timescale being studied. While modern acquisition systems feature latencies in the order of 10ms, response to fast events such as high-frequency electrical transients created by neuronal activity require latencies under $1ms$. In addition, algorithms for triggering or generating closed-loop stimuli can be complex, integrating multiple inputs in real-time. Integration of algorithm development into acquisition tools becomes an important part of experiment design. For electrophysiology experiments featuring naturalistic behavior, animals must be able to move freely in ecologically meaningful environments, mimicking natural conditions. Experiments featuring elements such as large arenaa, environmental objects or the presence of another animals are, however, hindered by the wired nature of acquisition systems. Other physical constraints, such as implant weight or power restrictions can also affect experiment time, limiting their duration. Beyond the technical limits, complex experiments are enriched when electrophysiology data is integrated with multiple sources, for example animal tracking or brain microscopy. Tools allowing mixing data independently of the source open new experimental possibilities. The technological advances presented on this thesis addresses these topics. We have designed devices with closed-loop latencies under 200us while featuring high-bandwidth interfaces. These allow the simultaneous acquisition of hundreds of electrophysiological channels combined with other heterogeneous data sources, such as video or tracking. The control software for these devices was designed with flexibility in mind, allowing easy implementation of closed-loop algorithms. Open interface standards were created to encourage the development of interoperable tools for experimental data integration. To solve wiring issues in behavioral experiments, we followed two different approaches. One was the design of light headstages, coupled with ultra-thin coaxial cables and active commutator technology, making use of animal tracking. This allowed to reduce animal strain to a minimum allowing large arenas and prolonged experiments with advanced headstages. A different, wireless headstage was also developed. We created a digital compression algorithm specialized for neural electrophysiological signals able to reduce data bandwidth to less than 65.5% its original size without introducing distortions. Bandwidth has a large effect on power requirements. Thus, this reduction allows for lighter batteries and extended operational time. The algorithm is designed to be able to be implemented in a wide variety of devices, requiring low hardware resources and adding negligible power requirements to a system. Combined, the developments we present open new possibilities for neuroscience experiments combining electrophysiology acquisition with natural behaviors and complex, real-time, stimuli.The research described in this thesis was carried out at the Polytechnic University of Valencia (Universitat Politècnica de València), Valencia, Spain in an extremely close collaboration with the Neuroscience Institute - Spanish National Research Council - Miguel Hernández University (Instituto de Neurociencias - Consejo Superior de Investigaciones Cientí cas - Universidad Miguel Hernández), San Juan de Alicante, Spain. The projects described in chapters 3 and 4 were developed in collabo- ration with, and funded by, Open Ephys, Cambridge, MA, USA and OEPS - Eléctronica e produção, unipessoal lda, Algés, Portugal.Cuevas López, A. (2021). Development of Advanced Closed-Loop Brain Electrophysiology Systems for Freely Behaving Rodents [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/179718TESI

Development and use of bioanalytical instrumentation and signal analysis methods for rapid sampling microdialysis monitoring of neuro-intensive care patients

This thesis focuses on the development and use of analysis tools to monitor brain injury patients. For this purpose, an online amperometric analyzer of cerebral microdialysis samples for glucose and lactate has been developed and optimized within the Boutelle group. The initial aim of this thesis was to significantly improve the signal-to-noise ratio and limit of detection of the assay to allow reliable quantification of the analytical data. The first approach was to re-design the electronic instrumentation of the assay. Printed-circuit boards were fabricated and proved very low noise, stable and much smaller than the previous potentiostats. The second approach was to develop generic data processing algorithms to remove three complex types of noise that commonly contaminate analytical signals: spikes, non-stationary ripples and baseline drift. The general strategy consisted in identifying the types of noise, characterising them, and subsequently subtracting them from the otherwise unprocessed data set. Spikes were effectively removed with 96.8% success and ripples were removed with minimal distortion of the signal resulting in an increased signal-to-noise ratio by up to 250%. This allowed reliable quantification of traces from ten patients monitored with the online microdialysis assay. Ninety-six spontaneous metabolic events in response to spreading depolarizations were resolved. These were characterized by a fall in glucose by -32.0 μM and a rise in lactate by +23.1 μM (median values) for over a 20-minute time-period. With frequently repeating events, this led to a progressive depletion of brain glucose. Finally, to improve the temporal coupling between the metabolic data and the electro-cortical signals, a flow-cell was engineered to integrate a potassium selective electrode into the microdialysate flow stream. With good stability over hours of continuous use and a 90% response time of 65 seconds, this flow cell was used for preliminary in vivo experiments the Max Planck Institute in Cologne

A modular multi electrode array system for electrogenic cell characterisation and cardiotoxicity applications

Multi electrode array (MEA) systems have evolved from custom-made experimental tools, exploited for neural research, into commercially available systems that are used throughout non-invasive electrophysiological study. MEA systems are used in conjunction with cells and tissues from a number of differing organisms (e.g. mice, monkeys, chickens, plants). The development of MEA systems has been incremental over the past 30 years due to constantly changing specific bioscientific requirements in research. As the application of MEA systems continues to diversify contemporary commercial systems are requiring increased levels of sophistication and greater throughput capabilities. [Continues.

HIERARCHICAL NEURAL COMPUTATION IN THE MAMMALIAN VISUAL SYSTEM

Our visual system can efficiently extract behaviorally relevant information from ambiguous and noisy luminance patterns. Although we know much about the anatomy and physiology of the visual system, it remains obscure how the computation performed by individual visual neurons is constructed from the neural circuits. In this thesis, I designed novel statistical modeling approaches to study hierarchical neural computation, using electrophysiological recordings from several stages of the mammalian visual system. In Chapter 2, I describe a two-stage nonlinear model that characterized both synaptic current and spike response of retinal ganglion cells with unprecedented accuracy. I found that excitatory synaptic currents to ganglion cells are well described by excitatory inputs multiplied by divisive suppression, and that spike responses can be explained with the addition of a second stage of spiking nonlinearity and refractoriness. The structure of the model was inspired by known elements of the retinal circuit, and implies that presynaptic inhibition from amacrine cells is an important mechanism underlying ganglion cell computation. In Chapter 3, I describe a hierarchical stimulus-processing model of MT neurons in the context of a naturalistic optic flow stimulus. The model incorporates relevant nonlinear properties of upstream V1 processing and explained MT neuron responses to complex motion stimuli. MT neuron responses are shown to be best predicted from distinct excitatory and suppressive components. The direction-selective suppression can impart selectivity of MT neurons to complex velocity fields, and contribute to improved estimation of the three-dimensional velocity of moving objects. In Chapter 4, I present an extended model of MT neurons that includes both the stimulus-processing component and network activity reflected in local field potentials (LFPs). A significant fraction of the trial-to-trial variability of MT neuron responses is predictable from the LFPs in both passive fixation and a motion discrimination task. Moreover, the choice-related variability of MT neuron responses can be explained by their phase preferences in low-frequency band LFPs. These results suggest an important role of network activity in cortical function. Together, these results demonstrated that it is possible to infer the nature of neural computation from physiological recordings using statistical modeling approaches

Glucose-powered neuroelectronics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-164).A holy grail of bioelectronics is to engineer biologically implantable systems that can be embedded without disturbing their local environments, while harvesting from their surroundings all of the power they require. As implantable electronic devices become increasingly prevalent in scientific research and in the diagnosis, management, and treatment of human disease, there is correspondingly increasing demand for devices with unlimited functional lifetimes that integrate seamlessly with their hosts in these two ways. This thesis presents significant progress toward establishing the feasibility of one such system: A brain-machine interface powered by a bioimplantable fuel cell that harvests energy from extracellular glucose in the cerebrospinal fluid surrounding the brain. The first part of this thesis describes a set of biomimetic algorithms and low-power circuit architectures for decoding electrical signals from ensembles of neurons in the brain. The decoders are intended for use in the context of neural rehabilitation, to provide paralyzed or otherwise disabled patients with instantaneous, natural, thought-based control of robotic prosthetic limbs and other external devices. This thesis presents a detailed discussion of the decoding algorithms, descriptions of the low-power analog and digital circuit architectures used to implement the decoders, and results validating their performance when applied to decode real neural data. A major constraint on brain-implanted electronic devices is the requirement that they consume and dissipate very little power, so as not to damage surrounding brain tissue. The systems described here address that constraint, computing in the style of biological neural networks, and using arithmetic-free, purely logical primitives to establish universal computing architectures for neural decoding. The second part of this thesis describes the development of an implantable fuel cell powered by extracellular glucose at concentrations such as those found in the cerebrospinal fluid surrounding the brain. The theoretical foundations, details of design and fabrication, mechanical and electrochemical characterization, as well as in vitro performance data for the fuel cell are presented.by Benjamin Isaac Rapoport.Ph.D