Estimating effective connectivity in linear brain network models

Contemporary neuroscience has embraced network science to study the complex and self-organized structure of the human brain; one of the main outstanding issues is that of inferring from measure data, chiefly functional Magnetic Resonance Imaging (fMRI), the so-called effective connectivity in brain networks, that is the existing interactions among neuronal populations. This inverse problem is complicated by the fact that the BOLD (Blood Oxygenation Level Dependent) signal measured by fMRI represent a dynamic and nonlinear transformation (the hemodynamic response) of neuronal activity. In this paper, we consider resting state (rs) fMRI data; building upon a linear population model of the BOLD signal and a stochastic linear DCM model, the model parameters are estimated through an EM-type iterative procedure, which alternately estimates the neuronal activity by means of the Rauch-Tung-Striebel (RTS) smoother, updates the connections among neuronal states and refines the parameters of the hemodynamic model; sparsity in the interconnection structure is favoured using an iteratively reweighting scheme. Experimental results using rs-fMRI data are shown demonstrating the effectiveness of our approach and comparison with state of the art routines (SPM12 toolbox) is provided

White matter and task-switching in young adults: A Diffusion Tensor Imaging study

The capacity to flexibly switch between different task rules has been previously associated with distributed fronto-parietal networks, predominantly in the left hemisphere for phasic switching sub-processes, and in the right hemisphere for more tonic aspects of task-switching, such as rule maintenance and management. It is thus likely that the white matter (WM) connectivity between these regions is critical in sustaining the flexibility required by task-switching. This study examined the relationship between WM microstructure in young adults and task-switching performance in different paradigms: classical shape-color, spatial and grammatical tasks. The main results showed an association between WM integrity in anterior portions of the corpus callosum (genu and body) and a sustained measure of task-switching performance. In particular, a higher fractional anisotropy and a lower radial diffusivity in these WM regions were associated with smaller mixing costs both in the spatial task-switching paradigm and in the shape-color one, as confirmed by a conjunction analysis. No association was found with behavioral measures obtained in the grammatical task-switching paradigm. The switch costs, a measure of phasic switching processes, were not correlated with WM microstructure in any task. This study shows that a more efficient inter-hemispheric connectivity within the frontal lobes favors sustained task-switching processes, especially with task contexts embedding non-verbal components

The role of noise modeling in the estimation of resting-state brain effective connectivity

Causal relations among neuronal populations of the brain are studied through the so-called effective connectivity (EC) network. The latter is estimated from EEG or fMRI measurements, by inverting a generative model of the corresponding data. It is clear that the goodness of the estimated network heavily depends on the underlying modeling assumptions. In this present paper we consider the EC estimation problem using fMRI data in resting-state condition. Specifically, we investigate on how to model endogenous fluctuations driving the neuronal activity

TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction

OBJECTIVE: During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. METHODS: We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. RESULTS: Our results demonstrated that (1) the DA affected the EEG signal in the spatiotemporal domain; (2) ADA was able to completely remove the DA without affecting the TEP waveforms; (3). ICA corrections produced significant changes in peak-to-peak TEP amplitude. CONCLUSIONS: ADA is a reliable solution for the DA correction, especially considering that (1) it does not affect physiological responses; (2) it is completely data-driven and (3) its effectiveness does not depend on the characteristics of the artefact and on the number of recording electrodes. SIGNIFICANCE: We proposed a new reliable algorithm of correction for long-lasting TMS-EEG artifacts

Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role in the emergent brain dynamics is still lacking. The main reason is that estimating brain connectivity requires solving a formidable large-scale inverse problem from indirect and noisy measurements. Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. To this purpose sparse estimation methods are adapted to infer the parameters of our novel model, which is based on a linearized, region-specific haemodynamic response function. The resulting algorithm, referred to as sparse DCM, is shown to compare favorably with state-of-the art methods when tested on both synthetic and real data. We also provide a graph-theoretical analysis on the whole-brain effective connectivity estimated using data from a cohort of healthy individuals, which reveals properties such as asymmetry in the connectivity structure as well as the different roles of brain areas in favoring segregation or integration

ESTUDO DE CASO ACERCA DO MOBILIÁRIO DO EDIFÍCIO COPAN-SP

Este trabalho apresenta o estudo de caso de um edifício situado no centro da cidade de São Paulo (SP) o Copan cujo foco da pesquisa é voltado para o espaço dos apartamentos do bloco E. Dessa forma, o objetivo da pesquisa é identificar os problemas relacionados ao espaço de cada apartamento e criar um meio de padronizar o mobiliário dos apartamentos de acordo com a área de cada unidade, evitando maiores gastos com móveis planejadas. Foram feitas pesquisas referentes ao tamanhos dos móveis, e com a ABNT foram calculados os espaço mínimo que cada cômodo do apartamento. Destacamos que  o  problema definido pela pesquisa pode ser solucionado, pois casas ou apartamentos pequenos podem ser muito aconchegantes. Integrar de maneira harmônica os espaços, apostar em acessórios e usar as peças de mobiliário de forma inteligente. Futuramente podemos aprofundar essa pesquisa pensando em projetos com tamanho padrão de apartamento que siga as normas de espaçamento mínimo com custo acessível visando sempre o melhor para o usuário

Performance of Circulating Placental Growth Factor as A Screening Marker for Diagnosis of Ovarian Endometriosis: A Pilot Study

The aim of this study is to compare the circulating placental growth factor (PlGF) concentration in women with and without endometrioma to verify the performance of this marker to diagnose the disease