Authors:
Wei Hong Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China

Search for other papers by Wei Hong in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0009-0000-9481-8556
,
Peipeng Liang School of Psychology, Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Haidian District, Beijing, 100048, China

Search for other papers by Peipeng Liang in
Current site
Google Scholar
PubMed
Close
,
Yu Pan Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China

Search for other papers by Yu Pan in
Current site
Google Scholar
PubMed
Close
,
Jia Jin Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China

Search for other papers by Jia Jin in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-1124-2999
,
Lijuan Luo Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China

Search for other papers by Lijuan Luo in
Current site
Google Scholar
PubMed
Close
,
Ying Li Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China

Search for other papers by Ying Li in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-6571-1637
,
Chen Jin Department of Psychology, School of Humanities & Social Science, University of Science & Technology of China, Hefei, Anhui, 230026, China

Search for other papers by Chen Jin in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-6394-9489
,
Wanwan Lü Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China

Search for other papers by Wanwan Lü in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0009-0004-1210-6707
,
Min Wang School of Mathematics and Big Data, Guizhou Education University, Guiyang, Guizhou 550018, P.R. China

Search for other papers by Min Wang in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-0560-1983
,
Yan Liu Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science & Technology of China, Hefei, 230031, China

Search for other papers by Yan Liu in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-7682-7894
,
Hui Chen Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China

Search for other papers by Hui Chen in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-7635-3684
,
Huixing Gou Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China

Search for other papers by Huixing Gou in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-2938-519X
,
Wei Wei School of Mathematics and Big Data, Guizhou Education University, Guiyang, Guizhou 550018, P.R. China

Search for other papers by Wei Wei in
Current site
Google Scholar
PubMed
Close
,
Zhanyu Ma Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China

Search for other papers by Zhanyu Ma in
Current site
Google Scholar
PubMed
Close
,
Ran Tao Beijing Shijian Integrated Medicine Science Institute, Beijing, 100000, China

Search for other papers by Ran Tao in
Current site
Google Scholar
PubMed
Close
,
Rujing Zha Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
Department of Psychology, School of Humanities & Social Science, University of Science & Technology of China, Hefei, Anhui, 230026, China

Search for other papers by Rujing Zha in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-0457-096X
, and
Xiaochu Zhang Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
Department of Psychology, School of Humanities & Social Science, University of Science & Technology of China, Hefei, Anhui, 230026, China
Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science & Technology of China, Hefei, 230031, China
Institute of Health and Medicine, Hefei Comprehensive Science Center, Hefei, 230071, China

Search for other papers by Xiaochu Zhang in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-7541-0130
Open access

Abstract

Background and aims

Impaired value-based decision-making is a feature of substance and behavioral addictions. Loss aversion is a core of value-based decision-making and its alteration plays an important role in addiction. However, few studies explored it in internet gaming disorder patients (IGD).

Methods

In this study, IGD patients (PIGD) and healthy controls (Con-PIGD) performed the Iowa gambling task (IGT), under functional magnetic resonance imaging (fMRI). We investigated group differences in loss aversion, brain functional networks of node-centric functional connectivity (nFC) and the overlapping community features of edge-centric functional connectivity (eFC) in IGT.

Results

PIGD performed worse with lower average net score in IGT. The computational model results showed that PIGD significantly reduced loss aversion. There was no group difference in nFC. However, there were significant group differences in the overlapping community features of eFC1. Furthermore, in Con-PIGD, loss aversion was positively correlated with the edge community profile similarity of the edge2 between left IFG and right hippocampus at right caudate. This relationship was suppressed by response consistency3 in PIGD. In addition, reduced loss aversion was negatively correlated with the promoted bottom-to-up neuromodulation from the right hippocampus to the left IFG in PIGD.

Discussion and conclusions

The reduced loss aversion in value-based decision making and their related edge-centric functional connectivity support that the IGD showed the same value-based decision-making deficit as the substance use and other behavioral addictive disorders. These findings may have important significance for understanding the definition and mechanism of IGD in the future.

Abstract

Background and aims

Impaired value-based decision-making is a feature of substance and behavioral addictions. Loss aversion is a core of value-based decision-making and its alteration plays an important role in addiction. However, few studies explored it in internet gaming disorder patients (IGD).

Methods

In this study, IGD patients (PIGD) and healthy controls (Con-PIGD) performed the Iowa gambling task (IGT), under functional magnetic resonance imaging (fMRI). We investigated group differences in loss aversion, brain functional networks of node-centric functional connectivity (nFC) and the overlapping community features of edge-centric functional connectivity (eFC) in IGT.

Results

PIGD performed worse with lower average net score in IGT. The computational model results showed that PIGD significantly reduced loss aversion. There was no group difference in nFC. However, there were significant group differences in the overlapping community features of eFC1. Furthermore, in Con-PIGD, loss aversion was positively correlated with the edge community profile similarity of the edge2 between left IFG and right hippocampus at right caudate. This relationship was suppressed by response consistency3 in PIGD. In addition, reduced loss aversion was negatively correlated with the promoted bottom-to-up neuromodulation from the right hippocampus to the left IFG in PIGD.

Discussion and conclusions

The reduced loss aversion in value-based decision making and their related edge-centric functional connectivity support that the IGD showed the same value-based decision-making deficit as the substance use and other behavioral addictive disorders. These findings may have important significance for understanding the definition and mechanism of IGD in the future.

Introduction

Playing internet games is a kind of leisure activity in our daily lives. There has been a substantial increase in playing internet games and related activities during the coronavirus (COVID-19) stay-at-home mandates and quarantines (King, Delfabbro, Billieux, & Potenza, 2020; Kiraly et al., 2020; Zha, Li, et al., 2022). However, compulsive uncontrolled, and excessive internet use could lead to problematic internet use such as the internet gaming disorder (IGD) for some individuals, particularly adolescents and young adults (D. King, Koster, & Billieux, 2019).

IGD is a behavioral addiction (Holden, 2001), that refers to excessive indulgence in online games and leads to consequences, including physical and psychological disorders, social impairments, and poor work performance (King & Delfabbro, 2018; Lukavska, 2018; Potenza, 2015). The IGD is currently in the Appendix of Section III of the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) as a non-substance disorder deserving further research. Recently, IGD has also been designated as an addictive disorder by the World Health Organization in the “Clinical Descriptions and Diagnostic Guidelines for ICD-11 Mental and Behavioral Disorders” (https://icd.who.int/dev11/l-m/en).

Impaired value-based decision-making is a feature of both substance-related disorders and pathological gambling (Diekhof, Falkai, & Gruber, 2008; Wiehler & Peters, 2015). Loss aversion is a central facet of value-based decision-making (Genauck et al., 2020) and is a concept developed by prospect theory, one of the most classic behavioral models of value-based decision-making, which means that people are more sensitive to the possibility of losing objects or money than the possibility of gaining the same objects or amounts of money (Tom, Fox, Trepel, & Poldrack, 2007). The Iowa Gambling Task (IGT) (Meshi, Elizarova, Bender, & Verdejo-Garcia, 2019) is one of the most common and popular paradigms used to assess value-based decision-making (Ahn et al., 2014; Ferraro et al., 2012; Fridberg et al., 2010; Vassileva et al., 2013). While there are a few studies have applied the IGT to investigate the group difference between IGD and healthy comparison participants and almost focused on the relative preferences for “advantageous” decks over “disadvantageous” decks (Lin, Wang, Sun, Ko, & Chiu, 2019; Metcalf & Pammer, 2014; Yao et al., 2015), this approach does not leverage the full potential of the IGT. Computational neuroscientists interested in the IGT have mainly focused their efforts on the value-based learning and decision-making components of the task (Ahn et al., 2014; Ferraro et al., 2012; Fridberg et al., 2010; Vassileva et al., 2013). A few previous studies using the PVL-DecayRI (Vassileva et al., 2013) or the PVL-Delta (Fridberg et al., 2010) models to study decision-making processes in the IGT in drug users. Consistent findings, both chronic (current) marijuana users (Fridberg et al., 2010) and polysubstance (former) users (Vassileva et al., 2013) showed reduced loss aversion compared to healthy control in the IGT. Few studies have investigated loss aversion in value-based decision-making in the IGD by using the IGT in conjunction with the computation model like the PVL-DecayRI or the PVL-Delta models. Recent neuroscientific researches showed that individual differences in loss aversion in substance use and behavioral addictive disorders were associated with the reward evaluation and processing network, including regions like the inferior frontal gyrus (IFG), hippocampus, and caudate (Genauck et al., 2017; Gianelli, Basso, Manera, Poggi, & Canessa, 2022; Quester & Romanczuk-Seiferth, 2015; Zha, Li, et al., 2022). However, it is currently unknown whether the IGD shows the same decision-making deficit.

Cognition and behavior stem from the interaction of brain networks, and the interactions between neural units are central to neurocognitive function research (Reid et al., 2019). Node-centric functional connectivity (nFC) is the traditional approach to investigating functional brain networks which emphasizes interactivity among pairs of nodes (Craddock et al., 2013; Rogers, Morgan, Newton, & Gore, 2007) and the nFC construct relies on forcing each brain node into one and only one community. Although the nFC has been useful in cognitive and network neuroscience (Di Martino et al., 2014; Fornito, Zalesky, & Breakspear, 2015), it cannot capture potentially meaningful features or patterns of inter-relationships among edges. And it divides brain regions into non-overlapping communities which conflicts with evidence that cognition and behavior require contributions from regions that span multiple node-defined communities and systems (Anderson, Kinnison, & Pessoa, 2013). Recently Faskowitz et al. proposed the edge-centric functional connectivity (eFC) to investigate functional brain networks that represent pairwise functional interactions among a network's edges (Faskowitz, Esfahlani, Jo, Sporns, & Betzel, 2020). In the eFC construct, overlapping community features are inherent and the definition of community comes closer to matching the brain's multifunctional nature (Faskowitz et al., 2020; Jo, Faskowitz, Esfahlani, Sporns, & Betzel, 2021; Jo, Zamani Esfahlani, et al., 2021). Therefore, the nFC and the eFC provide complementary insights into the organization and function of brain networks.

In this study, we hypothesized that the PIGD would exhibit worse performance (lower average net score) and reduce loss aversion in IGT. To better characterize the behavioral performance of IGT, including loss aversion, we used computational modeling approaches to disentangle the distinct neurocognitive processes. Furthermore, to reveal the possible neural mechanisms of reduced loss aversion in the PIGD, we examined whether the functional network of nFC and eFC altered in the IGT in the PIGD and whether loss aversion was associated with these functional network alterations.

Methods

Participants

Forty-nine participants, including patients with the IGD (PIGD, n = 27) and their controls (Con-PIGD, n = 22), were recruited. The PIGD were recruited through the General Hospital of Beijing Military Region's Addiction Medicine Center. The Con-PIGD were recruited through advertisements from the Beijing local community. The PIGD met the DSM-5 criteria for internet gaming disorder (i.e., meet at least 5 of 9 criteria within a 12-month-period). Structured clinical interviews were conducted by two experienced psychiatrists for the diagnoses. See details in Supplemental Methods (SM).

Measures

Demographic variables

The survey assessed the subjects' age, education, and gender.

Clinical variables

The survey measured symptom severity of the internet game disorder, including the duration of internet gaming exposure (years), the longest once internet gaming exposure (hours), weekly internet gaming exposure (hours), and frequency of weekly internet gaming exposure.

The settings and the study protocol and procedure

We recruited the PIGD and the Con-PIGD to perform the IGT under functional magnetic resonance imaging (fMRI) and to complete some surveys. Behavioral measures included symptom severity of IGD, net-score and loss aversion acquired from the IGT. We calculated nFC (Luo, Liu, Jin, Chang, & Peng, 2021; Zha, Li, et al., 2022), eFC and overlapping community features of eFC. And we used the generalized mediation analysis and dynamic causal modeling (DCM) analysis. Finally, we used the machine learning methods to classify two groups. See details in SM.

The IGT

The IGT was the same as that we used in several studies (Fig. 1) (Li et al., 2020; Wang et al., 2017; Wei et al., 2018; Zha, Li, et al., 2022). Four decks labeled with A, B, C, and D. Decks C and D were the advantageous because they had a positive payoff in the long run, and decks A and B were the disadvantageous because they had a negative payoff in the long run. The performance of IGT was net score, which calculated by subtracting the total number of selections on decks AB from the total number of selections on decks CD (Christakou, Brammer, Giampietro, & Rubia, 2009). See details in SM.

Fig. 1.
Fig. 1.

The Iowa gambling Task. The task contained 180 trials, which were divided into three runs. There were three blocks for each run. There were 20 trials for each block, and the inter-block interval was a 30 s resting interval. Two phases were performed for each trial. The first phase was the selection phase in which participants were asked to select one of four decks within 4 s. The second phase was the outcome phase, in which the outcome was shown for 1 s. The decks were labeled with A, B, C, and D

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

The computational model

Based on the literature, we compare the most promising the IGT models involving loss aversion: the Prospect Valence Learning (PVL) model with delta learning rule (PVL-Delta) (Ahn, Busemeyer, Wagenmakers, & Stout, 2008), the PVL model with decay reinforcement learning rule (PVL-DecayRI) (Ahn, Krawitz, Kim, Busmeyer, & Brown, 2011). See details in SM.

The fMRI data acquisition and pre-processing

All MRI data were acquired using 3-T Siemens Magnetom Trio scanners in the Xuanwu Hospital Capital Medical University, Beijing. Pre-processing was similar to that used in a study (Zha et al., 2019; Zha et al., 2022) and was processed with the Analysis of Functional Neuroimages (Version AFNI_18.2.03) (Cox, 1996). See details in SM.

Functional network of nFC

Following previous studies, we defined nodes and calculated the nFC Matrix (Luo et al., 2021; Zha, Li, et al., 2022). Nodes were automatically divided by anatomical automatic labeling (AAL) parcellation (Tzourio-Mazoyer et al., 2002). nFC describes the spatiotemporal correlation between spatially distinct brain regions.

Functional network of eFC

Overlapping community structural features are inherent in the eFC construct. Clustering the eFC matrix assigns each edge to a community. Each edge is associated with two brain regions (the nodes it connects). Thus, edge community assignments can be mapped back onto individual brain regions and, because every region is associated with N1 edges, allow regions to simultaneously maintain a plurality of community assignments.

eFC matrix

Following the study, we calculated the eFC Matrix (Faskowitz et al., 2020). Nodes were the same as those in the nFC. The first step was to z-score each time series for all nodes. Next, for all pairs of nodes, we referred to the product of the elements-wise of their z-scored time series as ‘edge time series’, representing the magnitude of time-resolved co-fluctuation between node pairs. The final step was to calculate the element-wise product between pairs of edge time series, resulting in a set of co-fluctuation time series. See details in SM.

Overlapping community feature-Normalized entropy

Following a study (Faskowitz et al., 2020), the recommended modified k-means (k = 10, the clustering algorithm repeated 250 times) algorithm was applied to divide the eFC matrix into non-overlapping communities of co-fluctuating edges and then map the edge assignments back to a single node, yielding overlapping regional community assignments.

The normalized entropy indicates the distribution of edge community assignments and measures the extent to which region i’s community affiliations are distributed evenly across all communities or concentrated within a small number of communities. See details in SM.

Overlapping community features-Edge community profile similarity

We assigned each edge to a single community. These edge communities can be rearranged into the upper triangle of an N×N matrix, X, whose element xij denoted the edge community assignment of the edge between nodes i and j. The i th column of X, edge community profile of nodes i, which we denote as xi=[xi1,,xiN], encodes the community labels of all edges in which node i participates.

From the X matrix, we extracted the edge community profile of nodes i and j and compared the edge community profile of nodes i and j by calculating the similarity of vectors xi and xj. Here, the edge community profile similarity of the edge between nodes i and j is the fraction of elements in both vectors with the same community label. That is:
Sij=1N2ui,jδ(xiu,xju)

Here, δ(xiu,xju) is the Kronecker delta and takes on a value of 1 when xiu and xju were co-assigned to the same community but is 0 otherwise, and u is the third nodes. Note the normalization of over N2 because we ignore the self-connection xii and xjj. Repeating this comparison for all pairs of nodes generates the similarity matrix, S={sij}.

To understand which brain regions may be responsible for the group difference of the edge community profile similarity of the edge between nodes i and, Sij. We compared the edge community profile similarity of the edge between nodes i and j at each third brain nodes (u) and generated the edge community assignment similarity of the edge between nodes i and j at each third brain region (u), Siuj.

All data were analyzed using MATLAB v2018a (MATLAB, MathWorks Inc., Natick, MA, PC), the Statistical Package for Social Science (SPSS v.22, Chicago, Illinois, PC), the Analysis of Functional Neuroimages (Version AFNI_18.2.03) (Cox, 1996), and the hBayesDM R package (Ahn, Haines, & Zhang, 2017) (https://cran.r-project.org/web/packages/hBayesDM/index.html).

Statistical analysis

We tested whether PIGD showed alterations in average net score, loss aversion, nFC, overlapping community features of eFC and effective connectivity. Correlations between loss aversion and overlapping community features of eFC networks were performed. Generalized mediation analysis of whether response consistency suppressed the association between loss aversion and overlapping community features of eFC networks in the PIGD. Calculated the effective connectivity using DCM analysis. Correlations between loss aversion and effective connectivity were also performed. Machine learning methods were used to test whether the loss aversion, overlapping community features of edge-centric functional networks, and effective connectivity could classify two groups. See details in SM.

Ethics

This study was approved by the Human Research Ethics Committee of the University of Science and Technology of China and the General Hospital of Beijing Military Region. Written informed consent was obtained from participants or their parents before the study. The research was conducted according to the principles of the Declaration of Helsinki.

Results

Demographic results

There were no differences in demographic variables between groups. However, the PIGD significantly increased the symptom severity of the IGD (Table 1).

Table 1.

Participants demographics and clinical variables

Demographics and Clinical VariablesControl-for-PIGD (n = 22)PIGD (n = 27)
MeanSDMeanSDTestp Value
Age, Years, Mean (SD)20.5003.09819.7043.244t47=0.8720.388
Education, Years, Mean (SD)13.1361.85911.8892.563t47=1.9090.062
Duration of Internet Gaming Exposure, years0.5110.9332.6761.827t47=5.0410.000
The longest Once Internet Gaming Exposure, hours3.0914.87616.14813.651t47=4.2630.000
Weekly Internet Gaming Exposure, hours12.68420.47466.81526.335t47=7.8870.000
Frequency of Weekly Internet Gaming Exposure3.0463.9227.0194.360t47=3.3170.002

The IGT performance results

The average net score of the PIGD was significantly lower in the IGT (t47=2.375,p=0.022,,cohensd=0.680,95CI=[0.102,1.259]) (Fig. 2).

Fig. 2.
Fig. 2.

The performance of the IGT. The average net score of the PIGD was significantly lower than that of the Con-PIGD in the IGT task. The error bars indicate the SEM. *: p<0.05

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

Computational modeling results

We used the Watanabe-Akaike Information Criterion (WAIC) (Watanabe, 2010) to compare the post-hoc fits of models (PVL-DecayRI model: WAICPIGD=7846.68, WAICConPIGD=5288.54; PVL-Delta model: WAICPIGD=10573.7, WAICConPIGD=7510.92). Therefore, we chose second best-fit model, the PVL_DecayRI, to investigate loss aversion.

Loss aversion was significantly reduced in the PIGD (t47=28.70,p<0.0001,cohensd=8.241,95CI=[6.516,9.968]) (Fig. 3_a). The PIGD also showed reduced outcome sensitivity (t47=14.81,p<0.0001,cohensd=4.253,95CI=[3.241,5.266]) (Fig. 3_b). However, the remaining two parameters, response consistency and decay rate, were not significantly different between groups (Fig. 3_c, Fig. 3_d). The estimated loss aversion parameter in PVL-DecayRI model in our study is reliable, which can be replicated with PVL-delta model. Specifically, we also found significantly reduced loss aversion among PIGD compared to Con-PIGD (t47=37.74,p<0.0001,cohensd=10.841,95CI=[8.621,13.100]).

Fig. 3.
Fig. 3.

Behavioral modeling results. a. The loss aversion parameter (λ) was significantly reduced in the PIGD. b. The outcome sensitivity parameter (α) was significantly reduced in the PIGD. c-d. The response consistency parameter (cons) and decay rate parameter (A), were not significantly different between the PIGD and the Con-PIGD. The error bars indicate the SEM. *: p<0.05; **: p<0.01; ***: p<0.001. ns: no significance

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

Simulations assess the accuracy of a model's predictions for the entire selection sequence based on the model parameters, independent of the subject's selection history. A posteriori prediction check can be used to evaluate whether the model produces valid predictions. The simulated data generated by the PVL-Decay model is similar to the real data of PIGD and Con-PIGD (Fig. 4_a, Fig. 4_b).

Fig. 4.
Fig. 4.

Posterior prediction checks (PPC) of the PVL-DecayRI model. a. The posterior prediction checks for the Con-PIGD. b. The posterior prediction checks for the PIGD. Red dotted lines were simulated data and black lines were the true data. The simulated data were similar to the real data in both PIGD and Con-PIGD

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

Results of the nFC

To test whether the PIGD altered the nFC network. We calculated the nFC of all node pairs and compared them between groups. To correct for the 4,005 independent tests, an alpha level of 1/4005(p<0.0002) was used to declare significance for the local measures (Cocchi et al., 2012). As results, nFC weren't significantly different between groups.

Results of the overlapping community features of eFC networks

Overlap is inherent within the eFC construct. To capture the potentially meaningful overlapping community features of the eFC network, we measured the community overlap of the eFC at the level of individual brain regions (nodes) i.e., the normalized entropy.

We compared the normalized community entropy of each node between groups. To correct for the 90 independent tests, an alpha level of 1/90(p<0.01) was used to declare significance for the local measures (Cocchi et al., 2012). We found that the normalized community entropy of the left triangle IFG was reduced significantly in the PIGD (t47=2.643,p=0.011,cohensd=0.759,95CI=[0.177,1.342]) (Fig. 5_a, Fig. 5_b).

Fig. 5.
Fig. 5.

The Edge functional connectivity results. a. The left triangle IFG was defined according to automated anatomical labeling (AAL) template labels. b. At the node level, the normalized community entropy within the left triangle IFG was significantly reduced in the PIGD compared to the Con-PIGD. c. The right hippocampus was defined according to the automated anatomical labeling (AAL) template labels. d. At the level of the brain system, the average edge community profile similarity of the edge between the left IFG and the right hippocampus (SleftIFGrighthippocampus) was significantly reduced in the PIGD compared to the Con-PIGD. The error bars indicate the SEM. *: p<0.05; **: p<0.01; ***: p<0.001; ns: no significance; R: right; L: left

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

Furthermore, to investigate the internal overlapping community features of the left IFG at the level of the brain system, we calculated the average edge community profile similarity of all edges between the left IFG and all other nodes. Then compared the average edge community profile similarity of each edge between groups. To correct for the 89 independent tests, an alpha level of 1/89(p<0.01) was used to declare significance for the local measure (Cocchi et al., 2012). As a result, the average edge community profile similarity of the edge between the left IFG and the right hippocampus (SleftIFGrighthippocampus) was reduced significantly in the PIGD (t47=2.469,p=0.011,cohensd=0.709,95CI=[0.129,1.289]) (Fig. 5_c, Fig. 5_d).

To understand which nodes may be responsible for the group difference of the average edge community profile similarity of the edge between the left IFG and the right hippocampus (SleftIFGrighthippocampus). We compared the edge community profile similarity of the edge between the left IFG and the right hippocampus at each third node and found that the PIGD group showed significantly reduced edge community profile similarity of the edge between the left IFG and the right hippocampus at these nodes, including the right frontal inferior orbital gyrus Sleft IFG-right IFO-right hippocampus (t47=2.948,p=0.005,cohensd=0.846,95CI=[0.259,1.433]) (Fig. 6_a), the right insular Sleft IFG-right insular-right hippocampus (t47=2.551,p=0.004,cohensd=0.869,95CI=[0.281,1.458]) (Fig. 6_b), the right caudate Sleft IFG-right caudate-right hippocampus (t47=2.558,p=0.014,cohensd=0.733,95CI=[0.151,1.315]) (Fig. 6_c), the left amygdala Sleft IFG-left amygdala-right hippocampus (t47=2.887,p=0.006,cohensd=0.827,95CI=[0.240,1.413]) (Fig. 6_d), the left hippocampus Sleft IFG-left hippocampus-right hippocampus (t47=3.016,p=0.004,cohensd=0.869,95CI=[0.281,1.458]) (Fig. 6_e) the left frontal superior medial gyrus Sleft IFG-left MFG-right hippocampus (t47=3.226,p=0.002,cohensd=0.94,95CI=[0.347,1.353]) (Fig. 6_f).

Fig. 6.
Fig. 6.

Edge community profile similarity of the edge between the left IFG and the right hippocampus at third nodes. The PIGD group showed significantly reduced edge community profile similarity of the edge between the left IFG and the right hippocampus compared to the Con-PIGD at these nodes, including a. the right frontal inferior orbital gyrus (Sleft IFG-right IFO-right hippocampus); b. the right insula (Sleft IFG-right insular-right hippocampus); c. the right caudate (Sleft IFG-right caudate-right hippocampus); d. the left amygdala (Sleft IFG-left amygdala-right hippocampus)); e. the left hippocampus (Sleft IFG-left hippocampus-right hippocampus); f. the left frontal superior medial gyrus (Sleft IFG-left MFG-right hippocampus), which were defined according to the Automated Anatomical Labeling (AAL) template labels. The error bars indicate the SEM. *: p<0.05; **: p<0.01; ***: p<0.001. ns: no significance; R: right; L: left

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

Relationship between loss aversion and overlapping community features of edge-centric functional networks

What overlapping community features of the edge-centric functional networks are associated with loss aversion in the IGT? Follow-up correlation analysis indicated that in the Con-PIGD loss aversion was significantly positively correlated with Sleft IFG-right caudate-right hippocampus (r=0.596,p=0.003) (Fig. 7). However, there were no significant associations in the PIGD (r=0.016,p=0.945) (Fig. 7). Fisher r-to-z transformation were used to assess the significance of the difference between the two correlation coefficients. The PIGD and the Con-PIGD had a significantly different relationship between the Sleft IFG-right caudate-right hippocampus and the loss aversion the IGT (z=2.290,p=0.022) (Fig. 7). Loss aversion was linearly related to response consistency (Molins, Serrano, & Alacreu-Crespo, 2021). Response consistency was related to expected value and impulsivity (Lorains et al., 2014). To investigate whether response consistency mediated the suppressed association between loss aversion and the Sleft IFG-right caudate-right hippocampus, we performed the generalized mediation analysis proposed by Wen et al. (Wen & Ye, 2014). As a result, the response consistency was fully mediated the suppressed association between loss aversion and Sleft IFG-right caudate-right hippocampus in the PIGD (a:95%CI[0.761,0.093],p=0.002;b:95%CI[0.023,0.765],p=0.004;ab:95%CI[0.405,0.005],p=0.106;c:95%CI[0.263,0.006],p=0.199;c:95%CI[0.129,0.546],p=0.420) (Fig. 8).

Fig. 7.
Fig. 7.

Relationship between the loss aversion and the Sleft IFG-right caudate-right hippocampus. Loss aversion was significantly positively correlated with the Sleft IFG-right caudate-right hippocampus in the Con-PIGD. However, there was no significant association in the PIGD. The dashed area in the panels shows the 95% confidence interval in the present study

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

Fig. 8.
Fig. 8.

Generalized mediation result. In the PIGD, the suppressed association between loss aversion and overlapping community features of the edge-centric functional network (Sleft IFG-right caudate-right hippocampus) in the IGT was fully mediated by the response consistency. *: p < 0.05, **: p < 0.01, ***: p < 0.001

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

Result of the DCM

Whether changes in effective connectivity within loss-aversion-correlated overlapping community features of edge-centric functional networks was associated with loss aversion in the PIGD? We calculated the effective connectivity within these nodes 1) the left IFG; 2) the right hippocampus; and 3) the right caudate using the DCM. The PIGD significantly increased effective connectivity from the right hippocampus to the left IFG in the outcome phase (t47=2.756,p=0.008,cohensd=0.79,95CI=[0.207,1.376]). Correlation analysis indicated that loss aversion was significantly positively correlated with it (r=0.516,p=0.006) in the PIGD. However, there was no significant association in the Con-PIGD (r=0.274,p=0.218). And the Fisher r-to-z transformation result showed that there was no different relationship between groups (z=0.940,p=0.347).

Results of machine learning classification

Machine learning methods were used to test whether the loss aversion, the Sleft IFG-right caudate-right hippocampus, and the effective connectivity from the right hippocampus to the left IFG could classify the PIGD from the Con-PIGD. The area under the receiver operating characteristic curve (ROC_AUC) for classifying the PIGD versus Con-PIGD reached 0.85 (Fig. 9_a) and the accuracy of the classification reached 0.71, suggesting that loss aversion related behavioral and neuroimaging features provided a way to distinguish PIGD from Con-PIGD. We also performed a permutation test (permutationtimes=1000) to validate this result and found that the accuracy of the classification mentioned above reached the top 1% in the random permutation distribution (permutationtests,99thcutoff=0.69,p=0.006) (Fig. 9_b).

Fig. 9.
Fig. 9.

Machine learning classification results. a. The area under the receiver operating characteristic curve (ROC_AUC) for classifying the PIGD versus Con-PIGD reached 0.85. Positive label: 1.0 means the result the classification prediction of PIGD and CON-PIGD was PIGD and counted it as 1. b. The permutation test result show that the accuracy of the classification mentioned above reached the top 1% in the random permutation distribution

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00014

Discussion

The present study results were consistent with our hypothesis. We found that the average net score of the IGT was significantly lower than that of the Con-PIGD, indicating that the PIGD group performed worse than the Con-PIGD in value-based decision-making. The PVL-DecayRI model was used to disentangle the underlying psychological processes involved in IGT performance. Consistent with our hypothesis, we observed a significantly reduced loss aversion in the PIGD group compared to the Con-PIGD group, suggesting that the reduced loss aversion may be a psychological mechanism for poor performance in value-based decision-making tasks. In addition, a study on value-based decision-making deficits also observed that gambling disorder and alcohol dependence exhibited reduced loss aversion compared to the healthy controls (Genauck et al., 2017). Therefore, our findings also suggested that the IGD, as a new behavioral addiction, is similar to other substance and other behavioral addictions, significantly reduced loss aversion in value-based decision-making.

Our study provided novel insights into the brain functional networks of the PIGD in the value-based decision-making from the perspective of the nFC and the eFC. nFC, is often interpreted as reflecting the time-averaged strength of “communication” between brain regions. Our findings demonstrated that reduced loss aversion in the PIGD is independent of the strength of communication in value-based decision making. eFC tracks how communication patterns evolve over time and ultimately assesses whether similar patterns are occurring in the brain simultaneously. Our findings showed the potential of the overlapping community features to serve as biomarkers for the PIGD diagnosis. The edge community profile similarity of the edge between the left IFG and the right hippocampus at right caudate was significantly reduced in the PIGD, which suggested that function integration ability and diversity of the reward evaluation and processing network was reduced. The IFG mainly involved in the executive control processing of the sense of control over the gain/loss outcome and the evaluation of the gain/loss information (Friese, Binder, Luechinger, Boesiger, & Rasch, 2013; Lee, Chatzisarantis, & Hagger, 2016; Zhang, Hu, Wang, Wang, & Dong, 2020). The hippocampus participates in reward processing (Zarrindast, Nouri, & Ahmadi, 2007). The right caudate mainly involved in processing the magnitude of received losses (Volkow, Wang, Fowler, Tomasi, & Telang, 2011). Therefore, the left IFG, the right hippocampus, and the right caudate were part of an integral edge network involved in reward evaluation and processing. A possible mechanism is that response consistency suppressed the association between loss aversion and the overlapping community features of edge-centric reward evaluation and processing functional network. Response consistency measures the consistency of decision makers' choices with value expectations (Molins et al., 2021) and is related to impulsivity, which is common in IGD (Lorains et al., 2014; Zha et al., 2019). Furthermore, DCM result indicated that it may be due to the promoted bottom-up neuromodulation (right hippocampus to left IFG) in the PIGD. In other words, the motivation to obtain rewards represents a central feature of addictions and seems to be more pronounced with the PIGD. Meanwhile, the PIGD cannot process the sense of control over the gain/loss outcome and evaluate the gain/loss information well. This may be why the PIGD find it easy to indulge in the game and difficult to withdraw from playing games.

Our study has several theoretical implications. First, existing theories of IGD largely neglect decreased loss aversion in value-based decision-making. Reduced loss aversion in value-based decision-making also seems to be a general feature of IGD, like other addictive disorders (Balodis et al., 2012; Ersche et al., 2016; Fauth-Buhler & Mann, 2017; Yao et al., 2015; Yip et al., 2018). Therefore, more consideration of loss aversion in value-based decision-making might be valuable for both neurobiological and clinical research in IGD. Second, our results proposed an idea about IGD. IGD may not inhibit a specific cognitive function of the individual brain region, but by dispersing the edge network functions, thereby simultaneously processing multiple functional processes in a complex environment. Therefore, it is impossible to concentrate more resources on goal-directed cognitive control, such as value-based decision-making.

Limitations

First, the majority of the sample in the present study was male. Despite the fact that the ratio of female IGD patients in China is very low (Qi et al., 2016), we still tried to recruit some female patients. The ratio of female participants in the PIGD group was comparable with that in the healthy control groups. Second, loss aversion has not yet been used to directly compare the IGD to other addiction disorders. Future work is needed to directly investigate the similarities and differences in the loss aversion and related brain networks between behavioral and substance addiction. Third, IGT may not be the best paradigm to study loss aversion in value-based decision making. In order to deepen the research on loss aversion impairment in value-based decision-making of IGD, more other loss aversion related task paradigms and research methods would be adopted in the future research on loss aversion impairment in value-based decision-making of IGD. It may be helpful to further understand the mechanism of IGD and provide more evidence for the treatment of IGD.

Conclusion

PIGD performed worse in IGT. IGD, as a new behavioral addiction, is similar to the substance and other behavioral addictions, with significantly reduced loss aversion in value-based decision-making. The possible neural mechanism of reduced loss aversion in the PIGD was the reduction in communication patterns of the edge-centric reward evaluation and processing functional network (the left IFG, the right hippocampus, and the right caudate). Loss aversion, overlapping community features of the edge-centric reward evaluation and processing functional network (left IFG, right hippocampus, and right caudate), and bottom-up neuromodulation (right hippocampus to left IFG) in IGT may provide consistent and novel behavioral and neuroimaging evidence supporting loss aversion as a specific factor for distinguishing of PIGD from Con-PIGD. These findings may have important significance for understanding the definition and mechanism of IGD in the future.

Funding sources

This work was supported by grants from the Chinese National Programs for Brain Science and Brain-like Intelligence Technology (2021ZD0202101), The National Natural Science Foundation of China (32171080, 32161143022, 71942003, 31800927, 31900766 and 71874170), Major Project of Philosophy and Social Science Research, Ministry of Education of China (19JZD010), CAS-VPST Silk Road Science Fund 2021 (GLHZ202128), Collaborative Innovation Program of Hefei Science Center, CAS (2020HSC-CIP001), National Key Research and Development Project of China (2020YFC2007302), the key research project of Academy for Multidisciplinary Studies, Capital Normal University (JCKXYJY2019019).

Authors’ contribution

W. H., P. L., R. T., R. Z., and X. Z. were responsible for study concept and design and interpretation of data. X. Z. obtained funding. R. Z., and X. Z. responsible for study supervision. W. H., P. L., and Y. L. performed research. W. H., P. L., C. J., Y. L., R. Z., and M. W., analyzed data. W. H. drafted the manuscript. W. H., P. L., Y. L., C. J., W. L., M. W., Y. P., J. J., L. L., H. C., H. G., W. W., Z. M., R. T., R. Z., and X. Z. provided critical revision of the manuscript. All authors critically reviewed the content and approved final version for publication.

Conflict of interest

The authors have no conflicts of interest.

Acknowledgment

We thank the Bioinformatics Center of the University of Science and Technology of China, School of Life Science, for providing supercomputing resources for this project.

Supplementary material

Supplementary data to this article can be found in a separate file: https://doi.org/10.1556/2006.2023.00014.

References

  • Ahn, W. Y., Busemeyer, J. R., Wagenmakers, E. J., & Stout, J. C. (2008). Comparison of decision learning models using the generalization criterion method. Cognitive Science, 32(8), 13761402. https://doi.org/10.1080/03640210802352992.

    • Search Google Scholar
    • Export Citation
  • Ahn, W. Y., Haines, N., & Zhang, L. (2017). Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computers in Psychiatry, 1, 2457. https://doi.org/10.1162/CPSY_a_00002.

    • Search Google Scholar
    • Export Citation
  • Ahn, W. Y., Krawitz, A., Kim, W., Busmeyer, J. R., & Brown, J. W. (2011). A model-based fMRI analysis with hierarchical bayesian parameter estimation. Journal of Neuroscience Psychology and Economic, 4(2), 95110. https://doi.org/10.1037/a0020684.

    • Search Google Scholar
    • Export Citation
  • Ahn, W. Y., Vasilev, G., Lee, S. H., Busemeyer, J. R., Kruschke, J. K., Bechara, A., & Vassileva, J. (2014). Decision-making in stimulant and opiate addicts in protracted abstinence: Evidence from computational modeling with pure users. Frontiers in Psychology, 5, 849. https://doi.org/10.3389/fpsyg.2014.00849.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. L., Kinnison, J., & Pessoa, L. (2013). Describing functional diversity of brain regions and brain networks. Neuroimage, 73, 5058. https://doi.org/10.1016/j.neuroimage.2013.01.071.

    • Search Google Scholar
    • Export Citation
  • Balodis, I. M., Kober, H., Worhunsky, P. D., Stevens, M. C., Pearlson, G. D., & Potenza, M. N. (2012). Diminished frontostriatal activity during processing of monetary rewards and losses in pathological gambling. Biological Psychiatry, 71(8), 749757. https://doi.org/10.1016/j.biopsych.2012.01.006.

    • Search Google Scholar
    • Export Citation
  • Christakou, A., Brammer, M., Giampietro, V., & Rubia, K. (2009). Right ventromedial and dorsolateral prefrontal cortices mediate adaptive decisions under ambiguity by integrating choice utility and outcome evaluation. Journal of Neuroscience, 29(35), 1102011028. https://doi.org/10.1523/JNEUROSCI.1279-09.2009.

    • Search Google Scholar
    • Export Citation
  • Cocchi, L., Bramati, I. E., Zalesky, A., Furukawa, E., Fontenelle, L. F., Moll, J., … Mattos, P. (2012). Altered functional brain connectivity in a non-clinical sample of young adults with attention-deficit/hyperactivity disorder. Journal of Neuroscience, 32(49), 1775317761. https://doi.org/10.1523/JNEUROSCI.3272-12.2012.

    • Search Google Scholar
    • Export Citation
  • Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162173. https://doi.org/10.1006/cbmr.1996.0014.

    • Search Google Scholar
    • Export Citation
  • Craddock, R. C., Jbabdi, S., Yan, C. G., Vogelstein, J. T., CastellanosF. X., Di Martino, A., … Milham, M. P. (2013). Imaging human connectomes at the macroscale. Nature Methods, 10(6), 524539. https://doi.org/10.1038/nmeth.2482.

    • Search Google Scholar
    • Export Citation
  • Di Martino, A., Fair, D. A., Kelly, C., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., … Milham, M. P. (2014). Unraveling the miswired connectome: A developmental perspective. Neuron, 83(6), 13351353. https://doi.org/10.1016/j.neuron.2014.08.050.

    • Search Google Scholar
    • Export Citation
  • Diekhof, E. K., Falkai, P., & Gruber, O. (2008). Functional neuroimaging of reward processing and decision-making: A review of aberrant motivational and affective processing in addiction and mood disorders. Brain Research Reviews, 59(1), 164184. https://doi.org/10.1016/j.brainresrev.2008.07.004.

    • Search Google Scholar
    • Export Citation
  • Ersche, K. D., Gillan, C. M., Jones, P. S., Williams, G. B., Ward, L. H., Luijten, M., … Robbins, T. W. (2016). Carrots and sticks fail to change behavior in cocaine addiction. Science, 352(6292), 14681471. https://doi.org/10.1126/science.aaf3700.

    • Search Google Scholar
    • Export Citation
  • Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience, 23(12), 16441654. https://doi.org/10.1038/s41593-020-00719-y.

    • Search Google Scholar
    • Export Citation
  • Fauth-Buhler, M., & Mann, K. (2017). Neurobiological correlates of internet gaming disorder: Similarities to pathological gambling. Addictive Behaviors, 64, 349356. https://doi.org/10.1016/j.addbeh.2015.11.004.

    • Search Google Scholar
    • Export Citation
  • Ferraro, S., Grazzi, L., Muffatti, R., Nava, S., Ghielmetti, F., Bertolino, N., … Chiapparini, L. (2012). In medication-overuse headache, fMRI shows long-lasting dysfunction in midbrain areas. Headache, 52(10), 15201534. https://doi.org/10.1111/j.1526-4610.2012.02276.x.

    • Search Google Scholar
    • Export Citation
  • Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews. Neuroscience, 16(3), 159172. https://doi.org/10.1038/nrn3901.

    • Search Google Scholar
    • Export Citation
  • Fridberg, D. J., Queller, S., Ahn, W. Y., Kim, W., Bishara, A. J., Busemeyer, J. R., … Stout, J. C. (2010). Cognitive mechanisms underlying risky decision-making in chronic cannabis users. Journal of Mathematical Psychology, 54(1), 2838. https://doi.org/10.1016/j.jmp.2009.10.002.

    • Search Google Scholar
    • Export Citation
  • Friese, M., Binder, J., Luechinger, R., Boesiger, P., & Rasch, B. (2013). Suppressing emotions impairs subsequent stroop performance and reduces prefrontal brain activation. Plos One, 8(4), e60385. https://doi.org/10.1371/journal.pone.0060385.

    • Search Google Scholar
    • Export Citation
  • Genauck, A., Andrejevic, M., Brehm, K., Matthis, C., Heinz, A., Weinreich, A., … Romanczuk-Seiferth, N. (2020). Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls. Addiction Biology, 25(6), e12841. https://doi.org/10.1111/adb.12841.

    • Search Google Scholar
    • Export Citation
  • Genauck, A., Quester, S., Wustenberg, T., Morsen, C., Heinz, A., & Romanczuk-Seiferth, N. (2017). Reduced loss aversion in pathological gambling and alcohol dependence is associated with differential alterations in amygdala and prefrontal functioning. Scientific Reports, 7(1), 16306. https://doi.org/10.1038/s41598-017-16433-y.

    • Search Google Scholar
    • Export Citation
  • Gianelli, C., Basso, G., Manera, M., Poggi, P., & Canessa, N. (2022). Posterior fronto-medial atrophy reflects decreased loss aversion, but not executive impairment, in alcohol use disorder. Addiction Biology, 27(1), e13088. https://doi.org/10.1111/adb.13088.

    • Search Google Scholar
    • Export Citation
  • Holden, C. (2001). ‘Behavioral’ addictions: Do they exist? Science, 294(5544), 980982. https://doi.org/10.1126/science.294.5544.980.

    • Search Google Scholar
    • Export Citation
  • Jo, Y., Faskowitz, J., Esfahlani, F. Z., Sporns, O., & Betzel, R. F. (2021). Subject identification using edge-centric functional connectivity. Neuroimage, 238. ARTN 118204. https://doi.org/10.1016/j.neuroimage.2021.118204.

    • Search Google Scholar
    • Export Citation
  • Jo, Y., Zamani Esfahlani, F., Faskowitz, J., Chumin, E. J., Sporns, O., & Betzel, R. F. (2021). The diversity and multiplexity of edge communities within and between brain systems. Cell Reports, 37(7), 110032. https://doi.org/10.1016/j.celrep.2021.110032.

    • Search Google Scholar
    • Export Citation
  • King, D. L., & Delfabbro, P. H. (2018). The concept of “harm” in Internet gaming disorder. Journal of Behavioral Addictions, 7(3), 562564. https://doi.org/10.1556/2006.7.2018.24.

    • Search Google Scholar
    • Export Citation
  • King, D. L., Delfabbro, P. H., Billieux, J., & Potenza, M. N. (2020). Problematic online gaming and the COVID-19 pandemic. Journal of Behavioral Addictions, 9(2), 184186. https://doi.org/10.1556/2006.2020.00016.

    • Search Google Scholar
    • Export Citation
  • King, D., Koster, E., & Billieux, J. (2019). Study what makes games addictive. Nature, 573(7774), 346. https://doi.org/10.1038/d41586-019-02776-1.

    • Search Google Scholar
    • Export Citation
  • Kiraly, O., Potenza, M. N., Stein, D. J., King, D. L., Hodgins, D. C., Saunders, J. B., … Demetrovics, Z. (2020). Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Comprehensive Psychiatry, 100, 152180. https://doi.org/10.1016/j.comppsych.2020.152180.

    • Search Google Scholar
    • Export Citation
  • Lee, N., Chatzisarantis, N., & Hagger, M. S. (2016). Adequacy of the sequential-task paradigm in evoking ego-depletion and how to improve detection of ego-depleting phenomena. Frontiers in Psychology, 7 ARTN 136 https://doi.org/10.3389/fpsyg.2016.00136.

    • Search Google Scholar
    • Export Citation
  • Li, J. A., Dong, D., Wei, Z., Liu, Y., Pan, Y., Nori, F., & Zhang, X. (2020). Quantum reinforcement learning during human decision-making. Nature Human Behaviour, 4(3), 294307. https://doi.org/10.1038/s41562-019-0804-2.

    • Search Google Scholar
    • Export Citation
  • Lin, C. H., Wang, C. C., Sun, J. H., Ko, C. H., & Chiu, Y. C. (2019). Is the clinical version of the Iowa gambling task relevant for assessing choice behavior in cases of internet addiction? Frontiers in Psychiatry, 10, 232. https://doi.org/10.3389/fpsyt.2019.00232.

    • Search Google Scholar
    • Export Citation
  • Lorains, F. K., Dowling, N. A., Enticott, P. G., Bradshaw, J. L., Trueblood, J. S., & Stout, J. C. (2014). Strategic and non-strategic problem gamblers differ on decision-making under risk and ambiguity. Addiction, 109(7), 11281137. https://doi.org/10.1111/add.12494.

    • Search Google Scholar
    • Export Citation
  • Lukavska, K. (2018). The immediate and long-term effects of time perspective on Internet gaming disorder. Journal of Behavioral Addictions, 7(1), 4451. https://doi.org/10.1556/2006.6.2017.089.

    • Search Google Scholar
    • Export Citation
  • Luo, Q., Liu, W., Jin, L., Chang, C., & Peng, Z. (2021). Classification of obsessive-compulsive disorder using distance correlation on resting-state functional MRI images. Frontiers in Neuroinformatics, 15, 676491. https://doi.org/10.3389/fninf.2021.676491.

    • Search Google Scholar
    • Export Citation
  • Meshi, D., Elizarova, A., Bender, A., & Verdejo-Garcia, A. (2019). Excessive social media users demonstrate impaired decision making in the Iowa Gambling Task. Journal of Behavioral Addictions, 8(1), 169173. https://doi.org/10.1556/2006.7.2018.138.

    • Search Google Scholar
    • Export Citation
  • Metcalf, O., & Pammer, K. (2014). Impulsivity and related neuropsychological features in regular and addictive first person shooter gaming. Cyberpsychology, Behavior and Social Networking, 17(3), 147152. https://doi.org/10.1089/cyber.2013.0024.

    • Search Google Scholar
    • Export Citation
  • Molins, F., Serrano, M. A., & Alacreu-Crespo, A. (2021). Early stages of the acute physical stress response increase loss aversion and learning on decision making: A Bayesian approach. Physiology & Behavior, 237, 113459. https://doi.org/10.1016/j.physbeh.2021.113459.

    • Search Google Scholar
    • Export Citation
  • Potenza, M. (2015). Perspective: Behavioural addictions matter. Nature, 522(7557), S62. https://doi.org/10.1038/522S62a.

  • Qi, X., Yang, Y., Dai, S., Gao, P., Du, X., Zhang, Y., … Zhang, Q. (2016). Effects of outcome on the covariance between risk level and brain activity in adolescents with internet gaming disorder. Neuroimage Clin, 12, 845851. https://doi.org/10.1016/j.nicl.2016.10.024.

    • Search Google Scholar
    • Export Citation
  • Quester, S., & Romanczuk-Seiferth, N. (2015). Brain imaging in gambling disorder. Current Addiction Reports, 2(3), 220229. https://doi.org/10.1007/s40429-015-0063-x.

    • Search Google Scholar
    • Export Citation
  • Reid, A. T., Headley, D. B., Mill, R. D., Sanchez-Romero, R., Uddin, L. Q., Marinazzo, D., … Cole, M. W. (2019). Advancing functional connectivity research from association to causation. Nature Neuroscience, 22(11), 17511760. https://doi.org/10.1038/s41593-019-0510-4.

    • Search Google Scholar
    • Export Citation
  • Rogers, B. P., Morgan, V. L., Newton, A. T., & Gore, J. C. (2007). Assessing functional connectivity in the human brain by fMRI. Magnetic Resonance Imaging, 25(10), 13471357. https://doi.org/10.1016/j.mri.2007.03.007.

    • Search Google Scholar
    • Export Citation
  • Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515518. https://doi.org/10.1126/science.1134239.

    • Search Google Scholar
    • Export Citation
  • Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273289. https://doi.org/10.1006/nimg.2001.0978.

    • Search Google Scholar
    • Export Citation
  • Vassileva, J., Ahn, W. Y., Weber, K. M., Busemeyer, J. R., Stout, J. C., Gonzalez, R., & Cohen, M. H. (2013). Computational modeling reveals distinct effects of HIV and history of drug use on decision-making processes in women. Plos One, 8(8), e68962. https://doi.org/10.1371/journal.pone.0068962.

    • Search Google Scholar
    • Export Citation
  • Volkow, N. D., Wang, G. J., Fowler, J. S., Tomasi, D., & Telang, F. (2011). Addiction: Beyond dopamine reward circuitry. Proceedings of the National Academy of Sciences of the United States of America, 108(37), 1503715042. https://doi.org/10.1073/pnas.1010654108.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., Ma, N., He, X., Li, N., Wei, Z., Yang, L., … Zhang, X. (2017). Neural substrates of updating the prediction through prediction error during decision making. Neuroimage, 157, 112. https://doi.org/10.1016/j.neuroimage.2017.05.041.

    • Search Google Scholar
    • Export Citation
  • Watanabe, S. (2010). Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11, 35713594. Retrieved from https://www.jmlr.org/papers/volume11/watanabe10a/watanabe10a.pdf.

    • Search Google Scholar
    • Export Citation
  • Wei, Z., Han, L., Zhong, X., Liu, Y., Zha, R., Wang, Y., … Zhang, X. (2018). Chronic nicotine exposure impairs uncertainty modulation on reinforcement learning in anterior cingulate cortex and serotonin system. Neuroimage, 169, 323333. https://doi.org/10.1016/j.neuroimage.2017.11.048.

    • Search Google Scholar
    • Export Citation
  • Wen, Z., & Ye, B. (2014). Analyses of mediating effects: The development of methods and models. [中介效应分析:方法和模型发展]. Advances in Psychological Science, 22(5), 731745. https://doi.org/10.3724/SP.J.1042.2014.00731.

    • Search Google Scholar
    • Export Citation
  • Wiehler, A., & Peters, J. (2015). Reward-based decision making in pathological gambling: The roles of risk and delay. Neuroscience Research, 90, 314. https://doi.org/10.1016/j.neures.2014.09.008.

    • Search Google Scholar
    • Export Citation
  • Yao, Y. W., Wang, L. J., Yip, S. W., Chen, P. R., Li, S., Xu, J., … Fang, X. Y. (2015). Impaired decision-making under risk is associated with gaming-specific inhibition deficits among college students with Internet gaming disorder. Psychiatry Research, 229(1–2), 302309. https://doi.org/10.1016/j.psychres.2015.07.004.

    • Search Google Scholar
    • Export Citation
  • Yip, S. W., Gross, J. J., Chawla, M., Ma, S. S., Shi, X. H., Liu, L., … Zhang, J. (2018). Is neural processing of negative stimuli altered in addiction independent of drug effects? Findings from drug-naive youth with internet gaming disorder. Neuropsychopharmacology, 43(6), 13641372. https://doi.org/10.1038/npp.2017.283.

    • Search Google Scholar
    • Export Citation
  • Zarrindast, M. R., Nouri, M., & Ahmadi, S. (2007). Cannabinoid CB1 receptors of the dorsal hippocampus are important for induction of conditioned place preference (CPP) but do not change morphine CPP. Brain Research, 1163, 130137. https://doi.org/10.1016/j.brainres.2007.06.015.

    • Search Google Scholar
    • Export Citation
  • Zha, R., Bu, J., Wei, Z., Han, L., Zhang, P., Ren, J., … Zhang, X. (2019). Transforming brain signals related to value evaluation and self-control into behavioral choices. Human Brain Mapping, 40(4), 10491061. https://doi.org/10.1002/hbm.24379.

    • Search Google Scholar
    • Export Citation
  • Zha, R., Li, P., Liu, Y., Alarefi, A., Zhang, X., & Li, J. (2022). The orbitofrontal cortex represents advantageous choice in the Iowa gambling task. Human Brain Mapping. https://doi.org/10.1002/hbm.25887.

    • Search Google Scholar
    • Export Citation
  • Zhang, J. L., Hu, Y., Wang, Z. L., Wang, M., & Dong, G. H. (2020). Males are more sensitive to reward and less sensitive to loss than females among people with internet gaming disorder: fMRI evidence from a card-guessing task. BMC Psychiatry, 20(1) ARTN 357 https://doi.org/10.1186/s12888-020-02771-1.

    • Search Google Scholar
    • Export Citation
  • Zha, R. J., Tao, R., Kong, Q. M., Li, H., Liu, Y., Huang, R. Q., … Rao, H. Y. (2022). Impulse control differentiates Internet gaming disorder from non-disordered but heavy Internet gaming use: Evidence from multiple behavioral and multimodal neuroimaging data. Computers in Human Behavior, 130. ARTN 107184. https://doi.org/10.1016/j.chb.2022.107184.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

  • Ahn, W. Y., Busemeyer, J. R., Wagenmakers, E. J., & Stout, J. C. (2008). Comparison of decision learning models using the generalization criterion method. Cognitive Science, 32(8), 13761402. https://doi.org/10.1080/03640210802352992.

    • Search Google Scholar
    • Export Citation
  • Ahn, W. Y., Haines, N., & Zhang, L. (2017). Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computers in Psychiatry, 1, 2457. https://doi.org/10.1162/CPSY_a_00002.

    • Search Google Scholar
    • Export Citation
  • Ahn, W. Y., Krawitz, A., Kim, W., Busmeyer, J. R., & Brown, J. W. (2011). A model-based fMRI analysis with hierarchical bayesian parameter estimation. Journal of Neuroscience Psychology and Economic, 4(2), 95110. https://doi.org/10.1037/a0020684.

    • Search Google Scholar
    • Export Citation
  • Ahn, W. Y., Vasilev, G., Lee, S. H., Busemeyer, J. R., Kruschke, J. K., Bechara, A., & Vassileva, J. (2014). Decision-making in stimulant and opiate addicts in protracted abstinence: Evidence from computational modeling with pure users. Frontiers in Psychology, 5, 849. https://doi.org/10.3389/fpsyg.2014.00849.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. L., Kinnison, J., & Pessoa, L. (2013). Describing functional diversity of brain regions and brain networks. Neuroimage, 73, 5058. https://doi.org/10.1016/j.neuroimage.2013.01.071.

    • Search Google Scholar
    • Export Citation
  • Balodis, I. M., Kober, H., Worhunsky, P. D., Stevens, M. C., Pearlson, G. D., & Potenza, M. N. (2012). Diminished frontostriatal activity during processing of monetary rewards and losses in pathological gambling. Biological Psychiatry, 71(8), 749757. https://doi.org/10.1016/j.biopsych.2012.01.006.

    • Search Google Scholar
    • Export Citation
  • Christakou, A., Brammer, M., Giampietro, V., & Rubia, K. (2009). Right ventromedial and dorsolateral prefrontal cortices mediate adaptive decisions under ambiguity by integrating choice utility and outcome evaluation. Journal of Neuroscience, 29(35), 1102011028. https://doi.org/10.1523/JNEUROSCI.1279-09.2009.

    • Search Google Scholar
    • Export Citation
  • Cocchi, L., Bramati, I. E., Zalesky, A., Furukawa, E., Fontenelle, L. F., Moll, J., … Mattos, P. (2012). Altered functional brain connectivity in a non-clinical sample of young adults with attention-deficit/hyperactivity disorder. Journal of Neuroscience, 32(49), 1775317761. https://doi.org/10.1523/JNEUROSCI.3272-12.2012.

    • Search Google Scholar
    • Export Citation
  • Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162173. https://doi.org/10.1006/cbmr.1996.0014.

    • Search Google Scholar
    • Export Citation
  • Craddock, R. C., Jbabdi, S., Yan, C. G., Vogelstein, J. T., CastellanosF. X., Di Martino, A., … Milham, M. P. (2013). Imaging human connectomes at the macroscale. Nature Methods, 10(6), 524539. https://doi.org/10.1038/nmeth.2482.

    • Search Google Scholar
    • Export Citation
  • Di Martino, A., Fair, D. A., Kelly, C., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., … Milham, M. P. (2014). Unraveling the miswired connectome: A developmental perspective. Neuron, 83(6), 13351353. https://doi.org/10.1016/j.neuron.2014.08.050.

    • Search Google Scholar
    • Export Citation
  • Diekhof, E. K., Falkai, P., & Gruber, O. (2008). Functional neuroimaging of reward processing and decision-making: A review of aberrant motivational and affective processing in addiction and mood disorders. Brain Research Reviews, 59(1), 164184. https://doi.org/10.1016/j.brainresrev.2008.07.004.

    • Search Google Scholar
    • Export Citation
  • Ersche, K. D., Gillan, C. M., Jones, P. S., Williams, G. B., Ward, L. H., Luijten, M., … Robbins, T. W. (2016). Carrots and sticks fail to change behavior in cocaine addiction. Science, 352(6292), 14681471. https://doi.org/10.1126/science.aaf3700.

    • Search Google Scholar
    • Export Citation
  • Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience, 23(12), 16441654. https://doi.org/10.1038/s41593-020-00719-y.

    • Search Google Scholar
    • Export Citation
  • Fauth-Buhler, M., & Mann, K. (2017). Neurobiological correlates of internet gaming disorder: Similarities to pathological gambling. Addictive Behaviors, 64, 349356. https://doi.org/10.1016/j.addbeh.2015.11.004.

    • Search Google Scholar
    • Export Citation
  • Ferraro, S., Grazzi, L., Muffatti, R., Nava, S., Ghielmetti, F., Bertolino, N., … Chiapparini, L. (2012). In medication-overuse headache, fMRI shows long-lasting dysfunction in midbrain areas. Headache, 52(10), 15201534. https://doi.org/10.1111/j.1526-4610.2012.02276.x.

    • Search Google Scholar
    • Export Citation
  • Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews. Neuroscience, 16(3), 159172. https://doi.org/10.1038/nrn3901.

    • Search Google Scholar
    • Export Citation
  • Fridberg, D. J., Queller, S., Ahn, W. Y., Kim, W., Bishara, A. J., Busemeyer, J. R., … Stout, J. C. (2010). Cognitive mechanisms underlying risky decision-making in chronic cannabis users. Journal of Mathematical Psychology, 54(1), 2838. https://doi.org/10.1016/j.jmp.2009.10.002.

    • Search Google Scholar
    • Export Citation
  • Friese, M., Binder, J., Luechinger, R., Boesiger, P., & Rasch, B. (2013). Suppressing emotions impairs subsequent stroop performance and reduces prefrontal brain activation. Plos One, 8(4), e60385. https://doi.org/10.1371/journal.pone.0060385.

    • Search Google Scholar
    • Export Citation
  • Genauck, A., Andrejevic, M., Brehm, K., Matthis, C., Heinz, A., Weinreich, A., … Romanczuk-Seiferth, N. (2020). Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls. Addiction Biology, 25(6), e12841. https://doi.org/10.1111/adb.12841.

    • Search Google Scholar
    • Export Citation
  • Genauck, A., Quester, S., Wustenberg, T., Morsen, C., Heinz, A., & Romanczuk-Seiferth, N. (2017). Reduced loss aversion in pathological gambling and alcohol dependence is associated with differential alterations in amygdala and prefrontal functioning. Scientific Reports, 7(1), 16306. https://doi.org/10.1038/s41598-017-16433-y.

    • Search Google Scholar
    • Export Citation
  • Gianelli, C., Basso, G., Manera, M., Poggi, P., & Canessa, N. (2022). Posterior fronto-medial atrophy reflects decreased loss aversion, but not executive impairment, in alcohol use disorder. Addiction Biology, 27(1), e13088. https://doi.org/10.1111/adb.13088.

    • Search Google Scholar
    • Export Citation
  • Holden, C. (2001). ‘Behavioral’ addictions: Do they exist? Science, 294(5544), 980982. https://doi.org/10.1126/science.294.5544.980.

    • Search Google Scholar
    • Export Citation
  • Jo, Y., Faskowitz, J., Esfahlani, F. Z., Sporns, O., & Betzel, R. F. (2021). Subject identification using edge-centric functional connectivity. Neuroimage, 238. ARTN 118204. https://doi.org/10.1016/j.neuroimage.2021.118204.

    • Search Google Scholar
    • Export Citation
  • Jo, Y., Zamani Esfahlani, F., Faskowitz, J., Chumin, E. J., Sporns, O., & Betzel, R. F. (2021). The diversity and multiplexity of edge communities within and between brain systems. Cell Reports, 37(7), 110032. https://doi.org/10.1016/j.celrep.2021.110032.

    • Search Google Scholar
    • Export Citation
  • King, D. L., & Delfabbro, P. H. (2018). The concept of “harm” in Internet gaming disorder. Journal of Behavioral Addictions, 7(3), 562564. https://doi.org/10.1556/2006.7.2018.24.

    • Search Google Scholar
    • Export Citation
  • King, D. L., Delfabbro, P. H., Billieux, J., & Potenza, M. N. (2020). Problematic online gaming and the COVID-19 pandemic. Journal of Behavioral Addictions, 9(2), 184186. https://doi.org/10.1556/2006.2020.00016.

    • Search Google Scholar
    • Export Citation
  • King, D., Koster, E., & Billieux, J. (2019). Study what makes games addictive. Nature, 573(7774), 346. https://doi.org/10.1038/d41586-019-02776-1.

    • Search Google Scholar
    • Export Citation
  • Kiraly, O., Potenza, M. N., Stein, D. J., King, D. L., Hodgins, D. C., Saunders, J. B., … Demetrovics, Z. (2020). Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Comprehensive Psychiatry, 100, 152180. https://doi.org/10.1016/j.comppsych.2020.152180.

    • Search Google Scholar
    • Export Citation
  • Lee, N., Chatzisarantis, N., & Hagger, M. S. (2016). Adequacy of the sequential-task paradigm in evoking ego-depletion and how to improve detection of ego-depleting phenomena. Frontiers in Psychology, 7 ARTN 136 https://doi.org/10.3389/fpsyg.2016.00136.

    • Search Google Scholar
    • Export Citation
  • Li, J. A., Dong, D., Wei, Z., Liu, Y., Pan, Y., Nori, F., & Zhang, X. (2020). Quantum reinforcement learning during human decision-making. Nature Human Behaviour, 4(3), 294307. https://doi.org/10.1038/s41562-019-0804-2.

    • Search Google Scholar
    • Export Citation
  • Lin, C. H., Wang, C. C., Sun, J. H., Ko, C. H., & Chiu, Y. C. (2019). Is the clinical version of the Iowa gambling task relevant for assessing choice behavior in cases of internet addiction? Frontiers in Psychiatry, 10, 232. https://doi.org/10.3389/fpsyt.2019.00232.

    • Search Google Scholar
    • Export Citation
  • Lorains, F. K., Dowling, N. A., Enticott, P. G., Bradshaw, J. L., Trueblood, J. S., & Stout, J. C. (2014). Strategic and non-strategic problem gamblers differ on decision-making under risk and ambiguity. Addiction, 109(7), 11281137. https://doi.org/10.1111/add.12494.

    • Search Google Scholar
    • Export Citation
  • Lukavska, K. (2018). The immediate and long-term effects of time perspective on Internet gaming disorder. Journal of Behavioral Addictions, 7(1), 4451. https://doi.org/10.1556/2006.6.2017.089.

    • Search Google Scholar
    • Export Citation
  • Luo, Q., Liu, W., Jin, L., Chang, C., & Peng, Z. (2021). Classification of obsessive-compulsive disorder using distance correlation on resting-state functional MRI images. Frontiers in Neuroinformatics, 15, 676491. https://doi.org/10.3389/fninf.2021.676491.

    • Search Google Scholar
    • Export Citation
  • Meshi, D., Elizarova, A., Bender, A., & Verdejo-Garcia, A. (2019). Excessive social media users demonstrate impaired decision making in the Iowa Gambling Task. Journal of Behavioral Addictions, 8(1), 169173. https://doi.org/10.1556/2006.7.2018.138.

    • Search Google Scholar
    • Export Citation
  • Metcalf, O., & Pammer, K. (2014). Impulsivity and related neuropsychological features in regular and addictive first person shooter gaming. Cyberpsychology, Behavior and Social Networking, 17(3), 147152. https://doi.org/10.1089/cyber.2013.0024.

    • Search Google Scholar
    • Export Citation
  • Molins, F., Serrano, M. A., & Alacreu-Crespo, A. (2021). Early stages of the acute physical stress response increase loss aversion and learning on decision making: A Bayesian approach. Physiology & Behavior, 237, 113459. https://doi.org/10.1016/j.physbeh.2021.113459.

    • Search Google Scholar
    • Export Citation
  • Potenza, M. (2015). Perspective: Behavioural addictions matter. Nature, 522(7557), S62. https://doi.org/10.1038/522S62a.

  • Qi, X., Yang, Y., Dai, S., Gao, P., Du, X., Zhang, Y., … Zhang, Q. (2016). Effects of outcome on the covariance between risk level and brain activity in adolescents with internet gaming disorder. Neuroimage Clin, 12, 845851. https://doi.org/10.1016/j.nicl.2016.10.024.

    • Search Google Scholar
    • Export Citation
  • Quester, S., & Romanczuk-Seiferth, N. (2015). Brain imaging in gambling disorder. Current Addiction Reports, 2(3), 220229. https://doi.org/10.1007/s40429-015-0063-x.

    • Search Google Scholar
    • Export Citation
  • Reid, A. T., Headley, D. B., Mill, R. D., Sanchez-Romero, R., Uddin, L. Q., Marinazzo, D., … Cole, M. W. (2019). Advancing functional connectivity research from association to causation. Nature Neuroscience, 22(11), 17511760. https://doi.org/10.1038/s41593-019-0510-4.

    • Search Google Scholar
    • Export Citation
  • Rogers, B. P., Morgan, V. L., Newton, A. T., & Gore, J. C. (2007). Assessing functional connectivity in the human brain by fMRI. Magnetic Resonance Imaging, 25(10), 13471357. https://doi.org/10.1016/j.mri.2007.03.007.

    • Search Google Scholar
    • Export Citation
  • Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515518. https://doi.org/10.1126/science.1134239.

    • Search Google Scholar
    • Export Citation
  • Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273289. https://doi.org/10.1006/nimg.2001.0978.

    • Search Google Scholar
    • Export Citation
  • Vassileva, J., Ahn, W. Y., Weber, K. M., Busemeyer, J. R., Stout, J. C., Gonzalez, R., & Cohen, M. H. (2013). Computational modeling reveals distinct effects of HIV and history of drug use on decision-making processes in women. Plos One, 8(8), e68962. https://doi.org/10.1371/journal.pone.0068962.

    • Search Google Scholar
    • Export Citation
  • Volkow, N. D., Wang, G. J., Fowler, J. S., Tomasi, D., & Telang, F. (2011). Addiction: Beyond dopamine reward circuitry. Proceedings of the National Academy of Sciences of the United States of America, 108(37), 1503715042. https://doi.org/10.1073/pnas.1010654108.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., Ma, N., He, X., Li, N., Wei, Z., Yang, L., … Zhang, X. (2017). Neural substrates of updating the prediction through prediction error during decision making. Neuroimage, 157, 112. https://doi.org/10.1016/j.neuroimage.2017.05.041.

    • Search Google Scholar
    • Export Citation
  • Watanabe, S. (2010). Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11, 35713594. Retrieved from https://www.jmlr.org/papers/volume11/watanabe10a/watanabe10a.pdf.

    • Search Google Scholar
    • Export Citation
  • Wei, Z., Han, L., Zhong, X., Liu, Y., Zha, R., Wang, Y., … Zhang, X. (2018). Chronic nicotine exposure impairs uncertainty modulation on reinforcement learning in anterior cingulate cortex and serotonin system. Neuroimage, 169, 323333. https://doi.org/10.1016/j.neuroimage.2017.11.048.

    • Search Google Scholar
    • Export Citation
  • Wen, Z., & Ye, B. (2014). Analyses of mediating effects: The development of methods and models. [中介效应分析:方法和模型发展]. Advances in Psychological Science, 22(5), 731745. https://doi.org/10.3724/SP.J.1042.2014.00731.

    • Search Google Scholar
    • Export Citation
  • Wiehler, A., & Peters, J. (2015). Reward-based decision making in pathological gambling: The roles of risk and delay. Neuroscience Research, 90, 314. https://doi.org/10.1016/j.neures.2014.09.008.

    • Search Google Scholar
    • Export Citation
  • Yao, Y. W., Wang, L. J., Yip, S. W., Chen, P. R., Li, S., Xu, J., … Fang, X. Y. (2015). Impaired decision-making under risk is associated with gaming-specific inhibition deficits among college students with Internet gaming disorder. Psychiatry Research, 229(1–2), 302309. https://doi.org/10.1016/j.psychres.2015.07.004.

    • Search Google Scholar
    • Export Citation
  • Yip, S. W., Gross, J. J., Chawla, M., Ma, S. S., Shi, X. H., Liu, L., … Zhang, J. (2018). Is neural processing of negative stimuli altered in addiction independent of drug effects? Findings from drug-naive youth with internet gaming disorder. Neuropsychopharmacology, 43(6), 13641372. https://doi.org/10.1038/npp.2017.283.

    • Search Google Scholar
    • Export Citation
  • Zarrindast, M. R., Nouri, M., & Ahmadi, S. (2007). Cannabinoid CB1 receptors of the dorsal hippocampus are important for induction of conditioned place preference (CPP) but do not change morphine CPP. Brain Research, 1163, 130137. https://doi.org/10.1016/j.brainres.2007.06.015.

    • Search Google Scholar
    • Export Citation
  • Zha, R., Bu, J., Wei, Z., Han, L., Zhang, P., Ren, J., … Zhang, X. (2019). Transforming brain signals related to value evaluation and self-control into behavioral choices. Human Brain Mapping, 40(4), 10491061. https://doi.org/10.1002/hbm.24379.

    • Search Google Scholar
    • Export Citation
  • Zha, R., Li, P., Liu, Y., Alarefi, A., Zhang, X., & Li, J. (2022). The orbitofrontal cortex represents advantageous choice in the Iowa gambling task. Human Brain Mapping. https://doi.org/10.1002/hbm.25887.

    • Search Google Scholar
    • Export Citation
  • Zhang, J. L., Hu, Y., Wang, Z. L., Wang, M., & Dong, G. H. (2020). Males are more sensitive to reward and less sensitive to loss than females among people with internet gaming disorder: fMRI evidence from a card-guessing task. BMC Psychiatry, 20(1) ARTN 357 https://doi.org/10.1186/s12888-020-02771-1.

    • Search Google Scholar
    • Export Citation
  • Zha, R. J., Tao, R., Kong, Q. M., Li, H., Liu, Y., Huang, R. Q., … Rao, H. Y. (2022). Impulse control differentiates Internet gaming disorder from non-disordered but heavy Internet gaming use: Evidence from multiple behavioral and multimodal neuroimaging data. Computers in Human Behavior, 130. ARTN 107184. https://doi.org/10.1016/j.chb.2022.107184.

    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

Indexing and Abstracting Services:

  • Web of Science [Science Citation Index Expanded (also known as SciSearch®)
  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
  • PsycINFO
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI
  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Sophia ACHAB (Faculty of Medicine, University of Geneva, Switzerland)
  • Alex BALDACCHINO (St Andrews University, United Kingdom)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Maria BELLRINGER (Auckland University of Technology, Auckland, New Zealand)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Damien BREVERS (University of Luxembourg, Luxembourg)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Julius BURKAUSKAS (Lithuanian University of Health Sciences, Lithuania)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Silvia CASALE (University of Florence, Florence, Italy)
  • Luke CLARK (University of British Columbia, Vancouver, B.C., Canada)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Geert DOM (University of Antwerp, Belgium)
  • Nicki DOWLING (Deakin University, Geelong, Australia)
  • Hamed EKHTIARI (University of Minnesota, United States)
  • Jon ELHAI (University of Toledo, Toledo, Ohio, USA)
  • Ana ESTEVEZ (University of Deusto, Spain)
  • Fernando FERNANDEZ-ARANDA (Bellvitge University Hospital, Barcelona, Spain)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Sally GAINSBURY (The University of Sydney, Camperdown, NSW, Australia)
  • Belle GAVRIEL-FRIED (The Bob Shapell School of Social Work, Tel Aviv University, Israel)
  • Biljana GJONESKA (Macedonian Academy of Sciences and Arts, Republic of North Macedonia)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Joshua GRUBBS (University of New Mexico, Albuquerque, NM, USA)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Zsolt HORVÁTH (Eötvös Loránd University, Hungary)
  • Susana JIMÉNEZ-MURCIA (Clinical Psychology Unit, Bellvitge University Hospital, Barcelona, Spain)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Chih-Hung KO (Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan)
  • Shane KRAUS (University of Nevada, Las Vegas, NV, USA)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Bernadette KUN (Eötvös Loránd University, Hungary)
  • Katerina LUKAVSKA (Charles University, Prague, Czech Republic)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Gemma MESTRE-BACH (Universidad Internacional de la Rioja, La Rioja, Spain)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Ståle PALLESEN (University of Bergen, Norway)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Michael SCHAUB (University of Zurich, Switzerland)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Hermano TAVARES (Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN (Ariel University, Israel)
  • Anise WU (University of Macau, Macao, China)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Dec 2023 0 331 96
Jan 2024 0 324 82
Feb 2024 0 271 62
Mar 2024 0 178 86
Apr 2024 0 108 63
May 2024 0 74 32
Jun 2024 0 0 0