Disrupted Functional Brain Network Topology in Etomidate Misuse.

IF 2.9 Q3 PSYCHIATRY
Alpha psychiatry Pub Date : 2026-06-25 eCollection Date: 2026-06-01 DOI:10.31083/AP49872
Ying Tang, Xingmin Wang, Juan Le, Qiuping Huang, Hao Chen, Xinxin Chen, Xuhao Wang, Yunde Tang, Lishun Zhao, Lin Zhao, Hongxian Shen, Zhenjiang Liao
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Abstract

Background: Etomidate misuse (EM) has recently become an increasing public health concern in East and Southeast Asia, but its neurobiological mechanisms are still not well understood. Although substance use disorders (SUDs) are commonly associated with disruptions in large-scale brain network organization, the effects of EM on brain network topology remain largely unexplored.

Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from individuals with EM and healthy controls (HC). Graph theoretical analyses were employed to and characterize global and nodal topological properties of functional brain networks. Clinical assessments captured substance use characteristics, craving, impulsivity, and addiction severity. Partial correlation analyses were conducted to examine associations between network metrics and substance use characteristics. Additonally, a support vector machine (SVM) classifier was implemented to discriminate individuals with EM from HC based on network features.

Results: A total of 103 individuals with EM and 57 HC were included in the final analysis. Global topological organization that appeared was largely preserved in the EM group, with the exception of a significantly reduced clustering coefficient. At the nodal level, individuals with EM exhibited significant alterations in degree centrality, betweenness centrality, and nodal efficiency across regions predominantly distributed within the default mode, attention, and sensorimotor networks. Correlation analyses revealed no significant associations between network metrics and substance use characteristics following correction for multiple comparisons. Furthermore, the SVM model achieved moderate classification performance (accuracy = 66.7%) with an area under the curve (AUC) of 0.711.

Conclusions: This study provides the first systematic investigation of the brain network topology in EM. The findings indicate widespread alterations in nodal network properties alongside relatively preserved global topological organization. While these results may offer preliminary indicators related to EM, their clinical relevance requires further in future research.

依托咪酯误用导致脑功能网络拓扑紊乱。
背景:依托咪酯滥用(EM)近年来已成为东亚和东南亚日益关注的公共卫生问题,但其神经生物学机制仍未得到很好的理解。虽然物质使用障碍(sud)通常与大规模大脑网络组织的中断有关,但EM对大脑网络拓扑结构的影响在很大程度上仍未被探索。方法:静息状态功能磁共振成像(rs-fMRI)数据来自EM个体和健康对照(HC)。图论分析被用于描述脑功能网络的全局和节点拓扑特性。临床评估捕获了物质使用特征、渴望、冲动和成瘾严重程度。进行偏相关分析以检验网络度量和物质使用特征之间的关联。在此基础上,利用支持向量机(SVM)分类器实现基于网络特征的EM和HC个体识别。结果:共有103例EM患者和57例HC患者被纳入最终分析。除了聚类系数显着降低外,EM组中出现的全局拓扑组织在很大程度上保留了下来。在节点水平上,EM个体在程度中心性、中间中心性和节点效率上表现出显著的变化,这些变化主要分布在默认模式、注意和感觉运动网络中。相关分析显示,在多重比较校正后,网络度量和物质使用特征之间没有显著关联。此外,SVM模型的分类性能中等,准确率为66.7%,曲线下面积(AUC)为0.711。结论:本研究首次对EM中的大脑网络拓扑结构进行了系统研究。研究结果表明,节点网络特性在相对保存的全局拓扑组织的基础上发生了广泛的变化。虽然这些结果可能提供了与EM相关的初步指标,但其临床相关性需要进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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