Navegando por Autor "Sturzbecher, Marcio"
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Artigo Application of Partial Directed Coherence to the Analysis of Resting-State EEG-fMRI Data(2013) Biazoli Jr., Claudinei E.; Sturzbecher, Marcio; White, Thomas P.; Onias, Heloisa Helena dos Santos; Andrade, Katia Cristine; Araújo, Dráulio Barros de; Sato, João R.The simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data potentially allows measurement of brain signals with both high spatial and temporal resolution. Partial directed coherence (PDC) is a Granger causality measure in the frequency domain, which is often used to infer the intensity of information flow over the brain from EEG data. In the current study, we propose a new approach to investigate functional connectivity in resting-state (RS) EEG-fMRI data by combining time-varying PDC with the analysis of blood oxygenation level-dependent (BOLD) signal fluctuations. Basically, we aim to identify brain circuits that are more active when the information flow is increased between distinct remote neuronal modules. The usefulness of the proposed method is illustrated by application to simultaneously recorded EEG-fMRI data from healthy subjects at rest. Using this approach, we decomposed the nodes of RS networks in fMRI data according to the frequency band and directed flow of information provided from EEG. This approach therefore has the potential to inform our understanding of the regional characteristics of oscillatory processes in the human brain.Artigo Brain complex network analysis by means of resting state fMRI and graph analysis: Will it be helpful in clinical epilepsy?(2014) Onias, Heloisa; Viol, Aline; Palhano-Fontes, Fernanda; Andrade, Katia C.; Sturzbecher, Marcio; Viswanathan, Gandhimohan; Araújo, Dráulio Barros deFunctional magnetic resonance imaging (fMRI) has just completed 20 years of existence. It currently serves as a research tool in a broad range of human brain studies in normal and pathological conditions, as is the case of epilepsy. To date, most fMRI studies aimed at characterizing brain activity in response to various active paradigms. More recently, a number of strategies have been used to characterize the low-frequency oscillations of the ongoing fMRI signals when individuals are at rest. These datasets have been largely analyzed in the context of functional connectivity, which inspects the covariance of fMRI signals from different areas of the brain. In addition, resting state fMRI is progressively being used to evaluate complex network features of the brain. These strategies have been applied to a number of different problems in neuroscience, which include diseases such as Alzheimer's, schizophrenia, and epilepsy. Hence, we herein aimed at introducing the subject of complex network and how to use it for the analysis of fMRI data. This appears to be a promising strategy to be used in clinical epilepsy. Therefore, we also review the recent literature that has applied these ideas to the analysis of fMRI data in patients with epilepsy.