Skip to content

Advertisement

Behavioral and Brain Functions

Short paper
Open Access

Reduced orbitofrontal cortical thickness in male adolescents with internet addiction

  • Soon-Beom Hong1, 2, 3,
  • Jae-Won Kim3,
  • Eun-Jung Choi4,
  • Ho-Hyun Kim5,
  • Jeong-Eun Suh6,
  • Chang-Dai Kim7,
  • Paul Klauser1,
  • Sarah Whittle1,
  • Murat Yűcel1,
  • Christos Pantelis1 and
  • Soon-Hyung Yi4Email author
Behavioral and Brain Functions20139:11

https://doi.org/10.1186/1744-9081-9-11

©  Hong et al.; licensee BioMed Central Ltd. 2013

Received: 17 October 2012

Accepted: 6 March 2013

Published: 12 March 2013

Abstract

Background

The orbitofrontal cortex (OFC) has consistently been implicated in the pathology of both drug and behavioral addictions. However, no study to date has examined OFC thickness in internet addiction. In the current study, we investigated the existence of differences in cortical thickness of the OFC in adolescents with internet addiction. On the basis of recently proposed theoretical models of addiction, we predicted a reduction of thickness in the OFC of internet addicted individuals.

Findings

Participants were 15 male adolescents diagnosed as having internet addiction and 15 male healthy comparison subjects. Brain magnetic resonance images were acquired on a 3T MRI and group differences in cortical thickness were analyzed using FreeSurfer. Our results confirmed that male adolescents with internet addiction have significantly decreased cortical thickness in the right lateral OFC (p<0.05).

Conclusion

This finding supports the view that the OFC alterations in adolescents with internet addiction reflect a shared neurobiological marker of addiction-related disorders in general.

Keywords

Internet addictionMagnetic resonance imagingCortical thicknessOrbitofrontal cortex

Introduction

Internet addiction has been increasingly recognized as a mental disorder. Recent estimates of its high prevalence in young people, combined with evidence that problematic internet use is a maladaptive behavior with potentially serious occupational and mental health consequences, support the validity of the diagnosis [1]. Nevertheless, there has been much disagreement in the planning for DSM-V about how to conceptualize this relatively new condition, or its core psychopathology [2]. Given this background, identifying the existence of any biological markers would help improve the diagnostic validity [3].

The neural basis of substance addiction has been more extensively studied and is better established compared to other forms of ‘addiction’ (e.g., behavioral addictions). So far, numerous studies in the literature have implicated the role of the orbitofrontal cortex (OFC) in addiction [4–6]. Volkow and colleagues (2000, 2002) suggest that the OFC is one of the most implicated frontal cortical areas in drug addiction [4, 7]. Recently, researchers in our group have reported a longitudinal prospective study result suggesting that structural abnormalities in the OFC might predate and contribute to risk for later cannabis use in young people [8]. Similarly, although few studies have been conducted, the reported functional and structural alterations observed in internet addiction have been relatively consistent in demonstrating altered OFC function (especially in the right hemisphere) and structure [9–14].

We hypothesized that adolescents with internet addiction would show structural abnormalities of the OFC, preferentially in the right hemisphere. Specifically, we conducted a case–control comparison of cortical thickness in adolescents with and without internet addiction, particularly focusing on excessive online gaming, which is among the major subtypes of this disorder [15].

Materials and methods

Subjects

Fifteen right-handed male adolescents with internet addiction were recruited through advertisement in Seoul National University Hospital. The recruitment was performed between February and June 2011. In order to establish the diagnosis of internet addiction we used the Young Internet Addiction Scale (YIAS) [16]. In addition, participants were confined to those reporting to have experienced typical components of addiction with their online gaming including: tolerance, withdrawal, preoccupation with playing it, repeated unsuccessful attempts to reduce or stop it, negatively influenced mood when attempting to reduce it, and neglecting important relationships or activities because of it [17, 18]. In order to exclude any comorbid psychiatric disorders, we used the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (K-SADS-PL) [19]. Healthy male adolescents were recruited through advertisement in local schools and were screened using the same assessment tools described above. Exclusion criteria for both groups were any axis I psychiatric disorder including substance abuse, epilepsy or other neurological disorders, and past history of severe head trauma. The prevalence of internet addiction has been estimated to be much higher in males than in females [1]. Given that males are over-represented in internet-addicted populations, and given time and budget limitations, we decided to focus only on male adolescents. This study was approved by the institutional review board for human subjects at the Seoul National University. All the adolescents and their parents provided written informed consent prior to study entry.

Data acquisition

Whole brain T1-weighted MR images were acquired on a 3T Siemens scanner (Siemens Magnetom Trio Tim Syngo MR B17, Germany) with the following parameters: TR 1900 ms; TE 2.36 ms; inversion time 700 ms; flip angle 9°; voxel size 1.0 mm3; slices 224. Head motions were minimized by filling the empty space around the head with sponge material and fixing the lower jaw with tape.

Image processing

Cortical thickness was estimated using FreeSurfer 5.1.0 (Massachusetts General Hospital, Boston, MA, US), a set of software tools that provides a semi-automated method to investigate brain morphometry. The surface-based stream involves (i) normalization of brain signal intensity, (ii) skull-stripping, (iii) segmentation of gray and white matter, (iv) delineation of the gray-white interface (inner surface), and (v) tracing of the pial (outer) surface. The distance between equivalent vertices in these two surfaces represents the cortical thickness. The whole cortex of each subject was visually inspected and systematically corrected for errors in a blind manner to the group status of the participants. We used our sample to generate an average target surface and the data for each participant were pre-smoothed with a full-width half-maximum Gaussian kernel of 10 mm prior to statistical analyses.

Data analysis

Region-of-interest (ROI)-based analyses were conducted comparing the cortical thickness of the OFC generated by FreeSurfer based on the Desikan-Killiany atlas [20]. More specifically, the Desikan-Killiany atlas defines the lateral and medial divisions of the OFC. The rostral/caudal and medial/lateral boundaries of these two structures are the rostral extent of the lateral orbital gyrus/the caudal portion of the lateral orbital gyrus and the midpoint of the olfactory sulcus/the lateral bank of the lateral orbital sulcus (and/or the circular insular sulcus) for the lateral OFC; and the rostral extent of the medial orbital gyrus/the caudal portion of the medial orbital gyrus (or gyrus rectus) and the cingulate cortex/the medial bank of the superior frontal gyrus for the medial OFC, respectively [20]. Analysis of covariance (ANCOVA) using the general linear model (GLM) was performed with SPSS 19.0 (SPSS Inc., Chicago, IL, USA), and the main effect of group (internet addiction vs. healthy control) was analyzed controlling for age, intelligence quotient (IQ), and intracranial volume (ICV). Results of this analysis were reported with a significance threshold of p<0.05 (two-tailed).

Results

Participant characteristics

The groups of adolescents with and without internet addiction significantly differed in age (13.33±2.84 for internet addiction; 15.40±1.24 for control; p=0.018). IQ was comparable across both groups (103.80±15.84 for internet addiction; 109.06±9.84 for control; p=0.283), and a significant difference in ICV was found (1434.42±158.33 cm3 for internet addiction; 1577.21±183.12 cm3 for control; p=0.030). The YIAS score was significantly higher in the internet addiction group (57.26±16.11 for internet addiction; 37.60±9.72 for control; p=0.000).

ROI-based analyses

The analysis revealed four ROIs with significant differences in cortical thickness (p<0.05), which were the lateral OFC, isthmus of the cingulate cortex, and pars orbitalis in the right hemisphere and lateral occipital cortex in the left hemisphere, all displaying cortical thinning in adolescents with internet addiction compared to healthy controls (Table 1).
Table 1

Parcellated region-of-interest-based comparison of cortical thickness between adolescents with internet addiction and healthy controls

Comparison

Region

Side

Cortical thickness (mm)

F

p-value

R2

   

Internet

Control

   

Internet < Control

Lateral orbitofrontal

Right

2.78±0.21

2.80±0.18

6.609

0.016

0.395

Internet < Control

Cingulate (isthmus)

Right

2.81±0.31

2.84±0.26

6.813

0.015

0.437

Internet < Control

Lateral occipital

Left

2.48±0.15

2.48±0.08

9.323

0.005

0.581

Internet < Control

Pars orbitalis

Right

2.75±0.20

2.84±0.32

5.266

0.030

0.258

Cortical thickness is given as mean ± standard deviation.

Secondary analysis

To provide a complementary perspective of our finding, a surface-based whole brain analysis using FreeSurfer’s Qdec (version 1.4) was performed by fitting a between-subject GLM at each surface vertex to compare cortical thickness between the groups (uncorrected, p<0.001). As illustrated in Figure 1, reduction in lateral OFC thickness was replicated by this analysis.
Figure 1
Figure 1

Vertex-wise whole brain comparison of cortical thickness between adolescents with internet addiction and healthy controls. Red color indicates cortical thickness is greater in adolescents with internet addiction, and blue color indicates cortical thickness is greater in controls.

Discussion

This is the first structural brain imaging study of cortical thickness in adolescents with internet addiction. Consistent with our hypothesis, the results indicated a reduced thickness of OFC in the internet addiction group compared to healthy controls. This finding is in accord with the results of former neuroimaging studies of internet addiction [9–14], and supports the theoretical model of addiction disorders, which emphasizes the involvement of the OFC.

The current finding on internet addiction supports the results of former studies on substance addiction, including ours [8], that have argued that the right OFC plays an important role in the biological mechanism of addiction disorders more broadly. The result of this study is not only in line with numerous other findings in the literature implicating the role of the OFC in addiction [4–6], but also with those indicating that this brain region in the right hemisphere might be particularly important [21].

In this study, only the lateral and not the medial OFC was shown to be significantly different in adolescents with internet addiction. The reason for this finding is not clear, but there have been multiple studies reporting different functions between the lateral and medial OFC, especially in reward-associated decision-making [22]. For example, the medial OFC has been found to be preferentially activated in choices involving immediate rewards, whereas the lateral OFC has been implicated in choices concerning delayed rewards or suppression of previously rewarded responses [23, 24]. It is noteworthy that pars orbitalis, which is laterally adjacent to the lateral OFC, also showed significant cortical thinning in adolescents with internet addiction. This finding supports that the cortical thinning is particularly located in the lateral part of the OFC, without or less involving medial OFC. Further work is warranted on potentially different lateral and medial OFC functions.

Lateral OFC has also been implicated in cognitive flexibility deficits and in the genesis of pathological habits [25]. In this regard, Chamberlain and colleagues (2008) demonstrated that the lateral OFC can be central to neurobiological models of obsessive-compulsive disorder (OCD) [26]. Behavioral addictions are often considered to share similar features with other known disorders including OCD [2], involving specific difficulties in refraining from a certain behavior that causes serious personal consequences. Rotge and colleagues (2008, 2010), based on their previous meta-analyses [27, 28], investigated overlapping brain regions between the anatomical and functional brain maps that showed significant gray matter density change and activity during symptom provocation, respectively, in patients with OCD: the authors found that the only overlapping brain region was the lateral OFC. Recently, Zhou and colleagues (2012) have shown impaired mental flexibility along with poor response inhibition in young adults with internet addiction [29]. The implications of reduced cortical thickness in lateral OFC, in relation to its role in internet addiction as well as other conditions with similar neurobehavioral features, are subject to future studies.

The present study has some important limitations. Above all, a different age distribution between the groups was a critical limitation of this study. However, previous reports on normal brain development have shown that cortical thickness peaks at approximately 8–9 years of age and then global cortical thinning begins afterward [30]. Of note is that all the participants in our study were over this age. Therefore, in adolescents, younger individuals tend to have thicker cortex; our finding of thinner right OFC in the younger internet addiction group thus suggests that the age difference in groups was unlikely to have affected the results. Second, we did not measure the duration of internet addiction. Third, the study participants in the internet addiction group were excessive online gamers, and therefore, the current findings may entail limited generalizability to other subtypes of internet addiction [15].

In summary, the results of the present study indicate preliminary findings of reduced thickness of the right OFC in adolescents with internet addiction. The findings are further suggestive of a shared neurobiological mechanism between internet addiction and other addictive disorders.

Declarations

Acknowledgements

This work was supported by the Seoul National University Brain Fusion Program Research Fund. SBH was supported by a National Research Foundation of Korea (NRF) grant (Global Internship Program) funded by the Korean government (MEST). MY was supported by an NHMRC fellowship grant (#1021973). The funders had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Authors’ Affiliations

(1)
Melbourne Neuropsychiatry Center, Department of Psychiatry, University of Melbourne and Melbourne Health, Parkville, Australia
(2)
Florey Institute of Neuroscience and Mental Health, Parkville, Australia
(3)
Division of Child and Adolescent Psychiatry, Department of Psychiatry, College of Medicine, Seoul National University, Seoul, Republic of Korea
(4)
Department of Child Development and Family Studies, College of Human Ecology, Seoul National University, Seoul, Republic of Korea
(5)
Interdisciplinary Program (Early Childhood Education Major), College of Education, Seoul National University, Seoul, Republic of Korea
(6)
Center for Campus Life & Culture, Seoul National University, Seoul, Republic of Korea
(7)
Department of Education (Educational Counseling Major), College of Education, Seoul National University, Seoul, Republic of Korea

References

  1. Ko CH, Yen JY, Yen CF, Chen CS, Chen CC: The association between Internet addiction and psychiatric disorder: A review of the literature. Eur Psychiat. 2012, 27: 1-8.View ArticleGoogle Scholar
  2. Holden C: 'Behavioral' addictions: do they exist?. Sci. 2001, 294: 980-982. 10.1126/science.294.5544.980.View ArticleGoogle Scholar
  3. Goldney RD: The utility of the DSM nosology of mood disorders. Can J Psychiatry. 2006, 51: 874-878.PubMedGoogle Scholar
  4. Volkow ND, Fowler JS: Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex. Cereb Cortex. 2000, 10: 318-325. 10.1093/cercor/10.3.318.View ArticlePubMedGoogle Scholar
  5. London ED, Ernst M, Grant S, Bonson K, Weinstein A: Orbitofrontal cortex and human drug abuse: functional imaging. Cereb Cortex. 2000, 10: 334-342. 10.1093/cercor/10.3.334.View ArticlePubMedGoogle Scholar
  6. Dom G, Sabbe B, Hulstijn W, van den Brink W: Substance use disorders and the orbitofrontal cortex: systematic review of behavioural decision-making and neuroimaging studies. Br J Psychiatry. 2005, 187: 209-220. 10.1192/bjp.187.3.209.View ArticlePubMedGoogle Scholar
  7. Goldstein RZ, Volkow ND: Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry. 2002, 159: 1642-1652. 10.1176/appi.ajp.159.10.1642.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Cheetham A, Allen NB, Whittle S, Simmons JG, Yucel M, Lubman DI: Orbitofrontal volumes in early adolescence predict initiation of cannabis use: a 4-year longitudinal and prospective study. Biol Psychiatry. 2012, 71: 684-692. 10.1016/j.biopsych.2011.10.029.View ArticlePubMedGoogle Scholar
  9. Dong G, Huang J, Du X: Enhanced reward sensitivity and decreased loss sensitivity in Internet addicts: an fMRI study during a guessing task. J Psychiatr Res. 2011, 45: 1525-1529. 10.1016/j.jpsychires.2011.06.017.View ArticlePubMedGoogle Scholar
  10. Han DH, Bolo N, Daniels MA, Arenella L, Lyoo IK, Renshaw PF: Brain activity and desire for Internet video game play. Compr Psychiatry. 2011, 52: 88-95. 10.1016/j.comppsych.2010.04.004.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Han DH, Kim YS, Lee YS, Min KJ, Renshaw PF: Changes in cue-induced, prefrontal cortex activity with video-game play. Cyberpsychol Behav Soc Netw. 2010, 13: 655-661. 10.1089/cyber.2009.0327.View ArticlePubMedGoogle Scholar
  12. Ko CH, Liu GC, Hsiao S, Yen JY, Yang MJ, Lin WC, Yen CF, Chen CS: Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res. 2009, 43: 739-747. 10.1016/j.jpsychires.2008.09.012.View ArticlePubMedGoogle Scholar
  13. Yuan K, Qin W, Wang G, Zeng F, Zhao L, Yang X, Liu P, Liu J, Sun J, von Deneen KM: Microstructure abnormalities in adolescents with internet addiction disorder. PLoS One. 2011, 6: e20708-10.1371/journal.pone.0020708.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Park HS, Kim SH, Bang SA, Yoon EJ, Cho SS, Kim SE: Altered regional cerebral glucose metabolism in internet game overusers: a 18F-fluorodeoxyglucose positron emission tomography study. CNS Spectr. 2010, 15: 159-166.PubMedGoogle Scholar
  15. Block JJ: Issues for DSM-V: internet addiction. Am J Psychiatry. 2008, 165: 306-307. 10.1176/appi.ajp.2007.07101556.View ArticlePubMedGoogle Scholar
  16. Widyanto L, McMurran M: The psychometric properties of the internet addiction test. Cyberpsychol Behav. 2004, 7: 443-450. 10.1089/cpb.2004.7.443.View ArticlePubMedGoogle Scholar
  17. Christakis DA: Internet addiction: a 21st century epidemic?. BMC Med. 2010, 8: 61-10.1186/1741-7015-8-61.PubMed CentralView ArticlePubMedGoogle Scholar
  18. Flisher C: Getting plugged in: an overview of internet addiction. J Paediatr Child Health. 2010, 46: 557-559. 10.1111/j.1440-1754.2010.01879.x.View ArticlePubMedGoogle Scholar
  19. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, Williamson D, Ryan N: Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997, 36: 980-988. 10.1097/00004583-199707000-00021.View ArticlePubMedGoogle Scholar
  20. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006, 31: 968-980. 10.1016/j.neuroimage.2006.01.021.View ArticlePubMedGoogle Scholar
  21. Tessner KD, Hill SY: Neural circuitry associated with risk for alcohol use disorders. Neuropsychol Rev. 2010, 20: 1-20. 10.1007/s11065-009-9111-4.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Mar AC, Walker ALJ, Theobald DE, Eagle DM, Robbins TW: Dissociable Effects of Lesions to Orbitofrontal Cortex Subregions on Impulsive Choice in the Rat. J Neurosci. 2011, 31: 6398-6404. 10.1523/JNEUROSCI.6620-10.2011.PubMed CentralView ArticlePubMedGoogle Scholar
  23. Elliott R, Dolan RJ, Frith CD: Dissociable functions in the medial and lateral orbitofrontal cortex: Evidence from human neuroimaging studies. Cereb Cortex. 2000, 10: 308-317. 10.1093/cercor/10.3.308.View ArticlePubMedGoogle Scholar
  24. McClure SM, Laibson DI, Loewenstein G, Cohen JD: Separate neural systems value immediate and delayed monetary rewards. Sci. 2004, 306: 503-507. 10.1126/science.1100907.View ArticleGoogle Scholar
  25. Rotge JY, Langbour N, Jaafari N, Guehl D, Bioulac B, Aouizerate B, Allard M, Burbaud P: Anatomical alterations and symptom-related functional activity in obsessive-compulsive disorder are correlated in the lateral orbitofrontal cortex. Biol Psychiatry. 2010, 67: e37-e38. 10.1016/j.biopsych.2009.10.007.View ArticlePubMedGoogle Scholar
  26. Chamberlain SR, Menzies L, Hampshire A, Suckling J, Fineberg NA, del Campo N, Aitken M, Craig K, Owen AM, Bullmore ET: Orbitofrontal dysfunction in patients with obsessive-compulsive disorder and their unaffected relatives. Sci. 2008, 321: 421-422. 10.1126/science.1154433.View ArticleGoogle Scholar
  27. Rotge JY, Langbour N, Guehl D, Bioulac B, Jaafari N, Allard M, Aouizerate B, Burbaud P: Gray matter alterations in obsessive-compulsive disorder: an anatomic likelihood estimation meta-analysis. Neuropsychopharmacol. 2010, 35: 686-691. 10.1038/npp.2009.175.View ArticleGoogle Scholar
  28. Rotge JY, Guehl D, Dilharreguy B, Cuny E, Tignol J, Bioulac B, Allard M, Burbaud P, Aouizerate B: Provocation of obsessive-compulsive symptoms: a quantitative voxel-based meta-analysis of functional neuroimaging studies. J Psychiatry Neurosci. 2008, 33: 405-412.PubMed CentralPubMedGoogle Scholar
  29. Zhou Z, Yuan G, Yao J: Cognitive biases toward Internet game-related pictures and executive deficits in individuals with an Internet game addiction. PLoS One. 2012, 7: e48961-10.1371/journal.pone.0048961.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Shaw P, Greenstein D, Lerch J, Clasen L, Lenroot R, Gogtay N, Evans A, Rapoport J, Giedd J: Intellectual ability and cortical development in children and adolescents. Nature. 2006, 440: 676-679. 10.1038/nature04513.View ArticlePubMedGoogle Scholar

Copyright

© Hong et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement

Behavioral and Brain Functions

ISSN: 1744-9081

Contact us