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手机成瘾倾向对自发脑微状态的负面影响:来自静息态脑电图的证据

Negative Effects of Mobile Phone Addiction Tendency on Spontaneous Brain Microstates: Evidence From Resting-State EEG.

作者信息

Li Hao, Yue Jingyi, Wang Yufeng, Zou Feng, Zhang Meng, Wu Xin

机构信息

School of Psychology, Xinxiang Medical University, Xinxiang, China.

出版信息

Front Hum Neurosci. 2021 Apr 28;15:636504. doi: 10.3389/fnhum.2021.636504. eCollection 2021.

Abstract

The prevalence of mobile phone addiction (MPA) has increased rapidly in recent years, and it has had a certain negative impact on emotions (e.g., anxiety and depression) and cognitive capacities (e.g., executive control and working memory). At the level of neural circuits, the continued increase in activity in the brain regions associated with addiction leads to neural adaptations and structural changes. At present, the spontaneous brain microstates that could be negatively influenced by MPA are unclear. In this study, the temporal characteristics of four resting-state electroencephalogram (RS-EEG) microstates (MS1, MS2, MS3, and MS4) related to mobile phone addiction tendency (MPAT) were investigated using the Mobile Phone Addiction Tendency Scale (MPATS). We attempted to analyze the correlation between MPAT and corresponding microstates and provide evidence to explain the brain and behavioral changes caused by MPA. The results showed that the total score of the MPATS was positively correlated with the duration of MS1, related to phonological processing and negatively correlated with the duration of MS2, related to visual or imagery processing, and MS4, related to the attentional network; the score of the withdrawal symptoms subscale was additionally associated with duration of MS3, related to the cingulo-opercular emotional network. Based on these results, we believe that MPAT may have some negative effects on attentional networks and sensory brain networks; moreover, withdrawal symptoms may induce some negative emotions.

摘要

近年来,手机成瘾(MPA)的患病率迅速上升,并且它对情绪(如焦虑和抑郁)和认知能力(如执行控制和工作记忆)产生了一定的负面影响。在神经回路层面,与成瘾相关的大脑区域活动持续增加会导致神经适应和结构变化。目前,尚不清楚可能受到MPA负面影响的自发脑微状态。在本研究中,使用手机成瘾倾向量表(MPATS)研究了与手机成瘾倾向(MPAT)相关的四种静息态脑电图(RS-EEG)微状态(MS1、MS2、MS3和MS4)的时间特征。我们试图分析MPAT与相应微状态之间的相关性,并提供证据来解释MPA引起的大脑和行为变化。结果表明,MPATS总分与与语音处理相关的MS1持续时间呈正相关,与与视觉或意象处理相关的MS2以及与注意力网络相关的MS4持续时间呈负相关;戒断症状子量表得分还与与扣带回-脑岛情绪网络相关的MS3持续时间相关。基于这些结果,我们认为MPAT可能对注意力网络和感觉脑网络有一些负面影响;此外,戒断症状可能会诱发一些负面情绪。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f274/8113394/828c1b960b9d/fnhum-15-636504-g001.jpg

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本文引用的文献

1
The Relationships Between Trait Creativity and Resting-State EEG Microstates Were Modulated by Self-Esteem.
Front Hum Neurosci. 2020 Nov 11;14:576114. doi: 10.3389/fnhum.2020.576114. eCollection 2020.
2
EEG microstates associated with intra- and inter-subject alpha variability.
Sci Rep. 2020 Feb 12;10(1):2469. doi: 10.1038/s41598-020-58787-w.
3
Deviant Dynamics of Resting State Electroencephalogram Microstate in Patients With Subjective Tinnitus.
Front Behav Neurosci. 2018 Jun 22;12:122. doi: 10.3389/fnbeh.2018.00122. eCollection 2018.
5
The influence of alexithymia on mobile phone addiction: The role of depression, anxiety and stress.
J Affect Disord. 2018 Jan 1;225:761-766. doi: 10.1016/j.jad.2017.08.020. Epub 2017 Sep 1.
6
Prognostic Value of EEG Microstates in Acute Stroke.
Brain Topogr. 2017 Sep;30(5):698-710. doi: 10.1007/s10548-017-0572-0. Epub 2017 May 25.
7
The role of emotional inhibitory control in specific internet addiction - an fMRI study.
Behav Brain Res. 2017 May 1;324:1-14. doi: 10.1016/j.bbr.2017.01.046. Epub 2017 Feb 4.
9
Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG.
Int J Neural Syst. 2017 Jun;27(4):1750005. doi: 10.1142/S0129065717500058. Epub 2016 Sep 13.
10
Cognitive manipulation of brain electric microstates.
Neuroimage. 2017 Feb 1;146:533-543. doi: 10.1016/j.neuroimage.2016.10.002. Epub 2016 Oct 11.

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