Format

Send to

Choose Destination
Addict Behav. 2019 Feb 26;95:49-57. doi: 10.1016/j.addbeh.2019.02.015. [Epub ahead of print]

Altered topological connectivity of internet addiction in resting-state EEG through network analysis.

Author information

1
School of Psychology, Liaoning Normal University, Da Lian, China. Electronic address: sun9199@163.com.
2
School of Psychology, Liaoning Normal University, Da Lian, China.

Abstract

The results of some neuroimaging studies have revealed that people with internet addiction (IA) exhibit structural and functional changes in specific brain areas and connections. However, the understanding about global topological organization of IA may also require a more integrative and holistic view of brain function. In the present study, we used synchronization likelihood combined with graph theory analysis to investigate the functional connectivity (FC) and topological differences between 25 participants with IA and 27 healthy controls (HCs) based on their spontaneous EEG activities in the eye-closed resting state. There were no significant differences in FC (total network or sub-networks) between groups (p > .05 for all). Graph analysis showed significantly lower characteristic path length and clustering coefficient in the IA group than in the HC group in the beta and gamma bands, respectively. Altered nodal centralities of the frontal (FP1, FPz) and parietal (CP1, CP5, PO3, PO7, P5, P6, TP8) lobes in the IA group were also observed. Correlation analysis demonstrated that the observed regional alterations were significantly correlated with the severity of IA. Collectively, our findings showed that IA group demonstrated altered topological organization, shifting towards a more random state. Moreover, this study revealed the important role of altered brain areas in the neuropathological mechanism of IA and provided further supportive evidence for the diagnosis of IA.

KEYWORDS:

Functional connectivity; Graph theory; Internet addiction; Resting state EEG; Synchronization likelihood

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center