Connectome‐based prediction of craving for gaming in internet gaming disorder

Abstract

Background: Craving-related brain responses have been associated with the emergence and maintenance of addictions. However, little is known about brain network organizations underlying cravings in internet gaming disorder (IGD).Methods: Sixty-six IGD subjects and 61 matched individuals with recreational game use (RGU) were scanned while performing a cue-craving task. A recently developed whole-brain analysis approach, connectome-based predictive modelling (CPM) with leave-one-out cross-validation was conducted to identify networks that predicted craving responses in IGD. Then, the craving network was tested in different brain states (cue-craving under deprivation) to investigate replicability.Results: CPM identified an IGD craving network, as indicated by a significant correspondence between predicted and actual craving values (r = 0.49, p < 0.001), characterized by within-network default mode (DMN) connectivity and connectivity between canonical networks implicated in executive/cognitive control (frontoparietal, medial frontal, DMN) and reward responsiveness (subcortical, motor/sensory).Network strength in the cue-craving task during gaming deprivation also predicted IGD craving scores (r = 0.43, p = 0.017), indicating network replication across brain states. Conclusions:The CPM results demonstrate that individual differences in cognitive, attention, and control network function can predict craving intensities in IGD subjects. These networks may be targets for potential interventions using brain modulation.

Connectome‐based predictive modelling of smoking severity in smokers

“…The dynamics of connectivity changes between these large‐scale networks provide an inspiring framework to characterize the neurobiological mechanism of nicotine addiction and predict abstinence‐induced craving. Functional communication within and between the SN, FPN, and DMN has also been disturbed in other addiction disorders, such as opioid use, cocaine use, and internet gaming disorder 17–19 . However, the functional communication within and between these large‐scale networks and their relationship with smoking severity remains unclear in nicotine addiction.…”

“…The CPM was also used to do individual traits prediction. Therefore, CPM is a reliable method to understand human behaviours and personality features, 21 as well as opioid and cocaine use disorders 17–19 . In addition, CPM revealed the specific neural network integration and segregation patterns, which could predict the existence of neural substrates of abstinence and treatment outcomes in substance use disorders 17–19 …”

“…Therefore, CPM is a reliable method to understand human behaviours and personality features, 21 as well as opioid and cocaine use disorders. [17][18][19] In addition, CPM revealed the specific neural network integration and segregation patterns, which could predict the existence of neural substrates of abstinence and treatment outcomes in substance use disorders. [17][18][19] The present study used CPM to examine the whole-brain neural network underlying cue-elicited reactivity that could predict the severity of nicotine addiction in smokers.…”

“…[17][18][19] In addition, CPM revealed the specific neural network integration and segregation patterns, which could predict the existence of neural substrates of abstinence and treatment outcomes in substance use disorders. [17][18][19] The present study used CPM to examine the whole-brain neural network underlying cue-elicited reactivity that could predict the severity of nicotine addiction in smokers. Previous studies suggested that the functional connectivity within the reward system, FPN, SN, visual network, and the interaction between these systems contribute to smoking severity in smokers.…”

“…Functional communication within and between the SN, FPN, and DMN has also been disturbed in other addiction disorders, such as opioid use, cocaine use, and internet gaming disorder. [17][18][19] However, the functional communication within and between these large-scale networks and their relationship with smoking severity remains unclear in nicotine addiction.…”