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

Outcome assessment of different reward stimuli in Internet gaming disorder by event-related potentials

An imbalance in sensitivity to different types of reward stimuli may be an important cause of addiction that is mainly manifested in high sensitivity to addictive substance rewards and blunting of natural rewards. However, contradictions remain in the research results on the sensitivity of individuals with Internet gaming disorder (IGD) to different reward stimuli. Based on participants' neural responses to win and loss feedback (in door task), the event-related potential (ERP) technique was used to investigate the effects of different types of reward stimuli on the assessment of reward-processing outcomes in individuals with IGD. The results showed that in the gain condition, the FB-P3 amplitude induced by game stimuli in the IGD group was significantly higher than that in the control group, and the FN amplitude induced by money stimuli was significantly lower than that in the control group. However, the FB-P3 and FN amplitudes induced by food were not significantly different from those in the control group. In the loss condition, there were no between-group differences in the FB-P3 and FN amplitudes evoked by the three reward stimuli. This indicates that the IGD group showed increased hedonic responses to game stimuli and decreased hedonic responses to money but no differences in hedonic responses to food during the reward assessment phase. Therefore, heightened sensitivity to game rewards and diminished sensitivity to monetary rewards during outcome assessment may play a crucial role in the development of IGD.

Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking

Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These "neural fingerprints" elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.

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.

Acupuncture modulates the functional connectivity among the subcortical nucleus and fronto-parietal network in adolescents with internet addiction

BACKGROUND

Internet addiction (IA), recognized as a behavioral addiction, is emerging as a global public health problem. Acupuncture has been demonstrated to be effective in alleviating IA; however, the mechanism is not yet clear. To fill this knowledge gap, our study aimed to investigate the modulatory effects of acupuncture on the functional interactions among the addiction-related networks in adolescents with IA.

METHODS

Thirty individuals with IA and thirty age- and sex-matched healthy control subjects (HCs) were recruited. Subjects with IA were given a 40-day acupuncture treatment, and resting-state functional magnetic resonance imaging (fMRI) data were collected before and after acupuncture sessions. HCs received no treatment and underwent one fMRI scan after enrollment. The intergroup differences in functional connectivity (FC) among the subcortical nucleus (SN) and fronto-parietal network (FPN) were compared between HCs and subjects with IA at baseline. Then, the intragroup FC differences between the pre- and post-treatment were analyzed in the IA group. A multiple linear regression model was further employed to fit the FC changes to symptom relief in the IA group.

RESULTS

In comparison to HCs, subjects with IA exhibited significantly heightened FC within and between the SN and FPN at baseline. After 40 days of acupuncture treatment, the FC within the FPN and between the SN and FPN were significantly decreased in individuals with IA. Symptom improvement in subjects with IA was well fitted by the decrease in FC between the left midbrain and ventral prefrontal cortex and between the left thalamus and ventral anterior prefrontal cortex.

CONCLUSION

These findings confirmed the modulatory effects of acupuncture on the aberrant functional interactions among the SN and FPN, which may partly reflect the neurophysiological mechanism of acupuncture for IA.

Brain responses to decision-making in easy and hard choices in internet gaming disorder: Implications for irrepressible gaming behaviours

BACKGROUND

Impaired decision-making was observed in internet gaming disorder (IGD), however, these studies did not differentiate 'hard' to 'easy' decisions, and only the 'hard' decision-making could reveal the mechanism underlying this issue.

METHODS

We recruited forty-eight individuals with IGD and forty-six recreational internet game users (RGUs) as a control group in this study. fMRI data were collected when they were finishing a value-matching delayed discount task (DDT), which included easy and hard decisions judging based on the indifference points of every participant. The correlations between brain responses during DDT and IGD severity and the effective connectivity between brain regions were calculated.

RESULTS

Compared to RGUs, IGD subjects showed enhanced activation in the orbitofrontal cortex (OFC) when facing hard choices, and this feature was associated with IGD severity. In addition, individuals with IGD showed increased effective connectivity from the OFC to the dorsolateral prefrontal cortex and the OFC to the occipital lobe and decreased effective connectivity from the occipital lobe to the OFC.

CONCLUSION

The current study showed that the abnormal activation in the OFC was associated with IGD severity and higher OFC-DLPFC/OFC-occipital lobe effective connectivity and lower occipital lobe-OFC effective connectivity when individuals with IGD faced different choices in the DDT. These findings suggest the neural mechanisms of impulsive decision-making in individuals with IGD due to dysfunction with subjective evaluation and dysfunction of the connection with the executive control system.

Connectome-based predictive modeling of smoking severity using individualized structural covariance network in smokers

Introduction

Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers.

Methods

A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing.

Results

As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edges mainly located between the subcortical-cerebellum network and networks including the frontoparietal default model and motor and visual networks.

Discussion

These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity.

Deficient dynamics of prefrontal-striatal and striatal-default mode network (DMN) neural circuits in internet gaming disorder

BACKGROUND

Studies have proven that individuals with internet gaming disorder (IGD) show impaired cognitive control over game craving; however, the neural mechanism underlying this process remains unclear. Accordingly, the present study aimed to investigate the dynamic features of brain functional networks of individuals with IGD during rest, which have barely been understood until now.

METHODS

Resting-state fMRI data were collected from 333 subjects (123 subjects with IGD (males/females: 73/50) and 210 healthy controls (males/females: 135/75)). First, the data-driven methodology, named co-activation pattern analysis, was applied to investigate the dynamic features of nucleus accumbens (the core region involved in craving/reward processing and addiction)-centered brain networks in IGD. Further, machine learning analysis was conducted to investigate the prediction effect of the dynamic features on participants' addiction severity.

RESULTS

Compared to controls, subjects in the IGD group showed decreased resilience, betweenness centrality and occurrence in the prefrontal-striatal neural circuit, and decreased in-degree in the striatal-default mode network (DMN) circuit. Moreover, these decreased dynamic features could significantly predict participants' addiction severity.

LIMITATIONS

The causal relationship between IGD and the abnormal dynamic features cannot be identified in this study. All the subjects were university students.

CONCLUSIONS

The present results revealed the underlying brain networks of uncontrollable craving and game-seeking behaviors in individuals with IGD during rest. The decreased dynamics of the prefrontal-striatal and striatal-DMN neural circuits might be potential biomarkers for predicting the addiction severity of IGD and potential targets for effective interventions to reduce game craving of this disorder.

Neural cue-reactivity in pathological gambling as evidence for behavioral addiction: a systematic review

Increasing incidence of problem gambling has led to prioritizing the problem from the point of view of public health. Additionally, gambling disorder has been recently classified as a behavioral addiction, with implications for both its diagnosis and treatment. However, the shared neural substrate of addictions, to substances and behavioral, is still discussed. Thus, this systematic review aims to provide up-to-date knowledge from the past five years (2017-2022) concerning the neural correlates of gambling related stimuli (cue-reactivity) on the basis of a previous review (Brevers et al., Cognitive, Affective and Behavioral Neuroscience 18:718-729, 2019). A total of five studies were included in the review. Activation of brain areas related to memory, reward and executive functions could be the underlying mechanism of this behavioral addiction. Specifically, nucleus accumbens and striatum (ventral and dorsal), parahippocampal regions, the right amygdala and several prefrontal cortex regions have systematically been found more active in those subjects exposed to gambling-related cues. Also, the insula could play a pivotal role connecting these three systems in a highly integrated neural network with several implications for reward processing modulation, associative learning and top-down attentional regulation to improve saliency of addiction-related cues. These results are consistent with previous findings on other substance addictions, such as alcohol, tobacco, marijuana or cocaine. The study of neural reactivity to stimuli related to addiction could be useful as a biomarker of the severity of the disorder, the efficacy of the treatment, the risk of relapse, in addition to being an objective criterion to measure the effectiveness of prevention campaigns.

Connectome-based predictive modelling of smoking severity in smokers

The functional connectivity within and between networks could provide a framework to characterize the neurobiological mechanism of nicotine addiction. This study examined the brain regions that were functionally connected in response to smoking cues and established the brain-behaviour relationships in smokers. Sixty-seven male smokers were enrolled and scanned while performing the cue-reactivity and Stroop task. A whole-brain analysis approach, connectome-based predictive modelling (CPM), was conducted on the data from the cue-reactivity task to identify the networks that could predict the smoking severity with the Shen atlas as templates. Then, the brain-behaviour relationships were verified in a different brain state (Stroop task). CPM identified the smoking severity-related network, as indicated by a significant correlation between predicted and actual smoking severity scores (r = 0.31, p = 0.02). Identified networks mainly involved the canonical networks implicated in the reward process (motor/sensory network and salience network) and executive control (frontoparietal network). Network strength in the Stroop task marginally significantly predicted smoking severity scores (r = 0.23, p = 0.06), partially replicating the brain-behaviour relationship. The CPM results identified the whole-brain neural network related to smoking severity, which was cross-validated by the AAL and Shen atlas. These findings contribute to more profound insights into neural substrates underlying the smoking severity.

Considering gender differences in the study and treatment of internet gaming disorder

BACKGROUND AND AIMS

Internet gaming disorder (IGD) is a multifaceted and complex addictive disorder characterized by poor impulse control and altered reward sensitivities, among other features. IGD is a male-predominant condition, and gender-related differences may contribute importantly to the development and maintenance of, and recovery from, IGD.

METHODS

The current manuscript proposes a framework that incorporates gender-related considerations at different stages of IGD and proposes potential strategies for the prevention and treatment of IGD.

RESULTS

At the development stage, high impulsivity, impaired inhibitory control, and aggressive behaviors have been more frequently observed among males than females, suggesting potential risk factors for the development of IGD. Loneliness and other emotional dysregulation have been more frequently noted among females (than among males) with IGD. At the maintenance stage, males may be more sensitive to gaming-related rewards than females, and acute gaming behaviors may elicit higher cravings among males than among females and thus weaken their inhibitory control. In recovery, females with IGD have reported more negative mood states than males, and females' gaming behaviors have been linked with affective disorders.

DISCUSSIONS

The model proposed here highlights gender-related differences in modulating the behavioral and neural systems that contribute to a three-stage framework of IGD. Additional investigation into gender-related differences in IGD may further advance the field by uncovering complex relationships that may underlie vulnerability and provide insights into the development of gender-specific prevention and treatment strategies.

Similarities and differences between internet gaming disorder and tobacco use disorder: A large-scale network study

Studies have shown that internet gaming disorder (IGD) has the potential to be a type of addiction; however, direct comparisons (similarities and differences) between IGD and traditional addictions remain scarce, especially at the neuroimaging level. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 92 individuals with IGD, 96 individuals with tobacco use disorders (TUDs) and 107 individuals who served as healthy controls (HCs). Independent component analysis (ICA) was performed to explore the similarities and differences among these three groups; Granger causality analysis (GCA) was further performed based on the ICA results to determine potential neural features underlying the differences and similarities among the groups. The ICA results indicated significant differences in the subcortical network and cerebellar network. GCA results found that significant differences in bilateral caudate among three groups, and the efferents of dorsal frontostriatal circuit showed significant differences in insula among three groups, whereas efferents of ventral frontostriatal circuit showed significant differences in the medial prefrontal cortex (mPFC). Two kinds of addiction showed differences in thalamus and frontostriatal circuits, and similar changes found in cerebellum and mPFC regions. It suggested that addiction disorders have psychopathology features, and the craving and reward dysfunctions may be the key reasons. Although both substance addiction and behaviour addiction showed craving dysfunction in cerebellum, however, the key reward dysfunction of substance addiction was found in subcortical regions, whereas behaviour addiction located in cortical regions.