Differences in the structural brain network analysis between internet addicted adolescents and healthy adolescents
Min-Hee Lee1, Yoon Ho Hwang1, Areum Min1, Dong Youn Kim1, Bong Soo Han2, and Hyung Suk Seo3

1Department of Biomedical Engineering, Yonsei University, Wonju, Korea, Republic of, 2Department of Radiological Science, Yonsei University, Wonju, Korea, Republic of, 3Department of Radiology, Korea University Ansan Hospital, Ansan, Korea, Republic of


Although Internet addiction (IA) has been increasingly considered as a serious public health issue for adolescents, its neurobiological mechanisms remain poorly understood. Therefore, new biomarkers are needed to understanding IA. Using diffusion tensor images for IA and healthy adolescents, we analyzed brain network to reveal structural alterations in brain of IA adolescents. IA adolescents showed increase of regional efficiency in bilateral superior orbitofrontal cortex, right rectus and parahippocampal gyrus. Severity of IA is correlated with regional efficiency of brain regions which showed differences between groups. The brain network analysis can be used to disclose potential functional deficits in IA.


We attempted to find the alterations of the regional network property between IA and healthy adolescents.


1. Subjects. Twenty IA adolescents and twenty healthy adolescents were recruited from the Korea University Ansan Hospital. Commonly, IA was determined based on Young’s online internet addiction test (IAT)1 scores of higher than the proposed scores of 50 in Young’s criteria. The IAT has been considered as reliable instrument for classifying IA.2 Furthermore, smartphone addiction scale-short version for adolescents (SAS-SV) proposed by Kwon et al.3 is reliability and validity for assessment of smartphone addiction. In this study, we classified the healthy adolescents and IA adolescents following criteria: (1) adolescents which meet criteria of both higher than 70 of IAT score or 35 of SAS-SV score included IA group; (2) adolescents which meet criteria of both lower than 35 of IAT score and 30 of SAS-SV score included healthy group. Institutional Review Board approved the research protocol for present study and written informed consent was obtained from all subjects or their parents.

2. Data acquisition. All imaging data were acquired using a 3T MRI scanner (Siemens, Erlangen, Germany). The scanning parameters of DTI were as follows: 30 non-collinear diffusion weighting gradient directions (b=1000 s/mm2) and 12 additional images without diffusion weighting (b=0 s/mm2). In addition, T1-weighted structural images were obtained by a magnetization prepared rapid acquisition gradient echo sequence.

3. Data preprocessing. Correction for distortions due to eddy current and head motions was done by affine transformation on the b0 image using FSL.4 T1-weighted image of each subject was coregistered to the b0 image using affine transformation. The coregistered T1-weighted image was mapping to MNI space using non-linear transformation and then the transformation parameters were applied to DTI using SPM12. Whole brain fiber tracking was performed using fiber assignment by continuous tracking algorithm.

4. Network Construction. The structural brain networks were comprised of the nodes based on automated anatomical labeling template with 90 brain regions5 and the edges defined as the fiber tracts connected pair of nodes. If two endpoints of each tract belong to two nodes respectively, two nodes are considered as anatomically connected.

5. Regional property. Regional efficiency reflects the importance of the nodes for reciprocal communication within the network.6 The regional efficiency is defined as the average path length between any node and the other nodes in the brain network.7,8 For statistical analysis, we performed two sample t-test assuming equal variance.

6. Relationships between the severity of internet addiction and brain regions. To verify whether the degree of IA affect the brain regions, we used Pearson correlation analysis among IAT, SAS-SV scores and brain regions which showed significantly differences in regional efficiency.


1. Comparison of Regional property. IA adolescents had greater regional efficiency in bilateral superior orbitofrontal cortex (ORBsup), right rectus and parahippocampal gyrus (PHG) (p<0.05). No region showed lower regional efficiency in IA compared with healthy controls. The detailed information for regional efficiency is shown in Table 1 and Fig. 1.

2. Relations among IAT, SAS-SV scores and regional efficiency. As shown in Fig. 2, Pearson correlation analysis demonstrated significantly correlation between IAT scores and regional efficiency in left ORBsup (r=0.377, p=0.008), and IAT and regional efficiency in right ORBsup (r=0.328, p=0.019). SAS-SV scores were positively correlated with the regional efficiency in left ORBsup (r=0.309, p=0.026), right ORBsup (r=0.363, p=0.011), right rectus (r=0.265, p=0.049) and right PHG (r=0.328, p=0.019).


Consistent with previous IA or other addiction studies,9-11 the network analysis of the regional level showed abnormalities in multiple brain regions. The results of the regional level analysis may suggest that the structural brain connections undergo alteration during the course of the addiction and that the connections may be rewired for optimal information transfer, which result in some brain regions to be more or less crucial.12 It is likely that the brain regions may be affected by continuous visual, auditory13 and rewarding stimulation.14

We also provided the evidence that the severity of IA is correlated with regional efficiency in the brain regions with significant differences in the regional efficiency between the two groups. And according to the further progression of IA, ORBsup, rectus and PHG may be negatively affected. This may have caused progress of the functional impairments of the brains of IA adolescents.


Our findings suggest the regional efficiency as a good biomarker to assess the IA and provide understanding the reason for the functional impairments in the brains of IA adolescents.


This research was supported by National Research Foundation of Korea (NRF) funded by the Ministry of Science (2013R1A1A1012361) and the Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2010-00757).


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Table 1. The results of altered regional property.

Fig. 1. The abnormal brain regions. Red color: bilateral orbitofrontal cortex (superior); Blue color: Right rectus gyrus; Green color: Right parahippocampus gyrus.

Fig. 2. The correlation between severity of internet addiction and regional efficiency. ORBsup: orbitofrontal cortex (superior); REC: rectus gyrus; PHG: parahippocampus gyrus; L: left; R: right.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)