Disrupted Functional Brain Network Topology in Etomidate Misuse.
Authors
Affiliations (4)
Affiliations (4)
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, 410011 Changsha, Hunan, China.
- Department of Psychology, School of Humanities and Management, Hunan University of Chinese Medicine, 410208 Changsha, Hunan, China.
- Hunan Lituo Compulsory Isolation and Drug Rehabilitation Center, Hunan Lituo Drug Rehabilitation and Recovery Center, 410113 Changsha, Hunan, China.
- Hunan Provincial Lushan Compulsory Isolation Detoxification Center, 410000 Changsha, Hunan, China.
Abstract
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. 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. 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. 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.