Abstract
Aims
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.
Design
Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets.
Setting
Zhejiang province and Beijing, China.
Participants
169 IGD and 147 control subjects.
Measurements
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.
Findings
Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup1 (“craving-related subgroup”) and subgroup2 (“mixed psychological subgroup”). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity (89–93%) for IGD subgroups in 10-fold validation (n = 218) and out-of-sample replication (n = 98) data sets. Subgroup1 is characterized by high craving scores, cue-reactivity during fMRI, and responsiveness to a craving behavioral intervention therapy. Subgroup2 is characterized by high craving, behavioral inhibition and activations scores, non-adaptive emotion-regulation strategies and guessing-task fMRI measures. Subgroup1 and subgroup2 showed largely opposite functional-connectivity patterns in overlapping networks.
Conclusions
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.