Given the difficulty in predicting outcomes in persons with stroke-induced aphasia (PWA), neuroimaging-based biomarkers of recovery could provide invaluable predictive power to stroke models. However, the neural patterns that constitute beneficial neural organization of language in PWA remain debated. Thus, in this work, we propose a novel network theory of aphasia recovery and test our overarching hypothesis, i.e., that task-specific language processing in PWA requires the dynamic engagement of intact tissue within a bilateral network of anatomically-segregated but functionally and structurally connected language-specific and domain-general brain regions. We first present two studies in which we examined left frontotemporal connectivity during different language tasks (i.e., picture naming and semantic feature verification). Results suggest that PWA heavily rely on left middle frontal gyrus (LMFG)-driven connectivity for tasks requiring lexical-semantic processing and semantic control whereas controls prefer models with input to either LMFG or left inferior frontal gyrus (LIFG). Both studies also revealed several significant associations between spared tissue, connectivity and language skills in PWA. In the third study, we examined bilateral frontotemporoparietal connectivity and tested a lesion- and connectivity-based hierarchical model of chronic aphasia recovery. Between-group comparisons showed controls exhibited stronger left intra-hemispheric task-modulated connectivity than did PWA. Connectivity and language deficit patterns most closely matched predictions for patients with primarily anterior damage whereas connectivity results for patients with other lesion types were best explained by the nature of the semantic task. In the last study, we investigated the utility of lesion classification based on gray matter (GM) only versus combined GM plus white matter (WM) metrics. Results suggest GM only classification was sufficient for characterizing aphasia and anomia severity but the GM+WM classification better predicted naming treatment outcomes. We also found that fractional anisotropy of left WM association tracts predicted baseline naming and treatment outcomes independent of total lesion volume. Finally, results of a preliminary multimodal prediction analysis suggest that combined structural and functional metrics reflecting the integrity of regions and connections comprise optimal predictive models of behavior in PWA. To conclude this dissertation, we discuss how multimodal network models of aphasia recovery can guide future investigations. / 2020-10-23T00:00:00Z

Dynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing strategy for a given cognitive task. The logical network topology of the model is specified by a combination of prior knowledge and statistical analysis of the neuro-imaging signals. Parameters of this a-priori model are then estimated and competing models are compared to determine the most likely model given experimental data. Inter-subject analysis using DCM is complicated by differences in model topology, which can vary across subjects due to errors in the first-level statistical analysis of fMRI data or variations in cognitive processing. This requires considerable judgment on the part of the experimenter to decide on the validity of assumptions used in the modeling and statistical analysis; in particular, the dropping of subjects with insufficient activity in a region of the model and ignoring activation not included in the model. This manual data filtering is required so that the fMRI model's network size is consistent across subjects. This thesis proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various data set sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool. / Ph. D.

The development of functional connectivity is often described as changing from local to distributed connections which give rise to the functional brain networks observed in adulthood. In contrast to the well-explored pattern found in functional connectivity, no research has been published describing effective connectivity development. Also, there is a plethora of literature describing functional connectivity patterns in a variety of neurodevelopmental and internalizing disorders, but there is little consistency in the connectivity patterns discovered for each disorder. Hence, this study aimed to describe functional and effective resting-state connectivity during adolescent development in a typically developing adolescent (TDA) group (n = 128) and to determine how adolescents with comorbid neurodevelopmental disorders (CND) (n = 46) differed. This was accomplished through functional and effective connectivity analysis within and between four networks: the Default Mode Network (DMN), the Salience Network (SN), the Dorsal Attention Network (DAN), and the Frontal Parietal Control Network (FPCN). The results from this study indicate that within-network connectivity decreased across age in the TDA group, which is in opposition to previous work which suggests strengthening within-network connectivity. The CND group displayed hyper-connectivity compared to the TDA group in between-network connectivity with no effect of age. The effective connectivity in the TDA group displayed decreasing connectivity within networks with increasing age, a novel effect not previously reported in the literature. The CND group’s effective connectivity was overall hyper-connected (for within- and between-networks). The functional connectivity patterns in the TDA group suggest that functional connectivity has subtle developmental change during adolescence. Further, the CND group consistently displayed hyper-connectivity in functional and effective connectivity. The CND group, and perhaps similar comorbid groups, may have less efficient networks which could contribute to their disorder(s).

No description available.

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

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.