Brain complex network analysis by means of resting state fMRI and graph analysis: Will it be helpful in clinical epilepsy?

Abstract

Functional 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.

Structural and functional connectivity in newly diagnosed juvenile myoclonic epilepsy

“…5 Network-based approaches have been proposed to determine the pathophysiologic organization of the seizure-prone state in epilepsy, 6 and complex networks become of major interest in the epilepsy research with the advent of novel imaging and recording technologies allowing the analysis of high-resolution structural and functional data in the last ten years. 7 Alongside these developments, an increasing number of studies have demonstrated that JME is regarded as a disruption of structural and functional networks, and it can significantly contribute to the understandings of JME pathogenesis. However, most of the previous studies have shared the problem of enrolling heterogeneous groups, thereby mixing patients with newly diagnosed epilepsy and chronic epilepsy.…”

A Survey of Graph Based Complex Brain Network Analysis Using Functional and Diffusional MRI

“…This method have become a great tool in the field of technological, biological and amusing sciences such as the science of ecological networks, the World Wide Web, amusing networks and neuroscience. Onias et al (2014) described that a network is a way to code a set of elements together with their connections. The elements are identified as nodes and their connections are identified as edges.…”

A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

“…Therefore, the analysis of effective connectivity requires two important concepts; a selection of causal model and model parameter estimation. Common techniques of exploring the effective connectivity includes dynamic causal modeling (DCM) [39,6,13,40,41,42], Granger causal modeling (GCM) [42,43,41,13,44] and structural equation modeling (SEM) [45,46,47,20,48].…”