brain research 1586 (2014) 109–117

Available online at www.sciencedirect.com

www.elsevier.com/locate/brainres

Research Report

Reduced fiber integrity and cognitive control in adolescents with internet gaming disorder Lihong Xinga,b, Kai Yuana,b,n, Yanzhi Bia,b, Junsen Yina,b, Chenxi Caia,b, Dan Fenga,b, Yangding Lia,b, Min Songa,b, Hongmei Wangc, Dahua Yud, Ting Xuee, Chenwang Jinc, Wei Qina,b,n, Jie Tiana,b,f a

School of Life Science and Technology, Xidian University, Xi’an, People’s Republic of China Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, People’s Republic of China c Department of Medical Imaging, The First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China d Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, People’s Republic of China e School of Mathematics, Physics and Biological Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, People’s Republic of China f Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China b

art i cle i nfo

ab st rac t

Article history:

The association between the impaired cognitive control and brain regional abnormalities in

Accepted 16 August 2014

Internet gaming disorder (IGD) adolescents had been validated in numerous studies. However,

Available online 27 August 2014

few studies focused on the role of the salience network (SN), which regulates dynamic

Keywords:

communication among brain core neurocognitive networks to modulate cognitive control.

Diffusion tensor imaging (DTI)

Seventeen IGD adolescents and 17 healthy controls participated in the study. By combining

Fractional anisotropy (FA)

resting-state functional connectivity and diffusion tensor imaging (DTI) tractography methods,

Internet gaming disorder (IGD)

we examined the changes of functional and structural connections within SN in IGD

Resting-state

adolescents. The color-word Stroop task was employed to assess the impaired cognitive control

Salience network (SN)

in IGD adolescents. Correlation analysis was carried out to investigate the relationship between the neuroimaging indices and behavior performance in IGD adolescents. The impaired cognitive control in IGD was validated by more errors during the incongruent condition in color-word Stroop task. The right SN tract showed the decreased fractional anisotropy (FA) in IGD adolescents, though no significant differences of functional connectivity were detected. Moreover, the FA values of the right SN tract were negatively correlated with the errors during the incongruent condition in IGD adolescents. Our results revealed the disturbed structural

n Corresponding authors at: School of Life Science and Technology, Xidian University, Xi’an 710071, Shaanxi, People’s Republic of China. Fax: þ86 29 81891060. E-mail addresses: [email protected] (K. Yuan), [email protected] (W. Qin).

http://dx.doi.org/10.1016/j.brainres.2014.08.044 0006-8993/& 2014 Elsevier B.V. All rights reserved.

110

brain research 1586 (2014) 109–117

connectivity within SN in IGD adolescents, which may be related with impaired cognitive control. It is hoped that the brain–behavior relationship from network perspective may enhance the understanding of IGD. & 2014 Elsevier B.V. All rights reserved.

1.

Introduction

Adolescence is a tumultuous time, accompanied by significant changes in emotional and cognitive function (Arain et al., 2013; Spear, 2000). As one of the common mental health problems amongst adolescents, Internet gaming disorder (IGD) has been increasingly recognized both in public and the scientific community worldwide (Christakis, 2010; Durkee et al., 2012; Flisher, 2010; Ko et al., 2010). Data from a survey about adolescents’ Internet gaming disorder (IGD) in China, released on 2 February 2010, demonstrated that the incidence of internet addiction among Chinese urban youths was about 14.1%, with the total number of 24 million (http:// edu.qq.com/edunew/diaocha/2009wybg.htm). Overuse of online gaming is marked by an inability to control excessive gaming habits, which was associated with detrimental social and emotional consequences, such as poor psychological well-being, academic failure and reduced work performance (Chou et al., 2005; Young, 2010, 1998, 1999; Yuan et al., 2013a). Unfortunately, there is currently no standardized treatment for IGD. Clinics in China have implemented regimented timetables, strict discipline and electric shock treatment, which gained notoriety for these treatment approaches (Yuan et al., 2011b). Developing effective methods for intervention and treatment of IGD will require first establishing a clear understanding of the mechanisms underlying this disorder. However, the neurobiological mechanisms of IGD remain poorly understood (Flisher, 2010). Although IGD has not been officially codified within a psychopathological framework (Christakis, 2010; Yuan et al., 2011a), the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) identified IGD in Section 3 as a condition warranting more clinical research to provide mechanistic insight into its etiology and treatment (Meng et al., 2014). Moreover, regardless of whether IGD is conceptualized as a behavioral addiction or an impulse-control disorder, it is speculated to be associated with impaired

cognitive control (Dong et al., 2012). Previous IGD studies have revealed cognitive impairments among online gamers by using cognitive control tasks, such as color-word Stroop task, GO/NOGO task and gambling task (Dong et al., 2010, 2011b, 2012, 2013, Yuan et al., 2013a, 2013b). However, the majority of studies only focus on the regional brain abnormalities and impaired cognitive control in IGD, such as the correlation between the abnormal OFC and response errors during incongruent condition measured by Stroop task (Yuan et al., 2013a, 2013b), few studies mentioned that whether the cognitive control behavioral deficits can be regulated by connectivity of brain network. Neuroimaging studies have indicated that both ACC and insula are involved in the performance of the Stroop task (Leung et al., 2000). Meanwhile, the ACC and insula have shown functional and structural alterations in IGD (Feng et al., 2013; Weng et al., 2013; Zhou et al., 2011). As far as we know, brain regions do not work in isolation, they work in concert, forming neural networks to produce subjective perception, motivation and behavior. More and more scientific evidences demonstrated that the high-level cognitive control processes rely on the integrity of, and dynamic interactions between, core neurocognitive networks (Bressler and Menon, 2010; Meehan and Bressler, 2012). One theory is that the salience network (SN) – which includes the anterior cingulate cortex (ACC) and insula – regulates dynamic changes in other brain core networks to modulate cognitive control (Menon, 2011; Uddin et al., 2011). Consistent with this viewpoint, one traumatic brain injury study suggested that the disruption of white matter in the SN tract lead to inefficient cognitive control (Bonnelle et al., 2012). Therefore, we hypothesized that the connections within SN network was disrupted in IGD adolescents. In addition, the abnormal connection was correlated with the impaired cognitive control. In the present study, first, we combined multimodal MRI approach to investigate whether the structural connectivity and functional connectivity within SN were disturbed in IGD adolescents. Then, we assessed impaired cognitive control

Table 1 – Subject demographics for adolescents with internet gaming disorder (IGD) and control groups. Items

IGD N¼ 17

Control N¼ 17

t/χ2

P value

Age (years) Gender (M/F)a Education (years) IAT Hours of online gaming (/day) Days of online gaming (/week) IQ

19.170.7 10/7 12.270.6 65.7711.6 9.571.3 5.471.2 102.4710.4

19.871.3 11/6 12.470.7 29.274.5 2.271.4 2.972.3 103.177.7

1.889 0.245 0.768  12.067  15.304  3.972 0.243

0.071 0.621 0.448 o0.005n o0.005n o0.005n 0.810

Values are expressed as mean7standard deviation (SD). IGD ¼ internet gaming disorder. IAT ¼internet addiction test. IQ¼ intelligence quotient. a The P value for gender distribution in the two groups was obtained by chisquare test.

brain research 1586 (2014) 109–117

ability in IGD adolescents using a color-word Stroop task (Dong et al., 2011b, 2012; Yuan et al., 2013a, 2013b). Finally, the correlation of neuroimaging findings and cognitive behavior performance was evaluated in IGD adolescents.

2.

Results

2.1.

Demographic data results

All participants were right-handed adolescents. No significant difference was found in age, gender, years of education, IQ score measured by Wechsler intelligence Scale between the two groups. However, IGD group spent 9.571.3 h/day and 5.471.2 days/week on online gaming, which is significantly more than the healthy controls. In addition, internet addiction test (IAT) score was significantly higher in IGD group (Table 1).

2.2.

Behavioral data results

Both groups showed a significant Stroop effect, where the reaction time was longer during the incongruent than the congruent condition (IGD: 670.2758.7 ms vs 568.4738.7 ms and controls: 585.0752.5 ms vs 521.2734.0 ms; Po0.005). The IGD group committed more errors than the control group during the incongruent condition (IGD: 6.1273.10 vs controls: 2.9471.89; Po0.05), the response delay measured by RT during the incongruent condition minus congruent conditions was significantly different between these two groups (IGD: 101.8757.56 ms vs controls: 63.8730.86 ms; Po0.05).

2.3.

Functional connectivity within SN

We examined two pairwise functional connectivity between the ACC and bilateral insula. The results showed that temporal correlation coefficients (After Fisher z-transformed) between the ACC and bilateral insula were not significantly different in IGD adolescents when comparing to healthy controls (Table 2).

2.4.

Structural connectivity within SN

White matter tracts connecting the ACC to bilateral insula were detected in all participants. The results of two sample t-test showed significantly lower mean FA in ACC-right insula tracts in IGD adolescents compared to healthy controls (IGD: 0.36770.030 vs controls: 0.39270.031; Po0.05) (Fig. 1). There

111

was no group difference in the mean FA of the ACC-left insula tracts (Table 2).

2.5.

Correlation results

Correlation results indicated that the FA of the fiber bundle connecting ACC to the right insula negatively correlated to the number of errors during the incongruent condition in both the adolescents with IGD (r¼ 0.530, Po0.05) and healthy controls (r¼ 0.643, P ¼0.005) (Fig. 2a). The temporal correlation coefficients between the ACC and the bilateral insula showed no significant correlation with the number of errors during the incongruent condition in neither IGD adolescents nor healthy controls (Fig. 2b). In addition, there is no significant correlation between the SN connections (i.e. structural and functional connectivity) and IAT score (Table 3); the structural connectivity was not correlated with functional connectivity within SN in neither IGD adolescents nor healthy controls.

3.

Discussion

IGD is characterized by the inability of an individual to control the overuse of the internet game with negative consequences for aspects of psychological, social and/or work functioning and has become the major source of adolescent crime in China (Christakis, 2010; Flisher, 2010; Young, 1998). It has drawn scientific attention from academia around the world (Christakis, 2010; Durkee et al., 2012). Numerous studies had detected the possible neural mechanisms of the IGD and suggested that it may be related to the impaired cognitive control (Dong et al., 2011a, 2011b, 2013; Yuan et al., 2013a, 2013b). More and more findings suggested that cognitive control ability is not only associated with the isolated operations of single brain areas but also is related with the connections within brain core neurocognitive networks (Bressler and Menon, 2010; Menon and Uddin, 2010; Menon, 2011; Uddin et al., 2011). Although the accurate contribution of these brain networks to cognitive control remains controversial, one theory is that the salience network (SN) – which includes the ACC and insula – regulates dynamic changes in other networks to modulate cognitive function (Bonnelle et al., 2012; Uddin et al., 2011). Previous studies had connected the regional brain abnormalities and impaired cognitive control in IGD, such as the correlation between the abnormal OFC and response errors during incongruent condition measured by Stroop task (Yuan et al., 2013a, 2013b).

Table 2 – The differences of structural/functional connectivity within SN between the IGD adolescents and healthy controls.

ACC_Right insula FA ACC_Left insula FA ACC_Right insula FC ACC_Left insula FC

IGD N¼ 17

Control N ¼17

P value

0.36770.030 0.38370.037 0.17670.266  0.02670.369

0.39270.031 0.38170.028 0.04670.177 0.02270.148

0.024n 0.885 0.104 0.967

Values are expressed as mean7standard deviation (SD). FA ¼fractional anisotropy. FC ¼ functional connectivity. po0.05.

n

112

brain research 1586 (2014) 109–117

Fig. 1 – Examples of Structural connectivity between nodes of Salience network (SN). The diffusion results revealed the reduced FA values within SN (i.e. the tracts from the ACC to right insula) in IGD adolescents. For illustration purposes, the individual interconnecting tracts were derived in individual native diffusion space, which were then normalized to match standard space (MNI-152) and combined over the subjects to create the group tracts. SN ¼ Salience network, ROI¼ region of interest, ACC¼anterior cingulate cortex. IGD¼ internet gaming disorder.

Few studies focused on the relationship between the connections within SN and cognitive control deficits. From a network perspective, the interconnectivities of isolated brain regions may be more important to understand the neurobiological underpinnings of IGD adolescents. Therefore, we can make a bold speculation that the disturbed connectivity within SN may be related to a failure of SN regulation and this inefficient regulation was involved in impaired cognitive control in IGD adolescents. In the present study, we combined resting-state fMRI and DTI methods to explore the relationship between the connections within SN and cognitive control in IGD adolescents. First, ICA was used to extract the SN, including ACC and bilateral insula according to previous studies (Qi et al., 2012;

Uddin et al., 2011). Second, we explored the functional and structural connectivity within SN in IGD adolescents. The decreased FA in ACC-right insula tracts within SN was found in IGD adolescents relative to healthy controls, which may suggest reduced white matter integrity or disrupted structural connectivity within SN in IGD adolescents. Meanwhile, compared to the healthy controls, no significant differences were detected in mean temporal correlation coefficients of the ACC to right insula in IGD adolescents. In addition, neither FA values nor mean temporal correlation coefficients of the ACC to left insula were different between the IGD adolescents and healthy controls. Several studies have indicated that the right insula node of the SN plays a critical and causal role in switching among core neurocognitive networks

brain research 1586 (2014) 109–117

113

Fig. 2 – Correlation analysis results between connections within SN and color-word stroop task performance in both IGD adolescents and healthy controls. (a) The right salience network (SN) fiber integrity was significantly negative correlated with the errors during the incongruent condition in both groups. (b) There was no significant correlation between functional connectivity within SN and the errors during the incongruent condition in neither IGD adolescents nor healthy controls. SN¼salience network, IGD¼ internet gaming disorder.

in response to cognitive demands (Sridharan et al., 2008; Uddin et al., 2011). Consistently, our findings reveal that reduced fiber integrity within SN is constrained to rightlateralized. Third, we detected the cognitive control ability in the IGD adolescents using the color-word Stroop task. In detail, the IGD individuals committed more errors than the control group during the incongruent condition. The response delay measured by RT during the incongruent condition minus congruent conditions in IGD adolescents was larger than the healthy controls. Our results demonstrated that the IGD adolescents showed impaired cognitive control ability measured by the color-word Stroop task.

It is noteworthy that the significant correlation between the FA in ACC-right insula tracts within SN and task performance during the color-word Stroop task was found both in IGD adolescents and healthy controls in the current study. Previous neuroimaging studies have indicated that several brain regions are involved in the performance of the Stroop task, including ACC and insula (Leung et al., 2000). It has been shown that the ACC is involved in conflict monitoring and cognitive control (Azizian et al., 2009; Leung et al., 2000; Peterson et al., 2002) and participates in motor control by facilitating the execution of the appropriate responses and/or suppressing the execution of the inappropriate responses

114

brain research 1586 (2014) 109–117

Table 3 – The correlation between the SN connections (structural and functional connectivity) and stroop task performance (errors during the incongruent condition), as well as the relationship with the IAT score. IGD (N¼ 17)

Control (N ¼17) P value

Errors

IAT

ACC_Right insula FA ACC_Left insula FA ACC_Right insula FC ACC_Left insula FC ACC_Right insula FA ACC_Left insula FA ACC_Right insula FC ACC_Left insula FC

r

P value

r

0.530 0.136 0.007 0.279 0.114 0.286 0.176 0.113

0.029n 0.603 0.979 0.279 0.663 0.265 0.499 0.666

0.643 0.455 0.191 0.185 0.052 0.205 0.129 0.118

0.005n 0.067 0.462 0.476 0.844 0.430 0.622 0.651

Values are expressed as mean7standard deviation (SD). FA¼ fractional anisotropy. FC¼ functional connectivity. Po0.05.

n

(Pardo et al., 1990). Recently task-related fMRI studies have found that greater BOLD signal in the ACC was associated with slower incongruent reaction time in IGD (Dong et al., 2012). Apart from the activation in the ACC, the insula also should be concerned in IGD adolescents. Although the role of the insula in cognition need to be more clearly defined. It has been highlighted as a region integrating interoceptive states into conscious feelings and the decision-making process and dysfunction of the insula may lead to abnormal decision making (Critchley et al., 2004). Consistently, the brain–behavior relationship from network perspective in the present study demonstrated that the abnormal structural connectivity in the right SN was associated with impaired executive function in IGD adolescents. Our results provided more evidence for the structural changes in the interconnectivity within SN in IGD adolescents. Apart from the observation mentioned above, our findings demonstrated that the structural connectivity differences were observed between IGD adolescents and healthy controls in the absence of functional connectivity differences. The results suggested that functional connectivity method may be not sensitive enough to detect the significant functional changes in IGD adolescents, due to that the sample size was too small. In future study, larger sample was necessary to investigate the functional connectivity changes within SN between IGD and healthy controls. In addition, multivariate Granger Causality Analysis should be taken into our consideration, which examine causal interactions between the brain regions by detecting causal interactions between brain regions. This method assessed the predictability of signal changes in one brain region based on the time course of responses in another brain region. In our opinion, this effective functional connectivity can give us more clues to the absence of functional connectivity in present study. In conclusion, compared to healthy controls, IGD adolescents shown decreased FA in right SN, which may be related to poor behavior performance in color-word Stroop task. The connection between the structural connectivity findings and the behavioral assessments could improve our understanding of the SN as a critical role in IGD. It is hoped that the brain–behavior relationship from network perspective may enhance the understanding of IGD.

4.

Experimental procedure

4.1.

Subjects

All research procedures were approved by the Ethical Committee of Xi’an Jiaotong University and were conducted in accordance with the Declaration of Helsinki. Prior to this study, all participants and their guardians received a complete description about the experiment and gave their written informed consent. IGD is a primary subtype of internet addiction disorder (IAD), which was determined based on Young’s online internet addiction test (IAT) in the current study. IAT consists of 20 self-rating questions with the Likert scale of one (rarely) to five (always) (Widyanto and McMurran, 2004). To exclude the possible effect of different games on our results, we chose the subjects who played League of Legends (LOL) as their primary mainly Internet activity. LOL, which is a multiplayer online battle arena video game developed and published by Riot Games, inspired by the model Defense of the Ancients for the famous video game Warcraft III. Meanwhile, we defined the subjects with scores over 50 as the IGD group as previously described (Pawlikowski and Brand, 2011). In addition, the subjects were asked to fill in a basic information questionnaire, such as hours of online gaming per day, days of online gaming per week and to recall their life-style when they were initially addicted to online games, e.g. why do you like online gaming or what would you like to do after school. We also confirmed the reliability of the self-report from the IGD individuals by talking with their parents via telephone and the roommates and classmates. Seventeen students with IGD (10 males, mean age¼ 19.17 0.7 years, education 12.270.6 years) and 17 age-, gender- and education-matched healthy controls without IGD (11 males, mean age¼ 19.871.3 years, education 12.470.7 years) were enrolled. In addition, IGD group spent 9.571.3 h/day and 5.471.2 days/week on online gaming. All recruited participants screened were native right-handed Chinese and were assessed by a personal self-report and Edinburgh Handedness Questionnaire. Exclusion criteria for both groups were (1) existence of a neurological disorder evaluated by the

brain research 1586 (2014) 109–117

Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV); (2) alcohol, nicotine or drug abuse by urine drug screening; (3) pregnancy or menstrual period in women; and (4) any physical illness such as a brain tumor, hepatitis, or epilepsy as assessed according to clinical evaluations and medical records. More detailed demographic information is given in Table 1.

4.2.

Behavioral data collection

The color-word Stroop task design was implemented by using E-prime 2.0 software (http://www.pstnet.com/eprime.cfm), which has been used in previous studies (Xu et al., 2006; Yuan et al., 2013a, 2013b). This task employed a block design with three conditions, i.e. rest, congruent and incongruent. Three words (red, blue and green) were displayed in three colors (red, blue and green) as the congruent and incongruent stimuli (for example: congruent, green painted in green; incongruent, green painted in red or blue). During rest, a cross was displayed at the center of the screen, and subjects were required to fix their eyes on this cross without responding. All events were programmed into two runs with different sequences of congruent and incongruent blocks. Each run consisted of four congruent, four incongruent, and nine rest blocks. There were seven trials in each task block, and each stimulus was presented for 1 s with an inter-stimulus interval of 2 s. Thus, each task block lasted 21 s. All rest blocks lasted 17 s, except for the first one, which lasted 19 s. Before each task block, the instruction, ‘Identify the Color’ was presented; and before each rest block, the instruction was ‘Rest’. All instructions were presented for 2 s. The entire run lasted 367 s. Each participant was instructed to respond to the displayed color as fast as possible by pressing a button on a Serial Response Box with his right hand. Button presses by the index, middle, and ring finger corresponded to red, blue, and green, respectively. Participants were tested individually in a quiet room when they were in a calm state of mind. After the initial practice, the behavior data was collected two or three days before MRI scanning.

4.3.

MRI data acquisition

All MRI data were carried out in a 3.0 T Signa MRI system (EXCITE; General Electric; Milwaukee; Wisc.) at the First Affiliated Hospital of the Medical College; Xi’an Jiaotong University in China. The foam pads were used to diminish head motion and scanner noise. After conventional localizer scanning, the T1-weighted images were obtained with a spoiled gradient recall sequence (repetition time (TR) ¼ 8.5 ms; echo time (TE) ¼3.4 ms; flip angle (FA)¼121; field of data matrix¼ 240  240; view (FOV)¼ 240  240 mm2; slices¼140; voxel size¼1  1  1 mm3). Then, resting-state functional images were acquired using an echo-planarimaging sequence (30 contiguous slices with a slice thickness¼5 mm; TR¼2000 ms; TE¼30 ms; FA¼ 901; FOV¼ 240  240 mm2; data matrix¼64  64; and total volumes¼185) in one run of 6 min and 10 s. The subjects were instructed to close their eyes, not to think about anything and stay awake during entire session. After the scan, all subjects confirmed that they remained awake during the scanning period. Diffusion data was acquired as follows: the

115

diffusion sensitizing gradients were applied along 30 nonlinear directions (b ¼1000 s/mm2) together with an acquisition without diffusion weighting (b¼ 0 s/mm2). The imaging parameters were 45 continuous axial slices with a slice thickness of 3 mm and no gap, FOV¼ 240  240 mm2; TR¼6800 ms; TE¼ 93 ms; data matrix¼ 128  128.

4.4.

SN network key nodes extraction

Data preprocessing was performed with FMRIB’s Software Library (FSL) (http://www.fmrib.ox.ac.uk/fsl/) (Smith et al., 2004). Preprocessing for resting-state data included discarding the first 5 echo-planar imaging volumes from each resting-state scan to allow for signal equilibration; motion correction; removal of non-brain structures; spatial smoothing (Gaussian kernel of 5-mm full width at half maximum); grand mean intensity normalization of all volumes by the same factor (4-dimensional grand-mean scaling in order to ensure comparability between datasets at the group level); high-pass temporal filtering (100 s); registration to individual’s structural scan and normalization to the Montreal Neurological Institute-152 (MNI-152) standard space. Preprocessed functional data were temporally concatenated across subjects (including both IGD and controls) to create a single 4D data set for the following analysis. No participants had a range of movement 43 mm translation or 3 degrees of rotation. Motion parameters did not differ between IGD and controls. To extract the SN in both groups, the 4D dataset were analyzed with the algorithm of ICA using the Multivariate Exploratory Linear Optimized Decomposition (MELODIC) software from FSL tools (Beckmann et al., 2005). We limited the number of independent components (ICs) to 25 to limit IC splitting into subcomponents described previously (Napadow et al., 2010; Xue et al., 2012). We defined regions of interest (ROIs) in key nodes of the SN based on the peaks of the ICA clusters. After selecting voxels with the highest z values within each cluster on the spatial map, the final ROIs were constructed by drawing spheres with centers as the seed point and a radius of 8 mm (Uddin et al., 2011) (Fig. 3).

4.5.

Functional connectivity within the SN

The time series of each ROI was preprocessed as follows: first, the regional resting-state fMRI time series was computed for

Fig. 3 – The nodes of Salience network (SN) extraction. (a) SN identified from ICA of resting-state fMRI data. (b) Definition of SN nodes. Network nodes for subsequent analyses were based on 8-mm-radius spheres created around peak voxels defined from this analysis.

116

brain research 1586 (2014) 109–117

each of the ROIs by averaging all the voxels within each region at each time point in the time series. Then, the mean time series from each ROI was extracted for all subjects. To quantify the functional connectivity between each pair of ROIs within the SN, we used the Pearson correlation to calculate temporal correlation coefficients (r) between each pair of regions. Statistical tests on regional functional connectivity results were computed after application of Fisher’s r-to-z transform, which yields variates that are approximately normally distributed.

4.6.

Structural connectivity within the SN

DTI preprocessing was performed with FMRIB Software Library (FSL) Version 4.1.9 (Smith et al., 2004) (http://www.fmrib.ox.ac. uk/fsl/) and comprised the following steps: removal of nonbrain tissue using the Brain Extraction Tool (BET) 2.1; correction of eddy current distortion and head motion using EDDYCORRECT from FMRIB’s Diffusion Toolbox; rotation of the gradients according to the corrected parameters from the second step; construction of individual fractional anisotropy (FA) maps by fitting a tensor model at each voxel of the diffusion data using DTIFIT within the FMRIB Diffusion Toolbox. The preprocessed data from FSL were further processed in the DTI native space using Diffusion Toolkit 0.6.2 and visualized by freely available software TrackVis 0.5.2 (http://www.trackvis. org) (Wang et al., 2007). For each subject, the diffusion tensors were estimated according to the corrected gradients. During tracking, the fiber assignment continuous tracking (FACT) algorithm was employed (Mori et al., 1999). Tracking termination criteria were angle 4451 and FA o0.2 (Mori and van Zijl, 2002) (individual FA map as mask imaging in Diffusion Toolkit). In the present study, the bilateral insula and the ACC were defined from the Automated Anatomical Labeling (AAL) atlas according to previous studies (Uddin et al., 2011). To determine the nodes of structural connectivity network in each subject, ROIs were defined in native diffusion space. In detail, each individual structural image (i.e. T1-weighted image) was first co-registered to their b0 images in the native diffusion space using a linear transformation. Co-registered structural images were then mapped to the MNI-152 T1-template by applying an affine transformation with 12 degrees of freedom together with a series of non-linear warps (Xue et al., 2014). The derived transformation parameters were inverted and used to warp the AAL ROIs from MNI space to the native diffusion space. Because fiber tracking becomes unreliable in gray matter, we ensured that the ROIs expanded 2–3 mm into the white matter (Uddin et al., 2011; Xue et al., 2014). The fiber tracts were obtained through a twoROI approach (seed ROI and target ROI) with logical AND concatenation (Wang et al., 2007) of the two ROIs, such that only fibers that passed both ROIs were included in the reconstructed tract. Obviously spurious fibers were removed from the fiber tract by using an additional avoidance ROI (logical NOT operation) (Wang et al., 2007). In addition, the FA values of fiber connectivity within SN in individual DTI native space were obtained from TrackVis (Wang et al., 2007) to determine whether there were any abnormalities in IGD adolescents. Furthermore, the individual interconnecting tracts were normalized to match standard space (MNI-152) and combined over the group of subjects for display.

4.7.

Statistical analysis

The statistical analyses were conducted with SPSS 13.0 software (SPSS, Chicago, IL, USA). First, two sample t-test and/or χ2 test were used to compare demographic data and the behavior performance (errors, reaction time [RT]) between two groups. Second, the connections (FA values, temporal correlation coefficients) within SN were investigated by two sample t-test (Po0.05/2, corrected for multiple comparison using the Bonferroni correction). Third, Pearson’s correlation was employed to evaluate the relationship between neuroimaging findings (i.e. structural and functional connectivity within SN) and behavioral performance measured by the color-word Stroop task, as well as the relationship with the IAT score (Po0.05). Finally, the coupling of structural and functional connectivity within SN was also investigated by Pearson correlation coefficient in the current study.

Acknowledgments This paper is supported by the Project for the National Key Basic Research and Development Program (973) under Grant nos. 2014CB543203, 2011CB707700, 2012CB518501, the National Natural Science Foundation of China under Grant nos. 81227901, 81271644, 81271546, 30930112, 81000640, 81000641, 81101036, 81101108, 31200837, 81030027, 81301281, the Natural Science Basic Research Plan in Shaanxi Province of China under Grant no. 2014JQ4118, and the Fundamental Research Funds for the Central Universities under the Grant nos. 8002-72125760, 8002-72135767, 800272145760, the Natural Science Foundation of Inner Mongolia under Grant no. 2012MS0908. General Financial Grant the China Postdoctoral Science Foundation under Grant no. 2014M552416.

r e f e r e n c e s

Arain, M., Haque, M., Johal, L., Mathur, P., Nel, W., Rais, A., Sandhu, R., Sharma, S., 2013. Maturation of the adolescent brain. Neuropsychiatric Dis. Treat. 9, 449. Azizian, A., Nestor, L.J., Payer, D., Monterosso, J.R., Brody, A.L., London, E.D., 2009. Smoking reduces conflict-related anterior cingulate activity in abstinent cigarette smokers performing a Stroop task. Neuropsychopharmacology 35, 775–782. Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M., 2005. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. London, Ser. B 360, 1001–1013. Bonnelle, V., Ham, T.E., Leech, R., Kinnunen, K.M., Mehta, M.A., Greenwood, R.J., Sharp, D.J., 2012. Salience network integrity predicts default mode network function after traumatic brain injury. Proc. Nat. Acad. Sci. U.S.A. 109, 4690–4695. Bressler, S.L., Menon, V., 2010. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14, 277–290. Chou, C., Condron, L., Belland, J.C., 2005. A review of the research on Internet addiction. Educ. Psychol. Rev. 17, 363–388. Christakis, D., 2010. Internet addiction: a 21st century epidemic?. BMC Med. 8, 61. ¨ hman, A., Dolan, R.J., Critchley, H.D., Wiens, S., Rotshtein, P., O 2004. Neural systems supporting interoceptive awareness. Nat. Neurosci. 7, 189–195.

brain research 1586 (2014) 109–117

Dong, G., Lu, Q., Zhou, H., Zhao, X., 2010. Impulse inhibition in people with Internet addiction disorder: electrophysiological evidence from a Go/NoGo study. Neurosci. Lett. 485, 138–142. Dong, G., Lu, Q., Zhou, H., Zhao, X., 2011a. Precursor or sequela: pathological disorders in people with Internet addiction disorder. PLoS One 6, e14703. Dong, G., Zhou, H., Zhao, X., 2011b. Male Internet addicts show impaired executive control ability: evidence from a color-word Stroop task. Neurosci. Lett. 499, 114–118. Dong, G., DeVito, E.E., Du, X., Cui, Z., 2012. Impaired inhibitory control in ‘internet addiction disorder’: a functional magnetic resonance imaging study. Psychiatry Res.: Neuroimaging. Dong, G., Hu, Y., Lin, X., 2013. Reward/punishment sensitivities among internet addicts: implications for their addictive behaviors. Prog. Neuropsychopharmacol. Biol. Psychiatry 46, 139–145. Durkee, T., Kaess, M., Carli, V., Parzer, P., Wasserman, C., Floderus, B., Apter, A., Balazs, J., Barzilay, S., Bobes, J., 2012. Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction 107, 2210–2222. Feng, Q., Chen, X., Sun, J., Zhou, Y., Sun, Y., Ding, W., Zhang, Y., Zhuang, Z., Xu, J., Du, Y., 2013. Voxel-level comparison of arterial spin-labeled perfusion magnetic resonance imaging in adolescents with internet gaming addiction. Behav. Brain Funct. 9, 33. Flisher, C., 2010. Getting plugged in: an overview of internet addiction. J. Paediatr. Child Health 46, 557–559. Ko, C.-H., Hsiao, S., Liu, G.-C., Yen, J.-Y., Yang, M.-J., Yen, C.-F., 2010. The characteristics of decision making, potential to take risks, and personality of college students with Internet addiction. Psychiatry Res. 175, 121–125. Leung, H.-C., Skudlarski, P., Gatenby, J.C., Peterson, B.S., Gore, J.C., 2000. An event-related functional MRI study of the Stroop color word interference task. Cerebral Cortex 10, 552–560. Meehan, T.P., Bressler, S.L., 2012. Neurocognitive networks: findings, models, and theory. Neurosci. Biobehav. Rev.. Meng, Y., Deng, W., Wang, H., Guo, W., Li, T., 2014. The prefrontal dysfunction in individuals with Internet gaming disorder: a meta-analysis of functional magnetic resonance imaging studies. Addict. Biol.. Menon, V., Uddin, L.Q., 2010. Saliency, switching, attention and control: a network model of insula function. Brain Struct. Funct. 214, 655–667. Menon, V., 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15, 483–506. Mori, S., Crain, B.J., Chacko, V., Van Zijl, P., 1999. Threedimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269. Mori, S., van Zijl, P., 2002. Fiber tracking: principles and strategies —a technical review. NMR Biomed. 15, 468–480. Napadow, V., LaCount, L., Park, K., As-Sanie, S., Clauw, D.J., Harris, R.E., 2010. Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. Arthritis Rheum. 62, 2545–2555. Pardo, J.V., Pardo, P.J., Janer, K.W., Raichle, M.E., 1990. The anterior cingulate cortex mediates processing selection in the Stroop attentional conflict paradigm. Proc. Nat. Acad. Sci. U.S.A. 87, 256–259. Pawlikowski, M., Brand, M., 2011. Excessive Internet gaming and decision making: do excessive World of Warcraft players have problems in decision making under risky conditions?. Psychiatry Res. 188, 428–433. Peterson, B.S., Kane, M.J., Alexander, G.M., Lacadie, C., Skudlarski, P., Leung, H.-C., May, J., Gore, J.C., 2002. An event-related functional MRI study comparing interference effects in the Simon and Stroop tasks. Cogn. Brain Res. 13, 427–440.

117

Qi, R., Xu, Q., Zhang, L.J., Zhong, J., Zheng, G., Wu, S., Zhang, Z., Liao, W., Zhong, Y., Ni, L., 2012. Structural and functional abnormalities of default mode network in minimal hepatic encephalopathy: a study combining DTI and fMRI. PLoS One 7, e41376. Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., 2004. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23, S208–S219. Spear, L.P., 2000. The adolescent brain and age-related behavioral manifestations. Neurosci. Biobehav. Rev. 24, 417–463. Sridharan, D., Levitin, D.J., Menon, V., 2008. A critical role for the right fronto-insular cortex in switching between centralexecutive and default-mode networks. Proc. Natl. Acad. Sci. U. S.A. 105, 12569–12574. Uddin, L.Q., Supekar, K.S., Ryali, S., Menon, V., 2011. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J. Neurosci. 31, 18578–18589. Wang, R., Benner, T., Sorensen, A., Wedeen, V., 2007. Diffusion toolkit: a software package for diffusion imaging data processing and tractography (ed.\widehateds.,). Proc. Int. Soc. Magn. Reson. Med. 15, 3720. Weng, C.-B., Qian, R.-B., Fu, X.-M., Lin, B., Han, X.-P., Niu, C.-S., Wang, Y.-H., 2013. Gray matter and white matter abnormalities in online game addiction. Eur. J. Radiol.. Widyanto, L., McMurran, M., 2004. The psychometric properties of the internet addiction test. Cyberpsychol. Behav. 7, 443–450. Xu, J., Mendrek, A., Cohen, M.S., Monterosso, J., Simon, S., Jarvik, M., Olmstead, R., Brody, A.L., Ernst, M., London, E.D., 2006. Effect of cigarette smoking on prefrontal cortical function in nondeprived smokers performing the Stroop Task. Neuropsychopharmacology 32, 1421–1428. Xue, K., Luo, C., Zhang, D., Yang, T., Li, J., Gong, D., Chen, L., Medina, Y.I., Gotman, J., Zhou, D., 2014. Diffusion tensor tractography reveals disrupted structural connectivity in childhood absence epilepsy. Epilepsy Res. 108, 125–138. Xue, T., Yuan, K., Zhao, L., Yu, D., Zhao, L., Dong, T., Cheng, P., von Deneen, K.M., Qin, W., Tian, J., 2012. Intrinsic brain network abnormalities in migraines without aura revealed in restingstate fMRI. PLoS One 7, e52927. Young, K., 2010. Internet addiction over the decade: a personal look back. World Psychiatry 9, 91 (-91). Young, K.S., 1998. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol. Behav. 1, 237–244. Young, K.S., 1999. Internet addiction: symptoms, evaluation and treatment. Innovations in Clinical Practice: A Source Book, vol. 17; 19–31. Yuan, K., Qin, W., Liu, Y., Tian, J., 2011a. Internet addiction: neuroimaging findings. Communicative Integr. Biol. 4, 637–639. Yuan, K., Qin, W., Wang, G., Zeng, F., Zhao, L., Yang, X., Liu, P., Liu, J., Sun, J., von Deneen, K.M., 2011b. Microstructure abnormalities in adolescents with internet addiction disorder. PLoS One 6, e20708. Yuan, K., Cheng, P., Dong, T., Bi, Y., Xing, L., Yu, D., Zhao, L., Dong, M., von Deneen, K.M., Liu, Y., 2013a. Cortical thickness abnormalities in late adolescence with online gaming addiction. PLoS One 8, e53055. Yuan, K., Jin, C., Cheng, P., Yang, X., Dong, T., Bi, Y., Xing, L., von Deneen, K.M., Yu, D., Liu, J., 2013b. Amplitude of low frequency fluctuation abnormalities in adolescents with online gaming addiction. PLoS One 8, e78708. Zhou, Y., Lin, F.-c., Du, Y.-s., Qin, L.-d., Zhao, Z.-m., Xu, J.-r., Lei, H., 2011. Gray matter abnormalities in Internet addiction: a voxelbased morphometry study. Eur. J. Radiol. 79, 92–95.