EEG-based functional networks in schizophrenia |
| |
Authors: | Jalili Mahdi Knyazeva Maria G |
| |
Affiliation: | aDepartment of Computer Engineering, Sharif University of Technology, Tehran, Iran;bDepartment of Clinical Neuroscience, Centre Hospitalier Universitaire Vaudois (CHUV), and University of Lausanne, Lausanne, Switzerland;cDepartment of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland |
| |
Abstract: | Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the network properties with the graph metrics, including mall-worldness, vulnerability, modularity, assortativity, and synchronizability. The schizophrenic patients showed method-specific and frequency-specific changes especially pronounced for modularity, assortativity, and synchronizability measures. However, the differences between schizophrenia patients and normal controls in terms of graph theory metrics were stronger for the unpartial correlation method. |
| |
Keywords: | EEG Schizophrenia Functional connectivity Graph theory Unpartial cross-correlation Partial cross-correlation |
本文献已被 ScienceDirect PubMed 等数据库收录! |
|