Random topology organization and decreased visual processing of internet addiction: Evidence from a minimum spanning tree analysis. Issue 3 (31st January 2019)

• Record Type: Journal Article
• Authors: Wang, Hongxia; Sun, Yan; Lv, Jiaojiao; Bo, Siyu
• Is Part Of: Brain and behavior. Volume 9:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-31
• Subjects: EEG -- functional connectivity -- internet addiction -- minimum spanning tree -- phase lag index; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.1218
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Objectives: Internet addiction (IA) has been associated with widespread brain alterations. Functional connectivity (FC) and network analysis results related to IA are inconsistent between studies, and how network hubs change is not known. The aim of this study was to evaluate functional and topological networks using an unbiased minimum spanning tree (MST) analysis on electroencephalography (EEG) data in IA and healthy control (HC) college students. Methods: In this study, Young's internet addiction test was used as an IA severity measure. EEG recordings were obtained in IA ( n = 30) and HC participants ( n = 30), matched for age and sex, during rest. The phase lag index (PLI) and MST were applied to analyze FC and network topology. We expected to obtain evidence of underlying alterations in functional and topological networks related to IA. Results: IA participants showed higher delta FC between left‐side frontal and parieto‐occipital areas compared to the HC group ( p < 0.001), global MST measures revealed a more star‐like network in IA participants in the upper alpha and beta bands, and the occipital brain region was relatively less important in the IA relative to the HC group in the lower band. The correlation results were consistent with the MST results: higher IA severity correlated with higher Max degree and kappa, and lower eccentricity and diameter. Conclusions: Functional networks of the IA group were characterized by increased FC, a more random organization, and a decrease of relative functional importance of the visual processing area. Taken together, these alterations can help us understand the influence of IA to brain mechanism. Abstract : Minimum spanning tree network analysis was applied on resting‐state EEG recordings of internet addiction (IA). The IA group was characterized by increased functional connectivity and a more random network, and a loss of relative functional importance of the visual processing area. These findings may be explained as maturational delay and impaired visual function caused by IA.