Made available in DSpace on 2017-07-21T19:26:04Z (GMT). No. of bitstreams: 1 Ewandson Luiz Lameu.pdf: 6871045 bytes, checksum: 35133e0038b470fc95e483092d60973c (MD5) Previous issue date: 2013-02-22 / Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Paraná / Functional brain networks are composed of cortical areas that are anatomically and functionally connected. One of the cortical networks for which more information is available in the literature is the cat cerebral cortex. Statistical analyses of the latter suggest that its structure can be described as a clustered network, in which each cluster is a scale-free network possessing highly connected hubs. Those hubs are strongly connected together. We have built a clustered scale-free network inspired in the cat and human cortex structure so as to study their dynamical properties. We focus on the synchronization of bursting activity of the cortical areas and how it can be suppressed by means of neuron deactivation through suitably applied light pulses. We show that is possible to effectively suppress bursting synchronization by acting on a single hub, because it is highly connected and have a strong influence over the network. / A rede funcional do cérebro é composta por áreas corticais que são anatômica e funcionalmente conectadas. Uma das redes corticais que mais se tem informação disponível na literatura é o córtex cerebral do gato. Análises estatísticas deste último sugerem uma estrutura que pode ser descrita como uma rede de sub-redes, onde cada sub-rede é do tipo livre de escala possuindo hubs muito conectados. Estes hubs são, por sua vez, intensamente conectados entre si. Constru ´ımos uma rede de sub-redes inspirados na estrutura cerebral de gatos e humanos a fim de estudar suas propriedades dinâmicas. Focamos na sincronização dos disparos neurais das áreas corticais e em como esse efeito pode ser suprimido por meio da desativação neural causada pela aplicação de pulsos de luz de forma apropriada. Mostramos que é possível suprimir efetivamente a sincronização dos disparos neurais perturbando um único hub, pois este tem muitas conexões e influencia fortemente toda a rede.

Our ability to learn relies on the potential of neuronal networks to change through experience. The primary visual cortex (V1) has become a popular system for studying how experience shapes cortical neuronal networks. Experience-dependent plasticity in V1 has been extensively studied in young animals, revealing that experiences in early postnatal life substantially shape neuronal activity in the developing cortex. In contrast, less is known about how experiences modify the representation of visual stimuli in the adult brain. In addition, adult experience-dependent plasticity remains largely unexplored in neurodevelopmental disorders. To address this issue, we established a two-photon calcium imaging set-up, suitable for chronic imaging of neuronal activity in awake-behaving mice. We implemented protocols for the reliable expression of genetically encoded calcium indicators (GCaMP6), for the implantation of a chronic cranial window and for the analysis of chronic calcium imaging data. This approach enables us to monitor the activity of hundreds of neurons across days, and up to 4-5 weeks. We used this technique to determine whether the daily exposure to high-contrast gratings would induce experience-dependent changes in V1 neuronal activity. We monitored the activity of putative excitatory neurons and of three non-overlapping populations of inhibitory interneurons in layer 2/3 of adult mice freely running on a cylindrical treadmill. We compared the results obtained from mice that were exposed daily to either a high-contrast grating or to a grey screen and characterized their neuronal response properties. Our results did not reveal significant differences in neuronal properties between these two groups, suggesting a lack of stimulus-specific plasticity in our experimental conditions. However, we did observe and characterize, in both groups, a wide range of activity changes in individual cells over time. We finally applied the same method to investigate impairments in experience-dependent plasticity in a mouse model of intellectual disability (ID), caused by synaptic GTPase-activating protein (SynGAP) haploinsufficiency. SynGAP haploinsufficiency is a common de novo genetic cause of non-syndromic ID and is considered a Type1 risk for autism spectrum disorders. While the impact of Syngap gene mutations has been thoroughly studied at the molecular and cellular levels, neuronal network deficits in vivo remain largely unexplored. In this study, we compared in vivo neuronal activity before and after monocular deprivation in adult mutant mice and littermate controls. These results revealed differences in baseline network activity between both experimental groups. These impairments in cortical neuronal network activity may underlie sensory and cognitive deficits in patients with Syngap gene mutations.

Dans le but de mieux comprendre le fonctionnement du cerveau nous devons examiner les domaines structuraux qui le composent, de la simple cellule à des régions entières du cerveau interconnectées. Cependant, bien que le fonctionnement d’une ou plusieurs cellules soit relativement bien connu, il n’y a que peu d’informations concernant les groupements de neurones interagissant fonctionnellement dans une même tâche, les réseaux neuronaux. De plus, l'activité équilibrée et concertée des réseaux excitateurs et inhibiteurs joue un rôle clé pour les intégrations corticales appropriées. Par ailleurs, il existe plusieurs outils afin d’enregistrer l’activité des réseaux excitateurs, ce qui n’est pas le cas pour les réseaux inhibiteurs. L’imagerie du colorant sensible au voltage (VSDI) est une technique permettant l’enregistrement de l’activité neuronale au moyen d’une émission de fluorescence proportionnelle au changement de potentiel de membrane. Par rapport aux autres techniques employant des électrodes, le VSDI permet l’enregistrement non invasif de l’activité de centaines de sites en même temps. Au cours des dernières décennies, le VSDI a été largement utilisé tant in vitro qu’in vivo pour étudier l’activité d’une cellule et des réseaux excitateurs. Néanmoins, en utilisant le VSDI, les recherches quant à l’activité des réseaux excitateurs ont été principalement réalisées par quantification d’émission de fluorescence en définissant des régions d’intérêts à des temps fixes, alors que l’activité inhibitrice n’a été évaluée qu’à l’échelle cellulaire. La première approche ne permet pas l’obtention de toutes les informations de la dynamique de propagation de la transmission glutamatergique du fait qu’elle ne prend en considération ni la vitesse ni la direction de propagation du signal. En revanche, la seconde approche n’offre pas la possibilité d’étudier l’activité du réseau inhibiteur ce qui serait toutefois important de définir du fait de la propagation spatiale extensive des interneurones au sein des aires corticales. Durant mon doctorat, le but de mon travail a été d’étudier en détail les réseaux neuronaux excitateurs et inhibiteurs de l’aire CA1 de l’hippocampe de souris à l’aide du VSDI. Pour les étudier de façon plus compréhensive, en collaboration avec une équipe de mathématicien, nous avons développé un algorithme permettant de mesurer la vitesse et la direction de propagation du signal VSDI, ce qui représente une nouvelle méthode pour analyser le flux optique. Après la validation réussie de l’algorithme avec des données de substitution pour tester sa précision, nous avons analysé deux séries d’expériences dans lesquelles l’activité des réseaux excitateurs a été manipulée soit par augmentation de l’intensité de stimulation passant de 10 à 30 Volts ou en bloquant la transmission GABAergique avec la picrotoxine, un antagoniste du récepteur GABAA. Les résultats de ces manipulations montrent une diminution significative de la vitesse alors que l’application de picrotoxine modifie de façon significative la direction de propagation, ce qui rend le signal de dépolarisation médié par le VSDI moins dispersé par rapport au contrôle. L’utilisation du VSDI a permis l’entière caractérisation des signaux hyperpolarisants médiés par les récepteurs GABAA dans toutes les sous-couches de CA1 (champ IPSP), offrant ainsi une nouvelle façon d’étudier les événements inhibiteurs à l’échelle d’un réseau. De plus, j’ai montré qu’en activant les récepteurs mGluR5, j’étais capable d’augmenter de façon durable le champ IPSP du VSDI, avec la durée et l’ampleur au niveau des sous-couches spécifiques de CA1. Globalement, je présente dans cette thèse de nouvelles méthodes et nouveaux résultats qui peuvent représenter une avancée dans la quête d’une meilleure compréhension des réseaux neuronaux, excitateurs et inhibiteurs, ce qui, espérons-le, pourra contribuer à réduire l’écart de connaissance entre l’activité d’une seule cellule et celle du comportement. / In order to better understand brain functioning we need to investigate all the structural domains present in it, from single cell to interconnected entire brain regions. However, while our knowledge in terms of single/few cells functioning is vast, very little is known about neuronal networks, which are interacting collections of neurons functionally related to the same task. Moreover, the balanced and concerted activity of excitatory and inhibitory networks plays a key role for proper cortical computations. However, while exist several tools to record excitatory networks activity, this is not the case for inhibitory networks. Voltage sensitive dye imaging (VSDI) is a technique that allows the recording of neuronal activity by mean of proportional emission of fluorescence according to changes in membrane potential. The advantage of using VSDI over other recording techniques using electrodes is that VSDI allows not invasive recording of neuronal activity from hundreds of sites at the same time. During my doctoral course I aimed at studying in detail excitatory and inhibitory neuronal networks in the CA1 area of mouse hippocampus with VSDI. To study excitatory networks more comprehensively, in collaboration with a team of mathematicians, we developed a mathematical algorithm that allowed measuring the velocity and the direction of spreading of the VSDI signal and it represents a new method to determine an optical flow. After successful validation of the algorithm with surrogate data to test its accuracy, we analysed two set of experiments in which network excitatory activity has been manipulated either by increasing Schaffer’s collaterals stimulation intensity or by blocking GABAergic transmission with the GABAA receptor antagonist picrotoxin in order to increase the depolarization in the CA1 region of the hippocampus. The results of these manipulations significantly decreased signal velocity whereas picrotoxin application significantly modified the direction of spreading, making the depolarization-mediated VSDI signal less dispersed compared to control. Using VSDI I was able to fully characterize GABAA receptor-mediated hyperpolarizing signals in all the CA1 sublayers (field IPSPs), thus providing a new way of monitoring inhibitory events at network level. Moreover, I found that the activation of mGluR5 receptors induced an increase in a long-lasting manner of the VSDI-recorded field IPSPs, with duration and magnitude that relied on the specific CA1 sublayer considered. Overall, my work shows new methodologies and new findings that may represent a step forward in the quest for a better understanding of neuronal networks, both excitatory as well as inhibitory, which hopefully can contribute to reduce the gap of knowledge between single cell activity and behaviour.

It has only recently been acknowledged to what large extent the internal dynamics of neural networks could play a role in their function. In this respect, synaptic "noise" -- that is, the influence of the cortical network on single neurons exerted through the massive recurrent circuity that is the hallmark of neocortex -- has recently been shown to have a profound effect on neuronal integrative properties, changing the responses of single neurons across brain states, sometimes within the matter of a few seconds. These internally generated activity states, shaped by and continually shaping the plastic synaptic recurrent connections, then combine with the external inputs to produce a rich repertoire of responses to sensory stimuli in primary cortical regions. In this thesis, we have focused on the {\it spatial} aspect of these internal dynamics, specifically the spatial structure of cortical oscillations, spontaneous and stimulus-evoked. Along the way, we have made an extensive review of the literature concerning propagating waves in thalamus and cortex, and studied network models to investigate how waves depend on network state. We have also introduced new tools for the characterization of spatiotemporal activity patterns in noisy multichannel data. The culmination of this work is a demonstration, using voltage-sensitive dye imaging data taken from the awake monkey, that the population response to a small visual stimulus propagates like a wave across a large extent of primary visual cortex during the awake state, a result contradicting a range of previous studies which seemed to suggest that propagating waves disappear in this case. Moving forward, we have begun to investigate the spatiotemporal structure of local field potential and spiking activity in multielectrode recordings taken from the human and monkey in various states of arousal, to address questions prompted by our initial voltage-sensitive dye imaging study in the monkey. In parallel, we have initiated an analysis of the extent to which neural connectivity can be characterized by the "small-world" effect, the main result of which is that neural graphs may in fact reside outside the small-world regime. The results from these PhD studies thus span the spectrum of scales in neuroscience, from macroscopic activity patterns to microscopic connectivity profiles. It is my sincere hope to expound in these pages a unified theme for these results, and a foundation for further work in neuroscience -- a search for structure within the internal architecture of the system under study.

Major Depressive Disorder (MDD) is exceedingly prevalent and considered to be one of the leading cause of disability worldwide. Depression is also a heterogeneous disorder characterized by complex diagnotic approaches with a lack of diagnostic biomarker, an inconsistent response to treatment, no established mechanism, and affecting multiple physiological systems such as endocrine, immunological and cardiovasular as well. <p><p>The growing impact of the analysis of complex signals on biology and medicine is fundamentally changing our view of living organisms, physiological systems, and disease processes. In this endeavour, the basic challenge is to reveal how the coordinated, dynamical behavior of cells and tissues at the macroscopic level, emerges from the vast number of random molecular interactions at the microscopic level. In this way, the fundamental questions could be: (i) how physiological systems function as a whole, (ii) how they transduce and process dynamical information, (iii) how they respond to external stimuli, and mostly (iv), how they change during a pathological processus.<p><p>These challenges are of interest from a number of perspectives including basic modeling of physiology and practical bedside approaches to medical and risk stratification. <p><p>The general purpose of this thesis, therefore, is to study physiological time series to provide a new understanding of sleep dynamics in health, specifically as they apply to the pathological condition of MDD. More precisely: (1) to quantitatively characterize the complex, nonlinear behaviour of cardiovascular (ECG) and electroencephalographic (EEG) time series during sleep, in health and in MDD. This project will test the hypotheses that both the sleep EEG and ECG detects reorganization in the system dynamics in patient suffering from depression. (2) To develop new diagnostic and prognostic tests for MDD, by detecting and extracting “hidden information” in the ECG and EEG datasets.<p><p>Three different methods are introduced in this thesis for the analysis of dynamical systems. The first one, detrended fluctuation analysis, can reveal the presence of long-term correlations ("memory" in the physiological system) even when embedded in non-stationary time series. Graph theoretical measures were then applied to test whether disrupting an optimal pattern ["small-world network"] of functional brain connectivity underlies depression. Finally, multiscale entropy method, which is aimed at quantifying the complexity of the systems' output resulting from the presence of irregular structures on multiple scales, was applied on the ECG signal.<p><p>The results indicate that healthy physiologic systems, measured through the EEG and the ECG signals, are the most complex. According to the decomplexification theory, the depressive disease model exhibits a loss of system complexity, with potential important applications in the development and testing of basic physiologic models, of new diagnostic and prognostic tools in psychiatry, and of clinical risk stratification. / Doctorat en Sciences médicales / info:eu-repo/semantics/nonPublished

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Les neurones sensoriels réagissent à des stimuli externes en émettant des séquences de potentiels d’action (“spikes”). Ces spikes transmettent collectivement de l’information sur le stimulus en formant des motifs spatio-temporels qui constituent le code neural. On observe expérimentalement que ces motifs se produisent de façon irrégulière, mais avec une structure qui peut être mise en évidence par l’utilisation de descriptions probabilistes et de méthodes statistiques. Cependant, la caractérisation statistique des données expérimentales présente plusieurs contraintes majeures: en dehors de celles qui sont inhérentes aux statistiques empiriques comme la taille de l’échantillonnage, ‘le’ modèle statistique sous-jacent est inconnu. Dans cette thèse, nous abordons le problème d’un point de vue complémentaire à l’approche expérimentale. Nous nous intéressons à des modèles neuro-mimétiques permettant d’étudier la statistique collective des potentiels d’action et la façon dont elle dépend de l’architecture et l’histoire du réseau ainsi que du stimulus. Nous considérons tout d’abord un modèle de type Intègre-et-Tire à conductance incluant synapses électriques et chimiques. Nous montrons que la statistique des potentiels d’action est caractérisée par une distribution non stationnaire et de mémoire infinie, compatible avec les probabilités conditionnelles (left interval-specification), qui est non-nulle et continue, donc une distribution de Gibbs. Nous présentons ensuite une méthode qui permet d’unifier les modèles dits d’entropie maximale spatio-temporelle (dont la mesure invariante est une distribution de Gibbs dans le sens de Bowen) et les modèles neuro-mimétiques, en fou / Sensory neurons respond to external stimulus using sequences of action potentials (“spikes”). They convey collectively to the brain information about the stimulus using spatio-temporal patterns of spikes (spike trains), that constitute a “neural code”. Since spikes patterns occur irregularly (yet highly structured) both within and over repeated trials, it is reasonable to characterize them using statistical methods and probabilistic descriptions. However, the statistical characterization of experimental data presents several major constraints: apart from those inherent to empirical statistics like finite size sampling, ‘the’ underlying statistical model is unknown. In this thesis we adopt a complementary approach to experiments. We consider neuromimetic models allowing the study of collective spike trains statistics and how it depends on network architecture and history, as well as on the stimulus. First, we consider a conductance-based Integrate-and-Fire model with chemical and electric synapses. We show that the spike train statistics is characterized by non-stationary, infinite memory, distribution consistent with conditional probabilities (Left interval specifications), which is continuous and non null, thus a Gibbs distribution. Then, we present a novel method that allows us to unify spatio-temporal Maximum Entropy models (whose invariant measure are Gibbs distributions in the Bowen sense) and neuro-mimetic models, providing a solid ground towards biophysical explanation of spatio-temporal correlations observed in experimental data. Finally, using these tools, we discuss the stimulus response of retinal ganglion cells, and the possible generalization of the co

This thesis deals with the topic of the technology called data mining. First, the thesis describes the term data mining as an independent discipline and then its processing methods and the most common use. The term data mining is thereafter explained with the help of methodologies describing all parts of the process of knowledge discovery in databases -- CRISP-DM, SEMMA. The study's purpose is presenting new data mining methods and particular algorithms -- decision trees, neural networks and genetic algorithms. These facts are used as theoretical introduction, which is followed by practical application searching for causes of meningoencephalitis development of certain sample of patients. Decision trees in system Clementine, which is one of the top datamining tools, were used for the analysys.

Dissociated neuronal cell cultures grown on substrate integrated microelectrode arrays (MEAs) generate spontaneous activity that can be recorded for up to several weeks. The signature wave shapes from extracellular recording of neuronal activity display a great variety of shapes with triphasic signals predominating. I characterized extracellular recordings from over 600 neuronal signals. I have preformed a categorical study by dividing wave shapes into two major classes: (type 1) signals in which the large positive peak follows the negative spike, and (type 2) signals in which the large positive peak precedes the negative spike. The former are hypothesized to be active signal propagation that can occur in the axon and possibly in soma or dendrites. The latter are hypothesized to be passive which is generally secluded to soma or dendrites. In order to verify these hypotheses, I pharmacologically targeted ion channels with tetrodotoxin (TTX), tetraethylammonium (TEA), 4-aminopyridine (4-AP), and monensin.

Since the mid-1990s, formal verification has become increasingly important because it can provide guarantees that a software system is free of bugs and working correctly based on a provided model. Verification of biological and medical systems is a promising application of formal verification. Human neural networks have recently been emulated and studied as a biological system. Some recent research has been done on modelling some crucial neuronal circuits and using model checking techniques to verify their temporal properties. In large case studies, model checkers often cannot prove the given property at the desired level of generality. In this thesis, we provide a model using the Coq proof assistant and prove some properties concerning the dynamic behavior of some basic neuronal structures. Understanding the behavior of these modules is crucial because they constitute the elementary building blocks of bigger neuronal circuits. By using a proof assistant, we guarantee that the properties are true in the general case, that is, true for any input values, any length of input, and any amount of time. In this thesis, we define a model of human neural networks. We verify some properties of this model starting with properties of neurons. Neurons are the smallest unit in a human neuronal network. In the next step, we prove properties about functional structures of human neural networks which are called archetypes. Archetypes consist of two or more neurons connected in a suitable way. They are known for displaying some particular classes of behaviours, and their compositions govern several important functions such as walking, breathing, etc. The next step is verifying properties about structures that couple different archetypes to perform more complicated actions. We prove a property about one of these kinds of compositions. With such a model, there is the potential to detect inactive regions of the human brain and to treat mental disorders. Furthermore, our approach can be generalized to the verification of other kinds of networks, such as regulatory, metabolic, or environmental networks.