The neurobiology of smartphone addiction in emerging adults evaluated using brain morphometry and resting-state functional MRI
Keywords:behavioural addiction, Internet addiction, problematic smartphone use, resting-state functional magnetic resonance imaging, voxel-based morphometry
The characteristics of smartphone addiction (SPA) can be evaluated by neuroimaging studies. Information on the brain structural alterations, and effects on psychosocial wellbeing, however, have not been concurrently evaluated. The aim of this study was to identify abnormalities in gray matter volume using voxel-based morphometry (VBM) and neuronal functional alterations using resting-state functional MRI (rs-fMRI) in emerging adults with SPA. We correlated the neuroimaging parameters with indices for psychosocial wellbeing such as depression, anxiety, stress, and impulsivity. Forty participants (20 SPA and 20 age-matched healthy controls) were assessed using VBM and rs-fMRI. The smartphone addiction scale – Malay version (SAS-M) questionnaire scores were used to categorize the SPA and healthy control groups. DASS-21 and BIS-11 questionnaires were used to assess for psychosocial wellbeing and impulsivity, respectively. VBM identified the SPA group to have reduced gray matter volume in the insula and precentral gyrus; and increased grey matter volume in the precuneus relative to controls. Moderate correlation was observed between the precuneus volume and the SAS-M scores. Individuals with SPA showed significant rs-fMRI activations in the precuneus, and posterior cingulate cortex (FWE uncorrected, p<0.001). The severity of SPA was correlated with depression. Anxiety score was moderately correlated with reduced GMV at the precentral gyrus. Collectively, these results can be used to postulate that the structural and neuronal functional changes in the insula are linked to the neurobiology of SPA that shares similarities with other behavioural addictions.
Ching, S. M., Yee, A., Ramachandran, V., Sazlly Lim, S. M., Wan Sulaiman, W. A., Foo, Y. L., & Hoo, F. K. (2015). Validation of a Malay version of the smartphone addiction scale among medical students in Malaysia. PLoS ONE, 10(10), e0139337. https://doi.org/10.1371/journal.pone.0139337
Connolly, C. G., Bell, R. P., Foxe, J. J., & Garavan, H. (2013). Dissociated grey matter changes with prolonged addiction and extended abstinence in cocaine users. PLoS ONE, 8(3), e59645. https://doi.org/10.1371/journal.pone.0059645
Ding, W., Sun, J., Sun, Y., Zhou, Y., Li, L., Xu, J., & Du, Y. (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
Dong, G., Wang, M., Wang, Z., Zheng, H., Du, X., & Potenza, M. N. (2020). Addiction severity modulates the precuneus involvement in internet gaming disorder: Functionality, morphology and effective connectivity. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 98, 109829. https://doi.org/10.1016/j.pnpbp.2019.109829
Du, X., Qi, X., Yang, Y., Du, G., Gao, P., Zhang, Y., Qin, W., Li, X., & Zhang, Q. (2016). Altered structural correlates of impulsivity in adolescents with internet gaming disorder. Frontiers in Human Neuroscience, 10. https://doi.org/10.3389/fnhum.2016.00004
Ersche, K. D., Williams, G. B., Robbins, T. W., & Bullmore, E. T. (2013). Meta-analysis of structural brain abnormalities associated with stimulant drug dependence and neuroimaging of addiction vulnerability and resilience. Current Opinion in Neurobiology, 23(4), 615-624. https://doi.org/10.1016/j.conb.2013.02.017
Harris, B., McCredie, M., & Fields, S. (2020). Examining the psychometric properties of the smartphone addiction scale and its short version for use with emerging adults in the U.S. Computers in Human Behavior Reports, 1, 100011. https://doi.org/10.1016/j.chbr.2020.100011
He, Q., Turel, O., & Bechara, A. (2017). Brain anatomy alterations associated with social networking site (SNS) addiction. Scientific Reports, 7(1). https://doi.org/10.1038/srep45064
Horvath, J., Mundinger, C., Schmitgen, M. M., Wolf, N. D., Sambataro, F., Hirjak, D., Kubera, K. M., Koenig, J., & Christian Wolf, R. (2020). Structural and functional correlates of smartphone addiction. Addictive Behaviors, 105, 106334. https://doi.org/10.1016/j.addbeh.2020.106334
Ibrahim, B., Nasser, N. S., Ibrahim, N., Mohamed, M., Hassan, H. A., Saripan, M. I., & Suppiah, S. (2020). Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review. Human Brain Mapping, 42(9):2941-2968. https://doi.org/10.1002/hbm.25369
Kim, S. M., Huh, H. J., Cho, H., Kwon, M., Choi, J. H., Ahn, H. J., Lee, S. W., Kim, Y. J., & Kim, D. J. (2014). The effect of depression, impulsivity, and resilience on smartphone addiction in university students. Journal of Korean Neuropsychiatric Association, 53(4), 214. https://doi.org/10.4306/jknpa.2014.53.4.214
Ko, C., Hsieh, T., Wang, P., Lin, W., Yen, C., Chen, C., & Yen, J. (2015). Altered gray matter density and disrupted functional connectivity of the amygdala in adults with internet gaming disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 57, 185-192. https://doi.org/10.1016/j.pnpbp.2014.11.003
Kwon, M., Kim, D., Cho, H., & Yang, S. (2013a). The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE, 8(12), e83558. https://doi.org/10.1371/journal.pone.0083558
Kwon, M., Lee, J., Won, W., Park, J., Min, J., Hahn, C., Gu, X., Choi, J., & Kim, D. (2013b). Development and validation of a smartphone addiction scale (SAS). PLoS ONE, 8(2), e56936. https://doi.org/10.1371/journal.pone.0056936
Lin, Y., Chang, L., Lee, Y., Tseng, H., Kuo, T. B., & Chen, S. (2014). Development and validation of the smartphone addiction inventory (SPAI). PLoS ONE, 9(6), e98312. https://doi.org/10.1371/journal.pone.0098312
Liu, B., Song, M., Li, J., Liu, Y., Li, K., Yu, C., & Jiang, T. (2010). Prefrontal-related functional connectivities within the default network are modulated by COMT val158met in healthy young adults. Journal of Neuroscience, 30(1), 64-69. https://doi.org/10.1523/jneurosci.3941-09.2010
Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the depression anxiety stress scales (2nd ed.). Sydney: Psychology Foundation.
Boumosleh, J.M., & Jaalouk, D. (2017). Depression, anxiety, and smartphone addiction in university students- A cross sectional study. PLoS ONE, 12(8), e0182239. https://doi.org/10.1371/journal.pone.0182239
Nasser, N. S., Ling, L. J., Rashid, A. A., Sharifat, H., Ahmad, U., Ibrahim, B., Mustafa, S., Kee, H. F., Mooi, C. S., & Suppiah, S. (2020a). A cross-sectional survey on smartphone usage pattern, the level of mobile phone dependence and psychosocial effects among undergraduate students in a Malaysian University. The Medical Journal of Malaysia, 75(4), 356-362. https://doi.org/10.1101/2020.01.06.20016592
Nasser, N. S., Sharifat, H., Rashid, A. A., Hamid, S. A., Rahim, E. A., Loh, J. L., Ching, S. M., Hoo, F. K., Ismail, S., Tyagi, R., Mohamad, M., & Suppiah, S. (2020b). Cue-reactivity among young adults with problematic Instagram use in response to Instagram-themed risky behavior cues: a pilot fMRI study. Frontiers in Psychology, 11, 556060. https://doi.org/10.3389/fpsyg.2020.556060
Noël, X., Brevers, D., & Bechara, A. (2013). A triadic neurocognitive approach to addiction for clinical interventions. Frontiers in Psychiatry, 4. https://doi.org/10.3389/fpsyt.2013.00179
Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Barratt impulsiveness scale-11 (BIS-11). PsycTESTS Dataset. https://doi.org/10.1037/t05661-000
Stanford, M. S., Mathias, C. W., Dougherty, D. M., Lake, S. L., Anderson, N. E., & Patton, J. H. (2009). Fifty years of the Barratt impulsiveness scale: An update and review. Personality and Individual Differences, 47(5), 385-395. https://doi.org/10.1016/j.paid.2009.04.008
Sharifat, H., Rashid, A. A., & Suppiah, S. (2018). Systematic review of the utility of functional MRI to investigate internet addiction disorder: Recent updates on resting state and task-based fMRI. Malaysian Journal of Medicine and Health Sciences, 14(1), 21-33.
Suckling, J., & Nestor, L. J. (2016). The neurobiology of addiction: The perspective from magnetic resonance imaging present and future. Addiction, 112(2), 360-369. https://doi.org/10.1111/add.13474
Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363-371. https://doi.org/10.1056/nejmra1511480
Wang, Y., Zou, Z., Song, H., Xu, X., Wang, H., D’Oleire Uquillas, F., & Huang, X. (2016). Altered gray matter volume and white matter integrity in college students with mobile phone dependence. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.00597
Weinstein, A. M. (2017). An update overview on brain imaging studies of internet gaming disorder. Frontiers in Psychiatry, 8. https://doi.org/10.3389/fpsyt.2017.00185
Zhu, Y., Zhang, H., & Tian, M. (2015). Molecular and functional imaging of internet addiction. BioMed Research International, 2015, 1-9. https://doi.org/10.1155/2015/37867
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Copyright (c) 2021 Aida Abdul Rashid, Subapriya Suppiah, Nisha Syed Nasser, Hamed Sharifat, Mazlyfarina Mohamad, Jia Ling Loh, Buhari Ibrahim, Nur Shahidatul Nabila Ibrahim, Nur Hafizah Mohad Azmi, Ezamin Abdul Rahim, Laila Mastura Ahmad Apandi, Suzana Ab Hamid, Yap Ngee Thai, Siew Mooi Ching, Fan Kee Hoo
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