Random topology organization and decreased visual processing of internet addiction: Evidence from a minimum spanning tree analysis

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.

Negative emotions mediate the association between the topology of the complex brain network and smartphone use disorder: A resting-state EEG study

“…First, we performed correlation analyses to confirm the association between the alpha-bands K and D, beta-bands K and D, and smartphone use disorder. Consistent with a previous study ( H. Wang, Sun et al., 2019 ), smartphone use disorder was positively associated with alpha band K and negatively associated with alpha band D (see Table 3 ). However, we did not find a significant correlation between beta-bands K and D and smartphone use disorder.…”

“…Furthermore, this change increased the traffic loads at the hubs and the risk of hub overload, which, in turn, compromised the global network ( Stam, 2014 ). Previous research reported that internet use disorder and problematic social networking site use also showed a star-like topology of the rs-EEG brain network in the alpha band, like our results ( H. Wang, Sun et al., 2019 ; Yin et al., 2023 ). Thus, for the first time, our findings reveal a close relationship between topological deviations of rs-EEG brain networks and smartphone use disorder.…”

“…Topological metrics were log-transformed to meet the statistical assumption of a normal distribution. According to a previous study ( H. Wang, Sun et al., 2019 ), higher severity of internet use disorder was correlated with higher K and lower D in the alpha and beta-bands, respectively. Therefore, we performed correlation analyses to confirm the association between the four topological metrics and smartphone use disorder.…”

“…Existing studies have provided evidence for the abovementioned view. A recent study found that a higher level of internet use disorder was significantly correlated with a higher kappa and lower diameter in the alpha and beta-bands, respectively (H. Wang, Sun, Lv, & Bo, 2019 ). This indicated that participants with internet use disorder possessed a more scale-free network topology.…”

Rasch stacking analysis of student internet addiction based on gender

“…Namun, hal tersebut menimbulkan berbagai masalah bagi individu yang tidak bisa mengontrol penggunaan waktu, sehingga risiko penggunaan berlebih mungkin terjadi. Hal tersebut menyebabkan efek buruk perkembangan psikososial untuk remaja (Tahiroglu, Celik, Uzel, Ozcan, & Avci, 2008;Wang, Sun, Lv, & Bo, 2019). Kondisi ini disebabkan karena lemahnya kontrol diri (Davis, 2001b;Özdemir, Kuzucu, & Ak, 2014;Young & Rogers, 1998;Li, Hou, Yang, Jian, & Wang, 2019) mahasiswa untuk mengatur waktu dalam penggunaan internet setiap hari.…”