Li Shuang, Jiang Anhang, Wang Min, Ni Haosen, Fu Jiejie, Dong Guangheng
1Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, China.
2Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, China.
J Behav Addict. 2025 Jun 23;14(2):793-804. doi: 10.1556/2006.2025.00047. Print 2025 Jul 2.
Despite extensive research efforts aimed at unraveling the neural mechanisms underlying Internet gaming disorder (IGD), reproducibility remains a challenge, largely due to overlooking the clinical and biological diversity within individuals affected by IGD. Therefore, investigating the altered brain features associated with IGD within both individual-shared and individual-specific subspaces is crucial for understanding this complex and heterogeneous disorder.
This study included 555 participants, comprising 326 individuals with IGD and 229 recreational game users (RGUs). Firstly, we computed altered functional connectivity (AFC) matrices for individuals with IGD and compared them with those of RGUs. Subsequently, we applied the common orthogonal basis extraction algorithm to partition the AFC of individuals with IGD into individual-shared and individual-specific subspaces. Finally, we examined brain regions exhibiting generally abnormal patterns in the individual-shared subspace and employed multiple linear regression analysis to assess the predictive influence of AFC within the individual-specific subspace on clinical symptoms.
Our findings revealed individual-shared altered patterns in the visual network, medial frontal network (MFN), and frontoparietal network (FPN) among individuals with IGD, which are associated with executive control and visual processing. Within the individual-specific subspace, we observed that AFC within the default mode network could predict scores related to fun-seeking behavior in the behavioral activation system (BAS), while AFC within the MFN correlated with reward responsiveness and drive scores in the BAS. Additionally, AFC within the FPN was predictive of scores in the behavioral inhibition system.
This study successfully decomposed the AFC of IGD into individual-shared and individual-specific subspaces. The AFC within individual-specific subspaces holds promise as potential biomarkers for elucidating clinical symptoms in IGD, thereby offering an analytical framework for investigating heterogeneity in other addictive behaviors.
尽管针对揭示网络游戏障碍（IGD）潜在神经机制进行了广泛研究，但可重复性仍是一项挑战，这主要是因为忽视了IGD患者个体内的临床和生物学多样性。因此，在个体共享和个体特异性子空间中研究与IGD相关的大脑特征改变，对于理解这种复杂的异质性疾病至关重要。
本研究纳入了555名参与者，包括326名IGD患者和229名娱乐游戏使用者（RGU）。首先，我们计算了IGD患者的功能连接改变（AFC）矩阵，并将其与RGU的矩阵进行比较。随后，我们应用共同正交基提取算法，将IGD患者的AFC划分为个体共享和个体特异性子空间。最后，我们检查了在个体共享子空间中表现出普遍异常模式的脑区，并采用多元线性回归分析来评估个体特异性子空间内的AFC对临床症状的预测影响。
我们的研究结果揭示了IGD患者在视觉网络、内侧额叶网络（MFN）和额顶叶网络（FPN）中存在个体共享的改变模式，这些模式与执行控制和视觉处理有关。在个体特异性子空间中，我们观察到默认模式网络内的AFC可以预测行为激活系统（BAS）中与寻求乐趣行为相关的得分，而MFN内的AFC与BAS中的奖励反应性和驱动力得分相关。此外，FPN内的AFC可预测行为抑制系统中的得分。
本研究成功地将IGD的AFC分解为个体共享和个体特异性子空间。个体特异性子空间内的AFC有望成为阐明IGD临床症状的潜在生物标志物，从而为研究其他成瘾行为的异质性提供一个分析框架。
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