Altered topological connectivity of internet addiction in resting-state EEG through network analysis

Accepted Manuscript Altered topological connectivity of internet addiction in restingstate EEG through network analysis

Yan Sun, Hongxia Wang, Siyu Bo PII: DOI: Reference:

S0306-4603(18)30893-1 https://doi.org/10.1016/j.addbeh.2019.02.015 AB 5913

To appear in:

Addictive Behaviors

Received date: Revised date: Accepted date:

11 August 2018 17 January 2019 14 February 2019

Please cite this article as: Y. Sun, H. Wang and S. Bo, Altered topological connectivity of internet addiction in resting-state EEG through network analysis, Addictive Behaviors, https://doi.org/10.1016/j.addbeh.2019.02.015

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Altered topological connectivity of internet addiction in resting-state EEG through network analysis Yan Sun

1,2*

1

, Hongxia Wang , Siyu Bo

1

1

School of psychology, Liaoning Normal University, Da Lian, China

2

Liaoning Collaborative Innovation Center of Children and Adolescents Healthy Personality

PT

Assessment and Cultivation, Da Lian, China

RI

* Correspondence: Corresponding Author: Yan Sun

SC

email: [email protected]

Abstract

AC C

EP T

ED

MA

NU

The results of some neuroimaging studies have revealed that people with internet addiction (IA) exhibit structural and functional changes in specific brain areas and connections. However, the understanding about global topological organization of IA may also require a more integrative and holistic view of brain function. In the present study, we used synchronization likelihood combined with graph theory analysis to investigate the functional connectivity (FC) and topological differences between 25 participants with IA and 27 healthy controls (HCs) based on their spontaneous EEG activities in the eye-closed resting state. There were no significant differences in FC (total network or sub-networks) between groups (p>0.05 for all). Graph analysis showed significantly lower characteristic path length and clustering coefficient in the IA group than in the HC group in the beta and gamma bands, respectively. Altered nodal centralities of the frontal (FP1 , FPz) and parietal (CP1 , CP5 , PO3 , PO7 , P5 , P6, TP8 ) lobes in the IA group were also observed. Correlation analysis demonstrated that the observed regional alterations were significantly correlated with the severity of IA. Collectively, our findings showed that IA group demonstrated altered topological organization, shifting towards a more random state. Moreover, this study revealed the important role of altered brain areas in the neuropathological mechanism of IA and provides further supportive evidence for the diagnosis of IA. Keywords : internet addiction, graph theory, synchronization likelihood, resting state EEG

1

functional

connectivity,

ACCEPTED MANUSCRIPT 1 Introduction

AC C

EP T

ED

MA

NU

SC

RI

PT

Internet addiction (IA) is usually defined as an individual’s inability to control his or her internet use, which may lead to the deve lopment of addictive symptomology, functional impairment, and comorbidity in some users (Lopez-Fernandez, 2015). Converging evidence has indicated that IA is related to higher impulsivity (Ding et al., 2014; Sariyska et al., 2017), attention deficit (Kim e t al., 2017a) and reduced self-esteem (Nie et al., 2016; Yücens & Üzer, 2018). Multiple similarities have been observed in the behavioural studies of IA and other addictions. Both subjects with internet gaming disorder (IGD) and subjects with pathological gambling behaviour exhibit enhanced impulsive choice behaviour and an aberrant reward mechanism (Fauthbühler & Mann, 2015). Individuals with IA are more impulsive, and this impulsive trait has been observed in several substance and behavioural addictions, including food addiction (Omar et al., 2016), drug addiction (Fattore & Melis, 2016), alcohol addiction (Asensio et al., 2016), and gambling disorder (Choi et al., 2014). Recently, studies have explored the cognitive neurological characteristics of IA with the aim of understanding the mechanism of altered behaviours caused by IA. Accumulating evidence has demonstrated that the alterations of frontal and parietal lobes are frequently associated with those who suffer from IA (Hong et al., 2013a; Liu et al., 2013; Wang et al., 2017). Excessive use of the internet has been related to various negative social and psychological problems. Such as, the relationship between self-esteem and IA has been established by various behavioural studies, which have reported that greater degrees of IA are associated with lower self-esteem (Farah et al., 2016; Nie et al., 2016). Low self-esteem seems to contribute to depressive symptoms (Hilbert et al., 2018), addicted personality, sense of loss of control and a sense of failure (Yücens & Üzer, 2018), and even suicidal ideation (Wang et al., 2018b). A number of studies have examined the relationship between altered brain mechanisms of IA and impulsive, craving, and depression behaviours (Ding et al., 2014; Ko et al., 2009; Park et al., 2017). However, the relationship between the intrinsic connection pattern and topological organization of IA and self-esteem is still unclear. Resting-state spontaneous neural activities are increasingly considered to be related to behaviour and cognition (Barry et al., 2009). In this study, we used Electroencephalography (EEG) and graph theory to measure spontaneous brain activity of IA in the resting state and systemically explored the relationship between the intrinsic neural activity and the self-esteem and the severity of IA, respectively. EEG has been employed to identify the neural mechanisms of IA, which has revealed that altered band power can be used as a neurobiological marker of IA (Kim et al., 2017b; Son et al., 2015; Wang & Griskova-Bulanova, 2018a). Resting-state EEG has proven to be a powerful tool for studying whole-brain neural mechanisms (Babiloni et al., 2015; González et al., 2016; Kim et al., 2017b). Resting-state measure is thought to index the intrinsic properties of brain functional organization (Fox & Raichle, 2014). It enables to detect the underlying neuronal circuitry alterations in numerous addiction studies, and can serve as a helpful diagnostic tool (Han et al., 2017; Hong et 2

ACCEPTED MANUSCRIPT

PT

al., 2018; Hua et al., 2018; Sutherland et al., 2012; Yu et al., 2017). Various models combined with graph theory have been applied to study IA neural mechanisms, and different conclusions have been proposed. Some previous studies concluded that the network topology alterations of IA were tiny, significant changes were only observed for regional nodal metrics, there was no difference in global network topology between the IA and HC groups (Lee et al., 2017; Wee et al., 2014). In another study, the IGD was characterized by a less global integrated network organization with decreased efficiency and increased shortest path length (Zhai et al., 2016). Thus, whether IA will cause regional and global network topology changes still need to be further explored.

ED

MA

NU

SC

RI

The extent to which prolonged internet using ‘damage the brain’ or ‘boost the brain power’ is uncertain. A few studies have reported that long-term internet activities can be expected to drive positive neurological changes in the brain system (Bavelier et al., 2011; Han et al., 2017; Liu et al., 2010). But, on the other hand, there are also opinions that the brain addicted to internet is considered to be in an abnormal state (Ding et al., 2013; Fauthbühler & Mann, 2015; Hong et al., 2018; Park et al., 2015). Park et al. (2015) applied graph-theoretical approach suggested that internet gaming addiction induced brain functional networks to shift toward the random topological architecture from the network perspective. IA as a behavioral addiction that was considered to share similar neurobiological abnormalities with substance addiction (Ding et al., 2013). Using graph theoretical analysis, studies revealed the brain network in heroin-dependent individuals and young smokers may shift towards a random network (Zhang et al., 2016, 2017). Therefore, one of the hypotheses of this study is that the brain topological organization in IA may tend to be more random.

AC C

EP T

The present study explored differences in brain networks derived from resting-state EEG between IA and HC groups as well as whether the differences are correlated with behavioural measures. We hypothesized that the IA subjects would show altered functional connectivity (FC) and topological patterns compared with the HC subjects. More specifically, on a functional level, we hypothesized that activities in the frontal and parietal brain regions differ between the IA and HC groups and that the brain network in IA subjects would tend to be more random. Finally, we tested whether t he graph-based values of altered brain regions were related to the Internet Addiction Test (IAT) and Rosenberg Self-Esteem Scale (RSES) scores, respectively. 2 Methods and materials 2.1 Participants Twenty-five IA (6 males and 19 females) and twenty-seven matched HC participants (4 males and 23 females) were included in this study. The exclusion criteria were as follows: 1) symptoms of mental illness, such as depression, anxiety or attention-deficit/hyperactivity disorder; 2) a history of alcohol, nicotine or drug use; 3) pregnant or menstruating women; and 4) a history of brain injury. The Young’s 3

ACCEPTED MANUSCRIPT

NU

SC

RI

PT

Internet Addiction Test (1998) was applied in this study, and scores over 50 indicate occasional or frequent internet-related problems. Accordingly, we chose 50 as the threshold for inclusion in the IA. There was no significant difference between the ages of the two groups (t=0.443, p=0.659). The average ages of the IA and HC groups were 20.19±1.833 and 20.00±1.740 years, respectively. There was also no significant difference in sex between the two groups according to a chi-square test (2 =0.705, p=0.401). An independent sample t test was performed on the IAT and RSES scores of the two groups, and significant differences were found (t=11.873, p<0.001 and t=-2.866, p=0.006). The specific demographic information can be found in Table 1. The participants did not work night shifts, use prescription medications or have medical contraindications, such as severe comorbidities, alcoholism, drug abuse, ets., that may limit psychological or intellectual compliance. All participants had normal or corrected-to-normal vision and were right-hand dominant according to self- reporting. After the end of the experiment, the participants were given a reward to show gratitude. All participants provided written informed consent, and all experimental procedures complied with the approval of the Institutional Review Board of Liaoning Normal University. TABLE 1 | Subject demographics for internet addiction (IA) individuals and healthy controls (HCs)

ED

MA

Items IA (N=25) HC (N=27) Age (years) 20.19±1.833 20.00±1.740 Gender (male/female) a 6/19 4/23 IAT score 60.44±6.571 30.11±5.508 IAT range 53-75 20-41 RSES score 30.34±4.115 33.11±3.401 Values are expressed as the mean±standard deviation (SD).

t/ 0.443 0.705 11.873 \ -2.866 2

p 0.659 0.401 <0.001 \ 0.006

EP T

IAT, internet addiction test, RSES, Rosenberg Self-Esteem Scale a The p value for gender distribution in the two groups was obtained by the chi-square test.

AC C

2.2 Equipment and procedure 2.2.1 Internet Addiction Test (IAT) The IAT compiled by the University of Pittsburgh, Young (1998). The scale, which is self-reported, contains 20 items concerning the extent of preoccupation, compulsive use, behavioural problems, emotional changes, and general functioning associated with computer use. The total score is between 20 and 100. Scores of 20-49 indicate normal internet use, scores of 50-79 indicate excessive internet addiction, and scores of 80-100 indicate severe internet addiction. The Cronbach’s alpha-coefficient of IAT was 0.90 (Rodgers et al., 2013). 2.2.2 Rosenberg Self-Esteem Scale (RSES) Our study measured the level of explicit self-esteem using the RSES, which is the most widely used tool for measuring self-esteem (Yen et al., 2014; Nie et al., 2016; Servidio & Gentile, 2018). The scores contain four grades, strongly disagree (1) to strongly agree (4) for items 1, 2, 4, 6, and 7 and an opposite rating scale for items 3, 5, 8, 9, and 10. Total scores are obtained by summing all responses and may range from 4

ACCEPTED MANUSCRIPT 10 to 40, with higher scores indicating higher self-esteem (Rosenberg, 1965).

PT

2.3 Experimental procedure Participants first came to the laboratory to complete the RSES test. Then, all participants underwent resting-state EEG recordings in a noiseless, dimly lit laboratory room. EEG signals were collected for six minutes during the eyes-closed resting state condition. Participants were instructed to relax, avoid movement, not think about anything and remain awake. The EEG recordings of all participants were monitored throughout to ensure that they followed the instructions and did not show signs of drowsiness.

MA

NU

SC

RI

2.4 EEG recording and signal processing The EEG signals were collected using a 64-channel electrode cap that complied with the 10–20 International System (Brain Product, Germany). The electrode locations, corresponding channel numbers and names are shown in Figure 3. The vertical channel of the electro-oculogram (EOG) was recorded to monitor eye movements and blinks. The unipolar reference region was linked at the right and left earlobes, and the ground electrode was located at AFz. The sampling frequency was 500_Hz with a bandpass of 0.01–100_Hz and a 50_Hz notch, and the electrode impedance was maintained below 10 kΩ.

AC C

EP T

ED

The acquired EEG signals were pre-processed using MATLAB and the EEGLAB toolbox (Delorme & Makeig, 2004). First, the data were re-referenced to the mastoid channels and then low-pass- filtered using a cut-off frequency of 256_Hz and bandpass-filtered between 1 and 50_Hz to exclude very low- frequency artefacts and line noise. Data portions contaminated by eye movements, electromyography, or any other non-physiological artefacts were corrected using the Independent Component Analysis algorithm (Makeig et al., 1997; Jung et al., 2001). Then, the pre-processed continuous EEG data were segmented into dozens of epochs, with an epoch length of 2000 ms. All further analyses were conducted for the delta (1–4_Hz), theta (4–8_Hz), alpha (8–12_Hz), beta (12–30_Hz) and gamma (30–45_Hz) frequencies. 2.5 Functional connectivity analysis Among the measures of FC, we chose to use synchronization likelihood (SL), which is able to estimate both linear and non- linear dependencies between EEG time courses of different brain areas and is more suitable for the analysis of non-stationary signals such as EEG than other algorithms. Reduced SL may represent two independently active populations, while an increased SL may relate to the more collaborative activities of each brain area (Coullaut-Valera et al., 2014; Wyczesany et al., 2011). Therefore, if the FC is reduced between two brain regions, they tend to exhibit more segregation activity. Some studies of decreased FC correlated with IA have indicated that the decreased coherence of brain activity in IA participants may underlie the impaired executive function and weakened inhibition control of internet-using behaviours (Dong et al., 2015; Hong et al., 2013b). Conversely, increased FC between 5

ACCEPTED MANUSCRIPT two regions tends to reflect more integration activity. Some researchers hold the view that higher FC in IA may be interpreted as a constructive, adaptive effect of prolonged internet use, forming a training effect (Han et al., 2017) or altered functional connections (Wang et al., 2017). Many studies have shown that the SL is indicative of changes in FC during resting state (Chriskos et al., 2018; Lv et al., 2014; Orgo et al., 2017; Yang & Lin, 2015).

EP T

ED

MA

NU

SC

RI

PT

2.6 Network analysis On the topological analysis of the FC network, each recorded EEG electrode is defined as a network node. We selected the most appropriate density to retain a certain proportion of the most closely connected edges and to eliminate false or weak connections. Density is defined as the number of existing edges divided by the maximum possible number of edges within a network. In two networks with the same number of nodes, the density is used to define edges so that the number of edges of the two networks is the same at the same density (Cao et al., 2014; Va n et al., 2015; Wijk et al., 2010). Thus, we constructed the binary network by setting a series of density values to avoid the effect of network size. To ensure the small-world property in this study, the choice of density was subject to the following principles: (1) The average degree of the constructed network should be larger than 2×logN, where N represents the number of nodes; and (2) the small- world property σ of the constructed network of all participants should be larger than 1.2 (Cao et al., 2014). As a result, 10-50% of the density scale was selected, and brain networks for each density were constructed with a 2% increasing step size. We compared the two networks constructed under all densities and ensured that the network average degree was larger than 2×log64 and that the small-worldness was greater than 1.2 in the whole density range.

AC C

It has been shown that graph metrics have different properties and highlight different topological characteristics of the graphs. The clustering coefficient (C) and the characteristic path length (L) are associated with segregation and integration functions, respectively (Rubinov & Sporns, 2010). We also chose three additional properties to assess nodal centrality, including degree (D), local efficiency (El) and betweenness centrality (bc). Detailed information about each attribute is summarized in Table 2 (Rubinov & Sporns, 2010). Network parameter computation was performed in MATLAB using the Brain Connectivity Toolbox (BCT). TABLE 2 | Descriptions of the network measures Character Parameter

Description

Fraction of the neighbours of a node i that C

Clustering coefficient

are also neighbours of each other. E Ci  i 1 K i ( K i  1) 2 6

ACCEPTED MANUSCRIPT Where Ei is the actual number of edges between neighbours of node i , and K i is the number of neighbours of node i .

2  dij N ( N  1) i , jN ,i  j

RI

L

L

Characteristic path length

PT

Average of the path lengths of all nodes.

Degree

Total number of edges connected to a node. Di   aij

NU

D

SC

where d ij is the shortest path between i and j .

bc

Global efficiency of a node calculated on the sub-graph created by the node’s neighbours.

El (i) 

1 1 G  i j N 1 d ij

Local efficiency

AC C

El

EP T

ED

MA

jV

Betweenness centrality

where d ij is the shortest path length between node i and node j , and N is the number of nodes in the network.

Fraction of all shortest paths in the graph that pass through a node.  j ,m (i ) BC (i)   j , mV i j m

where

 j,m

between

7

 j ,m

is the number of shortest paths

j and m , and  j ,m (i) is the number

ACCEPTED MANUSCRIPT of shortest paths between j and m that pass through i .

NU

SC

RI

PT

2.7 Statistical analysis Group differences in demographic variables were analysed using the independent t-test and the chi-square test. The tests were two tailed, and the level of significant was p < 0.05. SL and graph properties were averaged separately for all epochs and all participants in the IA and HC groups. Group (IA vs. HC) as independent variable, dependent variables including SL, C, L, D, El and bc. Between-group differences of SL and graph measures for each individual frequency were compared by One-way ANCOVA , with age and sex as covariates. False discovery rate (FDR) correction was used to address multiple comparisons and q<0.05 was considered to indicate corrected significant differences. After significant between- group differences were identified in the regional network metrics, partial correlation was computed to examine relationships between these metrics and clinical measures including age and sex as covariates. All statistical analyses were performed with IBM SPSS Statistics 20.0 (IBM Corp, Armonk, NY, USA).

MA

3 Results

AC C

EP T

ED

3.1 EEG functional connectivity SL produced a dimensional time-dependent matrix, and a 64×64 undirected matrix was conducted for each subject. The FC matrices of the IA and HC groups in the beta band are illustrated in Figure 1. The SL total values and sub-averages were calculated for each frequency band. There were no significant differences in FC (total network or sub-networks) between groups (p>0.05 for all).

FIGURE 1 | The SL between electrode pairs of the IA and HC groups in the beta band. The size of the matrix is 64×64. In the matrix map, each chromatic point represents the synchronization of two corresponding channels. The horizontal and vertical axes denote 64 channels.

3.2 Graph theory network measures 8

ACCEPTED MANUSCRIPT

EP T

ED

MA

NU

SC

RI

PT

3.2.1 Global measures To explore whether there are topological differences between the two groups, C and L, integration and segregation functions were measured. Figure 2 shows the average values of C and L between the two analysed groups in the beta and gamma bands at different densities. No significant effect was observed in other frequencies. In the beta band, in the 0.22-0.38 density intervals, L was significantly lower in the IA group than in the HC group. The differences between groups at 0.28 and 0.32-0.38 densities were still significant after FDR correction (Table 3). The most significant difference between groups occurred at a density of 0.32, indicating that the L of the IA group was smaller than that of the HC group, but C was not significantly different. In the gamma band, there was a significant decrease in C in the IA group only at a density of 0.16, while L did not significantly differ. Therefore, 0.32 and 0.16 are used to illustrate the following results in the beta and gamma bands, respectively (see Table 3).

AC C

FIGURE 2 | Average values of the global measures in the two groups in the beta and gamma bands at different densities. The left column represents the beta band, while the right column represents the gamma band. The red line represents values of the IA group, whereas the blue line refers to the HC group. The asterisk (☆) indicates that the level of significance was less than 0.05. The short vertical lines denote the 95% confidence intervals for each density in the interval of 0.1-0.5 with a step of 0.02 (C and L indicate the clustering coefficient and the characteristic path length, respectively).

TABLE 3 | Significant group differences in C and L in the beta and gamma bands at different densities L C Bands Densities 2 p-values η p-values η2 0.22 0.049*↓ 0.077 0.808 0.009 Beta 0.24 0.017*↓ 0.111 0.768 0.011 9

ACCEPTED MANUSCRIPT

PT

0.26 0.032*↓ 0.090 0.747 a 0.28 0.009 ↓ 0.132 0.616 0.30 0.026*↓ 0.097 0.716 a 0.32 0.004 ↓ 0.156 0.597 a 0.34 0.011 ↓ 0.124 0.460 a 0.36 0.007 ↓ 0.141 0.459 a 0.38 0.011 ↓ 0.125 0.707 Gamma 0.16 0.859 0.001 0.044*↓ Notes: C, clustering coefficient; L, characteristic path length. *significant results (p<0.05); asignificant after false discovery rate (FDR) 2

correction;

RI

↓, IAs
0.012 0.020 0.014 0.021 0.031 0.031 0.014 0.080

MA

NU

SC

3.2.2 Regional nodal centralities For nodal- level networks, D, El, and bc were selected to investigate the regional nodal centralities. A brain region may be significantly altered if at least one of its three regional nodal metrics has a p-value smaller than 0.05 (Wee et al., 2014). The results of the current study indicated that the alterations in nodal centrality were mainly located in the frontal (FP1 , FPz) and parietal (CP1 , CP5 , PO3 , PO 7 , P5 , P6, TP8 ) lobes in the current study (see Table 4). The specific locations of inter-group topological differences are listed in Figure 3.

AC C

EP T

ED

TABLE 4 | Regions showing altered nodal centralities D El Band Electrodes 2 p η p η2 FPz 0.168 0.039 0.706 0.003 FP1 0.016* 0.112 0.894 0.000 CP1 0.040* 0.077 0.842 0.001 PO3 0.046* 0.078 0.327 0.020 Beta PO7 0.011* 0.019 0.159 0.040 P5 0.048* 0.077 0.495 0.010 P6 0.740 0.002 0.039* 0.084 TP8 0.108 0.052 0.024* 0.099 CP5 0.449 0.012 0.219 0.031 Gamma TP8 0.681 0.003 0.044* 0.080

bc p 0.007** 0.057 0.049* 0.417 0.006** 0.658 0.597 0.155 0.043* 0.741

Regions η2 0.140 frontal 0.072 0.077 0.014 0.143 parietal 0.004 0.006 0.041 0.081 parietal 0.002

This table indicates the electrode points that showed significant group differences in each local attribute (D, degree; El, local efficiency; bc, betweenness centrality); the right column of ‘regions’ refers to the brain areas corresponding to the electrode points. *, **significant results (p<0.05, p<0.01, respectively); η 2 , effect size.

10

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

FIGURE 3 | EEG topological differences in nodal centrality between the IA and HC groups.

MA

The alterations in nodal centrality were mainly located in the frontal (FP1, FPz) and parietal (CP1 , CP5 , TP8 , P5 , P6 , PO3 , PO7 ) lobes and are marked in red.

AC C

EP T

ED

3.3 Correlation between the network metrics and behavioural measures Partial correlation analysis was conducted to assess the association between the nodal centrality values for electrode points with significant beta and gamma group differences and the corresponding IAT and RSES scores. In the beta band, significant correlations (p<0.05) between the bc values of the frontal (FPz) and left parietal-occipital (PO 7 ) lobes and the severity of IA were found. The correlation between El of the right parietal lobe (TP8 , P6 ) and the severity of IA was also significant. In addition, the D of the left parietal-occipital (PO 7 ) lobe was also significantly correlated with IAT scores (see Figure 4). However, correlations were not significant at other brain regions, and there was no significant correlation between the altered brain regions and the severity of IA in the gamma band. The RSES scores were not related to any of the measures in the beta and gamma bands.

11

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

EP T

4 Discussion

ED

FIGURE 4 | Correlation between regional nodal metrics and the severity of IA in the beta band, (the left column corresponds to the bc of at the FPz and PO7 electrodes, the right row refers to the El of the P6 and TP8 electrodes, the middle column refers to the degree of PO7, and the X-axis in each figure indicates IA scores).

AC C

This study used EEG to measure spontaneous brain activity in the resting state and systemically explored the neural characteristics of IA at functional and topological network levels. The results demonstrated that the IA exhibited a more random network organization with decreased C and L. Our findings may provide different evidence into the neural mechanism of IA with alterations both in global and regional levels compared with previous studies (Hong et al., 2013b; Lee et al., 2017; Wee et al., 2014; Zhai et al., 2016). However, the FC analysis failed to reveal a difference in connectivity strength between groups. Our results revealed an obvious dissociation between connectivity and network analysis of EEG data (González et al., 2016). In addition, altered frontal and parietal activities were significantly correlated with the severity of IA. In short, our results suggested that the spontaneous neuronal fluctuations may differ between the IA group and the HC group. The changes in some brain areas and their associated connection paths may be interpreted as reflecting alterations in the efficiency of information processing in IA. 4.1 Alterations in EEG functional connectivity changes 12

ACCEPTED MANUSCRIPT

MA

NU

SC

RI

PT

The current study just revealed a trend of increased global connectivity in the IA group relative to the HC group, the differences did not reach significance. However, we verified the significant increase of FC in IA by performed a sub-study in which we selected subjects from the existing dataset with the highest level of addiction (Table S3 and Table S4 in supplementary materials). Increased FC between any two brain regions may reflect a higher synchronization level (Coullaut-Valera et al., 2014), indicating IA are more inclined to have integrated brain activity, and might reflect a compensatory mechanism served as a protective factor against the decline in behavioral performance (Du et al., 2016). The integrated activity is inseparable from the long-term use of the internet by addicts to increase the ability of visual-auditory integration, object and movement detection and a tremendous amount of semantic information (Levac et al., 2014; Williams & Kirschner, 2012). Specifically, long-term internet contact may have multiple and complex effects on human cognition and brain development. The repeated behavior of internet surfing or using, such as contacting the network picture frequently, indulging in the noisy internet bar or in the game sound. The visual, auditory centers, which have been stimulated repeatedly for a long time, become easily to excited or have a raised excitability (Liu et al., 2010). This increased FC related to IA can also be seen in previous studies (Hong et al., 2018; Park et al., 2017; Seok and Sohn, 2018; Zhang et al., 2018).

AC C

EP T

ED

4.2 Alterations in global network measures Our results also showed a lower C and L of the IA group than the HC group. From a topology perspective, the IA group in the current study tended to exhibit a random network organization (Stam & van Straaten, 2012), which indicates an alteration in the normal balance of network function. Park et al. (2015) also indicated that IA induced brain functional networks to shift towards a random topological architecture. Random network topology has also been observed in various disorders, such as depression (Li et al., 2017), focal epilepsy (Park et al., 2018) and ADHD (Twp et al., 2017). This random network topology may be indicate that the brain networks may keep high wiring cost or break up the trade-off between the efficiency and cost (Zhang et al., 2016). Another interpretation is that the random network implies a less segregated and more integrated network connectivity pattern. The considerably lower segregation could translate into a network that lacks the capacity to contain functional processes into a specific community, such as memory, reward and inhibition (Ding et al., 2014; Michela et al., 2017; Yan et al., 2017), within the brain making it less specialized, whereas high integration may be interpreted by internet activities require that participants engage multiple cognitive processes. To perform more effortful activities, it is necessary to expend the cost of integrating multiple clusters through long-distance connections, while a segregated information processing pattern is no longer sufficient (Finc et al., 2017). In summary, existing findings may provide new insights into the neural mechanism of IA from the network analysis level. 4.3 Alterations in regional nodal centralities The nodal centrality measures of the beta and gamma networks demonstrated altered 13

ACCEPTED MANUSCRIPT

NU

SC

RI

PT

frontal (FP1 , FPz) and parietal (CP1 , CP5 , PO3 , PO7 , P5 , P6, TP8 ) lobe performance in the IA group. The frontal and parietal regions are two important regions for regulating advanced cognition, such as attention, behaviour control, information processing and other activities (Bush, 2011). The cue-reactivity and Go-Nogo paradigm has been utilized to evaluate craving and impulsivity in online gaming disorder, and hyperactive neural activities have been found in the frontal and parietal areas (Ding et al., 2014; Ko et al., 2009). A stop signal task showed that the right frontoparietal network implements attentional monitoring, the left frontoparietal network implements response inhibition, which is a core component of impulsive behaviour (Zhang & Li, 2015), while impulsivity is the main feature of IA (Ding et al., 2014). Altered frontal and parietal brain activities associated with IA were found even at resting state (Wang et al., 2015, 2017), which is consistent with studies examining task-related activity (Ding et al., 2014; Ko et al., 2009). Therefore, from these findings, one can infer that the altered activities in the frontal and parietal regions may provide evidence to some extent of the impulsive and attention deficit behaviours of internet addicts.

AC C

EP T

ED

MA

4.4 Correlation between the network metrics and behavioural measures To our knowledge, IA has an adverse effect on daily life (Whang et al., 2003), and numerous studies have elucidated that individuals categorized to have internet addiction showed lower levels of explicit self-esteem (Farah et al., 2016; Nie et al., 2016). There are no previous reports in the literature about correlations between changes in EEG graph measures and self-esteem of IA participants. However, this result inconsistent with our expectations, and therefore, we are reluctant to interpret the current absence of a significant relationship between RSES scores and graph network metrics. We speculate that the lack of significance may be due to the small sample size or the mild degree of addiction of the included participants. Future studies should more systematically evaluate potential relationships among these measures by including subjects with greater addiction and comparing resting-state and task-related EEG measurements. Furthermore, the nodal network metrics of EEG were correlated with IA severity. Previously, Wee et al. (2014) observed in adolescents a positive relationship of IA severity with El of the right middle cingulate gyrus, D of the left thalamus and right middle cingulate gyrus, and bc of the right anterior cingulate gyrus in resting-state fMRI. These findings demonstrate the important role of brain topological changes derived from graph theory in understanding the neural mechanism of IA and will help determine affected regions related to IA. 4.5 Limitations This study has several limitations. First, the small sample size may limit the statistical power of the analytical results. Second, the subjects were generally recruited from normal universities, and the unbalanced male- female ratio resulted many more female participants than male participants. In future research, we plan to balance the gender 14

ACCEPTED MANUSCRIPT ratio and eliminate possible gender effects as much as possible. Third, the current study is the cross-sectional design may reflect baseline differences in cognitive abilities and not fully differentiate the effects of internet addiction in college students, therefore we are cautious in interpreting the study results. Future studies may benefit from longitudinal assessments of these effects. In follow-up studies, we will include participants with more severe IA and explore the differences observed in this study in the context of additional behavioural studies.

PT

5 Conclusion

NU

SC

RI

This study demonstrated that IA most likely modulates brain network organization. In the current study, we used the complex network approach to investigate the different brain network topologies between IA and HC groups. We found that the brain network topological organization of subjects with IA tends toward a more random graph, or a less segregated but more integrated network configuration, relative to HC participants. Moreover, the results revealed the important role of the frontal and parietal lobes in the neuropathological mechanism of IA and provide further supportive evidence for the diagnosis of IA.

MA

References

ED

Asensio, S., Morales, J. L., Senabre, I., Romero, M. J., Beltran, M. A., & Flores-Bellver, M., et al. (2016). Magnetic resonance imaging structural alterations in brain of alcohol abusers and its association with impulsivity. Addiction Biology, 21(4), 962-971. Babiloni, C., Lizio, R., Marzano, N., Capotosto, P., Soricelli, A., & Triggiani, A. I., et al. (2015).

EP T

Brain neural synchronization and functional coupling in alzheimer's disease as revealed by resting state EEG rhythms. International Journal of Psychophysiology, 103, 88-102. Barry, R. J., Clarke, A. R., Johnstone, S. J., & Brown, C. R. (2009). EEG differences in children between eyes-closed and eyes-open resting conditions. Clinical Neurophysiology, 120(10),

AC C

1806-1811. Bavelier, D., Green, C. S., Han, D. H., Renshaw, P. F., Merzenich, M. M., & Gentile, D. A. (2011). Brains on video games. Nature Reviews Neuroscience, 12(12), 763-8. Bush, G. (2011). Cingulate, frontal and parietal cortical dysfunction attention-deficit/hyperactivity disorder. Biological Psychiatry, 69(12), 1160-1167.

in

Cao, R., Wu, Z., Li, H., Xiang, J., & Chen, J. (2014). Disturbed connectivity of EEG functional networks in alcoholism: a graph-theoretic analysis. Bio-medical materials and engineering, 24(6), 2927-2936. Choi, S. W., Kim, H. S., Kim, G. Y., Jeon, Y., Park, S. M., & Lee, J. Y., et al. (2014). Similarities and differences among internet gaming disorder, gambling disorder and alcohol use disorder: a focus on impulsivity and compulsivity. J Behav Addict, 3(4), 246-253. Chriskos, P. , Frantzidis, C. A. , Gkivogkli, P. T. , Bamidis, P. D. , & Kourtidou-Papadeli, C. . (2018). Achieving accurate automatic sleep staging on manually pre-processed eeg data through synchronization feature extraction and graph metrics. Frontiers in Human Neuroscience, 12, 110.

15

ACCEPTED MANUSCRIPT Coullaut-Valera, R., Arbaiza, I., Bajo, R., Arrãºe, R., Lã³Pez, M. E., & Coullaut-Valera, J., et al. (2014). Drug polyconsumption is associated with increased synchronization of brain electrical-activity at rest and in a counting task. International Journal of Neural Systems, 24(1), 1450005. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. Ding, W. N., Sun, J. H., Sun, Y. W., Chen, X., Zhou, Y., Zhuang, Z. G., et al.(2014). Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet

PT

gaming addiction revealed by a Go/No-Go fMRI study. Behav. Brain Funct. 10-20. Ding, W. N. , Sun, J. H. , Sun, Y. W. , Zhou, Y. , Li, L. , & Xu, J. R. , et al. (2013). Altered default

RI

network resting-state functional connectivity in adolescents with internet gaming addiction.

SC

PLOS ONE, 8(3), e59902. Dong, G., Lin, X., & Potenza, M. N. (2015). Decreased functional connectivity in an executive control network is related to impaired executive function in internet gaming disorder. Prog Neuropsychopharmacol Biol Psychiatry, 3(57), 76-85.

MA

NU

Du, X. , Yang, Y. , Gao, P. , Qi, X. , Du, G. , & Zhang, Y. , et al. (2016). Compensatory increase of functional connectivity density in adolescents with internet gaming disorder. Brain Imaging and Behavior, 11(6), 1901-1909. Farah, Y., Ghinwa, H., Hicham, J., Nada, E. O., Latife, K., & Aline, H., et al. (2016). Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in

ED

university students: a cross-sectional designed study. Plos One, 11(9), e0161126. Fattore, L., & Melis, M. (2016). Sex differences in impulsive and compulsive behaviors: a focus on drug addiction. Addiction Biology, 21(5), 1043-1051. Fauthbühler, M., & Mann, K. (2015). Neurobiological correlates of internet gaming disorder:

EP T

similarities to pathological gambling. Addictive Behaviors, 64, 349-356. Finc, K., Bonna, K., Lewandowska, M., Wolak, T., Nikadon, J., & Dreszer, J., et al. (2017). Transition of the functional brain network related to increasing cognitive demands. Human Brain Mapping, 38(1), 3659-3674.

AC C

Fox, M. D., & Raichle, M. E. (2014). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8(9), 700-711. González, G. F., Molen, M. J. W. V. D., Žarić, G., Bonte, M., Tijms, J., & Blomert, L., et al. (2016). Graph analysis of EEG resting state functional networks in dyslexic readers. Clinical Neurophysiology Official Journal of the International Federation of Clinical Neurophysiology, 127(9), 3165-3175. Han, D. H., Kim, S. M., Bae, S., Renshaw, P. F., & Anderson, J. S. (2017). Brain connectivity and psychiatric comorbidity in adolescents with internet gaming disorder. Addiction Biology, 22(3), 802-812. Hilbert, S., Goerigk, S., Padberg, F., Nadjiri, A., Übleis, A., & Jobst, A., et al. (2018). The role of self-esteem in depression: a longitudinal study. Behavioural & Cognitive Psychotherapy, 1-7. Hong, J. S., Sun, M. K., Bae, S., & Han, D. H. (2018). Impulsive internet game play is associated with increased functional connectivity between the default mode and salience networks in depressed patients with short allele of serotonin transporter gene. Frontiers in Psychiatry, 9, 125.

16

ACCEPTED MANUSCRIPT Hong, S. B., Kim, J. W., Choi, E. J., Kim, H. H., Suh, J. E., & Kim, C. D., et al. (2013a). Reduced orbitofrontal cortical thickness in male adolescents with internet addiction. Behavioral and Brain Functions,9,1(2013-03-12), 9(1), 11. Hong, S. B., Zalesky, A., Cocchi, L., Fornito, A., Choi, E. J., & Kim, H. H., et al. (2013b). Decreased functional brain connectivity in adolescents with internet addiction. Plos One, 8(2), e57831. Hua, K., Wang, T., Li, C., Li, S., Ma, X., & Li, C., et al. (2018). Abnormal degree centrality in chronic users of codeine-containing cough syrups: a resting-state functional magnetic resonance imaging study. Neuroimage Clinical, 19, 775-781.

PT

Jung, T. P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2001). Analys is and visualization of single-trial event-related potentials. Human Brain

RI

Mapping, 14(3), 166-185.

SC

Kim, D., Lee, D., Lee, J., Namkoong, K., & Jung, Y. C. (2017a). Association between childhood and adult attention deficit hyperactivity disorder symptoms in korean young adults with internet addiction:. Journal of Behavioral Addictions, 6(3), 345-353. Kim, Y. J., Lee, J. Y., Oh, S., Park, M., Jung, H. Y., & Sohn, B. K., et al. (2017b). Associations

MA

NU

between prospective symptom changes and slow-wave activity in patients with internet gaming disorder: a resting-state eeg study. Medicine, 96(8), e6178. Ko, C. H., Liu, G. C., Hsiao, S., Yen, J. Y., Yang, M. J., & Lin, W. C., et al. (2009). Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research, 43(7), 739-747.

ED

Lee, M., Min, A., Hwang, Y. H., Dong, Y. K., Han, B. S., & Seo, H. S. (2017). Structural brain network abnormalities in subjects with internet addiction. Journal of Mechanics in Medicine & Biology, 17(7), 1740031. Levac, D. , Espy, D. , Fox, E. , Pradhan, S. , & Deutsch, J. E. . (2015). \"kinect-ing\" with

EP T

clinicians : a knowledge translation resource to support decision making about video game use in rehabilitation. Physical Therapy, 95(3), 426-440. Li, X. W., Jing. Z., B, H., Zhu, J., Zhong, N., & Li, M., et al. (2017). A resting-state brain functional network study in MDD based on minimum spanning tree analysis and the

AC C

hierarchical clustering. Complexity, 2017(1), 1-11. Liu, J., Esmail, F., Li, L., Kou, Z., Li, W., & Gao, X., et al. (2013). Decreased frontal lobe function in people with internet addiction disorder. Neural Regeneration Research, 8(34), 3225-3232. Liu, J., Gao, X. P., Osunde, I., Li, X., Zhou, S. K., & Zheng, H. R., et al. (2010). Increased regional homogeneity in internet addiction disorder: a resting state functional magnetic resonance imaging study. Chinese Medical Journal, 123(14), 1904-1908. Lopez-Fernandez, O. (2015). How has internet addiction research evolved since the advent of internet gaming disorder? an overview of cyberaddictions from a psychological perspective. Current Addiction Reports, 2(3), 263-271. Lv, B., Su, C., Yang, L., Xie, Y., & Wu, T. (2014). Whole brain EEG synchronization likelihood modulated by long term evolution electromagnetic fields exposure. Conf Proc IEEE Eng Med Biol Soc,2014, 986-989. Makeig, S., Jung, T. P., Bell, A. J., Ghahremani, D., & Sejnowski, T. J. (1997). Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences of the United States of America, 94(20), 10979-10984.

17

ACCEPTED MANUSCRIPT Michela, B. , Irene, V. , & Roberta, F. (2017). Evidences from rewarding system, FRN and p300 effect in internet-addiction in young people. Brain Sciences, 7(12), 81. Nie, J., Zhang, W., & Liu, Y. (2016). Exploring depression, self-esteem and verbal fluency with different degrees of internet addiction among chinese college students. Comprehensive Psychiatry, 72, 114-120. Omar, Abd, E. M., ElRasheed, Amany, H., Azzam, & Hanan, M. E. E., et al. (2016). Personality profile and affect regulation in relation to food addiction among a sample of egyptian females. Addictive Disorders & Their Treatment, 15(3), 143-148. Orgo, L., Bachmann, M., Kalev, K., Hinrikus, H., & Järvelaid, M. (2017). Brain functional

PT

connectivity in depression: Gender differences in EEG. Biomedical Engineering and Sciences (pp.270-273). IEEE.

RI

Park, C. H., Chun, J. W., Cho, H., Jung, Y. C., Choi, J., & Kim, D. J. (2015). Is the internet

SC

gaming-addicted brain close to be in a pathological state?. Addiction Biology, 114(1), 169-176. Park, K. M. , Lee, B. I. , Shin, K. J. , Ha, S. Y. , Park, J. , & Kim, T. H. , et al. (2018). Progressive topological disorganization of brain network in focal epilepsy. Acta Neurologica

MA

NU

Scandinavica, 137(4), 425-431. Park, S. M., Lee, J. Y., Kim, Y. J., Lee, J. Y., Jung, H. Y., & Sohn, B. K., et al. (2017). Neural connectivity in internet gaming disorder and alcohol use disorder: a resting state EEG coherence study. Sci Rep, 7(1), 1333. Rodgers, R. F., Melioli, T., Laconi, S., Bui, E., & Chabrol, H. (2013). Internet addiction symptoms,

ED

disordered eating, and body image avoidance. Cyberpsychology Behavior & Social Networking, 16(1), 56-60. Rosenberg, M. (1965). Society and the adolescent self-image. NJ: Princeton University Press. Rubinov, M. O. Sporns.(2010). Complex network measures of brain connectivity: uses and

EP T

interpretations. Neuroimage, 52(3), 1059-1069. Sariyska, R., Lachmann, B., Markett, S., Reuter, M., & Montag, C. (2017). Individual differences in implicit learning abilities and impulsive behavior in the context of internet addiction and internet gaming disorder under the consideration of gender. Addictive Behaviors Reports,

AC C

5(C), 19-28. Seok, J. W., & Sohn, J. H. (2018). Altered gray matter volume and resting-state connectivity in individuals with internet gaming disorder: a voxel-based morphometry and resting-state functional magnetic resonance imaging study. Frontiers in Psychiatry, 9, 77. Servidio, R., & Gentile, A. (2018). The mediational role of coping strategies in the relationship between self-esteem and risk of internet addiction. Europes Journal of Psychology, 14(1), 176-187. Son, K. L., Choi, J. S., Lee, J., Park, S. M., Lim, J. A., & Lee, J. Y., et al. (2015). Neurophysiological features of internet gaming disorder and alcohol use disorder: a resting state EEG study. Translational Psychiatry, 5(9), e628. Stam, C. J., & van Straaten, E. C. (2012). The organization of physiological brain networks. Clinical Neurophysiology, 123(6), 1067-1087. Sutherland, M. T., Mchugh, M. J., Pariyadath, V., & Stein, E. A. (2012). Resting state functional connectivity in addiction: lessons learned and a road ahead. Neuroimage, 62(4), 2281-2295. Twp, J., Hillebrand, A., Gouw, A., Geladã©, K., Van, M. R., & Maras, A., et al. (2017). Neural

18

ACCEPTED MANUSCRIPT network topology in ADHD: evidence for maturational delay and default-mode network alterations. Clinical Neurophysiology, 128(11), 2258-2267. Van, D. E., Numan, T., Van, D. E., Aw, V. D. K., Boersma, M., & Hofman, D., et al. (2015). Opportunities and methodological challenges in eeg and meg resting state functional brain network research. Clinical Neurophysiology, 126(8), 1468-1481. Wang, G. Y., & Griskova-Bulanova, I. (2018a). Electrophysiological activity is associated with vulnerability of internet addiction in non-clinical population. Addictive Behaviors, 33-39. Wang, L., Shen, H., Lei, Y., Zeng, L. L., Cao, F., & Su, L., et al. (2017). Altered default mode, fronto-parietal and salience networks in adolescents with internet addiction. Addictive

PT

Behaviors, 70, 1-6. Wang, W., Xiao, C., Yao, X., Yang, Y., Yan, H., & Li, S. (2018b). Psychosocial health and suicidal

RI

ideation among people living with hiv/aids: a cross-sectional study in nanjing, china. Plos

SC

One, 13(2), e0192940. Wang, Y., Yin, Y., Sun, Y., Zhou, Y., Chen, X., & Ding, W., et al. (2015). Decreased prefrontal lobe interhemispheric functional connectivity in adolescents with internet gaming disorder: a primary study using resting state fMRI. Plos One, 10(3), e0118733.

MA

NU

Wee, C. Y., Zhao, Z., Yap, P. T., Wu, G., Shi, F., & Price, T., et al. (2014). Disrupted brain functional network in internet addiction disorder: a resting state functional magnetic resonance imaging study. Plos One,9(9), e107306. Whang, L. S., Lee, S., & Chang, G. (2003). Internet over-users' psychological profiles : a behavior sampling analys is on internet addiction. Cyberpsychology & Behavior the Impact of the

ED

Internet Multimedia & Virtual Reality on Behavior & Society, 6(2), 143. Wijk, B. C. M. V., Stam, C. J., & Daffertshofer, A. (2010). Comparing brain networks of different size and connectivity density using graph theory. Plos One, 5(10), e13701. Williams JP, Kirschner D (2012) Coordinated action in the massively multiplayer online game

EP T

World of Warcraft. Symb Interact, 35, 340-367. Wyczesany, M., Grzybowski, S. J., Barry, R. J., Kaiser, J., Coenen, A. M., & Potoczek, A. (2011). Covariation of EEG synchronization and emotional state as modified by anxiolytics. Journal of Clinical Neurophysiology Official Publication of the American Electroencephalographic

AC C

Society,28(3), 289-296. Yan, S. , Xiaodan, W. , Sha, L. , & Zhenzhu, L. (2017). The brain mechanism on working memory level of internet addicts:from the evidence of ERP. Journal of Psychological Science, 40(5), 1208-1214. Yang, C. Y., & Lin, C. P. (2015). Time-varying network measures in resting and task states using graph theoretical analysis. Brain Topography, 28(4), 1-12. Yen, C. F., Chou, W. J., Liu, T. L., Yang, P., & Hu, H. F. (2014). The association of internet addiction symptoms with anxiety, depression and self-esteem among adolescents with attention-deficit/hyperactivity disorder. Comprehensive Psychiatry, 55(7), 1601-1608. Young K．S. (1998). Internet addiction： the emergence of a new disorder[J]．Cyber Psychology and Behavior, l(3), 237-244. Yu, D., Yuan, K., Bi, Y., Luo, L., Zhai, J., & Liu, B., et al. (2017). Altered interhemispheric resting-state functional connectivity in young male smokers. Addiction Biology, 23(2), 772-780. Yücens, B., & Üzer A. (2018). The relationship between internet addiction, social anxiety,

19

ACCEPTED MANUSCRIPT impulsivity, self-esteem, and depression in a sample of turkish undergraduate medical students. Psychiatry Research, 267, 313-318. Zhai, J., Luo, L., Qiu, L., Kang, Y., Liu, B., & Yu, D., et al. (2016). The topological organization of white matter network in internet gaming disorder individuals. Brain Imaging & Behavior, 1-10. Zhang, J., Ma, S., Li, C. R., Liu, L., Xia, C., & Lan, J., et al. (2018). Craving behavioral intervention for internet gaming disorder: remediation of functional connectivity of the ventral striatum. Addiction Biology, 23(1), 337-346. Zhang, R. , Jiang, G. , Tian, J. , Qiu, Y. , Wen, X. , & Zalesky, A. , et al. (2016). Abnormal white

PT

matter structural networks characterize heroin-dependent individuals: a network analysis. Addiction Biology, 21(3), 667-678.

RI

Zhang, S., & Li, C. S. (2015). Functional networks for cognitive control in a stop signal task:

SC

independent component analysis. Human Brain Mapping, 33(1), 89-104. Zhang, Y. , Li, M. , Wang, R. , Bi, Y. , Li, Y. , & Yi, Z. , et al. (2017). Abnormal brain white matter

AC C

EP T

ED

MA

NU

network in young smokers: a graph theory analysis study. Brain Imaging and Behavior, 12(2), 345-356.

20

ACCEPTED MANUSCRIPT Highlights 

Synchronization likelihood combined with graph theory to investigate the neural mechanism of internet addiction in resting state EEG.



Internet addiction tends to exhibit a more global integrated network configuration

EP T

ED

MA

NU

SC

neuropathological mechanism of internet addiction.

RI

This study revealed the important role of frontal and parietal areas in the

AC C



PT

with decreased shortest path length.

21

Figure 1

Figure 2

Figure 3

Figure 4