Abstract
Objective neuroimaging markers are imminently in need for more accurate clinical diagnosis of Internet gaming disorder (IGD). Recent neuroimaging evidence suggested that IGD is associated with abnormalities in the mesolimbic dopamine (DA) system. As the key nodes of the DA pathways, ventral tegmental area (VTA) and substantia nigra (SN) and their connected brain regions may serve as potential markers to identify IGD. Therefore, we aimed to develop optimal classifiers to identify IGD individuals by using VTA and bilateral SN resting-state functional connectivity (RSFC) patterns. A dataset including 146 adolescents (66 IGDs and 80 healthy controls (HCs)) was used to build classification models and another independent dataset including 28 subjects (14 IGDs and 14 HCs) was employed to validate the generalization ability of the models. Multi-voxel pattern analysis (MVPA) with linear support vector machine (SVM) was used to select the features. Our results demonstrated that the VTA RSFC circuits successfully identified IGD individuals (mean accuracy: 86.1%, mean sensitivity: 84.5%, mean specificity: 86.6%, the mean area under the receiver operating characteristic curve: 0.91). Furthermore, the independent generalization ability of the VTA RSFC classifier model was also satisfied (accuracy = 78.5%, sensitivity = 71.4%, specificity = 85.8%). The VTA connectivity circuits that were selected as distinguishing features were mainly included bilateral thalamus, right hippocampus, right pallidum, right temporal pole superior gyrus and bilateral temporal superior gyrus. These findings demonstrated that the potential of the resting-state neuroimaging features of VTA RSFC as objective biomarkers for the IGD clinical diagnosis in the future.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381. https://doi.org/10.1146/annurev.ne.09.030186.002041.
Aston-Jones, G. (2015). Introduction to the special issue: "Role of corticostriatal circuits in addiction". Brain Res, 1628(Pt A), 1. https://doi.org/10.1016/j.brainres.2015.10.046.
Behrens, T. E., Johansen-Berg, H., Woolrich, M. W., Smith, S. M., Wheeler-Kingshott, C. A., Boulby, P. A., et al. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience, 6(7), 750–757. https://doi.org/10.1038/nn1075.
Cabral, C., Kambeitz-Ilankovic, L., Kambeitz, J., Calhoun, V. D., Dwyer, D. B., von Saldern, S., Urquijo, M. F., Falkai, P., & Koutsouleris, N. (2016). Classifying schizophrenia using multimodal multivariate pattern recognition analysis: Evaluating the impact of individual clinical profiles on the Neurodiagnostic performance. Schizophrenia Bulletin, 42(Suppl 1), S110–S117. https://doi.org/10.1093/schbul/sbw053.
Chakraborty, S., Kolling, N., Walton, M. E., & Mitchell, A. S. (2016). Critical role for the mediodorsal thalamus in permitting rapid reward-guided updating in stochastic reward environments. Elife, 5. doi:https://doi.org/10.7554/eLife.13588.
Cheng, H., Skosnik, P. D., Pruce, B. J., Brumbaugh, M. S., Vollmer, J. M., Fridberg, D. J., O’Donnell, B. F., Hetrick, W. P., & Newman, S. D. (2014). Resting state functional magnetic resonance imaging reveals distinct brain activity in heavy cannabis users - a multi-voxel pattern analysis. Journal of Psychopharmacology, 28(11), 1030–1040. https://doi.org/10.1177/0269881114550354.
Cui, Z., & Gong, G. (2018). The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage, 178, 622–637. https://doi.org/10.1016/j.neuroimage.2018.06.001.
Ding, W. N., Sun, J. H., Sun, Y. W., Zhou, Y., Li, L., Xu, J. R., & Du, Y. S. (2013). Altered default network resting-state functional connectivity in adolescents with internet gaming addiction. PLoS One, 8(3), e59902. https://doi.org/10.1371/journal.pone.0059902.
Ding, X., Yang, Y., Stein, E. A., & Ross, T. J. (2015). Multivariate classification of smokers and nonsmokers using SVM-RFE on structural MRI images. Human Brain Mapping, 36(12), 4869–4879. https://doi.org/10.1002/hbm.22956.
Dong, G., DeVito, E., Huang, J., & Du, X. (2012). Diffusion tensor imaging reveals thalamus and posterior cingulate cortex abnormalities in internet gaming addicts. Journal of Psychiatric Research, 46(9), 1212–1216. https://doi.org/10.1016/j.jpsychires.2012.05.015.
Dosenbach, N. U., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., et al. (2010). Prediction of individual brain maturity using fMRI. Science, 329(5997), 1358–1361. https://doi.org/10.1126/science.1194144.
Downing, P. E., Wiggett, A. J., & Peelen, M. V. (2007). Functional magnetic resonance imaging investigation of overlapping lateral occipitotemporal activations using multi-voxel pattern analysis. The Journal of Neuroscience, 27(1), 226–233. https://doi.org/10.1523/jneurosci.3619-06.2007.
Drui, G., Carnicella, S., Carcenac, C., Favier, M., Bertrand, A., Boulet, S., & Savasta, M. (2014). Loss of dopaminergic nigrostriatal neurons accounts for the motivational and affective deficits in Parkinson's disease. Molecular Psychiatry, 19(3), 358–367. https://doi.org/10.1038/mp.2013.3.
Fan, Y., Liu, Y., Wu, H., Hao, Y., Liu, H., Liu, Z., & Jiang, T. (2011). Discriminant analysis of functional connectivity patterns on Grassmann manifold. Neuroimage, 56(4), 2058–2067. https://doi.org/10.1016/j.neuroimage.2011.03.051.
Gu, H., Salmeron, B. J., Ross, T. J., Geng, X., Zhan, W., Stein, E. A., & Yang, Y. (2010). Mesocorticolimbic circuits are impaired in chronic cocaine users as demonstrated by resting-state functional connectivity. Neuroimage, 53(2), 593–601. https://doi.org/10.1016/j.neuroimage.2010.06.066.
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning, 46(1–3), 389–422.
Haber, S., & McFarland, N. R. (2001). The place of the thalamus in frontal cortical-basal ganglia circuits. Neuroscientist, 7(4), 315–324. https://doi.org/10.1177/107385840100700408.
Haxby, J. V. (2012). Multivariate pattern analysis of fMRI: The early beginnings. Neuroimage, 62(2), 852–855. https://doi.org/10.1016/j.neuroimage.2012.03.016.
Haxby, J. V., Connolly, A. C., & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37, 435–456. https://doi.org/10.1146/annurev-neuro-062012-170325.
Haynes, J. D., & Rees, G. (2005). Predicting the stream of consciousness from activity in human visual cortex. Current Biology, 15(14), 1301–1307. https://doi.org/10.1016/j.cub.2005.06.026.
Huang, A. S., Mitchell, J. A., Haber, S. N., Alia-Klein, N., & Goldstein, R. Z. (2018). The thalamus in drug addiction: From rodents to humans. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 373(1742), 20170028. https://doi.org/10.1098/rstb.2017.0028.
Jin, C., Zhang, T., Cai, C., Bi, Y., Li, Y., Yu, D., Zhang, M., & Yuan, K. (2016). Abnormal prefrontal cortex resting state functional connectivity and severity of internet gaming disorder. Brain Imaging and Behavior, 10(3), 719–729. https://doi.org/10.1007/s11682-015-9439-8.
Keleta, Y. B., & Martinez, J. L. (2012). Brain circuits of methamphetamine place reinforcement learning: The role of the Hippocampus-VTA loop. Brain and Behavior: A Cognitive Neuroscience Perspective, 2(2), 128–141. https://doi.org/10.1002/brb3.35.
Kim, S. H., Baik, S. H., Park, C. S., Kim, S. J., Choi, S. W., & Kim, S. E. (2011). Reduced striatal dopamine D2 receptors in people with internet addiction. Neuroreport, 22(8), 407–411. https://doi.org/10.1097/WNR.0b013e328346e16e.
Koepp, M. J., Gunn, R. N., Lawrence, A. D., Cunningham, V. J., Dagher, A., Jones, T., Brooks, D. J., Bench, C. J., & Grasby, P. M. (1998). Evidence for striatal dopamine release during a video game. Nature, 393(6682), 266–268. https://doi.org/10.1038/30498.
Markey, P. M., & Ferguson, C. J. (2017). Internet gaming addiction: Disorder or moral panic? The American Journal of Psychiatry, 174(3), 195–196. https://doi.org/10.1176/appi.ajp.2016.16121341.
Meng, X., Jiang, R., Lin, D., Bustillo, J., Jones, T., Chen, J., et al. (2017). Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data. Neuroimage, 145(Pt B), 218–229. https://doi.org/10.1016/j.neuroimage.2016.05.026.
Mitchell, A. S., Browning, P. G., & Baxter, M. G. (2007). Neurotoxic lesions of the medial mediodorsal nucleus of the thalamus disrupt reinforcer devaluation effects in rhesus monkeys. The Journal of Neuroscience, 27(42), 11289–11295. https://doi.org/10.1523/jneurosci.1914-07.2007.
Mori, T., Iwase, Y., Saeki, T., Iwata, N., Murata, A., Masukawa, D., & Suzuki, T. (2016). Differential activation of dopaminergic systems in rat brain basal ganglia by morphine and methamphetamine. Neuroscience, 322, 164–170. https://doi.org/10.1016/j.neuroscience.2016.01.043.
Murty, V. P., Shermohammed,M., Smith, D. V., Carter, R. M., Huettel, S. A., & Adcock, R. A. (2014). Resting state networks distinguish human ventral tegmental area from substantia nigra. NeuroImage, 100, 580–589
Petry, N. M., & O’Brien, C. P. (2013). Internet gaming disorder and the DSM-5. Addiction, 108(7), 1186–1187.
Przybylski, A. K., Weinstein, N., & Murayama, K. (2017). Internet gaming disorder: Investigating the clinical relevance of a new phenomenon. The American Journal of Psychiatry, 174(3), 230–236. https://doi.org/10.1176/appi.ajp.2016.16020224.
Ray, J. P., & Price, J. L. (1993). The organization of projections from the mediodorsal nucleus of the thalamus to orbital and medial prefrontal cortex in macaque monkeys. The Journal of Comparative Neurology, 337(1), 1–31. https://doi.org/10.1002/cne.903370102.
Sun, Y., Ying, H., Seetohul, R. M., Xuemei, W., Ya, Z., Qian, L., Guoqing, X., & Ye, S. (2012). Brain fMRI study of crave induced by cue pictures in online game addicts (male adolescents). Behavioural Brain Research, 233(2), 563–576. https://doi.org/10.1016/j.bbr.2012.05.005.
Tian, M., Chen, Q., Zhang, Y., Du, F., Hou, H., Chao, F., & Zhang, H. (2014). PET imaging reveals brain functional changes in internet gaming disorder. European Journal of Nuclear Medicine and Molecular Imaging, 41(7), 1388–1397. https://doi.org/10.1007/s00259-014-2708-8.
Tomasi, D., & Volkow, N. D. (2014). Functional connectivity of substantia nigra and ventral tegmental area: Maturation during adolescence and effects of ADHD. Cerebral Cortex, 24(4), 935–944. https://doi.org/10.1093/cercor/bhs382.
Volkow, N. D., & Morales, M. (2015). The brain on drugs: From reward to addiction. Cell, 162(4), 712–725. https://doi.org/10.1016/j.cell.2015.07.046.
Volkow, N. D., Wang, G. J., Fowler, J. S., Tomasi, D., & Telang, F. (2011). Addiction: Beyond dopamine reward circuitry. Proceedings of the National Academy of Sciences of the United States of America, 108(37), 15037–15042. https://doi.org/10.1073/pnas.1010654108.
Wang, R., Li, M., Zhao, M., Yu, D., Hu, Y., Wiers, C. E., Wang, G. J., Volkow, N. D., & Yuan, K. (2019). Internet gaming disorder: Deficits in functional and structural connectivity in the ventral tegmental area-Accumbens pathway. Brain Imaging and Behavior, 13(4), 1172–1181. https://doi.org/10.1007/s11682-018-9929-6.
Weinstein, A., Livny, A., & Weizman, A. (2017). New developments in brain research of internet and gaming disorder. Neuroscience and Biobehavioral Reviews, 75, 314–330. https://doi.org/10.1016/j.neubiorev.2017.01.040.
Wise, R. A. (2009). Roles for nigrostriatal--not just mesocorticolimbic--dopamine in reward and addiction. Trends in Neurosciences, 32(10), 517–524. https://doi.org/10.1016/j.tins.2009.06.004.
Young, S. K. (1998). Internet addiction: The emergence of a new clinical disorder. Cyberpsychology & Behavior, 1(3), 237–244.
Young, K. S. (1996). Psychology of computer use: XL. Addictive use of the internet: A case that breaks the stereotype. Psychological Reports, 79(3 Pt 1), 899–902. https://doi.org/10.2466/pr0.1996.79.3.899.
Yu, D., Yuan, K., Bi, Y., Luo, L., Zhai, J., Liu, B., Li, Y., Cheng, J., Guan, Y., Xue, T., Bu, L., Su, S., Ma, Y., Qin, W., Tian, J., & Lu, X. (2018). Altered interhemispheric resting-state functional connectivity in young male smokers. Addiction Biology, 23(2), 772–780. https://doi.org/10.1111/adb.12515.
Yuan, K., Qin, W., Liu, J., Guo, Q., & Tian, J. (2010). Altered small-world brain functional networks and duration of heroin use in male abstinent heroin-dependent individuals. Neuroscience Letters, 477(1), 37–42.
Yuan, K., Qin, W., Wang, G., Zeng, F., Zhao, L., Yang, X., Liu, P., Liu, J., Sun, J., von Deneen, K. M., Gong, Q., Liu, Y., & Tian, J. (2011). Microstructure abnormalities in adolescents with internet addiction disorder. PLoS One, 6(6), e20708. https://doi.org/10.1371/journal.pone.0020708.
Yuan, K., Yu, D., Bi, Y., Li, Y., Guan, Y., Liu, J., Zhang, Y., Qin, W., Lu, X., & Tian, J. (2016). The implication of frontostriatal circuits in young smokers: A resting-state study. Human Brain Mapping, 37(6), 2013–2026.
Yuan, K., Yu, D., Bi, Y., Wang, R., Li, M., Zhang, Y., et al. (2017a). The left dorsolateral prefrontal cortex and caudate pathway: New evidence for cue-induced craving of smokers. Human Brain Mapping, 38(9), 4644–4656.
Yuan, K., Yu, D., Cai, C., Feng, D., Li, Y., Bi, Y., Liu, J., Zhang, Y., Jin, C., Li, L., Qin, W., & Tian, J. (2017b). Frontostriatal circuits, resting state functional connectivity and cognitive control in internet gaming disorder. Addiction Biology, 22(3), 813–822. https://doi.org/10.1111/adb.12348.
Yuan, K., Zhao, L., Cheng, P., Yu, D., Zhao, L., Dong, T., et al. (2013). Altered structure and resting-state functional connectivity of the basal ganglia in migraine patients without Aura. Journal of Pain, 14(8), 836–844.
Zastrow, & Mark. (2017). News feature: Is video game addiction really an addiction? Proceedings of the National Academy of Sciences of the United States of America, 114(17), 4268–4272.
Zhang, J. T., & Brand, M. (2018). Editorial: Neural mechanisms underlying internet gaming disorder. Frontiers in Psychiatry, 9, 404. https://doi.org/10.3389/fpsyt.2018.00404.
Zhang, J. T., Ma, S. S., Yip, S. W., Wang, L. J., Chen, C., Yan, C. G., Liu, L., Liu, B., Deng, L. Y., Liu, Q. X., & Fang, X. Y. (2015). Decreased functional connectivity between ventral tegmental area and nucleus accumbens in internet gaming disorder: Evidence from resting state functional magnetic resonance imaging. Behavioral and Brain Functions, 11(1), 37. https://doi.org/10.1186/s12993-015-0082-8.
Zhang, S., Hu, S., Chao, H. H., & Li, C. S. (2016). Resting-state functional connectivity of the locus Coeruleus in humans: In comparison with the ventral tegmental area/Substantia Nigra pars Compacta and the effects of age. Cerebral Cortex, 26(8), 3413–3427. https://doi.org/10.1093/cercor/bhv172.
Zhong, X., Shi, H., Ming, Q., Dong, D., Zhang, X., Zeng, L. L., & Yao, S. (2017). Whole-brain resting-state functional connectivity identified major depressive disorder: A multivariate pattern analysis in two independent samples. Journal of Affective Disorders, 218, 346–352. https://doi.org/10.1016/j.jad.2017.04.040.
Zweig, M. H., & Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clinical Chemistry, 39(4), 561–577.
Acknowledgements
The authors thank the staff of Radiology Department of Ethical Committee of Inner Mongolia University of Science and Renji Hospital Affiliated to Shanghai Jiaotong University.
Funding
This work is supported by the National Natural Science Foundation of China under Grant Nos. 81871426, 81871430, 61771266, 31800926 and 81701780, the program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region NJYT-17-B11, the Natural Science Foundation of Inner Mongolia under Grant No. 2019JQ07, 2017MS(LH)0814, 2018LH08079, the Project of Guangxi Science and Technology (GuiKeAD19110133); the Guangxi Natural Science Foundation (Grant No: 2017GXNSFBA198221), the program of Science and Technology in Universities of Inner Mongolia Autonomous Region NJZY17262, the Innovation Fund Project of Inner Mongolia University of Science and Technology No. 2015QNGG03, National Natural Science Foundation of Shaanxi Province under Grant no.2018JM7075.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Informed consent
Informed consent was obtained from all individual participants included in the study.
Ethics approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Conflict of interest
The authors declare that we have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 707 kb)
Rights and permissions
About this article
Cite this article
Wen, X., Sun, Y., Hu, Y. et al. Identification of internet gaming disorder individuals based on ventral tegmental area resting-state functional connectivity. Brain Imaging and Behavior 15, 1977–1985 (2021). https://doi.org/10.1007/s11682-020-00391-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-020-00391-7