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Updated: Jan 12, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Abnormal white matter microstructure in tobacco use disorder: A machine learning study based on whole-brain
Lei Jiang1, Yan Kang1, Xu Han2
1State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Background:
There is growing evidence that cigarette smoking is associated with abnormal white matter (WM) microstructure. However, traditional research using group-level mass-univariate statistical analysis, despite strict multiple comparison corrections, may still yield false positives or false negatives. In this study, a multivariate machine learning method was performed to enhance the identification of smoking-related WM regions.
Methods:
The whole-brain skeletonized maps of diffusion metrics derived from diffusion tensor imaging of 60 tobacco use disorder (TUD) participants and 66 non-TUD subjects were utilized as classification features. A linear support vector machine (SVM) classifier was trained to differentiate between TUD and non-TUD individuals, and smoking-related WM regions were determined using discriminative score. Correlation analysis was conducted to evaluate the relationship between classification scores and smoking-related variables among TUD participants.
Results:
The SVM classifier achieved an accuracy over 0.80 and an area under the curve exceeding 0.91, with maximal discriminative weights localized to the anterior corona radiata, posterior thalamic radiation, and genu of the corpus callosum (i.e., forceps minor). Moreover, classification scores were positively correlated with the onset age of cigarette use.
Conclusions:
Our multivariate approach using whole-brain skeletonized maps of diffusion metrics outperformed those using regional features, demonstrating superior classification performance. The WM discriminative regions identified by our approach may offer a more effective way to distinguish individuals with nicotine addiction, potentially advancing our understanding of smoking-related brain alterations.
