Wang Weijian, Kang Yimeng, Niu Xiaoyu, Zhang Zanxia, Li Shujian, Gao Xinyu, Zhang Mengzhe, Cheng Jingliang, Zhang Yong
Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Neurosci. 2023 Jul 21;17:1227422. doi: 10.3389/fnins.2023.1227422. eCollection 2023.
Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the 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.
A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-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 = 0.020). Identified networks comprised of edges mainly located between the subcortical-cerebellum network and networks including the frontoparietal default model and motor and visual networks.
These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity.
大脑分布式系统之间的异常相互作用与尼古丁成瘾机制有关。然而，作为一种脑连接性测量指标的结构协方差网络与吸烟严重程度之间的关系仍不明确。为填补这一空白，本研究旨在探讨吸烟者结构协方差网络与吸烟严重程度之间的关系。
共招募了101名男性吸烟者和51名男性非吸烟者，并对他们进行了T1加权解剖图像扫描。首先，通过留一法偏差估计程序为每个参与者导出个性化的结构协方差网络。然后，采用一种名为基于连接组的预测建模（CPM）的数据驱动机器学习方法，使用个性化的结构协方差网络来推断用尼古丁依赖Fagerström测试（FTND）分数衡量的吸烟严重程度。使用留一法交叉验证和置换检验来评估CPM的性能。
结果显示，CPM识别出了与吸烟严重程度相关的结构协方差网络，预测的FTND分数与实际分数之间存在显著相关性（r = 0.23，置换检验p = 0.020）。识别出的网络中的边主要位于皮层下-小脑网络与包括额顶叶默认模式、运动和视觉网络在内的网络之间。
这些结果识别出了与吸烟严重程度相关的结构协方差网络，并为吸烟严重程度的神经基础提供了新的见解。
