State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Addiction. 2023 Feb;118(2):327-339. doi: 10.1111/add.16047. Epub 2022 Sep 24.
To identify subgroups of people with internet gaming disorder (IGD) based on addiction-related resting-state functional connectivity and how these subgroups show different clinical correlates and responses to treatment.
Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets.
Zhejiang province and Beijing, China.
One hundred and sixty-nine IGD and 147 control subjects.
k-Means algorithmic and support-vector machine-learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue-reactivity and guessing-related measures.
Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 ('craving-related subgroup') and subgroup 2 ('mixed psychological subgroup'). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10-fold validation (n = 218) and out-of-sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue-reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non-adaptive emotion-regulation strategies and guessing-task fMRI measures. Subgroups 1 and 2 showed largely opposite functional-connectivity patterns in overlapping networks.
There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.
基于与成瘾相关的静息态功能连接,确定互联网游戏障碍(IGD)患者的亚组,并探讨这些亚组如何表现出不同的临床相关性和对治疗的反应。
对两个功能磁共振成像(fMRI)数据集进行二次分析。
中国浙江省和北京市。
169 名 IGD 患者和 147 名对照者。
采用 K-均值算法和支持向量机学习方法来确定 IGD 患者的亚组。通过评估渴望、行为激活和抑制、情绪调节、线索反应和猜测相关措施来研究这些组。
确定了两组 IGD 患者,他们的大脑网络连接模式存在明显差异,这些网络先前与成瘾有关:亚组 1(“渴望相关亚组”)和亚组 2(“混合心理亚组”)。基于此对 IGD 进行聚类,使我们能够在 10 倍验证(n=218)和样本外复制(n=98)数据集中为 IGD 亚组开发具有高灵敏度和特异性的诊断分类器。亚组 1 的特点是高渴望评分、fMRI 期间的线索反应和对渴望行为干预治疗的反应。亚组 2 的特点是高渴望、行为抑制和激活评分、非适应性情绪调节策略和猜测任务 fMRI 测量。亚组 1 和 2 在重叠网络中表现出明显相反的功能连接模式。
似乎存在两种互联网游戏障碍患者亚组,每个亚组都与不同的大脑功能连接模式以及不同的临床症状谱和性别构成相关。