Social anxiety disorder (SAD) interferes with everyday life. It can, for instance, hinder careers, relationships, and leisure time. It is a common anxiety disorder that was neglected for decades. SAD individuals crave and fear social interactions simultaneously, leading to isolation in our highly social world. Therefore, research surrounding these kinds of disorders is essential. This systematic review has focused on the neural aspects and differences between SAD and healthy controls surrounding amygdala activation and connectivity. Functional magnetic resonance imaging (fMRI) studies conducted using social and emotional tasks were included. Findings include increased amygdala activation to fearful faces and words and a positive correlation between amygdala activation and symptom severity. Further, deficits in emotion regulation and a finding of gradual habituation have been found in SAD compared to healthy controls. Some limitations to this research are the small sample sizes used in the included articles and the use of both SAD and individuals with generalized SAD. The study is essential to assess future questions and directions regarding diagnosis, treatment, and understanding of SAD.

Social cognition is a set of complex processes that mediate much of human behavior. The development of these skills is related to and interdependent on other cognitive processes, particularly inhibitory control, which allows for willful suppression of dominant responses. Many aspects of social behavior rely on inhibitory control to moderate impulsive or socially inappropriate behaviors and process complex perspective-taking. Furthermore, the brain regions associated with inhibitory control and social cognition overlap functionally and structurally. I review neurodevelopmental literature to suggest that social cognition is developmentally dependent on inhibitory control and that the neural foundations of both these skills are measurable in infancy. I tested this model using growth curve and structural equation modeling and show that 10-month, but not 5-month, frontotemporal coherence predicts social cognition in late childhood through preschool inhibitory control. These findings provide insight into the neurodevelopmental trajectory of cognition and suggest that connectivity from frontal regions to other parts of the brain is a foundation for the development of these skills. / Doctor of Philosophy / Social cognition is the ability to understand and interpret another’s thoughts, words, and actions and inhibitory control is the ability to suppress one’s own thoughts, words, and actions. These two types of cognition are similar and use the same brain regions, and I suggest that inhibitory control underlies much of social cognition. In order to test this, I examined children’s inhibitory control and brain connectivity at 5 months, 10 months, 24 months, 48 months, and 9 years and measured social cognition at 9 years. I found that connectivity between the frontal and temporal lobes at 10 months predicted inhibitory control and 48-months, which then predicted social cognition at 9 years. This suggests that infant brain connectivity sets the stage for developing inhibitory control, which is important for later social cognition

Esta dissertação apresenta o desenvolvimento, a validação e a aplicação de algoritmos para inferência de conectividade neural em registros de EEG contendo potenciais evento-relacionados (ERP). Os sinais foram caracterizados via modelos auto-regressivos multivariados (MVAR) e empregou-se a coerência parcial direcionada (PDC) no estudo das relações de causalidade entre eles. Certas características dos ERPs, como sua transitoriedade intrínseca e as múltiplas repetições em experimentos, levaram ao desenvolvimento de novos algoritmos, como a estimação de modelos conjuntos a partir de vários segmentos de sinal e um procedimento em janela deslizante capaz de descrever a evolução temporal da estatística dos sinais de interesse. Ademais, mostrou-se a possibilidade de estender os resultados da análise assintótica da estatística da PDC ao caso multi-trecho, tornando possível o estudo de sua significância estatística sem recorrer a procedimentos de reamostragem. Os algoritmos foram validados em exemplos com neural mass models, modelos não-lineares capazes de gerar sinais com características muito semelhantes a sinais de EEG reais, e aplicados a uma base de dados pública contendo resultados de experimentos com ratos. / This dissertation presents the development, validation, and application of algorithms for inferring neural connectivity in EEG signals containing event-related potentials (ERP). The time series were described via multivariate auto-regressive models (MVAR) and partial directed coherence (PDC) was used to study causal relations between them. Certain features of the ERPs, such as their transitory behavior and the existence of multiple trials in an experiment, lead to the development of a new algorithm capable of estimating a joint model from multiple segments and a sliding-window procedure for describing the nonstationarity behavior of the signals of interest. Furthermore, the possibility of extending the asymptotic results for PDC\'s statistics to the multi-trial case was demonstrated, allowing, therefore, the study of its statistical significance without recurring to resampling methods. The algorithms were validated in examples with neural mass models, non-linear models capable of generating signals with features very similar to real EEG recordings, and then applied to a publicly available dataset of experiments in rats.

How do circuits in the mammalian cerebral cortex encode properties of the sensory environment in a way that can drive adaptive behavior? This question is fundamental to neuroscience, but it has been very difficult to approach directly. Various computational and theoretical models can explain a wide range of phenomena observed in the primary visual cortex (V1), including the anatomical organization of its circuits, the development of functional properties like orientation tuning, and behavioral effects like surround modulation. However, so far no model has been able to bridge these levels of description to explain how the machinery that develops directly affects behavior. Bridging these levels is important, because phenomena at any one specific level can have many possible explanations, but there are far fewer possibilities to consider once all of the available evidence is taken into account. In this thesis we integrate the information gleaned about cortical development, circuit and cell-type specific interactions, and anatomical, behavioral and electrophysiological measurements, to develop a computational model of V1 that is constrained enough to make predictions across multiple levels of description. Through a series of models incorporating increasing levels of biophysical detail and becoming increasingly better constrained, we are able to make detailed predictions for the types of mechanistic interactions required for robust development of cortical maps that have a realistic anatomical organization, and thereby gain insight into the computations performed by the primary visual cortex. The initial models focus on how existing anatomical and electrophysiological knowledge can be integrated into previously abstract models to give a well-grounded and highly constrained account of the emergence of pattern-specific tuning in the primary visual cortex. More detailed models then address the interactions between specific excitatory and inhibitory cell classes in V1, and what role each cell type may play during development and function. Finally, we demonstrate how these cell classes come together to form a circuit that gives rise not only to robust development but also the development of realistic lateral connectivity patterns. Crucially, these patterns reflect the statistics of the visual environment to which the model was exposed during development. This property allows us to explore how the model is able to capture higher-order information about the environment and use that information to optimize neural coding and aid the processing of complex visual tasks. Using this model we can make a number of very specific predictions about the mechanistic workings of the brain. Specifically, the model predicts a crucial role of parvalbumin-expressing interneurons in robust development and divisive normalization, while it implicates somatostatin immunoreactive neurons in mediating longer range and feature-selective suppression. The model also makes predictions about the role of these cell classes in efficient neural coding and under what conditions the model fails to organize. In particular, we show that a tight coupling of activity between the principal excitatory population and the parvalbumin population is central to robust and stable responses and organization, which may have implications for a variety of diseases where parvalbumin interneuron function is impaired, such as schizophrenia and autism. Further the model explains the switch from facilitatory to suppressive surround modulation effects as a simple by-product of the facilitating response function of long-range excitatory connections targeting a specialized class of inhibitory interneurons. Finally, the model allows us to make predictions about the statistics that are encoded in the extensive network of long-range intra-areal connectivity in V1, suggesting that even V1 can capture high-level statistical dependencies in the visual environment. The final model represents a comprehensive and well constrained model of the primary visual cortex, which for the first time can relate the physiological properties of individual cell classes to their role in development, learning and function. While the model is specifically tuned for V1, all mechanisms introduced are completely general, and can be used as a general cortical model, useful for studying phenomena across the visual cortex and even the cortex as a whole. This work is also highly relevant for clinical neuroscience, as the cell types studied here have been implicated in neurological disorders as wide ranging as autism, schizophrenia and Parkinson’s disease.

Esta dissertação apresenta o desenvolvimento, a validação e a aplicação de algoritmos para inferência de conectividade neural em registros de EEG contendo potenciais evento-relacionados (ERP). Os sinais foram caracterizados via modelos auto-regressivos multivariados (MVAR) e empregou-se a coerência parcial direcionada (PDC) no estudo das relações de causalidade entre eles. Certas características dos ERPs, como sua transitoriedade intrínseca e as múltiplas repetições em experimentos, levaram ao desenvolvimento de novos algoritmos, como a estimação de modelos conjuntos a partir de vários segmentos de sinal e um procedimento em janela deslizante capaz de descrever a evolução temporal da estatística dos sinais de interesse. Ademais, mostrou-se a possibilidade de estender os resultados da análise assintótica da estatística da PDC ao caso multi-trecho, tornando possível o estudo de sua significância estatística sem recorrer a procedimentos de reamostragem. Os algoritmos foram validados em exemplos com neural mass models, modelos não-lineares capazes de gerar sinais com características muito semelhantes a sinais de EEG reais, e aplicados a uma base de dados pública contendo resultados de experimentos com ratos. / This dissertation presents the development, validation, and application of algorithms for inferring neural connectivity in EEG signals containing event-related potentials (ERP). The time series were described via multivariate auto-regressive models (MVAR) and partial directed coherence (PDC) was used to study causal relations between them. Certain features of the ERPs, such as their transitory behavior and the existence of multiple trials in an experiment, lead to the development of a new algorithm capable of estimating a joint model from multiple segments and a sliding-window procedure for describing the nonstationarity behavior of the signals of interest. Furthermore, the possibility of extending the asymptotic results for PDC\'s statistics to the multi-trial case was demonstrated, allowing, therefore, the study of its statistical significance without recurring to resampling methods. The algorithms were validated in examples with neural mass models, non-linear models capable of generating signals with features very similar to real EEG recordings, and then applied to a publicly available dataset of experiments in rats.

Orientador: Gabriela Castellano / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Fisica Gleb Wataghin / Made available in DSpace on 2018-08-16T08:25:23Z (GMT). No. of bitstreams: 1 Silva_ElvisLirada_D.pdf: 63417799 bytes, checksum: f7f42b73809b23b9c1b761e184412c98 (MD5) Previous issue date: 2010 / Resumo: Nas últimas décadas, vêm crescendo muito o ramo da Neurociência que estuda a integração neuronal entre áreas cerebrais, onde tal integração é mediada pela chamada conectividade efetiva. A conectividade efetiva pode ser definida como a influência que um sistema neural exerce sobre o outro, tanto ao nível sináptico quanto ao nível cortical. Neste contexto, é cada vez maior a participação de físicos e matemáticos na elaboração de técnicas matemáticas que permitam investigar o comportamento desses sistemas neurais através de experimentos baseados na Ressonância Magnètica funcional (fMRI) e na Eletroencefalografia (EEG). Uma das técnicas que vem sendo amplamente utilizada para estimar a conectividade efetiva entre áreas cerebrais é a denominada Modelagem Causal Dinâmica (DCM), que é uma técnica que incorpora à sua teoria a não-linearidade e a dinâmica de sistemas biológicos. Este trabalho teve por objetivo estudar a conectividade entre áreas cerebrais através da DCM em experimentos de fMRI. Foram estudados dois sistemas neurais. O primeiro deles, o sistema motor, nos possibilitou verificar a plausibilidade da DCM, al'em de averiguarmos as diferenças na conectividade entre as áreas do sistema motor quando indivíduos destros movimentaram os dedos da mão direita e da mão esquerda. Encontramos que a conectividade efetiva é maior quando tais sujeitos movimentaram a mão esquerda, que supomos ser em decorrência da maior dificuldade (inerente às pessoas destras) em mover essa mão. O segundo sistema estudado foi o sistema de reconhecimento de faces emotivas (onde a emoção foi representada por níveis de tristeza) de indivíduos sadios, indivíduos com a doença de Parkinson e indivíduos com a doença de Parkinson e depressão. Neste estudo foi possível verificar através dos resultados da conectividade a falta de habilidade de sujeitos com Parkinson e sujeitos com Parkinson e depressão em reconhecer faces humanas emotivas. Sugerimos que esta falta de habilidade está relacionada principalmente com uma disfunção da atividade do córtex pré-frontal e consequentemente com um aumento da conectividade efetiva desta área com as outras áreas do sistema / Abstract: The branch of Neuroscience that studies functional integration between cerebral areas has recently shown a significant growth. Functional integration refers to the interactions among specialized neuronal populations, where the integration is mediated by the so called effective connectivity. Effective connectivity is defined as the influence that regions, which encompass given neuronal populations, exert on each other. In this process, physicists and mathematicians play an important role in the development of mathematical techniques that allow to investigate the behavior of these neuronal systems through experiments based on functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG). One technique that has been widely used to calculate the effective connectivity between brain areas is known as Dynamic Causal Modeling (DCM), which is a technique that embraces in its theory the nonlinearity and dynamics of biological systems. This work aimed to study the effective connectivity between brain areas through the DCM on fMRI experiments. Two neural systems were studied. The first one was the motor system, which allowed us to check the plausibility of DCM, and to investigate the differences in connectivity between areas of the motor system when right-handed subjects moved the fingers of their right and left hands. We found that the effective connectivity was larger when these individuals moved their left hands, due to a greater difficulty (inherent in right-handed people) in moving this hand. The second system studied was the system for recognition of emotional faces (with sadness as the emotion) of healthy subjects, subjects with Parkinson¿s disease and subjects with Parkinson¿s disease and depression. In this study we verified through the connectivity results the inability of subjects with Parkinson¿s disease and subjects with Parkinson¿s disease and depression to recognize human emotional faces. We suggest that this inability is mainly related to a dysfunction of the neuronal activity of the prefrontal cortex and a consequent increase in the effective connectivity of this area with other areas of the system / Doutorado / Física / Doutor em Ciências

Orientadores: Fernando Cendes, Benito Pereira Damasceno, Gabriela Castellano / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-17T05:21:26Z (GMT). No. of bitstreams: 1 Pereira_FabricioRamosSilvestre_M.pdf: 49779303 bytes, checksum: 3c2d2949aa4cb1bb74eaec511dfeb084 (MD5) Previous issue date: 2010 / Resumo: Um número crescente de estudos sobre conectividade cerebral tem-se destacado na área da Neurociência. Esses estudos almejam entender como diferentes regiões no cérebro estão relacionadas. Para isso, diversas técnicas podem ser empregadas, dentre elas, a ressonância magnética funcional (fMRI). Baseada no sinal BOLD (Blood Oxigenation Level Dependence), a fMRI constitui-se de séries temporais que permitem estimar padrões conectividade efetiva (ecMRI) e funcional (fcMRI). Esta é definida como uma sincronização entre atividades neurais de regiões cerebrais remotas, aquela, como a influência que a atividade neural em uma região cerebral exerce sobre outra área. O presente trabalho consiste no estudo da conectividade funcional em estado de repouso (resting-state) dos hipocampos de três grupos de indivíduos: controles, pacientes com ELTM esquerda e pacientes com ELTM direita. Os resultados mostraram diferenças na conectividade funcional tanto entre controles versus pacientes (apenas os controles apresentaram correlação entre ambos os hipocampos) quanto entre pacientes com ELTM esquerda versus pacientes com ELTM direita (os valores de conectividade funcional dos pacientes com ELTM à direita foram significativamente superiores aos valores do grupo com ELTM à esquerda). Os resultados demonstram que o uso de técnicas para avaliam a conectividade funcional pode representar uma potente ferramenta no estudo da plasticidade cerebral em pacientes com epilepsia mesial temporal além de possibilitar a análise da rede cerebral padrão em sujeitos controles / Abstract: A growing number of studies on brain connectivity have been deployed in the area of Neuroscience. These studies aim to understand how different brain regions are related. Indeed, several techniques can be employed such as the functional magnetic resonance imaging (fMRI). Based on the BOLD signal (Blood Oxigenation Level Dependence), the fRMI consists of time series to estimate functional (fcMRI) and effective connectivity (ecMRI) patterns. The former is defined as synchronization between neural activities in remote brain regions and the later as the influence that one neural activity exerts on another area. The present work studies functional connectivity at rest (resting-state) of hippocampi from three groups: controls, patients with left MTLE and patients with right MTLE. The results showed differences in functional connectivity between both patients versus controls (only the controls showed correlation between both hippocampi) and between patients with left MTLE versus right MTLE (values of functional connectivity in patients with right MTLE were significantly higher than the group with left MTLE). The results demonstrated that the use of techniques that assess functional connectivity can be a powerful tool in the study of brain plasticity in patients with MTLE / Mestrado / Ciencias Biomedicas / Mestre em Ciências Médicas