Aberrant structural network of comorbid attention deficit/hyperactivity disorder is associated with addiction severity in internet gaming disorder

Abstract

Background: Internet gaming disorder (IGD) is commonly comorbid with attention-deficit/hyperactivity disorder (ADHD). Although the addiction is more severe when comorbid with ADHD, little is known about the neural correlates of the association. This study aimed to identify whether an ADHD-related structural brain network exists in IGD patients with comorbid ADHD (IGDADHD+) by comparing them with those without comorbid ADHD (IGDADHD-) and elucidating how the sub-network is associated with addiction severity. Methods: Brain structural networks were constructed based on streamline tractography with diffusion tensor imaging in a cohort of 46 male IGDADHD+ patients, 48 male IGDADHD− patients, and 34 healthy controls (HC). We used network-based statistics (NBS) to identify the sub-network differences between the two IGD groups. Furthermore, the edges in the sub-network that significantly contributed to explaining the Young Internet Addiction Scale (YIAS) score were delineated using partial least square (PLS) regression analyses in IGD patients. Results: The YIAS score was higher in the IGDADHD+ group than in the IGDADHD- group and was correlated with the Korean Dupaul's ADHD scale score (r = 0.42, p <0.01). The NBS detected a sub-network with stronger connectivity in the IGDADHD+ group than in the IGDADHD−group. The PLS regression model showed that the sub-network is associated with the YIAS score in the IGDADHD+ group (q2 = 0.019). Edges connecting the left pre- and postcentral gyri, bilateral superior frontal gyri, medial orbital parts, and left fusiform to the inferior temporal gyrus were most important predictors in the regression model. Conclusion: Our results suggest that an aberrant increase in some structural connections within circuits related to inhibitory function or sensory integration can indicate how comorbid ADHD is associated with addiction severity in IGD. © 2020 The Author(s)