Balance of motor network activity between the two brain hemispheres after stroke is crucial for functional recovery. Several studies have extensively studied the role of the affected brain hemisphere to better understand changes in motor network activity following stroke. Very few studies have examined the role of the unaffected brain hemisphere and confirmed the testretest reliability of connectivity measures on unaffected hemisphere. We recorded blood oxygenation level dependent functional magnetic resonance imaging (fMRI) signals from nine stroke survivors with hemiparesis of the left or right hand. Participants performed a motor execution task with affected hand, unaffected hand, and both hands simultaneously. Participants returned for a repeat fMRI scan 1 week later. Using dynamic causal modeling (DCM), we evaluated effective connectivity among three motor areas: the primary motor area (M1), the premotor cortex (PMC) and the supplementary motor area for the affected and unaffected hemispheres separately. Five participants manual motor ability was assessed by Fugl-Meyer Motor Assessment scores and root-mean square error of participants tracking ability during a robot-assisted game. We found (i) that the task performance with the affected hand resulted in strengthening of the connectivity pattern for unaffected hemisphere, (ii) an identical network of the unaffected hemisphere when participants performed the task with their unaffected hand, and (iii) the pattern of directional connectivity observed in the affected hemisphere was identical for tasks using the affected hand only or both hands. Furthermore, paired t-test comparison found no significant differences in connectivity strength for any path when compared with one-week follow-up. Brain-behavior linear correlation analysis showed that the connectivity patterns in the unaffected hemisphere more accurately reflected the behavioral conditions than the connectivity patterns in the affected hemisphere. Above findings enrich our knowledge of unaffected brain hemisphere following stroke, which further strengthens our neurobiological understanding of stroke-affected brain and can help to effectively identify and apply stroke-treatments.

In neuroscience and biomedical engineering fields, one of the most important issues nowadays is finding a relationship between different brain regions when it is stimulated. Connectivity is an important research area in neuroscience which tries to determine the relationship between different brain region when the brain is stimulated externally or internally. Three main type of connectivity are discussed in this field: Anatomical, Functional and Effective connectivity. Importance of effective connectivity is its ability to detect brain disorders in early stages. Some brain disorders are Schizophrenia, MS and Major Depression disease. Comparing the effective connectivity between a healthy and unhealthy brain will help to diagnose brain disorder. In this master study, two methods named Dynamic Causal Modeling (DCM) and Psychophysiological Interaction (PPI) are used to compare effective connectivity and neuronal activity between different regions of brain when there are three different stimulations. Since the neural activity is latent in fMRI data, there is a need to a model which is able to transfer data from neuronal level to a visible data like Blood-Oxygen level dependent (BOLD) signal. DCM uses a haemodynamic balloon model (HD) to represent this data transfer. The hemodynamic model must be so that the parameters of neural and BOLD signal be the same. It should be noted that what is looked for is not the BOLD signal but the neuronal activity. In this study, as the first step, we did preprocessing of MR images and after ROI`s are created using the program MARSBAR. Ten ROIs, which are thought to have connections between them are selected by considering the stimulations used in the experiments in obtaining the data used in this thesis. The data used contains fMRI images of 11 healthy subjects. Stimulations of experiment are applied to images got from group analysis of 11 healthy subjects. These Stimulations are then used in preparing the design matrix and the parameters related to DCM. These parameters are the values related to connection matrices defining bilinear dynamic model on ROI. Bayesian method is used to select best model between all these models. Another method of PPI is also applied to analyze effective connectivity between 10 ROIs. This method considers two issues of physiological and psychological effects. Like DCM, the preprocessing steps and ROI selection is done for PPI and hemodynamic model is designed for this method. Neural and hemodynamic responses of ROIs are compared using this method.

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

Previous studies have shown that persons with Parkinson’s disease (pwPD) share specific deficits in learning new sequential movements, but the neural substrates of this impairment remain unclear. In addition, the degree to which striatal dopaminergic denervation in PD affects the cortico-striato-thalamo-cerebellar motor learning network remains unknown. We aimed to answer these questions using fMRI in 16 pwPD and 16 healthy age-matched control subjects while they performed an implicit motor sequence learning task. While learning was absent in both pwPD and controls assessed with reaction time differences between sequential and random trials, larger error-rates during the latter suggest that at least some of the complex sequence was encoded. Moreover, we found that while healthy controls could improve general task performance indexed by decreased reaction times across both sequence and random blocks, pwPD could not, suggesting disease-specific deficits in learning of stimulus-response associations. Using fMRI, we found that this effect in pwPD was correlated with decreased activity in the hippocampus over time. Importantly, activity in the substantia nigra (SN) and adjacent bilateral midbrain was specifically increased during sequence learning in pwPD compared to healthy controls, and significantly correlated with sequence-specific learning deficits. As increased SN activity was also associated (on trend) with higher doses of dopaminergic medication as well as disease duration, the results suggest that learning deficits in PD are associated with disease progression, indexing an increased drive to recruit dopaminergic neurons in the SN, however, unsuccessfully. Finally, there were no differences between pwPD and controls in task modulation of the cortico-striato-thalamo-cerebellar network. However, a restricted nigral-striatal model showed that negative modulation of SN to putamen connection was larger in pwPD compared to controls during random trials, while no differences between the groups were found during sequence learning. We speculate that learning-specific SN recruitment leads to a relative increase in SN- > putamen connectivity, which returns to a pathological reduced state when no learning takes place

L'objectif de cette thèse a été de caractériser les patrons spatiaux de diversité inter- et intraspécifique au sein des réseaux dendritiques, d'expliciter les processus évolutifs et écologiques qui les sous-tendent, et d'isoler les possibles covariations spatiales et interactions existant entre ces différentes facettes de biodiversité. Pour cela, j'ai tout d'abord développé de nouvelles méthodes statistiques permettant l'analyse, par des modèles causaux, de données sous la forme de matrices de distances, afin de pouvoir analyser plusieurs facettes de biodiversité dans un unique cadre statistique au niveau alpha et bêta. J'ai par la suite étudié de manière intégrative les patrons de diversité interspécifique et intraspécifique génétique d'une part, et intraspécifique génétique et intraspécifique phénotypique d'autre part, au sein du bassin versant Garonne- Dordogne. Enfin, j'ai utilisé un modèle de dynamique éco-évolutive afin d'étudier l'impact de la structure et des gradients environnementaux caractérisant les réseaux dendritiques sur l'adaptation locale au sein de ces réseaux. / The aim of this thesis was to characterized the spatial patterns of inter- and intraspecific diversity within riverine networks, to better understand the ecological and evolutionary processes underlying them and to explore how the different facets of biodiversity interact with one another. First, I developed novel statistical approaches allowing the application of causal modeling to data in the form of pairwise matrices, thus allowing the study within integrative frameworks of several biodiversity facets at the alpha and beta levels. I then studied integratively the patterns of interspecific and intraspecific genetic diversity and of intraspecific genetic and intraspecific phenotypic diversity within the Garonne-Dordogne river basin. Finally, I used an eco-evolutionary metapopulation dynamics model to assess the impacts of the structure and environmental gradients that characterize riverine networks on local adaptation.

Functional magnetic resonance imaging (fMRI) is recent important method, used in neuroimaging. The aim of this thesis is to develop software tool for comparison of two methods for functional and effective connectivity estimation. In this thesis are described the basics of magnetic resonance imaging, fMRI, basic terms of fMRI experiments and generally are described methods of functional and effective connectivity. Then are more detailed mentioned methods of dynamic causal modeling (DCM), Granger causal modeling (GCM) and independent component analysis (ICA). Practical implementation of DCM in toolbox SMP and ICA in toolbox GIFT is also mentioned. In purpose to describe behavior of DCM and GCM in dependence on several parameters are performed Monte Carlo simulations. Then the concept and realization of software tool for simulating connectivity and comparison of DCM and GCM are described. Finally results of DCM and GCM comparison and results of Monte Carlo simulations are discussed.

People are better able to empathize with others when they are given information concerning the context driving that person’s experiences. This suggests that people draw on prior memories when empathizing, but the mechanisms underlying this connection remain largely unexplored. The present study investigates how variations in episodic information shape the emotional response towards a movie character. Episodic information is either absent or provided by a written context preceding empathic film clips. It was shown that sad context information increases empathic concern for a movie character. This was tracked by neural activity in the temporal pole (TP) and anterior hippocampus (aHP). Dynamic causal modeling with Bayesian Model Selection has shown that context changes the effective connectivity from left aHP to the right TP. The same crossed-hemispheric coupling was found during rest, when people are left to their own thoughts. We conclude that (i) that the integration of episodic memory also supports the specific case of integrating context into empathic judgments, (ii) the right TP supports emotion processing by integrating episodic memory into empathic inferences, and (iii) lateral integration is a key process for episodic simulation during rest and during task. We propose that a disruption of the mechanism may underlie empathy deficits in clinical conditions, such as autism spectrum disorder.