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CNS Spectr. 2018 Oct;23(5):300-310. doi: 10.1017/S1092852917000360. Epub 2017 Aug 29.

Group independent component analysis reveals alternation of right executive control network in Internet gaming disorder.

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1Department of Psychology,Zhejiang Normal University,Jinhua,P.R. China.
2Peking-Tsinghua Center for Life Sciences,Peking University,Beijing,P.R. China.
3Department of Physics,Shanghai Key Laboratory of Magnetic Resonance,East China Normal University,Shanghai,P.R. China.



Previous studies have demonstrated that individuals with Internet gaming disorder (IGD) showed attentional bias toward gaming-related cues and exhibited impaired executive functions. The purpose of this study was to explore the alternations in related functional brain networks underlying attentional bias in IGD subjects.


Eighteen IGD subjects and 19 healthy controls (HC) were scanned with functional magnetic resonance imaging while they were performing an addiction Stroop task. Networks of functional connectivity were identified using group independent component analysis (ICA).


ICA identified 4 functional networks that showed differences between the 2 groups, which were related to the right executive control network and visual related networks in our study. Within the right executive control network, in contrast to controls, IGD subjects showed increased functional connectivity in the temporal gyrus and frontal gyrus, and reduced functional connectivity in the posterior cingulate cortex, temporal gyrus, and frontal gyrus.


These findings suggest that IGD is related to abnormal functional connectivity of the right executive control network, and may be described as addiction-related abnormally increased cognitive control processing and diminished response inhibition during an addiction Stroop task. The results suggest that IGD subjects show increased susceptibility towards gaming-related cues but weakened strength of inhibitory control.


Addiction Stroop task; Internet gaming disorder; attentional bias; executive control network; independent component analysis


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