Characterizing the Structural Pattern of Heavy Smokers Using Multivoxel Pattern Analysis - Ye, Yufeng; Zhang, Jian; Huang, Bingsheng; Cai, Xun; Wang, Panying; Zeng, Ping; Wu, Songxiong; Ma, Jinting; Huang, Han; Liu, Heng; Dan, Guo; Wu, Guangyao (Frontiers in Psychiatry, 2021)
Background Smoking addiction is one of the major public health issues which causes a series of chronic diseases and mortalities worldwide. We aimed to explore the most discriminative gray matter regions between heavy smokers and healthy controls with a data-driven multivoxel pattern analysis technique, and to explore the methodological difference between multivoxel pattern analysis and voxel-based morphometry. Methods Traditional voxel-based morphometry has continuously contributed in finding smoking addiction-related regions on structural magnetic resonance imaging. However, voxel-based morphometry has its inherent limitation. In this study, a multivoxel pattern analysis with searchlight algorithm and support vector machine was applied on structural magnetic resonance imaging to identify the spatial pattern of gray matter volume in heavy smokers. Results Our proposed method yielded a voxel-wise accuracy of at least 74% for classifying heavy smokers from healthy controls. Identified regions were primarily located at temporal cortex and prefrontal cortex, occipital cortex, thalamus (bilateral), insula (left), anterior and median cingulate gyri and precuneus (left). Conclusions Our results suggested several regions which were seldomly reported in voxel-based morphometry analysis might be latently correlated with smoking addiction. Such findings might provide insights in understanding the mechanism of chronic smoking and effective cessation treatment making. Multivoxel pattern analysis can be efficient in locating brain discriminative regions which were neglected by voxel-based morphometry.