Individualized prediction of online shopping addiction from whole-brain functional connectivity

Abstract

Online shopping addiction (OSA) is defined as a behavioral addiction where an individual exhibits an unhealthy and excessive attachment to shopping on the Internet. Since the OSA shown its adverse impacts on individuals' daily life and social functions, it is important to examine the neurobiological underpinnings of OSA that could be used in clinical practice to identify individuals with OSA. The present study addressed this question by employing a connectome-based prediction model approach to predict the OSA tendency of healthy subjects from whole-brain resting-state functional connectivity. The OSA connectome - a set of connections across multiple brain networks that contributed to predict individuals' OSA tendency was identified, including the functional connectivity between the frontal-parietal network (FPN) and cingulo-opercular network (CON) (i.e., positive network), as well as the functional connectivity within default mode network (DMN) and that between FPN and DMN (i.e., negative network). Key nodes that contributed to the prediction model included the middle frontal gyrus, inferior frontal gyrus, anterior cingulate cortex, and inferior temporal gyrus, which have been associated with impulsivity and emotional processing. Notably, this connectome has shown its specific role in predicting OSA by controlling for the influence of general Internet addiction. Moreover, the strength of the negative network mediated the relationship between OSA and impulsivity, highlighting that the negative network underlies the impulsivity characteristic of OSA. Together, these findings advanced our understanding of the neural correlates of OSA and provided a promising framework for diagnosing OSA.