No description available.

The current work investigated the stimulation induced spatial response of the cerebral microvascular network by reconstruction of the 3D microvascular morphology from in vivo two photon fluorescence microscopy (2PFM) volumes using an automated, model based tracking algorithm. In vivo 2PFM imaging of the vasculature in the forelimb representation of the primary somatosensory cortex of alpha-chloralose anesthetized rats was achieved via implantation of a closed cranial window, and intravascular injection of fluorescent dextran. The dilatory and constrictory responses of the cerebral microvascular network to functional stimulation were heterogeneous and depended on resting vascular radius and response latency. Capillaries experienced large relative dilations and constrictions, but the larger vessel absolute volume changes dominated the overall network cerebral blood volume change.

The current work investigated the stimulation induced spatial response of the cerebral microvascular network by reconstruction of the 3D microvascular morphology from in vivo two photon fluorescence microscopy (2PFM) volumes using an automated, model based tracking algorithm. In vivo 2PFM imaging of the vasculature in the forelimb representation of the primary somatosensory cortex of alpha-chloralose anesthetized rats was achieved via implantation of a closed cranial window, and intravascular injection of fluorescent dextran. The dilatory and constrictory responses of the cerebral microvascular network to functional stimulation were heterogeneous and depended on resting vascular radius and response latency. Capillaries experienced large relative dilations and constrictions, but the larger vessel absolute volume changes dominated the overall network cerebral blood volume change.

In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented as grids or sequences. Due to this limitation, many existing deep learning approaches do not generalize to problem domains where the data is represented as graphs – social networks in social network analysis or molecular graphs in chemoinformatics, for instance. The goal of this thesis is to help bridge the gap by studying deep learning solutions that can handle graph data naturally. In particular, we explore deep learning-based approaches in the following areas. 1. Graph Attention. In the real-world, graphs can be both large – with many complex patterns – and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to use an attention-based deep learning model. An attention mechanism allows the model to focus on task-relevant parts of the graph which helps the model make better decisions. We introduce a model for graph classification which uses an attention-guided walk to bias exploration towards more task-relevant parts of the graph. For the task of node classification, we study a different model – one with an attention mechanism which allows each node to select the most task-relevant neighborhood to integrate information from. 2. Graph Representation Learning. Graph representation learning seeks to learn a mapping that embeds nodes, and even entire graphs, as points in a low-dimensional continuous space. The function is optimized such that the geometric distance between objects in the embedding space reflect some sort of similarity based on the structure of the original graph(s). We study the problem of learning time-respecting embeddings for nodes in a dynamic network. 3. Brain Network Discovery. One of the fundamental tasks in functional brain analysis is the task of brain network discovery. The brain is a complex structure which is made up of various brain regions, many of which interact with each other. The objective of brain network discovery is two-fold. First, we wish to partition voxels – from a functional Magnetic Resonance Imaging scan – into functionally and spatially cohesive regions (i.e., nodes). Second, we want to identify the relationships (i.e., edges) between the discovered regions. We introduce a deep learning model which learns to construct a group-cohesive partition of voxels from the scans of multiple individuals in the same group. We then introduce a second model which can recover a hierarchical set of brain regions, allowing us to examine the functional organization of the brain at different levels of granularity. Finally, we propose a model for the problem of unified and group-contrasting edge discovery which aims to discover discriminative brain networks that can help us to better distinguish between samples from different classes.

In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented as grids or sequences. Due to this limitation, many existing deep learning approaches do not generalize to problem domains where the data is represented as graphs – social networks in social network analysis or molecular graphs in chemoinformatics, for instance. The goal of this thesis is to help bridge the gap by studying deep learning solutions that can handle graph data naturally. In particular, we explore deep learning-based approaches in the following areas. 1. Graph Attention. In the real-world, graphs can be both large – with many complex patterns – and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to use an attention-based deep learning model. An attention mechanism allows the model to focus on task-relevant parts of the graph which helps the model make better decisions. We introduce a model for graph classification which uses an attention-guided walk to bias exploration towards more task-relevant parts of the graph. For the task of node classification, we study a different model – one with an attention mechanism which allows each node to select the most task-relevant neighborhood to integrate information from. 2. Graph Representation Learning. Graph representation learning seeks to learn a mapping that embeds nodes, and even entire graphs, as points in a low-dimensional continuous space. The function is optimized such that the geometric distance between objects in the embedding space reflect some sort of similarity based on the structure of the original graph(s). We study the problem of learning time-respecting embeddings for nodes in a dynamic network. 3. Brain Network Discovery. One of the fundamental tasks in functional brain analysis is the task of brain network discovery. The brain is a complex structure which is made up of various brain regions, many of which interact with each other. The objective of brain network discovery is two-fold. First, we wish to partition voxels – from a functional Magnetic Resonance Imaging scan – into functionally and spatially cohesive regions (i.e., nodes). Second, we want to identify the relationships (i.e., edges) between the discovered regions. We introduce a deep learning model which learns to construct a group-cohesive partition of voxels from the scans of multiple individuals in the same group. We then introduce a second model which can recover a hierarchical set of brain regions, allowing us to examine the functional organization of the brain at different levels of granularity. Finally, we propose a model for the problem of unified and group-contrasting edge discovery which aims to discover discriminative brain networks that can help us to better distinguish between samples from different classes.

No description available.

Estudos em assimetria cerebral funcional (ACF) apontam que há diferenças entre os hemisférios cerebrais esquerdo (HE) e direito (HD). O HE é especializado para tarefas de linguagem enquanto o HD para tarefas espaciais. Ainda, pode ocorrer uma superestimação de tamanho no campo visual esquerdo (CVE) em relação ao direito (CVD). Já homens possuem melhor desempenho do HD em tarefas espaciais, mas nas mulheres o desempenho dos hemisférios é equivalente. Ainda, há evidências que homens são menos sensíveis ao contexto dos estímulos que mulheres. Mas não é claro como a forma do estímulo, a variação da sua distância ao centro da tela (excentricidade) e se um gradiente de textura podem afetar a ACF. Com base nisso, o objetivo desse trabalho foi verificar se a variação da excentricidade influenciaria a percepção de tamanho de dois tipos de estímulos no CVE e CVD em homens e mulheres (Experimento I). Também (Experimento II) verificar se um gradiente de textura influenciaria um possível efeito observado no Experimento I. Nos dois experimentos a técnica do campo visual dividido foi associada ao método dos estímulos constantes (30 repetições) com escolha forçada de duas alternativas (qual o maior?). Os estímulos no Experimento I foram círculos (um padrão e sete de comparação) e elipses horizontais (uma padrão e sete de comparação). Esses estímulos foram contrabalanceados em ambos os hemicampos visuais. Os estímulos foram apresentados em três excentricidades (2,5°, 5° e 7,5°) por 100ms num fundo cinza, para universitários destros (10 homens e 10 mulheres por tipo de estímulo). No Experimento II foram apresentados círculos a 2,5° num gradiente de textura (dividido verticalmente e com mesmas informações de profundidade no CVE e CVD), para 10 mulheres universitárias destras. A partir dos dados foram calculados o Erro Relativo e o coeficiente angular sensibilidade discriminativa. Os resultados do Experimento I mostram que a média do erro relativo do CVD para círculos a 2,5° foi maior que a 5° e 7,5°. Nas mulheres houve diferenças entre os hemicampos visuais a 2,5°, sendo o CVD superestimado. Os coeficientes angulares foram maiores a 2,5° de excentricidade e maiores também para os círculos. Os homens apresentaram diferença nos coeficientes angulares para a variação da excentricidade, sendo a de 2,5° maior que 5° e 7,5°. Já as mulheres tiveram coeficientes maiores para círculos. Nos círculos os coeficientes das mulheres a 2,5° foram maiores que a 7,5°. Nas elipses os coeficientes a 2,5° foram maiores em geral e nos homens. Nesses ainda houve uma diferença no CVD, em que os coeficientes a 2,5° foram maiores que a 7,5°. No Experimento II o erro relativo não mostrou diferenças significativas, exceto na comparação de resultados com o Experimento I. Nessa análise a média do CVE foi menor que do CVD. Os coeficientes não apresentaram diferenças significativas. Esses resultados mostram que a ACF não é um efeito absoluto, mas sim dependente das características dos estímulos, da tarefa e pode ser influenciada por diferentes estratégias de homens e mulheres. Apesar de haver diferenças na sensibilidade discriminativa, elas não resultaram numa distorção perceptual. Isso sugere que além da percepção, medidas de sensibilidade também devem ser analisadas para a ACF. Ainda, o efeito do gradiente de textura se sobrepõe a ACF. / Functional brain asymmetry (FBA) studies point out that there are differences between left (LH) and right (HD) brain hemispheres. LH is more specialized for processing language while HD for processing spatial information. Still, there may be a size overestimation in left visual field (LVF) compared to the right visual field (RVF).But men perform better on spatial tasks using LVF/RH, while women perform equivalently in each brain hemisphere. Also, there is evidence that men are less sensitive to stimuli context than women. However, it is not clear how the shape of the stimulus, variation of its distance from the center of the screen (eccentricity) and a texture gradient can affect FBA. Based on this, the aim of this study was to verify if eccentricity variation can influence size perception of two types of stimuli in LVF and RVF in men and women (Experiment I). Also (Experiment II) we investigate if a texture gradient can influence a possible effect observed in Experiment I. In both experiments we used the divided visual field technique associated with the method of constant stimuli (30 repetitions) with two alternative forced choice (\"what is the bigger?\"). Stimuli in Experiment I were circles (one standard and seven for comparison) and horizontal ellipses (one standard and seven for comparison). These stimuli were counterbalanced in both visual hemifields. Stimuli were presented at three eccentricities (2.5°, 5° and 7.5 °) for 100 ms on a gray background to right-handers (10 men and 10 women by stimulus type). In Experiment II circles were presented at 2.5° in a texture gradient (divided vertically and with same depth information on LVF and RVF), for 10 right-handed women. From data we calculated Relative Errors and psychometric slopes - discriminative sensitivity. Results of Experiment I show that relative error mean for 2.5° in RVF circles was greater than 5° and 7.5°. Women showed an overestimation for circles presented in RVF at 2.5° eccentricity. Slope coefficients were greater for 2.5 ° eccentricity and for circles. Men showed a difference in slope coefficients for eccentricity variation, with 2.5° mean greater than 5 ° and 7.5°. Women had higher coefficients for circles than ellipses. Mean circles coefficients were greater at 2.5° than 7.5° eccentricities. Mean ellipses coefficients were greater at 2.5° in general and in men. Men also showed a difference in RVF in which coefficients were greater at 2.5° than 7.5°. Experiment II showed only a difference for relative errors in comparison with Experiment I. These results show that FBA is not an absolute effect, but rely on stimuli characteristics and different strategies for men and women in the task. Although there are differences in discriminative sensitivity, they did not result in a perceptual distortion. This suggests that for FBA not only perceptual parameters should be analyzed, but also the psychometric slope and discrimination sensitivity. Furthermore, the effect of the gradient texture overlaps the FBA.

Estudos em assimetria cerebral funcional (ACF) apontam que há diferenças entre os hemisférios cerebrais esquerdo (HE) e direito (HD). O HE é especializado para tarefas de linguagem enquanto o HD para tarefas espaciais. Ainda, pode ocorrer uma superestimação de tamanho no campo visual esquerdo (CVE) em relação ao direito (CVD). Já homens possuem melhor desempenho do HD em tarefas espaciais, mas nas mulheres o desempenho dos hemisférios é equivalente. Ainda, há evidências que homens são menos sensíveis ao contexto dos estímulos que mulheres. Mas não é claro como a forma do estímulo, a variação da sua distância ao centro da tela (excentricidade) e se um gradiente de textura podem afetar a ACF. Com base nisso, o objetivo desse trabalho foi verificar se a variação da excentricidade influenciaria a percepção de tamanho de dois tipos de estímulos no CVE e CVD em homens e mulheres (Experimento I). Também (Experimento II) verificar se um gradiente de textura influenciaria um possível efeito observado no Experimento I. Nos dois experimentos a técnica do campo visual dividido foi associada ao método dos estímulos constantes (30 repetições) com escolha forçada de duas alternativas (qual o maior?). Os estímulos no Experimento I foram círculos (um padrão e sete de comparação) e elipses horizontais (uma padrão e sete de comparação). Esses estímulos foram contrabalanceados em ambos os hemicampos visuais. Os estímulos foram apresentados em três excentricidades (2,5°, 5° e 7,5°) por 100ms num fundo cinza, para universitários destros (10 homens e 10 mulheres por tipo de estímulo). No Experimento II foram apresentados círculos a 2,5° num gradiente de textura (dividido verticalmente e com mesmas informações de profundidade no CVE e CVD), para 10 mulheres universitárias destras. A partir dos dados foram calculados o Erro Relativo e o coeficiente angular sensibilidade discriminativa. Os resultados do Experimento I mostram que a média do erro relativo do CVD para círculos a 2,5° foi maior que a 5° e 7,5°. Nas mulheres houve diferenças entre os hemicampos visuais a 2,5°, sendo o CVD superestimado. Os coeficientes angulares foram maiores a 2,5° de excentricidade e maiores também para os círculos. Os homens apresentaram diferença nos coeficientes angulares para a variação da excentricidade, sendo a de 2,5° maior que 5° e 7,5°. Já as mulheres tiveram coeficientes maiores para círculos. Nos círculos os coeficientes das mulheres a 2,5° foram maiores que a 7,5°. Nas elipses os coeficientes a 2,5° foram maiores em geral e nos homens. Nesses ainda houve uma diferença no CVD, em que os coeficientes a 2,5° foram maiores que a 7,5°. No Experimento II o erro relativo não mostrou diferenças significativas, exceto na comparação de resultados com o Experimento I. Nessa análise a média do CVE foi menor que do CVD. Os coeficientes não apresentaram diferenças significativas. Esses resultados mostram que a ACF não é um efeito absoluto, mas sim dependente das características dos estímulos, da tarefa e pode ser influenciada por diferentes estratégias de homens e mulheres. Apesar de haver diferenças na sensibilidade discriminativa, elas não resultaram numa distorção perceptual. Isso sugere que além da percepção, medidas de sensibilidade também devem ser analisadas para a ACF. Ainda, o efeito do gradiente de textura se sobrepõe a ACF. / Functional brain asymmetry (FBA) studies point out that there are differences between left (LH) and right (HD) brain hemispheres. LH is more specialized for processing language while HD for processing spatial information. Still, there may be a size overestimation in left visual field (LVF) compared to the right visual field (RVF).But men perform better on spatial tasks using LVF/RH, while women perform equivalently in each brain hemisphere. Also, there is evidence that men are less sensitive to stimuli context than women. However, it is not clear how the shape of the stimulus, variation of its distance from the center of the screen (eccentricity) and a texture gradient can affect FBA. Based on this, the aim of this study was to verify if eccentricity variation can influence size perception of two types of stimuli in LVF and RVF in men and women (Experiment I). Also (Experiment II) we investigate if a texture gradient can influence a possible effect observed in Experiment I. In both experiments we used the divided visual field technique associated with the method of constant stimuli (30 repetitions) with two alternative forced choice (\"what is the bigger?\"). Stimuli in Experiment I were circles (one standard and seven for comparison) and horizontal ellipses (one standard and seven for comparison). These stimuli were counterbalanced in both visual hemifields. Stimuli were presented at three eccentricities (2.5°, 5° and 7.5 °) for 100 ms on a gray background to right-handers (10 men and 10 women by stimulus type). In Experiment II circles were presented at 2.5° in a texture gradient (divided vertically and with same depth information on LVF and RVF), for 10 right-handed women. From data we calculated Relative Errors and psychometric slopes - discriminative sensitivity. Results of Experiment I show that relative error mean for 2.5° in RVF circles was greater than 5° and 7.5°. Women showed an overestimation for circles presented in RVF at 2.5° eccentricity. Slope coefficients were greater for 2.5 ° eccentricity and for circles. Men showed a difference in slope coefficients for eccentricity variation, with 2.5° mean greater than 5 ° and 7.5°. Women had higher coefficients for circles than ellipses. Mean circles coefficients were greater at 2.5° than 7.5° eccentricities. Mean ellipses coefficients were greater at 2.5° in general and in men. Men also showed a difference in RVF in which coefficients were greater at 2.5° than 7.5°. Experiment II showed only a difference for relative errors in comparison with Experiment I. These results show that FBA is not an absolute effect, but rely on stimuli characteristics and different strategies for men and women in the task. Although there are differences in discriminative sensitivity, they did not result in a perceptual distortion. This suggests that for FBA not only perceptual parameters should be analyzed, but also the psychometric slope and discrimination sensitivity. Furthermore, the effect of the gradient texture overlaps the FBA.

abstract: Electromagnetic fields (EMFs) generated by biologically active neural tissue are critical in the diagnosis and treatment of neurological diseases. Biological EMFs are characterized by electromagnetic properties such as electrical conductivity, permittivity and magnetic susceptibility. The electrical conductivity of active tissue has been shown to serve as a biomarker for the direct detection of neural activity, and the diagnosis, staging and prognosis of disease states such as cancer. Magnetic resonance electrical impedance tomography (MREIT) was developed to map the cross-sectional conductivity distribution of electrically conductive objects using externally applied electrical currents. Simulation and in vitro studies of invertebrate neural tissue complexes demonstrated the correlation of membrane conductivity variations with neural activation levels using the MREIT technique, therefore laying the foundation for functional MREIT (fMREIT) to detect neural activity, and future in vivo fMREIT studies. The development of fMREIT for the direct detection of neural activity using conductivity contrast in in vivo settings has been the focus of the research work presented here. An in vivo animal model was developed to detect neural activity initiated changes in neuronal membrane conductivities under external electrical current stimulation. Neural activity was induced in somatosensory areas I (SAI) and II (SAII) by applying electrical currents between the second and fourth digits of the rodent forepaw. The in vivo animal model involved the use of forepaw stimulation to evoke somatosensory neural activations along with hippocampal fMREIT imaging currents contemporaneously applied under magnetic field strengths of 7 Tesla. Three distinct types of fMREIT current waveforms were applied as imaging currents under two inhalants – air and carbogen. Active regions in the somatosensory cortex showed significant apparent conductivity changes as variations in fMREIT phase (φ_d and ∇^2 φ_d) signals represented by fMREIT activation maps (F-tests, p <0.05). Consistent changes in the standard deviation of φ_d and ∇^2 φ_d in cortical voxels contralateral to forepaw stimulation were observed across imaging sessions. These preliminary findings show that fMREIT may have the potential to detect conductivity changes correlated with neural activity. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2020

Causal inference may help us to understand the underlying mechanisms and the risk factors of diseases. In Genetics, it is crucial to understand how the connectivity among variables is influenced by genetic and environmental factors. Family data have proven to be useful in elucidating genetic and environmental influences, however, few existing approaches are able of addressing structure learning of probabilistic graphical models (PGMs) and family data analysis jointly. We propose methodologies for learning, from observational Gaussian family data, the most likely PGM and its decomposition into genetic and environmental components. They were evaluated by a simulation study and applied to the Genetic Analysis Workshop 13 simulated data, which mimic the real Framingham Heart Study data, and to the metabolic syndrome phenotypes from the Baependi Heart Study. In neuroscience, one challenge consists in identifying interactions between functional brain networks (FBNs) - graphs. We propose a method to identify Granger causality among FBNs. We show the statistical power of the proposed method by simulations and its usefulness by two applications: the identification of Granger causality between the FBNs of two musicians playing a violin duo, and the identification of a differential connectivity from the right to the left brain hemispheres of autistic subjects. / Inferência causal pode nos ajudar a compreender melhor as relações de dependência direta entre variáveis e, assim, a identificar fatores de riscos de doenças. Em Genética, a análise de dados agrupados em famílias permite investigar influências genéticas e ambientais nas relações entre as variáveis. Neste trabalho, nós propomos métodos para aprender, a partir de dados Gaussianos agrupados em famílias, o mais provável modelo gráfico probabilístico (dirigido ou não dirigido) e também sua decomposição em dois componentes: genético e ambiental. Os métodos foram avaliados por simulações e aplicados tanto aos dados simulados do Genetic Analysis Workshop 13, que imitam características dos dados do Framingham Heart Study, como aos dados da síndrome metabólica do estudo Corações de Baependi. Em Neurociência, um desafio consiste em identificar interações entre redes funcionais cerebrais - grafos. Nós propomos um método que identifica causalidade de Granger entre grafos e, por meio de simulações, mostramos que o método tem alto poder estatístico. Além disso, mostramos sua utilidade por meio de duas aplicações: 1) identificação de causalidade de Granger entre as redes cerebrais de dois músicos enquanto tocam um dueto de violino e 2) identificação de conectividade diferencial do hemisfério cerebral direito para o esquerdo em indivíduos autistas.