Huang Hsu-Wen, Li Po-Yu, Chen Meng-Cin, Chang You-Xun, Liu Chih-Ling, Chen Po-Wei, Lin Qiduo, Lin Chemin, Huang Chih-Mao, Wu Shun-Chi
National Center for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan, Taiwan.
Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Taiwan.
Psychol Med. 2025 May 16;55:e148. doi: 10.1017/S0033291725001035.
Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods.
Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task.
Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. -test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization.
Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA.
网络成瘾（IA）是指过度使用互联网导致认知障碍或困扰。了解IA背后的神经生理机制对于准确诊断以及为治疗和预防策略提供依据至关重要。尽管最近研究IA神经生理特征的研究有所增加，但其结果往往各不相同。为了提高识别IA关键神经生理特征的准确性，本研究使用了相位滞后指数（PLI）和加权PLI（WPLI）方法，这些方法可将容积传导效应降至最低，以分析静息态脑电图（EEG）功能连接性。我们还使用各种机器学习方法评估了所识别特征用于IA分类的可靠性。
纳入92名参与者（42名IA患者和50名健康对照者（HCs））。计算每个参与者的PLI和WPLI值，并选择两组之间表现出显著差异的值作为后续分类任务的特征。
支持向量机（SVM）使用PLI特征的准确率达到83%，使用WPLI特征的准确率提高到86%。t检验结果显示WPLI和PLI具有相似的地形图模式。在δ和γ频段内发现了许多两组之间存在显著差异的连接，IA组表现出更高水平的相位同步。
功能连接性分析和机器学习算法可以根据EEG数据共同区分IA参与者和HCs。PLI和WPLI作为识别IA神经生理特征的生物标志物具有很大潜力。
