Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder.

Background: Internet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis.

Connectome-based prediction of the severity of autism spectrum disorder.

Background: Autism spectrum disorder (ASD) is characterized by social and behavioural deficits. Current diagnosis relies on behavioural criteria, but machine learning, particularly connectome-based predictive modelling (CPM), offers the potential to uncover neural biomarkers for ASD.

Objective: This study aims to predict the severity of ASD traits using CPM and explores differences among ASD subtypes, seeking to enhance diagnosis and understanding of ASD.

Increased functional integration of emotional control network in late adulthood.

Across the adult lifespan, emotion regulation ability remains stable or even improves. The corresponding effects, however, in the emotion regulation networks in the brain remain underexplored. By utilizing large-scale datasets such as the Human Connectome Project (HCP-Aging, N=621, 349 females) and Cambridge Centre for Ageing and Neuroscience (Cam-CAN, N=333, 155 females), we were able to investigate how emotion regulation networks' functional topography differs across the entire adult lifespan.

Individual differences in harm-related moral values are associated with functional integration of large-scale brain networks of emotional regulation.

Emotions affects moral judgements, and controlled cognitive processes regulate those emotional responses during moral decision making. However, the neurobiological basis of this interaction is unclear. We used a graph theory measurement called participation coefficient ('PC') to quantify the resting-state functional connectivity within and between four meta-analytic groupings (MAGs) associated with emotion generation and regulation, to test whether that measurement predicts individual differences in moral foundations-based values.

Predicting the severity of internet gaming disorder with resting-state brain features: A multi-voxel pattern analysis.

Background: Internet gaming disorder (IGD) has become a worldwide mental health concern; however, the neural mechanism underlying this disorder remains unclear. Multivoxel pattern analysis (MVPA), a newly developed data-driven approach, can be used to investigate the neural features of IGD based on massive neural data.

Methods: Resting-state fMRI data from four hundred and two participants with varying levels of IGD severity were recruited.

The unbalanced behavioral activation and inhibition system sensitivity in internet gaming disorder: Evidence from resting-state Granger causal connectivity analysis.

Background: Patients with behavioral or substance addiction show an unbalanced behavioral activation system (BAS) and behavioral inhibition system (BIS) sensitivity. However, the relationship between internet gaming disorder (IGD) and BAS/BIS is obscure and the neurobiological mechanism underlying this relationship remains unclear.

Methods: We recruited 154 IGDs and 229 recreational game users (RGUs) in the current study.

Altered effective connectivity from the posterior insula to the amygdala mediates the relationship between psychopathic traits and endorsement of the Harm foundation.

Psychopathic traits have been demonstrated to be associated with different moral foundations. However, the neuropsychological mechanism underlying the relationship between psychopathic traits and moral foundations remains obscure. Our study examined the effective connectivity (EC) of psychopathy-related brain regions and its association with endorsement to moral foundations (Harm, Fairness, Loyalty, Authority, and Purity)-combining questionnaire measures, resting-state fMRI (RS-fMRI), and Granger causality analysis.

Connectome-based model predicts individual psychopathic traits in college students.

Background: Psychopathic traits have been suggested to increase the risk of violations of socio-moral norms. Previous studies revealed that abnormal neural signatures are associated with elevated psychopathic traits; however, whether the intrinsic network architecture can predict psychopathic traits at the individual level remains unclear.

Methods: The present study utilized connectome-based predictive modeling (CPM) to investigate whether whole-brain resting-state functional connectivity (RSFC) can predict psychopathic traits in the general population.

More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses.

Background: Internet gaming disorder (IGD) is included in the DSM-5 as a provisional diagnosis. Whether IGD should be regarded as a disorder and, if so, how it should be defined and thresholded have generated considerable debate.

Methods: In the current study, machine learning was used, based on regional and interregional brain features.

High levels of psychopathic traits increase the risk of transferring reactive aggression to innocent people after provocation: Evidence from an ERP study.

This study investigated the neuropsychological underpinnings of reactive aggression toward innocent people in a student population with different levels of psychopathic traits. While recording event-related potentials, participants (divided into high/low psychopathic [HP/LP] traits groups) competed against two fictitious opponents in a modified Taylor Aggression Paradigm. We found that the HP group compared to the LP group selected more often high-intensity punishment for the second innocent opponent after being provoked by the first opponent.