White matter integrity in young smokers: a tract-based spatial statistics study.
Previous diffusion tensor imaging (DTI) studies revealed contradictory effects of smoking on fractional anisotropy (FA). Multiple DTI-derived indices may help to deduce the pathophysiological type of white matter (WM) changes and provide more specific biomarkers of WM neuropathology in the whole brain of young smokers. Twenty-three young smokers and 22 age-, education- and gender-matched healthy non-smoking controls participated in this study. Tract-based spatial statistics was employed to investigate the WM microstructure in young smokers by integrating multiple indices, including FA, mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). Compared with healthy non-smoking controls, young smokers showed significantly increased FA with increased AD and decreased RD in several brain regions, while no difference in MD was observed. Specifically, the overlapped WM regions with increased FA, increased AD and decreased RD were found in the right posterior limb of the internal capsule, the right external capsule and the right superior corona radiata. Additionally, average FA and RD values in the WM regions mentioned earlier were significantly correlated with pack-years and Fagerström Test for Nicotine Dependence, while no correlation in AD was found. The WM tracts with increased FA may be more associated with RD, rather than AD in young smokers. We suggested that WM properties of several fibres in young smokers may be the biomarker as the cumulative effect and severity of nicotine dependence.
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, China.
Dahua
D
Yu
Life Sciences Research Center, School of Life Science and Technology, Xidian University, China.
Kai
K
Yuan
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, China.
Baohua
B
Zhang
Life Sciences Research Center, School of Life Science and Technology, Xidian University, China.
Jixin
J
Liu
Life Sciences Research Center, School of Life Science and Technology, Xidian University, China.
Minghao
M
Dong
Department of Medical Imaging, The First Affiliated Hospital of Medical College, Xi'an Jiaotong University, China.
Chenwang
C
Jin
Department of Medical Imaging, The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, China.
Lin
L
Luo
Department of Medical Imaging, The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, China.
Jinquan
J
Zhai
The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, China.
Ling
L
Zhao
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, China.
Ying
Y
Zhao
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, China.
Yu
Y
Gu
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, China.
Ting
T
Xue
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, China.
Xin
X
Liu
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, China.
Xiaoqi
X
Lu
Life Sciences Research Center, School of Life Science and Technology, Xidian University, China.
Wei
W
Qin
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, China.|Institute of Automation, Chinese Academy of Sciences, China.
Jie
J
Tian
D000293:Adolescent; D016880:Anisotropy; D001921:Brain / Q000000981:diagnostic imaging; D016022:Case-Control Studies; D056324:Diffusion Tensor Imaging; D006801:Humans; D008297:Male; D009434:Neural Pathways / Q000000981:diagnostic imaging; D012907:Smoking; D066127:White Matter / Q000000981:diagnostic imaging*; D055815:Young Adult
Diffusion tensor imaging; smoking; tract-based spatial statistics