Mapping Internet gaming disorder using effective connectivity: A spectral dynamic causal modeling study

Accepted Manuscript Mapping Internet gaming disorder using effective connectivity: A spectral dynamic causal modeling study

Min Wang, Hui Zheng, Xiaoxia Du, Guangheng Dong PII: DOI: Reference:

S0306-4603(18)30907-9 doi:10.1016/j.addbeh.2018.10.019 AB 5740

To appear in:

Addictive Behaviors

Received date: Revised date: Accepted date:

11 August 2018 25 September 2018 15 October 2018

Please cite this article as: Min Wang, Hui Zheng, Xiaoxia Du, Guangheng Dong , Mapping Internet gaming disorder using effective connectivity: A spectral dynamic causal modeling study. Ab (2018), doi:10.1016/j.addbeh.2018.10.019

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ACCEPTED MANUSCRIPT Mapping Internet gaming disorder using effective connectivity: a spectral dynamic causal modeling study

Min Wanga, Hui Zhenga , Xiaoxia Dub, Guangheng Donga,c,* [email protected] a

Department of Psychology, Zhejiang Normal University, Jinhua, P.R. China.

b Department

of Physics, Shanghai Key Laboratory of Magnetic Resonance, East

China Normal University, Shanghai, PR China. of Psychological and Brain Sciences, Zhejiang Normal University, Jinhua,

P.R. China. *Corresponding author

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c Institute

at: Department of Psychology, Zhejiang Normal University,

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688 Yingbin Road, Jinhua, Zhejiang Province, PR China.

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Abstract

Objects: Understanding the neural basis underlying Internet gaming disorder (IGD)

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is essential for the diagnosis and treatment of this type of behavioural addiction. Aberrant resting-state functional connectivity (rsFC) of the default mode network

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(DMN) has been reported in individuals with IGD. Since rsFC is not a directional analysis, the effective connectivity within the DMN in IGD remains unclear. Here,

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we employed spectral dynamic causal modeling (spDCM) to explore this issue.

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Methods: Resting state fMRI data were collected from 64 IGD (age: 22.6± 2.2) and 63 well-matched recreational Internet game users (RGU, age: 23.1± 2.5). Voxel-based mean time series data extracted from the 4 brain regions within the

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DMN (medial prefrontal cortex, mPFC; posterior cingulate cortex, PCC; bilateral inferior parietal lobule, left IPL/right IPL) of two groups during the resting state were used for the spDCM analysis.

Results: Compared with RGU, IGD showed reduced effective connectivity from the mPFC to the PCC and from the left IPL to the mPFC, with reduced self-connection in the PCC and the left IPL.

Conclusions: The spDCM could distinguish the changes in the functional architecture between two groups more precisely than rsFC. Our findings suggest 1

ACCEPTED MANUSCRIPT that the decreased excitatory connectivity from the mPFC to the PCC may be a crucial biomarker for IGD. Future brain-based intervention should pay attention to dysregulation in the IPL-mPFC-PCC circuits.

Key words: Internet gaming disorder; effective connectivity; default mode

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network; medial prefrontal cortex; spectral dynamic causal modeling.

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Introduction

With an increasing number of people losing control to online games, Internet

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gaming disorder (IGD) has been officially listed as a mental illness by the World Health Organization (ICD-11,

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https://icd.who.int/browse11/l-m/en#/http://id.who.int/icd/entity/144859723 4). With a high prevalence in adolescents (K. Chen, Oliffe & Kelly, 2018), IGD is a serious problem that must be addressed. Studies have proved that IGD could

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impair players’ self-inhibition and decision-making (Kuss, Pontes & Griffiths, 2018; Y. Wang, et al., 2017). The involved brain regions include the prefrontal and parietal

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cortex, medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and cortical-ventral striatum circuitry (C. Chen, et al., 2015; Dong & Potenza, 2016; L.

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Wang, et al., 2017).

Regarding the interactions between different brain regions involved in IGD, most of the current work has used resting-state functional connectivity (rsFC) analysis to examine the intrinsic brain network related to IGD (Dong, et al., 2018a; Liu, et al., 2018). The rsFC studies have focused on abnormal functional brain networks to further understand the neurobiological characteristics of the IGD, involving poorer response-inhibition and working memory, impairment of neuronal reward system, and decreased audiovisual functioning (Argyriou, Davison & Lee, 2017; Dong, Lin & Potenza, 2015; Kuss, Pontes & Griffiths, 2018; Spada & 2

ACCEPTED MANUSCRIPT Caselli, 2017). Specifically, adolescents with IGD were found decreased rsFC of dorsal prefrontal cortex (DLPFC) – caudate pathway, indicating a high correlation between the cognitive control deficits and the frontostrital rsFC strength (Yuan, et al., 2017). Evidence from the dynamic functional connectivity also showed abnormal changes in dynamic characteristics between the DLPFC and the insula in IGD individuals (X. Han, et al., 2018), which indicated that IGD and substance use

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disorder (SUD) share certain neural mechanisms (J. Han, et al., 2015). In addition,

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the rsFC of ventral tegmental area-medial orbitofrontal cortex pathway in IGD was considered as a potential neurobiological marker involved in impaired reward

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circuit (R. Wang, et al., 2018). The resting-state functional connectivity density (rsFCD) analysis demonstrated increased rsFCD among the brain regions involved

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in working memory and spatial orientation in IGD, reflecting the compensation

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mechanism for maintaining the normal behavioral performance (Du, et al., 2017).

Nowadays, increasing numbers of studies found that the Default Mode

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Network (DMN) also played important roles in addiction (Bae, et al., 2018). The DMN is a unique brain connection region that preferentially activates when the

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individual is concerned with the inner world but not outside (Buckner, Andrews-Hanna & Schacter, 2008; Raichle, et al., 2001). Although the DMN usually

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refers to brain regions with high synchrony during the resting state, it also participates in important cognitive functions (e.g., introspection, emotional process and social cognition) (Andrews-Hanna, et al., 2010; Sheline, et al., 2010). The PCC and mPFC are important components of the DMN, which act as the brain network hubs (Raichle, 2015). Recent studies suggested that the IGD demonstrated decreased rsFC between the DMN-related regions (PCC, mPFC, precuneus) compared with healthy controls (Bae, et al., 2018; L. Wang, et al., 2017). Our recent study investigated the correlations between IGD individual mood states and rsFC among the DMN, which found that the negative mood states are associated with poorer rsFCs among the DMN, which may be a useful indicator for differentiating 3

ACCEPTED MANUSCRIPT IGD (Dong, et al., 2017). In addition, the decreased rsFC between the PCC and supplementary motor area may be neurological evidence of the efficacy of behavioural interventions for IGD (J. Zhang, et al., 2016). The role that the DMN plays in addiction has been widely discussed, as functional connectivity provides a good method for exploration (Bae, et al., 2018; Dong & Potenza, 2016). However, traditional functional connectivity evaluates the Pearson correlations among

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regions of interest (ROI) based on time series. This analytical approach could not

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answer questions about causal or directed connectivity among ROIs. Therefore, the

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interactions among the DMN regions involved in IGD remain unclear.

Dynamic causal modeling (DCM) could address this limitation. DCM is an

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analytic technique that is modeled at the neuronal level (Friston, Harrison & Penny, 2003). Therefore, it has the capacity to prompt the directional connectivity

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(effective connectivity) among functionally related brain areas, compared with rsFC (Marreiros, Kiebel & Friston, 2010). DCM could solve such problems, for

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example, when there are two hypothetical neural regions (A and B), whether A directionally affects B, B directionally affects A, or both reciprocally affect each

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other (Ma, et al., 2015a). Effective connectivity analysis might play a more important role in exploring changes between patients and healthy controls than functional connectivity, as it could provide a better understanding of

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interpretability (Geng, et al., 2018). Currently, DCM is widely used in SUD studies, including cocaine, tobacco, and alcohol (Ma, et al., 2015a; Ray, Di & Biswal, 2016; Tang, et al., 2016). Studies have proposed that certain neurological characteristics are shared between SUD and behavioural addiction (L. Wang, et al., 2017). Until now, only one effective connectivity study has evaluated Internet addiction. This study investigated the neuronal pathways associated with the response inhibition of Internet addiction by stochastic DCM analysis, elicited under the performance of a Go-Stop paradigm. The DCM analysis revealed that the indirect frontal-basal ganglia pathway was engaged by response inhibition in healthy individuals, but there is no equivalent effective connectivity in Internet addiction subjects (B. Li, et 4

ACCEPTED MANUSCRIPT al., 2014). From what we discussed, effective connectivity should be used to study the neural mechanism of IGD. As an extension of DCM at resting state, spectral dynamic causal modeling (spDCM) (Friston, 2014a, 2014b) estimates effective connectivity based on coupled brain regions in the frequency domain, and therefore it has a high computational efficiency. Furthermore, spDCM is more efficient than stochastic DCM and more sensitive to the differences between groups,

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which is also an important reason for the use of spDCM in this study (Razi, et al.,

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2015).

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Finally, it was also worth noting that the selection of control groups in this study. Previous IGD studies mainly selected healthy individuals with low frequency

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or even no games as control groups. In the this study, we included the recreational game users (RGU) as a control group, which could overcome the limitations of

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previous studies using low frequency game players as controls. The RGU was a special group that had more gaming experience and a longer game duration than

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the general control group but did not lose control of the game, which increases the reference meaning of the RGU (Kuss & Griffiths, 2012). Several studies in the field

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of SUD employed recreational drug users as control groups and observed a unique activation pattern (Parrott, 2013; Smith, et al., 2014). Our recent studies used RGU as a control group and found some functional and structural abnormalities in IGD

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that were different from previous studies using the general control group (Y. Wang, et al., 2017; Z. Wang, et al., 2018). Since addiction is an incremental process, recreational users may fall between addicts and healthy individuals (Kuss & Griffiths, 2012). Using RGU in the current research may provide a better understanding of the brain mechanism that prevents RGU from developing IGD (Dong, et al., 2018b).

In the current study, our main goal was to explore the effective connectivity within the DMN network, which is the most active network in the resting state (Du, et al., 2017). Many studies have revealed that the core regions of the DMN could 5

ACCEPTED MANUSCRIPT also be used to observe aberrant changes in task studies using IGD (L. Wang, et al., 2016; Yuan, et al., 2016). Thus, in the current study, we sought to evaluate the effective connectivity among four key regions within the DMN: the mPFC, the PCC, the left inferior parietal lobule (left IPL), and the right inferior parietal lobule (right IPL). Then, spDCM was applied to resting state fMRI data to quantify the effective connectivity among core regions implicated in IGD. Then, the model parameters

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within the DMN regions were compared at the group level. Given that this is the

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first exploratory study of the effective connectivity of IGD during the resting state and based on research on connectivity conducted by SUD (Ray, Di & Biswal, 2016;

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Tang, et al., 2016), we hypothesized that IGD showed a decreased effective connectivity pattern within the DMN as a result of chronic Internet gaming use

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during the resting state when comparing to RGU.

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Methods Participants

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The study was approved by the Human Investigations Committee of Zhejiang Normal University and conformed to the Declaration of Helsinki. All subjects

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signed informed consent forms before the experiment. We recruited 127 subjects (63 RGU and 64 IGD) by advertisements. The participants underwent assessment using structured psychiatric interviews (MINI) (Sheehan, et al., 1998), and those

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with other psychiatric disorders (e.g., depression, anxiety, schizophrenia and SUD) were excluded. The Beck Depression Inventory (Goldschmidt, 2008) was used to identify depression, excluding participants with more than 5 scores. All participants were university students with normal or corrected-to-normal vision. All participants were free of illegal drug use and did not consume any addictive substances (e.g., coffee, alcohol and tobacco) on the day of scanning. Demographic information for all subjects is shown in Table 1. The age, education, beck depression inventory score, and sex (2 = 1.037, p = 0.309) between two groups were matched. 6

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Insert Table 1 here

Addiction screening criteria were based on 2 items: the Internet addiction test (IAT) by Young (Chang & Law, 2008) and the DSM-5 (Petry, et al., 2014) proposed nine-item diagnostic criteria. The inclusion criteria for IGD are as follows: 1) more

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than 60 points in the IAT test (Dong, et al., 2018c; Lin, et al., 2015); 2) score greater

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than 5 points in DSM-5. The inclusion criteria for RGU were similar to IGD, but included a 1) score lower than 40 points in the IAT test and a 2) score less than 4

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points in the DSM-5 test. 3) Online game time more than 2 hours per day, and total game duration more than 2 years (to control the duration and frequency they used

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in the current days).

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Image acquisition

Resting-state fMRI (rs-fMRI) requires subjects to complete the examination at

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rest, without any structural thinking activity (van den Heuvel & Pol, 2010). Since it is spontaneous activity, rs-fMRI has less interference compared to task state-fMRI,

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resulting in a higher signal-to-noise ratio (Shmueli, et al., 2007). The rs-fMRI functional data (T2*-weighted images) were acquired using a 3T Siemens Trio MRI scanner at East China Normal University. Specific parameters are as follows:

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repetition time = 2000ms, interleaved 33 axial slices, echo time = 30ms, thickness = 3.0 mm, flip angle = 90°, field of view (FOV) = 220mm × 220mm, matrix = 64 × 64. Participants kept their eyes closed and were instructed not to think of anything in particular during scanning. To minimize head movement, the subject’s head was fixed by foam padding. Each fMRI scan lasted 7-min, including 210 imaging volumes. Data Pre-processing Pre-processing was conducted with SPM8 (Statistical Parametric Mapping, https://www.fil.ion.ucl.ac.uk/spm/software/spm8/) based on a MATLAB toolbox. 7

ACCEPTED MANUSCRIPT For each participant, the first 10 volumes of each participant were discarded to minimize the instability of the initial signal. The subsequent process included slice timing to correct for time differences, head-motion correction and spatial normalization by standard EPI template. Finally, a Gaussian filter of 6 mm full-width at half-maximum (FWHM) was applied to the dataset for spatial smoothing. Data with head motion exceeding 2.0 mm of maximum translation and

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2.0° of maximum rotation during the scanning process were excluded from the

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further analysis (Fox & Raichle, 2007).

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ROI Selection

The aim of the current study was to explore the effective connectivity

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differences between IGD and RGU (Kuss, Pontes & Griffiths, 2018) during the resting state. After collecting rs-fMRI pre-processed data from individuals with IGD

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and matched RGU, our first objective was to determine the ROI. Since no tasks were involved, the selection of ROIs in the resting state depended on our prior

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knowledge (Friston, et al., 2014a; Tang, et al., 2016).

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The nodes for the spDCM analysis in the present study were selected based on the following 2 criteria: (1) the node can be anatomically defined as part of the DMN (Crone, et al., 2015; Raichle, et al., 2015; Tang, et al., 2016); (2) the node must

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be regarded to be involved in IGD in previous literature (Choi, et al., 2018; Dong, et al., 2017; Y. Wang, et al., 2017). Thus, we identified four key nodes in the DMN region, including the mPFC, PCC, and left/right IPL for effective connectivity analysis (Tang, et al., 2016). For each participant, 4 identical nodes (e.g. the mPFC, PCC and bilateral IPL) were selected. For each node, a mask was created using the WFU PickAtlas Tool based on the SPM toolbox (http://uvasocialneuroscience.com/doku.php?id=uva_socia:wfu_pickatlas), including the PCC, Medial Frontal Gyrus, Parahippocampal Gyrus, Broadman areas 19 and 39 (Jann, et al., 2010; Di & Biswal, 2014). To extract BOLD fMRI time series corresponding to the aforementioned 4 nodes, the pre-processed data was used to 8

ACCEPTED MANUSCRIPT establish the residuals of a General Linear Model (GLM) (Fransson, 2005; Kahan, et al., 2014). Six head motion parameters and WM/CSF signals were added to the model as nuisance regressors. A high-pass filter was also used to remove possible ultraslow fluctuations (< 0.0078 Hz). We did not remove frequencies above 0.1 Hz, as they were confirmed to contain meaningful information in resting-state research (F. Lin, et al., 2015). Then, we selected the MNI coordinates of nodes that have been

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previously reported in the DMN: the mPFC [3, 54, -2], PCC [0, -52, 26], left IPL [-50,

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-63, 32] and right IPL [48, -69, 35] (Di & Biswal, 2014; Tang, et al., 2016; Razi, et al., 2015), as the (8 mm radius) centre of the sphere to compute the principal

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nodes with the corresponding time series.

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eigenvariate and correct for confounds. Fig.1 illustrates the location of the four

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Insert Fig.1 here

Spectral dynamic causal modeling

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The spDCM analysis was performed by DCM 12 implemented in SPM 12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). After extracting the

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mean-corrected time series values of all nodes, we assumed that all participants used the same model and specified a "full" model for each subject. Here, "full" means that each node was assumed to be connected to all other nodes (24 = 16

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connectivity parameters, including 4 intrinsic self-connections) (Ma, et al., 2015b). Unlike DCM under tasks, there were no experimental conditions in the model, i.e., the parameters of drive input and modulation input quantized by the B and C matrix were zero. In addition, this study selected a linear model, and the D matrix parameters were also set to zero. Therefore, the spDCM only contained endogenous connectivity and was quantified by A matrix parameters (Friston, et al., 2014a; Ray, Di & Biswal, 2016). The next step was Model Estimation based on standard variational Bayes procedures (variational Laplace) under the frequency domain. The convolution kernel of the model was converted into a spectrum and expressed in the frequency domain. This approximate Bayesian inference method 9

ACCEPTED MANUSCRIPT can quickly estimate the connectivity parameters and logarithmic model evidence of first-level DCMs and effectively optimize the posterior probability of model parameters (Friston, et al., 2014b). After estimating all "full" models, we employed a DCM network discovery (DND) routine based on Bayesian model selection to conduct DCM structure inference at the group level. The routine would implement a greedy search for all possible model connectivity parameters (28 = 256 reduced

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model space), and the model with the highest posterior probability was selected as

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the optimal model (Ma, et al., 2015b; Ray, Di & Biswal, 2016). Then, Bayesian parameter averaging (BPA) was adopted to obtain optimal sparse model

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parameters at the group level separately. For more fundamentals of spDCM analysis,

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please refer to the related papers of Friston et al. (2014a)

Statistical Analysis

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The demographic characteristics of the subjects as well as of the connectivity strength of the model were statistically analyzed using R (www.R-project.org). In

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order to examine changes in effective connectivity within the DMN between two groups, two-sample t-test was used to analyze the coupling parameters between

Results

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groups, with age, education and sex as covariate.

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DND compared 256 reduced model spaces to find the best model for 2 groups, separately. Fig.2 shows the DND results for IGD and RGU. The left column A represents the IGD and the right column B represents the RGU. The upper left column of A (corresponding to B) shows the distribution of log Bayesian model evidence for reduced models. The posterior probability distribution of 256 reduced models is given in the upper right column of A (corresponding to B). In both groups, comparing the log Bayesian model evidence for 256 reduced models, the "full" model wins in comparison and is considered to be the optimal model with a posterior probability of almost 1.

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The lower panel of column A (corresponding to B) shows the Bayesian parameter averaging model of IGD after DND. On the horizontal scale, there are 16 connectivity parameters ("full" model) that reflect the connection strength (Hertz). The horizontal axis represents the target area (i.e., the PCC, left IPL, mPFC and right

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IPL) of 16 connectivity parameters, while the colour represents the source area.

After obtaining BPA models, we performed a two-sample t-test (subtract the

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BPA of RGU from the IGD) to identify which averaged connectivity parameters in IGD were significantly different from RGU. The differences between groups are

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shown in Table 2. For the 4 self-connections, we observed that the intrinsic self-connections of the PCC (t = -3.299, p = 0.001, Bonferroni Corrected) and the left

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IPL (t = -2.505, p = 0.014, uncorrected) were inhibitory and reached significance after the t-test. Since the self-connections of nodes always showed inhibitory

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effects (see Fig.2), this means that the responses of the PCC and the left IPL in IGD were disinhibited compared to RGU. We further determined that the two extrinsic

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connections involving the mPFC showed significant differences. One was from the mPFC to the PCC (t = -2.033, p = 0.044, uncorrected) and the other was from the left IPL to the mPFC (t = -2.373, p = 0.019, uncorrected). In terms of the RGU, the

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connection from the mPFC to the PCC had almost no directional influences (BPA = -0.004), while for IGD, the connection from the mPFC to the PCC was inhibitory (see Fig.2). This indicates that IGD further reduced the connectivity from the mPFC to the PCC compared to RGU. Similarly, from the left IPL to the mPFC, IGD again showed this reduction in connectivity, which indicated that the coupling between the mPFC and the left IPL was unbalanced, which themselves became disinhibited. Differences between groups are shown in Fig.3.

Insert Table 2 here

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ACCEPTED MANUSCRIPT Insert Fig.3 here

Discussion In this work, we used sizable samples (63 RGU and 64 IGD) to study how IGD affects regional interactions within the DMN and compared the effective

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connectivity among 4 core brain regions within the DMN in IGD to RGU during the resting state. The spDCM based on spectral domain analysis was employed to

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examine effective connectivity. This analytical approach revealed changes in

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information flow among these 4 key nodes within the DMN of IGD. Our findings

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suggest abnormalities in effective connectivity among the DMN regions in IGD.

The DMN could be observed in primates and rodents (Mantini, 2011, 2013)

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which made it one of the ancient systems that have continued in evolutionary process (Mantini & Vanduffel, 2013). The main regions of the DMN include the PCC, mPFC, IPL and precuneus. In the past few decades, the DMN has been shown to

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play a major role in the distinction of neurological and psychiatric disorders (Geng, et al., 2018). The DMN is involved in important cognitive functions, including

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introspection, self-monitoring, and emotional processing (Andrews-Hanna, et al., 2010). The majority of IGD studies that combine rs-fMRI with ROI analysis report the results of functional connectivity within the DMN (Sheline, et al., 2010), which

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reflects the statistical properties (i.e., temporal correlation) among functional brain regions. However, correlation results cannot answer the question of whether a neural region directionally influences another. Based on this, we used spectral dynamic causal modeling analysis to investigate the directional relationships among different brain regions.

Our research observed the changes in regional interactions in the neural pathways of the left IPL-mPFC-PCC, showing a negative value. The negative value of the connectivity strength (ie., the rate constant of neuron response measured in Hz) 12

ACCEPTED MANUSCRIPT in the model was usually interpreted as inhibitory (Friston, et al., 2014a). Current findings reflected increased directional inhibition of the left IPL-mPFC-PCC neural pathway of IGD. Among them, IGD showed reduced coupling from the mPFC to the PCC than RGU. Given that the PCC has been shown to be a robustly driven hub that links to major brain structures and receives information from the whole brain (Hagmann, et al., 2008; Zuo, et al., 2012), the dysregulation from the mPFC to the

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PCC may be a potential biomarker to distinguish IGD from healthy individuals. As a

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hub connecting the DMN and task-related networks, the PCC is indispensable for attention and self-monitoring, as it involves the coupling of executive control and

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the coordination of the endogenous activity of the brain during the resting state (Kim, et al., 2015; Sharaev, et al., 2016). Functional connectivity analysis suggested

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that connectivity between the mPFC and the PCC was associated with self-relevant cognitive behaviours (Hong, et al., 2018; Peeters, et al., 2015). Previous studies

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found the abnormality of rsFC between the mPFC and the PCC in IGD, which suggested that the altered connectivity in IGD was associated with excessive

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Internet gaming and deficits in self-reference (Hong, et al., 2018). However, these studies did not further clarify the question of whether effective connectivity

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between the mPFC and the PCC was dysfunctional. Here, we applied spDCM for the first time to show the reduction of the effective connectivity from the mPFC to the PCC in IGD, reflecting the abnormality of dynamic interactions among the brain

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regions involved, expanding previous empirical findings. In addition, IGD also showed that the left IPL reduced the efferent coupling to the mPFC. In previous spDCM studies, the IPL is described as the necessary node to provide information input. In IGD individuals (X. Zhang, et al., 2018), the IPL is involved in attentional bias towards rewards and related cues (J. Zhang, et al., 2016), which might lead IGD to pursue high returns in the decision-making process regardless of risk (Liu, et al., 2017). Our findings are consistent with previous studies: the dis-inhibition and decreased efferent coupling of the left IPL reflected the changes in individual attentional bias during long-term Internet gaming (Liu, et al., 2017). These results might reveal the potential biomarkers of diagnosis and critical pathways in IGD 13

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Compared to RGU, IGD showed a reduction in afferent coupling (the left IPL-the mPFC) and efferent coupling (mPFC-PCC) of the mPFC. When integrating stimuli from different sources, the mPFC involves the function of switching, thereby supporting cognitive flexibility (Davey, et al., 2017). The rsFC analysis

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demonstrated that value signals in the ventromedial the mPFC might integrate

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information input from the complex network (Hare, et al., 2010). The above evidence seems to suggest that the mPFC is a key node in our model. Previous

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studies have reported that addicts, such as smoking, alcohol, and cocaine addicts, showed functional and structural abnormalities in the mPFC (Canterberry, et al.,

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2016; Hasler, et al., 2017; B. Li, et al., 2014; W. Li, et al., 2015). For example, the mPFC exhibited inactivation in cocaine-dependent groups when responding to

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neutral stimuli (Canterberry, et al., 2016). In addition, when IGD adolescents were thinking of their game characters, the mPFC involving self-reflection and

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metallizing processes showed dysfunction, suggesting that the game characters are more likely to be associated with self (Choi, et al., 2018). The mPFC is a complex

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region involving multiple cognitive functions, including self-evaluation and monitoring (Choi, et al., 2018; Hasler, et al., 2017). Wang et al. (2017) found that individuals with Internet addiction showed lower functional connectivity in the

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dorsal the mPFC of the anterior DMN by independent component analysis. In the present research, we speculated that during the resting state, decreased effective connectivity from the mPFC to the PCC within the DMN in IGD may reflect the negative self-assessment of continuous Internet gaming use. In a stochastic DCM study of depression, the authors (Davey, et al., 2017) noted that the mPFC acts to direct self-related processes by modulating PCC activity, suggesting that the mPFC has a “hyper-regulatory” influence in disease.

In the current study, compared to RGU, IGD showed increased inhibitory connectivity in the neural pathways of the left IPL-mPFC-PCC, and dis-inhibition of 14

ACCEPTED MANUSCRIPT both the PCC and left IPL (see Fig.3). In previous spDCM studies, the IPL provides information input for other nodes (X. Zhang, et al., 2018) while the PCC plays the role of information gathering from other DMN regions (Sharaev, et al., 2016). The mPFC is the core node for modulating PCC activity (Davey, et al., 2017). However, current results themselves indicated that regional interactions within the DMN were abnormal in IGD, involving the medial prefrontal cortex, parietal cortex, and

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limbic system. The effective connectivity may be more accurate for exploring

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changes between the patient and healthy controls than functional connectivity (Geng, et al., 2018). More specifically, IGD showed that the mPFC has reduced

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sensitivity to afferent coupling from the IPL. Meanwhile, the PCC also reduces the sensitivity to afferent coupling from the mPFC. These findings are consistent with

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previous research on IGD (Bae, et al., 2018; L. Wang, et al., 2017). In fact, IGD individuals often exhibit dysfunction of the fronto-cingulo-parietal network and

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decreased spontaneous activity in the DMN (Dong, Lin & Potenza, 2015), and after intervention, the activation of related functional areas is improved (D. Han, et al.,

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2012; Park, et al., 2016). In addition, a study on tobacco dependence using spDCM analysis found the disinhibition of the PCC and IPL in smokers (Tang, et al., 2016),

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which suggested that IGD and substance addiction might share certain neurological characteristics. the DMN is an indispensable network for coordinating the brain’s endogenous activity. Since endogenous activity is known to play a major role in

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neurological and psychiatric disorders as well as overall brain health (D. Zhang & Raichle, 2010), future IGD interventions should consider the abnormalities of effective connectivity within the DMN.

Limitations First, although we investigated IGD between recreational game users and addicted game users, there is no normal control group in this study. Incorporating healthy individuals into this study may better explain the effective connectivity changes from non-IGD to IGD. Second, since our subjects are mainly college students, the conclusions are only limited to addicted college students, which may 15

ACCEPTED MANUSCRIPT not be applicable to other ages of IGD individuals. Third, we have attempted to examine the correlations between effective connectivity with significant differences and severity of IGD (IAT scores), but even the results with highest correlation coefficient did not reach a significant level (effective connectivity f rom mPFC to PCC: r = 0.113, p = 0.205). It may be due to insufficient sample size, more consideration should be given to this in future neuroimage research. Finally, due to

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the limitation of the resting state that only allowed us to select ROI based on prior

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knowledge, we may have neglected other meaningful areas. In addition, the radius and location of ROIs are also extraneous variables that probably influence results.

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task state and pay attention to the above issues.

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In future work, we will explore the changes of effective connectivity in IGD under a

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Conclusions

Using spDCM, the current study found that IGD showed reduced effective

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connectivity from the mPFC to the PCC and from the left IPL to the mPFC, with reduced self-connection in the PCC and the left IPL when compared with RGU. IGD

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exhibits a regional interaction distinct from RGU in the neural pathway of the left IPL-mPFC-PCC, which may shed light on the future understanding of brain mechanisms for IGD. Future brain-based interventions should consider the

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dysregulation in the left IPL-mPFC-PCC circuits.

ACKNOWLEDGMENTS Dr. Dong was supported by the National Science Foundation of China (31371023)

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ACCEPTED MANUSCRIPT COMPETING INTERESTS The authors declare that no competing interests exist.

Authors’ contributions Min Wang analysed the data and wrote the first draft of the manuscript. Xiaoxia Du and Hui Zheng contributed to fMRI data collection. Guangheng Dong

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designed this research and edited the manuscript. All authors contributed to and

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approved the final manuscript.

Reference

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Fig.1: Illustration of four nodes within the DMN.

The nodes included the medial frontal cortex (mPFC), the posterior cingulate

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cortex (PCC), the left inferior parietal lobule (lIPL), and the right inferior parietal lobule (rIPL). Corresponding time-series are the principal eigenvariates of regions.

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Fig.2: Results of DCM Network Discovery (DND) for two groups. This figure shows the results of employing the DND routine. Column (A) represents

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IGD and column (B) represents RGU. The upper two panels of column (A) (corresponding to column B) show the log-posterior of all reduced models and the

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posterior probabilities of all evaluated models. The "full" model is the best model, with a posterior probability of almost one. The lower graph of column (A) (column B, respectively) represents the Bayesian parameter average (BPA) of the IGD (corresponding to RGU). The horizontal axis represents the target regions of the 16 model parameters, and the colour represents the source area.

Fig.3: Differences between groups This figure shows the Bayesian parametric average (BPA) differences of the two groups, including 2 extrinsic connections and 2 intrinsic connections. The left panel shows the difference when subtracting the BPA of RGU from the IGD. The 22

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right panel shows the effective connectivity with significant differences in the IGD.

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ACCEPTED MANUSCRIPT Table 1. Demographic information and group differences IGD (n=64) Male=36, Female=28 22.577±2.181 69.906±8.131 6.096±1.660 8.365±3.951 14.1±2.42 2.33±1.89

t -1.078 34.890 17.486 4.212 -0.832 0.470

p 0.284 0.000 0.000 0.000 0.407 0.640

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Age (mean±SD) IAT score (mean±SD) DSM-5 score (mean±SD) Game playing hours (mean±SD) Educations (years) (mean±SD) BDI

RGU (n=63) Male=41, Female=22 23.085±2.509 28.508±4.789 1.277±0.949 5.277±2.268 14.4±1.54 2.17±1.95

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GD: Internet gaming disorder; RGU: recreational game user; IAT: Internet addiction test; DSM-5: Diagnostic and Statistical anual of Mental Disorders-5; BDI: Beck Depression Inventory.

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ame playing hours: Game playing refers to how many hours per week

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ACCEPTED MANUSCRIPT Table 2. Parameter estimates for each group and their comparison a Model parameters

IGD(64) Mean

c

RGU(63) t

Mean

c

IGD vs. RGU t

t

p

Extrinsic Connectivity -0.012

-0.225

0.008

0.135

-0.250

0.803

mPFC→PCC rIPL→PCC

-0.166 0.050

-3.087* 0.879

-0.005 0.091

-0.083 1.321

-2.033 -0.450

0.044 0.653

PCC→lIPL

-0.076

-0.986

-0.086

-1.077

0.095

0.924

mPFC→lIPL

0.032

0.412

-0.031

-0.418

0.586

0.559

rIPL→lIPL PCC→mPFC lIPL→mPFC

-0.043 -0.039 -0.085

-0.677 -0.643 -1.574

0.082 -0.078 0.103

1.182 -1.279 1.774

-1.328 0.455 -2.373

0.187 0.650 0.019

rIPL→mPFC

0.026

0.439

0.026

0.434

0.003

0.998

PCC→rIPL lIPL→rIPL

-0.029 0.007

-0.415 0.121

-0.098 0.004

-1.200 0.061

0.649 0.036

0.518 0.971

mPFC→rIPL

-0.039

-0.556

0.046

0.727

-0.899

0.370

Self-Connectivity PCC→PCC

-0.818

-11.102***

-0.478

-6.622***

-3.299

0.001b

lIPL→lIPL

-0.884

-12.715***

-0.649

-10.362***

-2.505

0.014

mPFC→mPFC rIPL→rIPL

-0.862 -0.717

-12.742*** -10.479***

-0.819 -0.808

-12.609*** -12.341***

-0.458 0.965

0.648 0.336

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prefrontal cortex; PCC=posterior cingulate cortex; IPL=inferior parietal lobule. Significance indicated by asterisks is based on one-sample t tests. b Two-sample t test,

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a mPFC=medial

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lIPL→PCC

p<0.05, Bonferroni correction.

c Bayesian parameter average (BPA)

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*p<0.01; **p<0.001; *** p<0.0001. The parameters are tested by one-sample t-test to identify significant

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effective connectivity in each group compared to random effects.

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ACCEPTED MANUSCRIPT Highlights

 Using spDCM explore changes in directional connectivity in IGD.  Selecting RGU as control group overcome the limitations of low frequency

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game.

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 the number of subjects is sizeable to make the results more scientific.

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 IGD show dysregulation in the left IPL-mPFC-PCC circuits.

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Figure 1

Figure 2

Figure 3