Les variants du nombre de copies (CNV) offre un cadre riche pour étudier les mécanismes neurobiologiques qui sous tendent la vulnérabilité aux troubles neuropsychiatriques. Notamment, les délétions du 16p11.2 et 22q11.2 sont parmi les facteurs génétiques les plus fréquents associés au trouble du spectre de l’autisme (TSA) et à la schizophrénie (SCZ). À l’heure actuelle, les perturbations fonctionnelles cérébrales qui sous-tendent cette vulnérabilité cognitive restent mécomprises. Récemment, l’analyse par gradient du connectome humain a révélé une réorganisation le long de l’axe dominant sensorimoteur à transmodal dans le TSA et la SCZ. Dans cette étude, nous avons cherché à étendre cette approche analytique aux porteurs d’une délétion du 16p11.2 et du 22q11.2 conférant un risque élevé pour de mêmes conditions. À cette fin, nous avons utilisé les données d’imagerie par résonance magnétique au repos combinant les données de deux cohortes génétiques, pour un total de 180 sujets incluant 61 porteurs. Par le biais d’un paradigme cas-contrôle, nous rapportons la première évidence d’une compression du gradient fonctionnel sensorimoteur à transmodal chez les porteurs de telles délétions. En dernier lieu, nous présentons une étude exploratoire d’association endophénotype-phénome dans la population générale du UK Biobank. Nous démontrons que la ressemblance aux profils de compression corticale des délétions est reliée à plusieurs traits humains complexes, en concordance avec les dimensions cliniques impactées par ces mêmes CNV. / Copy number variants (CNVs) present a unique opportunity to study the neural mechanisms underlying vulnerability to neuropsychiatric disorders. Notably, deletions of the 16p11.2 and 22q11.2 region are among the most common genetic variations associated with autism spectrum disorder (ASD) and schizophrenia (SCZ). However, brain functional disruptions underlying this cognitive vulnerability remains unclear. Recent gradient analysis framework developed to study parsimonious connectome dimensions at the system-level have reported disruptions along the overarching sensorimotor-to-transmodal gradient in ASD and SCZ. In this study, we sought to extend this gradient approach to carriers of a deletion at the 16p11.2 and 22q11.2 region. To achieve this, we pooled resting-state functional magnetic resonance imaging data from a total of 180 subjects, including 61 carriers, distributed among two genetic cohorts. By the means of a case-control study design, we provide the first evidence of a compressed cortical functional gradient in CNV carriers compared to healthy controls. Finally, we provide an exploratory endophenotype-phenome association study in the general UK Biobank population. We demonstrate that resemblance to 16p11.2 and 22q11.2 deletion profiles of cortical compression is related to several complex human traits, in concordance with clinical dimensions known to be impacted by the same CNV.

Com o avanço da tecnologia, as estratégias tradicionais para identificação de pessoas se tornaram mais suscetíveis a falhas, de forma a superar essas dificuldades algumas abordagens vêm sendo propostas na literatura. Dentre estas abordagens destaca-se a Biometria. O campo da Biometria abarca uma grande variedade de tecnologias usadas para identificar e verificar a identidade de uma pessoa por meio da mensuração e análise de aspectos físicos e/ou comportamentais do ser humano. Em função disso, a biometria tem um amplo campo de aplicações em sistemas que exigem uma identificação segura de seus usuários. Os sistemas biométricos mais populares são baseados em reconhecimento facial ou de impressões digitais. Entretanto, existem outros sistemas biométricos que utilizam a íris, varredura de retina, voz, geometria da mão e termogramas faciais. Nos últimos anos, o reconhecimento biométrico obteve avanços na sua confiabilidade e precisão, com algumas modalidades biométricas oferecendo bom desempenho global. No entanto, mesmo os sistemas biométricos mais avançados ainda enfrentam problemas. Recentemente, esforços têm sido realizados visando empregar diversas modalidades biométricas de forma a tornar o processo de identificação menos vulnerável a ataques. Biometria multimodal é uma abordagem relativamente nova para representação de conhecimento biométrico que visa consolidar múltiplas modalidades biométricas. A multimodalidade é baseada no conceito de que informações obtidas a partir de diferentes modalidades se complementam. Consequentemente, uma combinação adequada dessas informações pode ser mais útil que o uso de informações obtidas a partir de qualquer uma das modalidades individualmente. As principais questões envolvidas na construção de um sistema biométrico unimodal dizem respeito à definição das técnicas de extração de característica e do classificador. Já no caso de um sistema biométrico multimodal, além destas questões, é necessário definir o nível de fusão e a estratégia de fusão a ser adotada. O objetivo desta dissertação é investigar o emprego de ensemble para fusão das modalidades biométricas, considerando diferentes estratégias de fusão, lançando-se mão de técnicas avançadas de processamento de imagens (tais como transformada Wavelet, Contourlet e Curvelet) e Aprendizado de Máquina. Em especial, dar-se-á ênfase ao estudo de diferentes tipos de máquinas de aprendizado baseadas em métodos de Kernel e sua organização em arranjos de ensemble, tendo em vista a identificação biométrica baseada em face e íris. Os resultados obtidos mostraram que a abordagem proposta é capaz de projetar um sistema biométrico multimodal com taxa de reconhecimento superior as obtidas pelo sistema biométrico unimodal. / With the advancement of technology, traditional strategies for identifying people become more susceptible to failure, in order to overcome these difficulties some approaches have been proposed in the literature. Among these approaches highlights the Biometrics. The field of Biometrics encompasses a wide variety of technologies used to identify and verify the person\'s identity through the measurement and analysis of physiological and behavioural aspects of the human body. As a result, biometrics has a wide field of applications in systems that require precise identification of their users. The most popular biometric systems are based on face recognition and fingerprint matching. Furthermore, there are other biometric systems that utilize iris and retinal scan, speech, face, and hand geometry. In recent years, biometrics authentication has seen improvements in reliability and accuracy, with some of the modalities offering good performance. However, even the best biometric modality is facing problems. Recently, big efforts have been undertaken aiming to employ multiple biometric modalities in order to make the authentication process less vulnerable to attacks. Multimodal biometrics is a relatively new approach to biometrics representation that consolidate multiple biometric modalities. Multimodality is based on the concept that the information obtained from different modalities complement each other. Consequently, an appropriate combination of such information can be more useful than using information from single modalities alone. The main issues involved in building a unimodal biometric System concern the definition of the feature extraction technique and type of classifier. In the case of a multimodal biometric System, in addition to these issues, it is necessary to define the level of fusion and fusion strategy to be adopted. The aim of this dissertation is to investigate the use of committee machines to fuse multiple biometric modalities, considering different fusion strategies, taking into account advanced methods in machine learning. In particular, it will give emphasis to the analyses of different types of machine learning methods based on Kernel and its organization into arrangements committee machines, aiming biometric authentication based on face, fingerprint and iris. The results showed that the proposed approach is capable of designing a multimodal biometric System with recognition rate than those obtained by the unimodal biometrics Systems.

Com o avanço da tecnologia, as estratégias tradicionais para identificação de pessoas se tornaram mais suscetíveis a falhas, de forma a superar essas dificuldades algumas abordagens vêm sendo propostas na literatura. Dentre estas abordagens destaca-se a Biometria. O campo da Biometria abarca uma grande variedade de tecnologias usadas para identificar e verificar a identidade de uma pessoa por meio da mensuração e análise de aspectos físicos e/ou comportamentais do ser humano. Em função disso, a biometria tem um amplo campo de aplicações em sistemas que exigem uma identificação segura de seus usuários. Os sistemas biométricos mais populares são baseados em reconhecimento facial ou de impressões digitais. Entretanto, existem outros sistemas biométricos que utilizam a íris, varredura de retina, voz, geometria da mão e termogramas faciais. Nos últimos anos, o reconhecimento biométrico obteve avanços na sua confiabilidade e precisão, com algumas modalidades biométricas oferecendo bom desempenho global. No entanto, mesmo os sistemas biométricos mais avançados ainda enfrentam problemas. Recentemente, esforços têm sido realizados visando empregar diversas modalidades biométricas de forma a tornar o processo de identificação menos vulnerável a ataques. Biometria multimodal é uma abordagem relativamente nova para representação de conhecimento biométrico que visa consolidar múltiplas modalidades biométricas. A multimodalidade é baseada no conceito de que informações obtidas a partir de diferentes modalidades se complementam. Consequentemente, uma combinação adequada dessas informações pode ser mais útil que o uso de informações obtidas a partir de qualquer uma das modalidades individualmente. As principais questões envolvidas na construção de um sistema biométrico unimodal dizem respeito à definição das técnicas de extração de característica e do classificador. Já no caso de um sistema biométrico multimodal, além destas questões, é necessário definir o nível de fusão e a estratégia de fusão a ser adotada. O objetivo desta dissertação é investigar o emprego de ensemble para fusão das modalidades biométricas, considerando diferentes estratégias de fusão, lançando-se mão de técnicas avançadas de processamento de imagens (tais como transformada Wavelet, Contourlet e Curvelet) e Aprendizado de Máquina. Em especial, dar-se-á ênfase ao estudo de diferentes tipos de máquinas de aprendizado baseadas em métodos de Kernel e sua organização em arranjos de ensemble, tendo em vista a identificação biométrica baseada em face e íris. Os resultados obtidos mostraram que a abordagem proposta é capaz de projetar um sistema biométrico multimodal com taxa de reconhecimento superior as obtidas pelo sistema biométrico unimodal. / With the advancement of technology, traditional strategies for identifying people become more susceptible to failure, in order to overcome these difficulties some approaches have been proposed in the literature. Among these approaches highlights the Biometrics. The field of Biometrics encompasses a wide variety of technologies used to identify and verify the person\'s identity through the measurement and analysis of physiological and behavioural aspects of the human body. As a result, biometrics has a wide field of applications in systems that require precise identification of their users. The most popular biometric systems are based on face recognition and fingerprint matching. Furthermore, there are other biometric systems that utilize iris and retinal scan, speech, face, and hand geometry. In recent years, biometrics authentication has seen improvements in reliability and accuracy, with some of the modalities offering good performance. However, even the best biometric modality is facing problems. Recently, big efforts have been undertaken aiming to employ multiple biometric modalities in order to make the authentication process less vulnerable to attacks. Multimodal biometrics is a relatively new approach to biometrics representation that consolidate multiple biometric modalities. Multimodality is based on the concept that the information obtained from different modalities complement each other. Consequently, an appropriate combination of such information can be more useful than using information from single modalities alone. The main issues involved in building a unimodal biometric System concern the definition of the feature extraction technique and type of classifier. In the case of a multimodal biometric System, in addition to these issues, it is necessary to define the level of fusion and fusion strategy to be adopted. The aim of this dissertation is to investigate the use of committee machines to fuse multiple biometric modalities, considering different fusion strategies, taking into account advanced methods in machine learning. In particular, it will give emphasis to the analyses of different types of machine learning methods based on Kernel and its organization into arrangements committee machines, aiming biometric authentication based on face, fingerprint and iris. The results showed that the proposed approach is capable of designing a multimodal biometric System with recognition rate than those obtained by the unimodal biometrics Systems.

Nous présentons dans cette thèse, l'approche non-paramétrique par noyaux associés mixtes, pour les densités àsupports partiellement continus et discrets. Nous commençons par rappeler d'abord les notions essentielles d'estimationpar noyaux continus (classiques) et noyaux associés discrets. Nous donnons la définition et les caractéristiques desestimateurs à noyaux continus (classiques) puis discrets. Nous rappelons aussi les différentes techniques de choix deparamètres de lissage et nous revisitons les problèmes de supports ainsi qu'une résolution des effets de bord dans le casdiscret. Ensuite, nous détaillons la nouvelle méthode d'estimation de densités par les noyaux associés continus, lesquelsenglobent les noyaux continus (classiques). Nous définissons les noyaux associés continus et nous proposons laméthode mode-dispersion pour leur construction puis nous illustrons ceci sur les noyaux associés non-classiques de lalittérature à savoir bêta et sa version étendue, gamma et son inverse, gaussien inverse et sa réciproque le noyau dePareto ainsi que le noyau lognormal. Nous examinons par la suite les propriétés des estimateurs qui en sont issus plusprécisément le biais, la variance et les erreurs quadratiques moyennes ponctuelles et intégrées. Puis, nous proposons unalgorithme de réduction de biais que nous illustrons sur ces mêmes noyaux associés non-classiques. Des études parsimulations sont faites sur trois types d’estimateurs à noyaux lognormaux. Par ailleurs, nous étudions lescomportements asymptotiques des estimateurs de densité à noyaux associés continus. Nous montrons d'abord lesconsistances faibles et fortes ainsi que la normalité asymptotique ponctuelle. Ensuite nous présentons les résultats desconsistances faibles et fortes globales en utilisant les normes uniformes et L1. Nous illustrons ceci sur trois typesd’estimateurs à noyaux lognormaux. Par la suite, nous étudions les propriétés minimax des estimateurs à noyauxassociés continus. Nous décrivons d'abord le modèle puis nous donnons les hypothèses techniques avec lesquelles noustravaillons. Nous présentons ensuite nos résultats minimax tout en les appliquant sur les noyaux associés non-classiquesbêta, gamma et lognormal. Enfin, nous combinons les noyaux associés continus et discrets pour définir les noyauxassociés mixtes. De là, les outils d'unification d'analyses discrètes et continues sont utilisés, pour montrer les différentespropriétés des estimateurs à noyaux associés mixtes. Une application sur un modèle de mélange des lois normales et dePoisson tronquées est aussi donnée. Tout au long de ce travail, nous choisissons le paramètre de lissage uniquementavec la méthode de validation croisée par les moindres carrés. / We present in this thesis, the non-parametric approach using mixed associated kernels for densities withsupports being partially continuous and discrete. We first start by recalling the essential concepts of classical continuousand discrete kernel density estimators. We give the definition and characteristics of these estimators. We also recall thevarious technical for the choice of smoothing parameters and we revisit the problems of supports as well as a resolutionof the edge effects in the discrete case. Then, we describe a new method of continuous associated kernels for estimatingdensity with bounded support, which includes the classical continuous kernel method. We define the continuousassociated kernels and we propose the mode-dispersion for their construction. Moreover, we illustrate this on the nonclassicalassociated kernels of literature namely, beta and its extended version, gamma and its inverse, inverse Gaussianand its reciprocal, the Pareto kernel and the kernel lognormal. We subsequently examine the properties of the estimatorswhich are derived, specifically, the bias, variance and the pointwise and integrated mean squared errors. Then, wepropose an algorithm for reducing bias that we illustrate on these non-classical associated kernels. Some simulationsstudies are performed on three types of estimators lognormal kernels. Also, we study the asymptotic behavior of thecontinuous associated kernel estimators for density. We first show the pointwise weak and strong consistencies as wellas the asymptotic normality. Then, we present the results of the global weak and strong consistencies using uniform andL1norms. We illustrate this on three types of lognormal kernels estimators. Subsequently, we study the minimaxproperties of the continuous associated kernel estimators. We first describe the model and we give the technicalassumptions with which we work. Then we present our results that we apply on some non-classical associated kernelsmore precisely beta, gamma and lognormal kernel estimators. Finally, we combine continuous and discrete associatedkernels for defining the mixed associated kernels. Using the tools of the unification of discrete and continuous analysis,we show the different properties of the mixed associated kernel estimators. All through this work, we choose thesmoothing parameter using the least squares cross-validation method.

In this thesis I hope to provide a clear and concise introduction to Feigenbaum scaling accessible to undergraduate students. This is accompanied by a description of how to obtain numerical results by various means. A more intricate approach drawing from renormalization theory as well as a short consideration of some of the topological properties will also be presented. I was furthermore trying to put great emphasis on diagrams throughout the text to make the contents more comprehensible and intuitive.