Introduction
Internet use and gaming technology have developed quickly in the last two decades. Consequently, the number of subjects with internet gaming disorder (IGD) has also grown extensively.1 2 In 2013, the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) included IGD in Section III and listed it as a disorder requiring further study.3 The World Health Organization (WHO) also listed gaming disorder (GD) in the 11th final revision of the International Classification of Diseases (ICD-11).4 Recently, the outbreak of the novel COVID-19 pandemic has also increased the risk of IGD because of higher stress during this period.5 However, the lack of knowledge about the biomarkers associated with IGD has restricted its diagnosis and treatment.
As a complex system, the behaviour of the brain—for example, in the addiction to internet games—is shaped by interactions among its constituent elements.6 7 Thus, the characteristics of the brain connection network can serve as the source of biomarkers of IGD. With the development of modern brain imaging technologies such as functional magnetic resonance imaging (fMRI), we can construct the overall functional network of the brain with millimetre resolution. Then, with the help of network analysis methods in complex systems physics, we can reliably quantify the connectivity patterns of this brain functional network.8 For example, resting-state brain functional network analysis has been used for the investigation of biomarkers of mental disorders in previous studies.9 Brain network analysis has also been used to explore IGD-related changes in network functional connectivity patterns10–12; the differences between those with IGD and healthy controls (HCs) mainly manifested in the hubness of nodes in the brain network.13 The concept of hubness has frequently been used in social network research14 and has recently been introduced into the analysis of brain networks. Various indices have been proposed to measure hubness, and they all assess a node’s involvement in the walk structure of a network.14 Thus, hubness serves as a valuable tool in quantifying a node’s significance in network organisation and identifying the key areas associated with brain disorders.15
However, another existing problem in dissecting biomarkers associated with the development of addiction is the interpretation of the findings: are such changes factors leading to addiction or just the consequences of addictive behaviour? This general question can be posed for virtually all addiction studies without prospective data on their pre-abuse state. However, it is indeed challenging to address.16 Similarly, the altered hubness of nodes found in patients with IGD compared with HCs might result from recurrent engagement in internet games rather than the core biomarkers associated with the development of IGD. To address this issue, we included excessive internet game users (EIUs) as a control group for IGD in this study. EIU is a group of subjects who also recurrently play internet games like those with IGD, but their engagement in internet games does not lead to clinically significant impairment or distress. Thus, including the EIUs as the control for the IGD group enables us to eliminate the effect of recurrent engagement in internet games and explore the core biomarkers of IGD in a cross-sectional study.
Additionally, there is a lack of explanation and evidence for the cognitive significance of the changed hubness of specific nodes. The decision-making paradigm is an effective tool for investigating cognitive significance and has been widely used to explore cognitive differences in addiction-related disorders in previous studies.17–23 For example, a delay discounting task (DDT) was used in earlier studies to measure impulsivity in decision-making. IGD and many other behavioural addictions were found to be associated with impulsivity measured by DDT.24 The Iowa gambling task (IGT), a classic risk decision-making task, has also been widely applied to investigate deficits in decision-making in the field of addiction. Evidence from IGT computational models suggests that subjects addicted to stimulants, opioids, etc, have deficits in decision-making.23 25 Few studies have used IGT to explore the decision-making process of IGD, and these studies have not been able to draw consistent conclusions.26–28
In the present study, we investigated the biomarkers of IGD through resting-state brain network analysis. Based on the review of the above literature, we hypothesised that nodal hubness—a measure quantifying the importance of a node to network organisation—would identify the core nodes associated with the development of IGD. To remove the effect of high engagement in internet games, we also included EIU as a control group. Some decision-making paradigms, DDT and IGT, were used to investigate the cognitive significance of nodal hubness further.