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. METHOD: Resting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns. RESULTS: IGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions. CONCLUSION: The dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets.

Increased functional integration of emotional control network in late adulthood.

Across the adult lifespan, there are changes in how emotions are perceived and regulated. As individuals age, there is an observed improvement in emotion regulation and overall quicker recovery from negative emotions. While previous studies have shown differences in emotion processing in late adulthood, the corresponding differences in large-scale brain networks remain largely underexplored. By utilizing large-scale datasets such as the Human Connectome Project (HCP-Aging, N=621) and Cambridge Centre for Ageing and Neuroscience (Cam-CAN, N=333), we were able to investigate how emotion regulation networks' functional topography differs across the entire adult lifespan. Based on previous meta-analytic work that identified four large-scale functional brain networks involved in emotion generation and regulation, we found an increase in the functional integration of the emotional control network among older adults. Additionally, confirming through the nonlinear model, individuals around the age of 70 showed a steadier decline in integration of a network mediating emotion generation and regulation via interoception. Furthermore, the analyses revealed a negative association between age and perceived stress and loneliness that could be attributed to differences in large-scale emotion regulation networks. Our study highlights the importance of identifying topological changes in the functional emotion network architecture across the lifespan, as it allows for a better understanding of emotional aging and psychological well-being in late adulthood.

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. We found that the PC of one of the MAGs (MAG2) was positively correlated with one of the five recognized moral foundations-the one based on harm avoidance. We also found that increased inter-module connectivity between the ventromedial prefrontal cortex, dorsolateral prefrontal cortex and middle temporal gyrus with other nodes in the four MAGs was likewise associated with higher endorsement of the Harm foundation. These results suggest that individuals' sensitivity to harm is associated with functional integration of large-scale brain networks of emotional regulation. These findings add to our knowledge of how individual variations in our moral values could be reflected by intrinsic brain network organization and deepen our understanding of the relationship between emotion and cognition during evaluations of moral values.

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. Methods: Resting-state functional magnetic resonance imaging data from 151 ASD patients were used in the model. CPM with leave-one-out cross-validation was conducted to identify intrinsic neural networks that predict Autism Diagnostic Observation Schedule (ADOS) scores. After the model was constructed, it was applied to independent samples to test its replicability (172 ASD patients) and specificity (36 healthy control participants). Furthermore, we examined the predictive model across different aspects of ASD and in subtypes of ASD to understand the potential mechanisms underlying the results. Results: The CPM successfully identified negative networks that significantly predicted ADOS total scores [r (df = 150) = 0.19, P = 0.008 in all patients; r (df = 104) = 0.20, P = 0.040 in classic autism] and communication scores [r (df = 150) = 0.22, P = 0.010 in all patients; r (df = 104) = 0.21, P = 0.020 in classic autism]. These results were reproducible across independent databases. The networks were characterized by enhanced inter- and intranetwork connectivity associated with the occipital network (OCC), and the sensorimotor network (SMN) also played important roles. Conclusions: A CPM based on whole-brain resting-state functional connectivity can predicted the severity of ASD. Large-scale networks, including the OCC and SMN, played important roles in the predictive model. These findings may provide new directions for the diagnosis and intervention of ASD, and maybe could be the targets in novel interventions.

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. Regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF) were calculated and subsequently decoded by applying MVPA. The highly weighted regions in both predictive models were selected as regions of interest for further graph theory and Granger causality analysis (GCA) to explore how they affect IGD severity. RESULTS: The results revealed that the neural patterns of ReHo and ALFF can independently and significantly predict IGD severity. The highly weighted regions that contributed to both predictive models were the right precentral gyrus and left postcentral gyrus. Moreover, topological properties of the right precentral gyrus were significantly correlated with IGD severity; further GCA revealed effective connectivity from the right precentral gyrus to left precentral gyrus and dorsal anterior cingulate cortex, both of which were significantly associated with IGD severity. CONCLUSIONS: The present study demonstrated that IGD has distinctive neural patterns, and this pattern could be found by machine learning. In addition, the neural features in the right precentral gyrus play a key role in predicting IGD severity. The current study revealed the neural features of IGD and provided a potential target for IGD interventions using brain modulation.

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. First, we explored the relationship between BAS/BIS and IGD. Second, subjects were subdivided into subgroups by BAS/BIS sensitivity. Third, whole-brain Granger causal connectivity (GCC) of striatum and amygdala subdivisions was estimated for the subgroup. Fourth, mediation analysis was performed to explore the role of connectivity in the relationship between IGD and BAS/BIS sensitivity. RESULTS: We found the IGD group scored higher than the RGU on BIS and BASf (fun-seeking) sensitivity. Then, we identified 4 (2*2) subgroups: low/high risk of IGD with low/high BAS/BIS sensitivity groups. Two-way ANCOVA main results of interaction effects showed that in the high BAS/BIS group, the RGU exhibited increased strength in the GCC from the left putamen to the right cuneus, and the IGD exhibited decreased strength in the GCC from the right medial frontal gyrus to the caudate, from the left superior frontal gyrus to the centromedial amygdala, and from the right superior parietal lobule to the left laterobasal amygdala. Moreover, the GCC from the centromedial amygdala to the middle frontal gyrus mediated the directional relationship between BIS and IAT (Young's internet addiction test) scores. CONCLUSIONS: The IGD individuals exhibited higher BIS and BAS-fun seeking sensitivity. Moreover, IGD with unbalanced BAS/BIS sensitivity exhibited alternative connectivity patterns involving amygdala and striatum subdivisions. These findings suggest a neurobiological mechanism for an alternation between IGD and RGU with different BAS/BIS sensitivity.

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. We administered the Levenson Self-Report Psychopathy Scale and Moral Foundation Questionnaire to 78 college students after RS-fMRI scanning. Our results showed that total and primary psychopathy negatively predicted endorsement of the Harm foundation. The EC from the posterior insula to the amygdala was negatively associated with primary psychopathy but positively associated with endorsement of the Harm foundation. Altered posterior insula-amygdala EC partially mediated the relationship between primary psychopathy and endorsement of the Harm foundation. Our findings demonstrated that individuals with elevated psychopathic traits may have atypical processes in recognizing and integrating bodily state information into emotional responses -leading to less concern for harm-related morality. Our findings deepen the understanding of the neuropsychological mechanism underlying the relationship between psychopathic traits and morality, providing potential neurobiological explanations for increased moral transgressions, especially those harm-related transgressions, committed by psychopathic individuals.

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. Resting-state fMRI data were collected from 84 college students with varying psychopathic traits measured by the Levenson Self-Report Psychopathy Scale (LSRP). RESULTS: Functional connections that were negatively correlated with psychopathic traits predicted individual differences in total LSRP and secondary psychopathy score but not primary score. Particularly, nodes with the most connections in the predictive connectome anchored in the prefrontal cortex (e.g., anterior prefrontal cortex and orbitofrontal cortex) and limbic system (e.g., anterior cingulate cortex and insula). In addition, the connections between the occipital network (OCCN) and cingulo-opercular network (CON) served as a significant predictive connectome for total LSRP and secondary psychopathy score. CONCLUSION: CPM constituted by whole-brain RSFC significantly predicted psychopathic traits individually in the general population. The brain areas including the prefrontal cortex and limbic system and large-scale networks including the CON and OCCN play special roles in the predictive model-possibly reflecting atypical cognitive control and affective processing for individuals with elevated psychopathic traits. These findings may facilitate detection and potential intervention of individuals with maladaptive psychopathic tendency.

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. Resting-state data from 374 subjects (including 148 IGD subjects with DSM-5 scores ≥5 and 93 IGD subjects with DSM-5 scores ≥6) were collected, and multivariate pattern analysis (MVPA) was employed to classify IGD from recreational game use (RGU) subjects based on regional brain features (ReHo) and communication between brain regions (functional connectivity; FC). Permutation tests were used to assess classifier performance. RESULTS: The results demonstrated that when using DSM-5 scores ≥5 as the inclusion criteria for IGD subjects, MVPA could not differentiate IGD subjects from RGU, whether based on ReHo or FC features or by using different templates. MVPA could differentiate IGD subjects from RGU better than expected by chance when using DSM-5 scores ≥6 with both ReHo and FC features. The brain regions involved in the default mode network and executive control network and the cerebellum exhibited high discriminative power during classification. DISCUSSION: The current findings challenge the current IGD diagnostic criteria thresholding proposed in the DSM-5, suggesting that more stringent criteria may be needed for diagnosing IGD. The findings suggest that brain regions involved in the default mode network and executive control network relate importantly to the core criteria for IGD.

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. Further, a more negative N2 and a smaller P3 was found in the HP group while punishing the innocents-reflecting a tendency on antisocial-aggressive behavior. Finally, both groups showed a more negative FRN for losing than winning trials when seeing the outcome of the game. Our results suggest that high psychopathic traits increase the risk of transferring provoked aggression to innocent people-offering a psychophysiological perspective for explaining and predicting aggression against the innocents in social interactions.