Abstract
Despite significant progress in treatments for smoking cessation, smoking continues to be a significant public health concern, especially in young adulthood. Thus, developing a predictive model that can classify and characterize the brain-based biomarkers predicting smoking status would be imperative to improving treatment development. In this study, we applied a support vector machine-based classification method to discriminate 70 young male smokers and 70 matched nonsmokers using their diffusion tensor imaging (DTI) data. The classification procedure achieved an average accuracy of 88.6% and an average area under the curve of 0.95. The most discriminative features that contributed to the classification were primarily located in the sagittal stratum (SS), external capsule (EC), superior longitudinal fasciculus (SLF), anterior corona radiata (ACR) and inferior front-occipital fasciculus (IFOF). The following regression analysis showed a significant negatively correlation between the average RD values of the left ACR (r = −0.247, p = 0.039) and FTND. The average MD values in the right EC (r = −0.254, p = 0.034) and RD values in the right IFOF (r = −0.240, p = 0.046) were inversely associated with pack-years. Our findings indicate that the discriminative white matter (WM) features as brain biomarkers provide great predictive power for smoking status and suggest that machine learning techniques can reveal underlying smoking-related neurobiology.
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Acknowledgements
The study was supported by the National Natural Science Foundation of China under Grant Nos. 81871426, 81871430, 81571751, 81571753, 61771266, 31800926, 81701780 and 8151650, the Fundamental Research Funds for the Central Universities under Grant No. JB151204, the program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region NJYT-17-B11, the Natural Science Foundation of Inner Mongolia under Grant No. 2017MS(LH)0814, the program of Science and Technology in Universities of Inner Mongolia Autonomous Region NJZY17262, the Innovation Fund Project of Inner Mongolia University of Science and Technology No. 2015QNGG03, Science Fund for Distinguished Young Scholars of Hunan Province under Grant no. 2019JJ20037, National Natural Science Foundation of Shaanxi Province under Grant no. 2018JM7075 and the US National Institutes of Health, Intramural Research program Y1AA3009.
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Zhao, M., Liu, J., Cai, W. et al. Support vector machine based classification of smokers and nonsmokers using diffusion tensor imaging. Brain Imaging and Behavior 14, 2242–2250 (2020). https://doi.org/10.1007/s11682-019-00176-7
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DOI: https://doi.org/10.1007/s11682-019-00176-7