Hostname: page-component-857557d7f7-qdphv Total loading time: 0 Render date: 2025-12-07T04:16:05.783Z Has data issue: false hasContentIssue false
  • English
  • Français

Similarity and difference in large-scale functional network alternations between behavioral addictions and substance use disorder: a comparative meta-analysis

Published online by Cambridge University Press:  04 December 2023

Xinglin Zeng ,
Xinyang Han ,
Dong Zheng ,
Ping Jiang  and
Zhen Yuan Open the ORCID record for Zhen Yuan [Opens in a new window]
Xinglin Zeng
Affiliation:
Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, 999078, China Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
Xinyang Han
Affiliation:
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, 999077, China
Dong Zheng
Affiliation:
The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
Ping Jiang*
Affiliation:
West China Medical Publishers, West China Hospital of Sichuan University, Chengdu, 610041, People's Republic of China Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
Zhen Yuan*
Affiliation:
Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, 999078, China Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
*
Corresponding authors: Ping Jiang; Email: jiangping@wchscu.cn Zhen Yuan; Email: zhenyuan@um.edu.mo
Corresponding authors: Ping Jiang; Email: jiangping@wchscu.cn Zhen Yuan; Email: zhenyuan@um.edu.mo
Get access Rights & Permissions [Opens in a new window]

Abstract

Behavioral addiction (BA) and substance use disorder (SUD) share similarities and differences in clinical symptoms, cognitive functions, and behavioral attributes. However, little is known about whether and how functional networks in the human brain manifest commonalities and differences between BA and SUD. Voxel-wise meta-analyses of resting-state functional connectivity (rs-FC) were conducted in BA and SUD separately, followed by quantitative conjunction analyses to identify the common and distinct alterations across both the BA and SUD groups. A total of 92 datasets with 2444 addicted patients and 2712 healthy controls (HCs) were eligible for the meta-analysis. Our findings demonstrated that BA and SUD exhibited common alterations in rs-FC between frontoparietal network (FPN) and other high-level neurocognitive networks (i.e. default mode network (DMN), affective network (AN), and salience network (SN)) as well as hyperconnectivity between SN seeds and the Rolandic operculum in SSN. In addition, compared with BA, SUD exhibited several distinct within- and between-network rs-FC alterations mainly involved in the DMN and FPN. Further, altered within- and between-network rs-FC showed significant association with clinical characteristics such as the severity of addiction in BA and duration of substance usage in SUD. The common rs-FC alterations in BA and SUD exhibited the relationship with consistent aberrant behaviors in both addiction groups, such as impaired inhibition control and salience attribution. By contrast, the distinct rs-FC alterations might suggest specific substance effects on the brain neural transmitter systems in SUD.

Keywords

behavioral addictionfrontoparietal networkmeta-analysisresting-state functional connectivitysubstance use disorderventral attention network

Information

Type
Review Article
Information
Psychological Medicine , Volume 54 , Issue 3 , February 2024 , pp. 473 - 487
Check for updates
Copyright
Copyright © University of Macau, 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

*

These authors have contributed equally to this work and share first authorship.

References

Acheson, A. (2020). Behavioral processes and risk for problem substance use in adolescents. Pharmacology Biochemistry and Behavior, 198, 173021. doi: 10.1016/j.pbb.2020.173021Google Scholar
Akbari, M., Bahadori, M. H., Mohammadkhani, S., Kolubinski, D. C., Nikcevic, A. V., & Spada, M. M. (2021). A discriminant analysis model of psychosocial predictors of problematic internet use and cannabis use disorder in university students. Addictive Behaviors Reports, 14, 100354. doi: 10.1016/j.abrep.2021.100354Google Scholar
Anticevic, A., Gancsos, M., Murray, J. D., Repovs, G., Driesen, N. R., Ennis, D. J., … Corlett, P. R. (2012). NMDA receptor function in large-scale anticorrelated neural systems with implications for cognition and schizophrenia. Proceedings of the National Academy of Sciences, 109(41), 16720–16725. doi: 10.1073/pnas.1208494109Google Scholar
Bae, S., Han, D. H., Jung, J., Nam, K. C., & Renshaw, P. F. (2017). Comparison of brain connectivity between internet gambling disorder and internet gaming disorder: A preliminary study. Journal of Behavioral Addictions, 6(4), 505–515. doi: 10.1556/2006.6.2017.061Google Scholar
Bari, A., & Robbins, T. W. (2013). Inhibition and impulsivity: Behavioral and neural basis of response control. Progress in Neurobiology, 108, 44–79. doi: 10.1016/j.pneurobio.2013.06.005Google Scholar
Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage, 76, 412–427. doi: 10.1016/j.neuroimage.2013.02.063Google Scholar
Blanco-Hinojo, L., Pujol, J., Harrison, B. J., Macià, D., Batalla, A., Nogué, S., … Martín-Santos, R. (2017). Attenuated frontal and sensory inputs to the basal ganglia in cannabis users. Addiction Biology, 22(4), 1036–1047. doi: 10.1111/adb.12370Google Scholar
Blefari, M. L., Martuzzi, R., Salomon, R., Bello-Ruiz, J., Herbelin, B., Serino, A., & Blanke, O. (2017). Bilateral Rolandic operculum processing underlying heartbeat awareness reflects changes in bodily self-consciousness. European Journal of Neuroscience, 45(10), 1300–1312. doi: 10.1111/ejn.13567Google Scholar
Boileau, I., Payer, D., Chugani, B., Lobo, D., Behzadi, A., Rusjan, P. M., … Zack, M. (2013). The D2/3 dopamine receptor in pathological gambling: A positron emission tomography study with [11C]-(+)-propyl-hexahydro-naphtho-oxazin and [11C]raclopride. Addiction, 108(5), 953–963. doi: 10.1111/add.12066Google Scholar
Brandl, F., Avram, M., Weise, B., Shang, J., Simões, B., Bertram, T., … Sorg, C. (2019). Specific substantial dysconnectivity in schizophrenia: A transdiagnostic multimodal meta-analysis of resting-state functional and structural magnetic resonance imaging studies. Biological Psychiatry, 85(7), 573–583. doi: 10.1016/j.biopsych.2018.12.003Google Scholar
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. (2011). The organization of the human cerebellum estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(5), 2322–2345. doi: 10.1152/jn.00339.2011Google Scholar
Çakıcı, M., Çakıcı, E., Babayiğit, A., & Karaaziz, M. (2021). Gambling behaviour: Prevalence, risk factors and relation with acculturation in 2007–2018 North Cyprus adult household surveys. Journal of Gambling Studies, 37(4), 1099–1111. doi: 10.1007/s10899-021-10008-3Google Scholar
Chen, Y., Sun, Y., Ai, S.-Z., Li, J. J., Lu, L., & Shi, J. (2017). Similarities and differences in psychology. In Zhang, X., Shi, J., & Tao, R. (Eds.), Substance and non-substance addiction (pp. 91–101). Singapore: Springer Singapore.Google Scholar
Chen, X., Wang, Y., Zhou, Y., Sun, Y., Ding, W., Zhuang, Z., … Du, Y. (2014). Different resting-state functional connectivity alterations in smokers and nonsmokers with internet gaming addiction. BioMed Research International, 2014, 825787. doi: 10.1155/2014/825787Google Scholar
Choi, E. Y., Yeo, B. T., & Buckner, R. L. (2012). The organization of the human striatum estimated by intrinsic functional connectivity. Journal of Neurophysiology, 108(8), 2242–2263. doi: 10.1152/jn.00270.2012Google Scholar
Clark, L., Boileau, I., & Zack, M. (2019). Neuroimaging of reward mechanisms in gambling disorder: An integrative review. Molecular Psychiatry, 24(5), 674–693. doi: 10.1038/s41380-018-0230-2Google Scholar
Cooper, S., Robison, A. J., & Mazei-Robison, M. S. (2017). Reward circuitry in addiction. Neurotherapeutics: The Journal of the American Society for Experimental NeuroTherapeutics, 14(3), 687–697. doi: 10.1007/s13311-017-0525-zCrossRefGoogle ScholarPubMed
Denier, N., Schmidt, A., Gerber, H., Vogel, M., Huber, C. G., Lang, U. E., … Borgwardt, S. (2015). Abnormal functional integration of thalamic low frequency oscillation in the BOLD signal after acute heroin treatment. Human Brain Mapping, 36(12), 5287–5300. doi: 10.1002/hbm.23011Google Scholar
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. doi: 10.1371/journal.pone.0059902Google Scholar
Eddie, D., Bates, M. E., & Buckman, J. F. (2022). Closing the brain-heart loop: Towards more holistic models of addiction and addiction recovery. Addiction Biology, 27(1), e12958. doi: 10.1111/adb.12958Google Scholar
Estévez, A., Jáuregui, P., Sánchez-Marcos, I., López-González, H., & Griffiths, M. D. (2017). Attachment and emotion regulation in substance addictions and behavioral addictions. Journal of Behavioral Addictions, 6(4), 534–544. doi: 10.1556/2006.6.2017.086Google Scholar
Fouad, H., Commar, A., Hamadeh, R., El-Awa, F., Shen, Z., & Fraser, C. (2021). Estimated and projected prevalence of tobacco smoking in males, Eastern Mediterranean Region, 2000–2025. Eastern Mediterranean Health Journal, 27(1), 76–82. doi: 10.26719/2021.27.1.76Google Scholar
Franklin, T. R., Wetherill, R. R., Jagannathan, K., Johnson, B., Mumma, J., Hager, N., … Childress, A. R. (2014). The effects of chronic cigarette smoking on gray matter volume: Influence of sex. PLoS ONE, 9(8), e104102. doi: 10.1371/journal.pone.0104102Google Scholar
Gardner, E. L. (2011). Addiction and brain reward and antireward pathways. Advances in Psychosomatic Medicine, 30, 22–60. doi: 10.1159/000324065Google Scholar
Ge, X., Sun, Y., Han, X., Wang, Y., Ding, W., Cao, M., … Zhou, Y. (2017). Difference in the functional connectivity of the dorsolateral prefrontal cortex between smokers with nicotine dependence and individuals with internet gaming disorder. BMC Neuroscience, 18(1), 54. doi: 10.1186/s12868-017-0375-yGoogle Scholar
Goldstein, R. Z., & Volkow, N. D. (2002). Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. American Journal of Psychiatry, 159(10), 1642–1652. doi: 10.1176/appi.ajp.159.10.1642Google Scholar
Gong, D., He, H., Ma, W., Liu, D., Huang, M., Dong, L., … Yao, D. (2016). Functional integration between salience and central executive networks: A role for action video game experience. Neural Plasticity, 2016, 9803165. doi: 10.1155/2016/9803165Google Scholar
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. doi: 10.1016/j.neuroimage.2010.06.066Google Scholar
Halcomb, M. E., Chumin, E. J., Goñi, J., Dzemidzic, M., & Yoder, K. K. (2019). Aberrations of anterior insular cortex functional connectivity in nontreatment-seeking alcoholics. Psychiatry Research: Neuroimaging, 284, 21–28. doi: 10.1016/j.pscychresns.2018.12.016Google Scholar
Hendrick, O. M., Luo, X., Zhang, S., & Li, C.-S. R. (2012). Saliency processing and obesity: A preliminary imaging study of the stop signal task. Obesity, 20(9), 1796–1802. doi: 10.1038/oby.2011.180Google Scholar
Huang, H., Jiang, Y., Xia, M., Tang, Y., Zhang, T., Cui, H., … Wang, J. (2018a). Increased resting-state global functional connectivity density of default mode network in schizophrenia subjects treated with electroconvulsive therapy. Schizophrenia Research, 197, 192–199. doi: 10.1016/j.schres.2017.10.044Google Scholar
Huang, Y., Mohan, A., De Ridder, D., Sunaert, S., & Vanneste, S. (2018b). The neural correlates of the unified percept of alcohol-related craving: A fMRI and EEG study. Scientific Reports, 8(1), 923. doi: 10.1038/s41598-017-18471-yGoogle Scholar
Jentsch, J. D., & Pennington, Z. T. (2014). Reward, interrupted: Inhibitory control and its relevance to addictions. Neuropharmacology, 76(Pt B), 479–486. doi: 10.1016/j.neuropharm.2013.05.022Google Scholar
Jiang, P., Sun, J., Zhou, X., Lu, L., Li, L., Huang, X., … Gong, Q. (2021). Functional connectivity abnormalities underlying mood disturbances in male abstinent methamphetamine abusers. Human Brain Mapping, 42(11), 3366–3378. doi: 10.1002/hbm.25439Google Scholar
Kohno, M., Morales, A. M., Ghahremani, D. G., Hellemann, G., & London, E. D. (2014). Risky decision making, prefrontal cortex, and mesocorticolimbic functional connectivity in methamphetamine dependence. JAMA Psychiatry, 71(7), 812–820. doi: 10.1001/jamapsychiatry.2014.399Google Scholar
Koob, G. F., & Volkow, N. D. (2016). Neurobiology of addiction: A neurocircuitry analysis. The Lancet Psychiatry, 3(8), 760–773. doi: 10.1016/s2215-0366(16)00104-8Google Scholar
Koziol, L. F., Barker, L. A., Joyce, A. W., & Hrin, S. (2014). Structure and function of large-scale brain systems. Applied Neuropsychology: Child, 3(4), 236–244. doi: 10.1080/21622965.2014.946797Google Scholar
Leech, R., Braga, R., & Sharp, D. J. (2012). Echoes of the brain within the posterior cingulate cortex. The Journal of Neuroscience, 32(1), 215. doi: 10.1523/JNEUROSCI.3689-11.2012Google Scholar
Liao, Y., Tang, J., Liu, J., Xie, A., Yang, M., Johnson, M., … Hao, W. (2016). Decreased thalamocortical connectivity in chronic ketamine users. PLoS ONE, 11(12), e0167381. doi: 10.1371/journal.pone.0167381Google Scholar
Liu, J., Cai, W., Zhao, M., Cai, W., Sui, F., Hou, W., … Yuan, K. (2019). Reduced resting-state functional connectivity and sleep impairment in abstinent male alcohol-dependent patients. Human Brain Mapping, 40(17), 4941–4951. doi: 10.1002/hbm.24749Google Scholar
Lin, L., & Chu, H. (2018). Quantifying publication bias in meta-analysis. Biometrics, 74(3), 785–794. doi: 10.1111/biom.12817Google Scholar
Lv, H., Wang, Z., Tong, E., Williams, L. M., Zaharchuk, G., Zeineh, M., … Wintermark, M. (2018). Resting-state functional MRI: Everything that nonexperts have always wanted to know. American Journal of Neuroradiology, 39(8), 1390–1399. doi: 10.3174/ajnr.A5527Google Scholar
Marek, S., & Dosenbach, N. U. F. (2018). The frontoparietal network: Function, electrophysiology, and importance of individual precision mapping. Dialogues in Clinical Neuroscience, 20(2), 133–140. doi: 10.31887/DCNS.2018.20.2/smarekGoogle Scholar
Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15(10), 483–506. doi: 10.1016/j.tics.2011.08.003CrossRefGoogle ScholarPubMed
Mesgarani, M., Shafiee, S., Ahmadi, E., & Zare, F. (2013). Studying the relationship between internet addiction and emotional intelligence, sensation seeking and metacognition among those who referred to cafes. International Research Journal of Applied and Basic Sciences, 4(4), 889–893.Google Scholar
Moeller, S. J., & Goldstein, R. Z. (2014). Impaired self-awareness in human addiction: Deficient attribution of personal relevance. Trends in Cognitive Sciences, 18(12), 635–641. doi: 10.1016/j.tics.2014.09.003Google Scholar
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. doi: 10.1371/journal.pmed.1000097Google Scholar
Nestor, L., McCabe, E., Jones, J., Clancy, L., & Garavan, H. J. N. (2011). Differences in “bottom-up” and “top-down” neural activity in current and former cigarette smokers: Evidence for neural substrates which may promote nicotine abstinence through increased cognitive control. Neuroimage, 56(4), 2258–2275.Google Scholar
Oh, H., Gosnell, S., Nguyen, T., Tran, T., Kosten, T. R., & Salas, R. (2021). Cingulate cortex structural alterations in substance use disorder psychiatric inpatients. The American Journal on Addictions, 30(1), 72–79. doi: 10.1111/ajad.13093Google Scholar
Pan, Y. C., Chiu, Y. C., & Lin, Y. H. (2020). Systematic review and meta-analysis of epidemiology of internet addiction. Neuroscience and Biobehavioral Reviews, 118, 612–622. doi: 10.1016/j.neubiorev.2020.08.013CrossRefGoogle ScholarPubMed
Pando-Naude, V., Toxto, S., Fernandez-Lozano, S., Parsons, C. E., Alcauter, S., & Garza-Villarreal, E. A. (2021). Gray and white matter morphology in substance use disorders: A neuroimaging systematic review and meta-analysis. Translational Psychiatry, 11(1), 29. doi: 10.1038/s41398-020-01128-2Google Scholar
Pang, J. C., Aquino, K. M., Oldehinkel, M., Robinson, P. A., Fulcher, B. D., Breakspear, M., & Fornito, A. (2023). Geometric constraints on human brain function. Nature, 618(7965), 566–574. doi: 10.1038/s41586-023-06098-1Google Scholar
Perales, J. C., King, D. L., Navas, J. F., Schimmenti, A., Sescousse, G., Starcevic, V., … Billieux, J. (2020). Learning to lose control: A process-based account of behavioral addiction. Neuroscience and Biobehavioral Reviews, 108, 771–780. doi: 10.1016/j.neubiorev.2019.12.025Google Scholar
Power, Y., Goodyear, B., & Crockford, D. (2012). Neural correlates of pathological gamblers preference for immediate rewards during the Iowa gambling task: An fMRI study. Journal of Gambling Studies, 28(4), 623–636. doi: 10.1007/s10899-011-9278-5Google Scholar
Qin, K., Zhang, F., Chen, T., Li, L., Li, W., Suo, X., … Gong, Q. (2020). Shared gray matter alterations in individuals with diverse behavioral addictions: A voxel-wise meta-analysis. Journal of Behavioral Addictions, 9(1), 44–57. doi: 10.1556/2006.2020.00006Google Scholar
Qiu, Z., & Wang, J. (2021). Altered neural activities during response inhibition in adults with addiction: A voxel-wise meta-analysis. Psychological Medicine, 51(3), 387–399. doi: 10.1017/S0033291721000362Google Scholar
Radua, J., & Mataix-Cols, D. (2009). Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. British Journal of Psychiatry, 195(5), 393–402. doi: 10.1192/bjp.bp.108.055046Google Scholar
Radua, J., Mataix-Cols, D., Phillips, M. L., El-Hage, W., Kronhaus, D. M., Cardoner, N., & Surguladze, S. (2012). A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry, 27(8), 605–611. doi: 10.1016/j.eurpsy.2011.04.001Google Scholar
Radua, J., Romeo, M., Mataix-Cols, D., & Fusar-Poli, P. (2013). A general approach for combining voxel-based meta-analyses conducted in different neuroimaging modalities. Current Medicinal Chemistry, 20(3), 462–466.Google Scholar
Radua, J., Rubia, K., Canales-Rodríguez, E. J., Pomarol-Clotet, E., Fusar-Poli, P., & Mataix-Cols, D. (2014). Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Frontiers in Psychiatry, 5, 13. doi: 10.3389/fpsyt.2014.00013Google Scholar
Redish, A. D. (2004). Addiction as a computational process gone awry. Science, 306(5703), 1944–1947. doi: 10.1126/science.1102384Google Scholar
Salimi-Khorshidi, G., Smith, S. M., Keltner, J. R., Wager, T. D., & Nichols, T. E. (2009). Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies. Neuroimage, 45(3), 810–823. doi: 10.1016/j.neuroimage.2008.12.039Google Scholar
Samaha, A. N., Khoo, S. Y., Ferrario, C. R., & Robinson, T. E. (2021). Dopamine ‘ups and downs’ in addiction revisited. Trends in Neurosciences, 44(7), 516–526. doi: 10.1016/j.tins.2021.03.003Google Scholar
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., … Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27(9), 2349–2356. doi: 10.1523/JNEUROSCI.5587-06.2007, %J The Journal of Neuroscience.Google Scholar
Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral Cortex, 22(1), 158–165. doi: 10.1093/cercor/bhr099Google Scholar
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., … Beckmann, C. F. (2009). Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences, 106(31), 13040–13045. doi: 10.1073/pnas.0905267106Google Scholar
Smitha, K. A., Akhil Raja, K., Arun, K. M., Rajesh, P. G., Thomas, B., Kapilamoorthy, T. R., & Kesavadas, C. (2017). Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. The Neuroradiology Journal, 30(4), 305–317. doi: 10.1177/1971400917697342Google Scholar
Solinas, M., Belujon, P., Fernagut, P. O., Jaber, M., & Thiriet, N. (2019). Dopamine and addiction: What have we learned from 40 years of research. The Journal of Neural Transmission, 126(4), 481–516. doi: 10.1007/s00702-018-1957-2Google Scholar
Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences, 105(34), 12569–12574. doi: 10.1073/pnas.0800005105Google Scholar
Stormezand, G. N., Doorduin, J., Chaves, L. T., García, D. V., Nienhuis, F. J., Schoevers, R. A., … Dierckx, R. A. J. O. (2021). No evidence for decreased D2/3 receptor availability and frontal hypoperfusion in subjects with compulsive pornography use. Psychiatry Research: Neuroimaging, 311, 111284. doi: 10.1016/j.pscychresns.2021.111284Google Scholar
Sutherland, M. T., McHugh, M. J., Pariyadath, V., & Stein, E. A. (2012). Resting state functional connectivity in addiction: Lessons learned and a road ahead. Neuroimage, 62(4), 2281–2295. doi: 10.1016/j.neuroimage.2012.01.117Google Scholar
Tanabe, J., Nyberg, E., Martin, L. F., Martin, J., Cordes, D., Kronberg, E., & Tregellas, J. R. (2011). Nicotine effects on default mode network during resting state. Psychopharmacology, 216(2), 287–295. doi: 10.1007/s00213-011-2221-8Google Scholar
Tang, S., Lu, L., Zhang, L., Hu, X., Bu, X., Li, H., … Huang, X. (2018). Abnormal amygdala resting-state functional connectivity in adults and adolescents with major depressive disorder: A comparative meta-analysis. EBioMedicine, 36, 436–445. doi: 10.1016/j.ebiom.2018.09.010Google Scholar
Tatari, F., Farnia, V., Momeni, K., Davarinejad, O., Salemi, S., Soltani, B., … Alikhani, M. (2020). Predicting addiction potential based on sensation-seeking, psychological hardiness and assertiveness in students in western Iran: An analytical study. Journal of Substance Use, 26(1), 7–12. doi: 10.1080/14659891.2020.1760371Google Scholar
Tolomeo, S., & Yu, R. (2022). Brain network dysfunctions in addiction: A meta-analysis of resting-state functional connectivity. Translational Psychiatry, 12(1), 41. doi: 10.1038/s41398-022-01792-6Google Scholar
Tsilosani, A., Chan, K., Steffens, A., Bolton, T. B., & Kowalczyk, W. J. (2023). Problematic social media use is associated with depression and similar to behavioral addictions: Physiological and behavioral evidence. Addictive Behaviors, 145, 107781. doi: 10.1016/j.addbeh.2023.107781Google Scholar
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519–534. doi: 10.1016/j.euroneuro.2010.03.008Google Scholar
Velezmoro, R., Lacefield, K., & Roberti, J. W. (2010). Perceived stress, sensation seeking, and college students’ abuse of the internet. Computers in Human Behavior, 26(6), 1526–1530. doi: 10.1016/j.chb.2010.05.020Google Scholar
Verdejo-Garcia, A., Contreras-Rodríguez, O., Fonseca, F., Cuenca, A., Soriano-Mas, C., Rodriguez, J., … de la Torre, R. (2014). Functional alteration in frontolimbic systems relevant to moral judgment in cocaine-dependent subjects. Addiction Biology, 19(2), 272–281. doi: 10.1111/j.1369-1600.2012.00472.xGoogle Scholar
Verdejo-Garcia, A., Garcia-Fernandez, G., & Dom, G. (2019). Cognition and addiction. Dialogues in Clinical Neuroscience, 21(3), 281–290. doi: 10.31887/DCNS.2019.21.3/gdomGoogle Scholar
Wang, C., Bai, J., Wang, C., von Deneen, K. M., Yuan, K., & Cheng, J. (2017). Altered thalamo-cortical resting state functional connectivity in smokers. Neuroscience Letters, 653, 120–125. doi: 10.1016/j.neulet.2017.05.038Google Scholar
Weinstein, A., Abu, H. B., Timor, A., & Mama, Y. (2016). Delay discounting, risk-taking, and rejection sensitivity among individuals with internet and video gaming disorders. Journal of Behavioral Addictions, 5(4), 674–682. doi: 10.1556/2006.5.2016.081Google Scholar
Wesley, M. J., Hanlon, C. A., & Porrino, L. J. (2011). Poor decision-making by chronic marijuana users is associated with decreased functional responsiveness to negative consequences. Psychiatry Research, 191(1), 51–59. doi: 10.1016/j.pscychresns.2010.10.002Google Scholar
Wise, R. A. (2004). Dopamine, learning and motivation. Nature Reviews Neuroscience, 5(6), 483–494. doi: 10.1038/nrn1406Google Scholar
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. doi: 10.1152/jn.00338.2011Google Scholar
Zhang, J. T., Ma, S. S., Yip, S. W., Wang, L. J., Chen, C., Yan, C. G., … 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. doi: 10.1186/s12993-015-0082-8Google Scholar
Zhang, R., & Volkow, N. D. (2019). Brain default-mode network dysfunction in addiction. Neuroimage, 200, 313–331. doi: 10.1016/j.neuroimage.2019.06.036Google Scholar
Zhao, Q., Li, H., Hu, B., Li, Y., Gillebert, C. R., Mantini, D., & Liu, Q. (2017). Neural correlates of drug-related attentional bias in heroin dependence. Frontiers in Human Neuroscience, 11, 646. doi: 10.3389/fnhum.2017.00646Google Scholar
Zilverstand, A., Huang, A. S., Alia-Klein, N., & Goldstein, R. Z. (2018). Neuroimaging impaired response inhibition and salience attribution in human drug addiction: A systematic review. Neuron, 98(5), 886–903. doi: 10.1016/j.neuron.2018.03.048Google Scholar
Zou, F., Wu, X., Zhai, T., Lei, Y., Shao, Y., Jin, X., … Yang, Z. (2015). Abnormal resting-state functional connectivity of the nucleus accumbens in multi-year abstinent heroin addicts. Journal of Neuroscience Research, 93(11), 1693–1702. doi: 10.1002/jnr.23608Google Scholar
Supplementary material: File

S0033291723003434sup001.docx

Zeng et al. supplementary material
Download S0033291723003434sup001.docx(File)
File 1.4 MB