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Brain Imaging and Behavior

, Volume 12, Issue 2, pp 345–356 | Cite as

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

  • Yajuan Zhang
  • Min Li
  • Ruonan Wang
  • Yanzhi Bi
  • Yangding Li
  • Zhang Yi
  • Jixin Liu
  • Dahua Yu
  • Kai Yuan
Original Research
First Online:

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.

Keywords

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

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.

Compliance with ethical standards

Ethical statements

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.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Yajuan Zhang
    • 1
    • 2
  • Min Li
    • 1
    • 2
  • Ruonan Wang
    • 1
    • 2
  • Yanzhi Bi
    • 1
    • 2
  • Yangding Li
    • 4
  • Zhang Yi
    • 1
    • 2
  • Jixin Liu
    • 1
    • 2
  • Dahua Yu
    • 3
  • Kai Yuan
    • 1
    • 2
    • 3
  1. 1.School of Life Science and TechnologyXidian UniversityXi’an ShaanxiPeople’s Republic of China
  2. 2.Engineering Research Center of Molecular and Neuro Imaging, Ministry of EducationXi’anPeople’s Republic of China
  3. 3.Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, Information Processing Laboratory, School of Information EngineeringInner Mongolia University of Science and TechnologyBaotouPeople’s Republic of China
  4. 4.Guangxi Key Laboratory of the Multi-source Information Mining and SecurityGuangxi Normal UniversityGulinPeople’s Republic of China

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