Abnormal brain white matter network in young smokers: a graph theory analysis study

Abstract

Previous diffusion tensor imaging (DTI) studies had investigated the white matter (WM) integrity abnormalities in some specific fiber bundles in smokers. However, little is known about the changes in topological organization of WM structural network in young smokers. In current study, we acquired DTI datasets from 58 male young smokers and 51 matched nonsmokers and constructed the WM networks by the deterministic fiber tracking approach. Graph theoretical analysis was used to compare the topological parameters of WM network (global and nodal) and the inter-regional fractional anisotropy (FA) weighted WM connections between groups. The results demonstrated that both young smokers and nonsmokers had small-world topology in WM network. Further analysis revealed that the young smokers exhibited the abnormal topological organization, i.e., increased network strength, global efficiency, and decreased shortest path length. In addition, the increased nodal efficiency predominately was located in frontal cortex, striatum and anterior cingulate gyrus (ACG) in smokers. Moreover, based on network-based statistic (NBS) approach, the significant increased FA-weighted WM connections were mainly found in the PFC, ACG and supplementary motor area (SMA) regions. Meanwhile, the network parameters were correlated with the nicotine dependence severity (FTND) scores, and the nodal efficiency of orbitofrontal cortex was positive correlation with the cigarette per day (CPD) in young smokers. We revealed the abnormal topological organization of WM network in young smokers, which may improve our understanding of the neural mechanism of young smokers form WM topological organization level.

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References

  1. Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, e17.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Almeida, O. P., Garrido, G. J., Lautenschlager, N. T., Hulse, G. K., Jamrozik, K., & Flicker, L. (2008). Smoking is associated with reduced cortical regional gray matter density in brain regions associated with incipient Alzheimer disease. The American Journal of Geriatric Psychiatry, 16, 92–98.

    Article  PubMed  Google Scholar 

  3. Baler, R. D., & Volkow, N. D. (2006). Drug addiction: The neurobiology of disrupted self-control. Trends in Molecular Medicine, 12, 559–566.

    CAS  Article  PubMed  Google Scholar 

  4. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D.-U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424, 175–308.

    Article  Google Scholar 

  5. Brody, A. L., Mandelkern, M. A., Lee, G., Smith, E., Sadeghi, M., Saxena, S., Jarvik, M. E., & London, E. D. (2004). Attenuation of cue-induced cigarette craving and anterior cingulate cortex activation in bupropion-treated smokers: A preliminary study. Psychiatry Research: Neuroimaging, 130, 269–281.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bruni, J. E., & Montemurro, D. G. (2009). Human neuroanatomy: A text, brain atlas, and laboratory dissection guide. USA: Oxford University Press.

    Google Scholar 

  7. Bruno, J., Hosseini, S. H., & Kesler, S. (2012). Altered resting state functional brain network topology in chemotherapy-treated breast cancer survivors. Neurobiology of Disease, 48, 329–338.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186–198.

    CAS  Article  PubMed  Google Scholar 

  9. Butts, C. T. (2009). Revisiting the foundations of network analysis. Science, 325, 414–416.

    CAS  Article  PubMed  Google Scholar 

  10. Cai, C., Yuan, K., Yin, J., Feng, D., Bi, Y., Li, Y., Yu, D., Jin, C., Wei, Q., & Tian, J. (2016). Striatum morphometry is associated with cognitive control deficits and symptom severity in internet gaming disorder. Brain Imaging and Behavior, 10, 1–9.

    Article  Google Scholar 

  11. Cui, Z., Zhong, S., Xu, P., He, Y., Gong, G., 2013. PANDA: A pipeline toolbox for analyzing brain diffusion images.

    Google Scholar 

  12. Fagerström, K.-O. (1978). Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addictive Behaviors, 3, 235–241.

    Article  PubMed  Google Scholar 

  13. Feng, D., Yuan, K., Li, Y., Cai, C., Yin, J., Bi, Y., Cheng, J., Guan, Y., Shi, S., & Yu, D. (2015). Intra-regional and inter-regional abnormalities and cognitive control deficits in young adult smokers. Brain Imaging and Behavior, 1–11.

  14. Fornito, A., Zalesky, A., Bullmore, E., 2016. Fundamentals of brain network analysis. Academic Press.

  15. Gong, G., He, Y., Concha, L., Lebel, C., Gross, D. W., Evans, A. C., & Beaulieu, C. (2009b). Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex, 19, 524–536.

    Article  PubMed  Google Scholar 

  16. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6, e159.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & FAGERSTROM, K. O. (1991). The Fagerström test for nicotine dependence: A revision of the Fagerstrom tolerance questionnaire. British Journal of Addiction, 86, 1119–1127.

    CAS  Article  PubMed  Google Scholar 

  18. Hoeft, F., Barneagoraly, N., Haas, B. W., Golarai, G., Ng, D., Mills, D., Korenberg, J., Bellugi, U., Galaburda, A., & Reiss, A. L. (2007). More is not always better: Increased fractional anisotropy of superior longitudinal fasciculus associated with poor visuospatial abilities in Williams syndrome. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience, 27, 11960–11965.

    CAS  Article  Google Scholar 

  19. Hudkins, M., O’Neill, J., Tobias, M. C., Bartzokis, G., & London, E. D. (2012). Cigarette smoking and white matter microstructure. Psychopharmacology, 221, 285–295.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Humphries, M. D., Gurney, K., & Prescott, T. J. (2005). Is there an integrative center in the vertebrate brain-stem? A robotic evaluation of a model of the reticular formation viewed as an action selection device. Adaptive Behavior, 13, 97–113.

    Article  Google Scholar 

  21. Jacobsen, L. K., Giedd, J. N., Gottschalk, C., Kosten, T. R., & Krystal, J. H. (2001). Quantitative morphology of the caudate and putamen in patients with cocaine dependence. American Journal of Psychiatry, 158, 486–489.

    CAS  Article  PubMed  Google Scholar 

  22. Jacobsen, L. K., Picciotto, M. R., Heath, C. J., Frost, S. J., Tsou, K. A., Dwan, R. A., Jackowski, M. P., Constable, R. T., & Mencl, W. E. (2007). Prenatal and adolescent exposure to tobacco smoke modulates the development of white matter microstructure. The Journal of Neuroscience, 27, 13491–13498.

    CAS  Article  PubMed  Google Scholar 

  23. Jasinska, A. J., Zorick, T., Brody, A. L., & Stein, E. A. (2014). Dual role of nicotine in addiction and cognition: A review of neuroimaging studies in humans. Neuropharmacology, 84, 111–122.

    CAS  Article  PubMed  Google Scholar 

  24. Kalivas, P. W., & Volkow, N. D. (2005). The neural basis of addiction: A pathology of motivation and choice. American Journal of Psychiatry, 162, 1403–1413.

    Article  PubMed  Google Scholar 

  25. Kim, D.-J., Skosnik, P. D., Cheng, H., Pruce, B. J., Brumbaugh, M. S., Vollmer, J. M., Hetrick, W. P., O'Donnell, B. F., Sporns, O., & Puce, A. (2011). Structural network topology revealed by white matter tractography in cannabis users: A graph theoretical analysis. Brain Connectivity, 1, 473–483.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Koehler, S., Hasselmann, E., Wüstenberg, T., Heinz, A., & Romanczuk-Seiferth, N. (2015). Higher volume of ventral striatum and right prefrontal cortex in pathological gambling. Brain Structure and Function, 220, 469–477.

    Article  PubMed  Google Scholar 

  27. Koob, G. F., & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35, 217–238.

    Article  PubMed  Google Scholar 

  28. Kringelbach, M. L. (2005). The human orbitofrontal cortex: Linking reward to hedonic experience. Nature Reviews Neuroscience, 6, 691–702.

    CAS  Article  PubMed  Google Scholar 

  29. Kühn, S., Schubert, F., & Gallinat, J. (2010). Reduced thickness of medial orbitofrontal cortex in smokers. Biological Psychiatry, 68, 1061–1065.

    Article  PubMed  Google Scholar 

  30. Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87, 198701.

    CAS  Article  PubMed  Google Scholar 

  31. Le Bihan, D. (2003). Looking into the functional architecture of the brain with diffusion MRI. Nature Reviews Neuroscience, 4, 469–480.

    CAS  Article  PubMed  Google Scholar 

  32. Li, Y., Yuan, K., Cai, C., Feng, D., Yin, J., Bi, Y., Shi, S., Yu, D., Jin, C., & von Deneen, K. M. (2015). Reduced frontal cortical thickness and increased caudate volume within fronto-striatal circuits in young adult smokers. Drug and Alcohol Dependence, 151, 211–219.

    Article  PubMed  Google Scholar 

  33. Liao, Y., Tang, J., Deng, Q., Deng, Y., Luo, T., Wang, X., Chen, H., Liu, T., Chen, X., & Brody, A. L. (2011). Bilateral fronto-parietal integrity in young chronic cigarette smokers: A diffusion tensor imaging study. PloS One, 6, e26460.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Limbrick-Oldfield, E. H., van Holst, R. J., & Clark, L. (2013). Fronto-striatal dysregulation in drug addiction and pathological gambling: Consistent inconsistencies? Neuro Image: Clinical, 2, 385–393.

    Google Scholar 

  35. Lin, F., Wu, G., Zhu, L., & Lei, H. (2015). Altered brain functional networks in heavy smokers. Addiction Biology, 20, 809–819.

    Article  PubMed  Google Scholar 

  36. Makris, N., Kennedy, D. N., McInerney, S., Sorensen, A. G., Wang, R., Caviness, V. S., & Pandya, D. N. (2005). Segmentation of subcomponents within the superior longitudinal fascicle in humans: A quantitative, in vivo, DT-MRI study. Cerebral Cortex, 15, 854–869.

    Article  PubMed  Google Scholar 

  37. Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296, 910–913.

    CAS  Article  PubMed  Google Scholar 

  38. Mori, S., 2013. MRI atlas of human white matter -, S. Wakana.

  39. Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9, 97–113.

    CAS  Article  PubMed  Google Scholar 

  40. Paul, R. H., Grieve, S. M., Niaura, R., David, S. P., Laidlaw, D. H., Cohen, R., Sweet, L., Taylor, G., Clark, C. R., & Pogun, S. (2008). Chronic cigarette smoking and the microstructural integrity of white matter in healthy adults: A diffusion tensor imaging study. Nicotine & Tobacco Research, 10, 137–147.

    CAS  Article  Google Scholar 

  41. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059–1069.

    Article  PubMed  Google Scholar 

  42. Shu, N., Liu, Y., Li, J., Li, Y., Yu, C., & Jiang, T. (2009). Altered anatomical network in early blindness revealed by diffusion tensor tractography. PloS One, 4, e7228.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Shu, N., Liu, Y., Li, K., Duan, Y., Wang, J., Yu, C., Dong, H., Ye, J., & He, Y. (2011). Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cerebral Cortex, 21, 2565–2577.

    Article  PubMed  Google Scholar 

  44. Sporns, O. (2011). The human connectome: A complex network. Annals of the New York Academy of Sciences, 1224, 109–125.

    Article  PubMed  Google Scholar 

  45. Sun, Y., Wang, G.B., Lin, Q.X., Lu, L., Shu, N., Meng, S.Q., Wang, J., Han, H.B., He, Y., Shi, J., 2015. Disrupted white matter structural connectivity in heroin abusers. Addiction biology.

    Google Scholar 

  46. Tuch, D. S., Wedeen, V. J., Dale, A. M., George, J. S., & Belliveau, J. W. (2001). Conductivity tensor mapping of the human brain using diffusion tensor MRI. Proceedings of the National Academy of Sciences, 98, 11697–11701.

    CAS  Article  Google Scholar 

  47. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393, 440–442.

    CAS  Article  PubMed  Google Scholar 

  48. Wise, R. A. (2009). Roles for nigrostriatal—Not just mesocorticolimbic—Dopamine in reward and addiction. Trends in Neurosciences, 32, 517–524.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. Wylie, K. P., Rojas, D. C., Tanabe, J., Martin, L. F., & Tregellas, J. R. (2012). Nicotine increases brain functional network efficiency. NeuroImage, 63, 73–80.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. Yu, D., Yuan, K., Zhang, B., Liu, J., Dong, M., Jin, C., Luo, L., Zhai, J., Zhao, L., Zhao, Y., 2015. White matter integrity in young smokers: A tract-based spatial statistics study. Addiction biology.

  51. 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.

  52. Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010). Whole-brain anatomical networks: Does the choice of nodes matter? NeuroImage, 50, 970–983.

    Article  PubMed  Google Scholar 

  53. Zhang, J., Wang, J., Wu, Q., Kuang, W., Huang, X., He, Y., & Gong, Q. (2011a). Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological Psychiatry, 70, 334–342.

    Article  PubMed  Google Scholar 

  54. Zhang, X., Salmeron, B. J., Ross, T. J., Geng, X., Yang, Y., & Stein, E. A. (2011b). Factors underlying prefrontal and insula structural alterations in smokers. NeuroImage, 54, 42–48.

    Article  PubMed  Google Scholar 

  55. Zhang, R., Jiang, G., Tian, J., Qiu, Y., Wen, X., Zalesky, A., Li, M., Ma, X., Wang, J., & Li, S. (2015). Abnormal white matter structural networks characterize heroin-dependent individuals: A network analysis. Addiction Biology. doi:10.1111/adb.12234.

    Google Scholar 

  56. Zhang, R., Jiang, G., Tian, J., Qiu, Y., Wen, X., Zalesky, A., Li, M., Ma, X., Wang, J., & Li, S. (2016). Abnormal white matter structural networks characterize heroin-dependent individuals: A network analysis. Addiction Biology, 21, 667.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

This paper is supported by the Project for the National Natural Science Foundation of China under Grant nos. 81571751, 81571753, 61502376, 81401478, 81401488, 81470816, 61431013, 81471737, 81301281, 81271644, 81271546, 81271549, the Natural Science Basic Research Plan in Shaanxi Province of China under Grant no. 2014JQ4118, and the Fundamental Research Funds for the Central Universities under the Grant nos. JBG151207, JB161201 JB151204, JB121405, the Natural Science Foundation of Inner Mongolia under Grant no. 2014BS0610, the Innovation Fund Project of Inner Mongolia University of Science and Technology Nos. 2015QNGG03, 2014QDL002, General Financial Grant the China Post- doctoral Science Foundation under Grant no.2014 M552416.

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Correspondence to Dahua Yu or Kai Yuan.

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Zhang, Y., Li, M., Wang, R. et al. Abnormal brain white matter network in young smokers: a graph theory analysis study. Brain Imaging and Behavior 12, 345–356 (2018). https://doi.org/10.1007/s11682-017-9699-6

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Keywords

  • Young smokers
  • White matter (WM)
  • Diffusion tensor imaging (DTI)
  • Graph theory analysis (GTA)