Random Topology Organization and Decreased Visual Processing of Internet Addiction: Evidence from a Minimum Spanning Tree Analysis

Overview

Journal Brain Behav

Specialty Psychology

Date 2019 Feb 2

PMID 30706671

Citations 2

Authors

Hongxia Wang

Yan Sun

Jiaojiao Lv

Siyu Bo

Affiliations

Soon will be listed here.

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.

Citing Articles

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

Yin Y, Li X, Lau J, Nan S, Ouyang M, Cai X J Behav Addict. 2024; 13(1):120-133.

PMID: 38324061 PMC: 10988397. DOI: 10.1556/2006.2023.00077.

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

Wang H, Sun Y, Lv J, Bo S Brain Behav. 2019; 9(3):e01218.

PMID: 30706671 PMC: 6422800. DOI: 10.1002/brb3.1218.

References

Block J . Issues for DSM-V: internet addiction. Am J Psychiatry. 2008; 165(3):306-7. DOI: 10.1176/appi.ajp.2007.07101556. View

Pezoulas V, Zervakis M, Michelogiannis S, Klados M . Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to [corrected] IQ and Gender. Front Hum Neurosci. 2017; 11:189. PMC: 5405083. DOI: 10.3389/fnhum.2017.00189. View

Khanna A, Pascual-Leone A, Michel C, Farzan F . Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev. 2014; 49:105-13. PMC: 4305485. DOI: 10.1016/j.neubiorev.2014.12.010. View

Yucens B, Uzer A . The relationship between internet addiction, social anxiety, impulsivity, self-esteem, and depression in a sample of Turkish undergraduate medical students. Psychiatry Res. 2018; 267:313-318. DOI: 10.1016/j.psychres.2018.06.033. View

Zhang R, Jiang G, Tian J, Qiu Y, Wen X, Zalesky A . Abnormal white matter structural networks characterize heroin-dependent individuals: a network analysis. Addict Biol. 2015; 21(3):667-78. DOI: 10.1111/adb.12234. View

Liu L, Xue G, Potenza M, Zhang J, Yao Y, Xia C . Dissociable neural processes during risky decision-making in individuals with Internet-gaming disorder. Neuroimage Clin. 2017; 14:741-749. PMC: 5385591. DOI: 10.1016/j.nicl.2017.03.010. View

Hong S, Kim J, Choi E, Kim H, Suh J, Kim C . Reduced orbitofrontal cortical thickness in male adolescents with internet addiction. Behav Brain Funct. 2013; 9:11. PMC: 3608995. DOI: 10.1186/1744-9081-9-11. View

Wen T, Hsieh S . Network-Based Analysis Reveals Functional Connectivity Related to Internet Addiction Tendency. Front Hum Neurosci. 2016; 10:6. PMC: 4740778. DOI: 10.3389/fnhum.2016.00006. View

Vecchio F, Miraglia F, Gorgoni M, Ferrara M, Iberite F, Bramanti P . Cortical connectivity modulation during sleep onset: A study via graph theory on EEG data. Hum Brain Mapp. 2017; 38(11):5456-5464. PMC: 6866973. DOI: 10.1002/hbm.23736. View

10.

Zhai J, Luo L, Qiu L, Kang Y, Liu B, Yu D . The topological organization of white matter network in internet gaming disorder individuals. Brain Imaging Behav. 2016; 11(6):1769-1778. DOI: 10.1007/s11682-016-9652-0. View

11.

van Dellen E, Douw L, Hillebrand A, de Witt Hamer P, Baayen J, Heimans J . Epilepsy surgery outcome and functional network alterations in longitudinal MEG: a minimum spanning tree analysis. Neuroimage. 2013; 86:354-63. DOI: 10.1016/j.neuroimage.2013.10.010. View

12.

Anjomshoa A, Dolatshahi M, Amirkhani F, Rahmani F, Mirbagheri M, Aarabi M . Structural brain network analysis in schizophrenia using minimum spanning tree. Annu Int Conf IEEE Eng Med Biol Soc. 2017; 2016:4075-4078. DOI: 10.1109/EMBC.2016.7591622. View

13.

Dong G, Lin X, Potenza M . Decreased functional connectivity in an executive control network is related to impaired executive function in Internet gaming disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2014; 57:76-85. PMC: 4473260. DOI: 10.1016/j.pnpbp.2014.10.012. View

14.

Corbetta M, Shulman G . Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002; 3(3):201-15. DOI: 10.1038/nrn755. View

15.

Dieter J, Hoffmann S, Mier D, Reinhard I, Beutel M, Vollstadt-Klein S . The role of emotional inhibitory control in specific internet addiction - an fMRI study. Behav Brain Res. 2017; 324:1-14. DOI: 10.1016/j.bbr.2017.01.046. View

16.

Lin F, Zhou Y, Du Y, Qin L, Zhao Z, Xu J . Abnormal white matter integrity in adolescents with internet addiction disorder: a tract-based spatial statistics study. PLoS One. 2012; 7(1):e30253. PMC: 3256221. DOI: 10.1371/journal.pone.0030253. View

17.

Panwar K, Rutherford H, Mencl W, Lacadie C, Potenza M, Mayes L . Differential associations between impulsivity and risk-taking and brain activations underlying working memory in adolescents. Addict Behav. 2014; 39(11):1606-1621. PMC: 4062599. DOI: 10.1016/j.addbeh.2013.12.007. View

18.

Wang L, Shen H, Lei Y, Zeng L, Cao F, Su L . Altered default mode, fronto-parietal and salience networks in adolescents with Internet addiction. Addict Behav. 2017; 70:1-6. DOI: 10.1016/j.addbeh.2017.01.021. View

19.

Boersma M, Smit D, de Bie H, van Baal G, Boomsma D, de Geus E . Network analysis of resting state EEG in the developing young brain: structure comes with maturation. Hum Brain Mapp. 2010; 32(3):413-25. PMC: 6870229. DOI: 10.1002/hbm.21030. View

20.

Fraga Gonzalez G, van der Molen M, Zaric G, Bonte M, Tijms J, Blomert L . Graph analysis of EEG resting state functional networks in dyslexic readers. Clin Neurophysiol. 2016; 127(9):3165-3175. DOI: 10.1016/j.clinph.2016.06.023. View