AUTHOR=Wang Weijian , Kang Yimeng , Niu Xiaoyu , Zhang Zanxia , Li Shujian , Gao Xinyu , Zhang Mengzhe , Cheng Jingliang , Zhang Yong 

TITLE=Connectome-based predictive modeling of smoking severity using individualized structural covariance network in smokers

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1227422

DOI=10.3389/fnins.2023.1227422

ISSN=1662-453X

ABSTRACT=Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers.One hundred and one male smokers and 51 male non-smokers were recruited and underwent T1weighted anatomical image scan. First, individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method, named connectome-based predictive modelling (CPM), was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores, using individualized structural covariance network. The performance of CPM was evaluated using leaveone-out cross-validation and a permutation testing. As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised edges mainly located between subcortical-cerebellum network and networks including frontopartial, default model network, motor and visual network. These results identify smoking-severity related structural covariance networks and provide a new insight into the neural underpinnings of smoking severity.