Increased interregional functional connectivity of anterior insula is associated with improved smoking cessation outcome
- 36 Downloads
Damage to the insular cortex has been shown to disrupt smoking behavior. However, whether smoking cessation outcomes are associated with abnormal functions of insula and its subregions remains unclear. In this study, we investigated the relationship between insular functions (interregional functional connectivity and regional activity) and treatment outcomes of cigarette smoking. Thirty treatment-seeking smokers were recruited into the treatment study and underwent magnetic resonance imaging (MRI) scans immediately before and after the treatment. Sixteen participants remained abstinent from smoking (quitters), while 14 relapsed to smoking (relapers). Changes in resting-state functional connectivity and fractional amplitude of low frequency fluctuation (fALFF) across groups and visits were assessed using repeated measures ANCOVA. Significant interaction effects were detected: 1) between the left anterior insula and left precuneus; and 2) between the right anterior insula and left precuneus and medial frontal gyrus. Post-hoc region-of-interest analyses in brain areas showing interaction effects indicated significantly increased functional connectivity after treatment compared with before treatment in quitters but opposite longitudinal changes in relapsers. However, no significant effects in fALFF were observed. These novel findings suggest that increased interregional functional connectivity of the anterior insula is associated with improved smoking cessation outcome: individuals with increased functional connectivity of the anterior insula during the treatment would more likely quit smoking successfully. These insular circuits may serve as therapeutic targets for more efficacious treatment of nicotine addiction.
KeywordsAnterior insula Resting state functional connectivity Nicotine dependence Smoking cessation
This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ18H180001, Zhejiang Medicine and Health Science and Technology Program under Grant nos.2017KY080 and 2018KY418, National Natural Science Foundation of China under Grant nos. 81,171,310 and 81,701,647. YY was supported by the Intramural Research Program of the National Institute on Drug Abuse, the National Institutes of Health.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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.
Informed consent was obtained from all individual participants included in the study.
- Addicott, M. A., Sweitzer, M. M., Froeliger, B., Rose, J. E., & McClernon, F. J. (2015). Increased functional connectivity in an insula-based network is associated with improved smoking cessation outcomes. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 40(11), 2648–2656.CrossRefGoogle Scholar
- Buhle, J. T., Silvers, J. A., Wager, T. D., Lopez, R., Onyemekwu, C., Kober, H., et al. (2014). Cognitive reappraisal of emotion: A meta-analysis of human neuroimaging studies. Cerebral cortex (New York, NY : 1991), 24(11), 2981–2990.Google Scholar
- Cahill, K., Stead, L. F., & Lancaster, T. (2010). Nicotine receptor partial agonists for smoking cessation. The Cochrane Database of Systematic Reviews, (12), CD006103.Google Scholar
- Chang, L. J., Yarkoni, T., Khaw, M. W., & Sanfey, A. G. (2013). Decoding the role of the insula in human cognition: Functional parcellation and large-scale reverse inference. Cerebral cortex (New York, NY : 1991), 23(3), 739–749.Google Scholar
- Deen, B., Pitskel, N. B., & Pelphrey, K. A. (2011). Three systems of insular functional connectivity identified with cluster analysis. Cerebral cortex (New York, NY : 1991), 21(7), 1498–1506.Google Scholar
- Dinur-Klein, L., Dannon, P., Hadar, A., Rosenberg, O., Roth, Y., Kotler, M., et al. (2014). Smoking cessation induced by deep repetitive transcranial magnetic stimulation of the prefrontal and insular cortices: A prospective, randomized controlled trial. Biological Psychiatry, 76(9), 742–749.CrossRefGoogle Scholar
- Dosenbach, N. U., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A., et al. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences of the United States of America, 104(26), 11073–11078.CrossRefGoogle Scholar
- Li, X., Hartwell, K. J., Borckardt, J., Prisciandaro, J. J., Saladin, M. E., Morgan, P. S., et al. (2013). Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: A preliminary real-time fMRI study. Addiction Biology, 18(4), 739–748.CrossRefGoogle Scholar
- Naqvi NH, Gaznick N, Tranel D, Bechara A (2014). The insula: a critical neural substrate for craving and drug seeking under conflict and risk. Annals of the New York Academy of Sciences 1316: 53–70.Google Scholar
- Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., et al. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of neuroscience : the official journal of the Society for Neuroscience, 27(9), 2349–2356.CrossRefGoogle Scholar
- Wang, C., Huang, P., Shen, Z., Qian, W., Li, K., Luo, X., et al. (2019). Gray matter volumes of insular subregions are not correlated with smoking cessation outcomes but negatively correlated with nicotine dependence severity in chronic smokers. Neuroscience Letters, 696, 7–12.CrossRefGoogle Scholar
- Whitney, C., Kirk, M., O'Sullivan, J., Lambon Ralph, M. A., & Jefferies, E. (2011). The neural organization of semantic control: TMS evidence for a distributed network in left inferior frontal and posterior middle temporal gyrus. Cerebral cortex (New York, NY : 1991), 21(5), 1066–1075.Google Scholar
- Yan, C. G., Wang, X. D., Zuo, X. N., Zang, Y. F. (2016). DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339–351. https://doi.org/10.1007/s12021-016-9299-4.