Yi Meizhi, Wang Tianyao, Li Xun, Jiang Yihong, Wang Yan, Zhang Luokai, Chen Wen, Hu Jun, Wu Huiting, Zhou Yang, Luo Guanghua, Liu Jun, Zhou Hong
Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Department of Radiology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Quant Imaging Med Surg. 2025 Mar 3;15(3):2076-2093. doi: 10.21037/qims-24-1516. Epub 2025 Feb 26.
Benzodiazepine use disorders (BUDs) have become a public health issue that cannot be ignored. We aimed to demonstrate that patients with BUDs might undergo changes in white matter (WM) integrity, which are related to impaired cognitive function.
We used diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) to observe changes in WM structure from 29 patients with sleep disorders with BUD (SDBUD), 33 patients with sleep disorders with non-BUD (SDNBUD), and 25 healthy participants. We also compared the diagnostic performance of the diffusion metrics and models in predicting the status of BUDs and evaluated the relationship between WM changes and cognitive impairment.
BUD was closely associated with WM damage in the corpus callosum (CC) and pontine crossing tract (PCT). There were 14 main diffusion metrics that could be used to predict BUD status (P=0.001-0.023). DTI, DKI, NODDI, and MAP had similar satisfactory performance for predicting BUD status (P=0.001-0.021). Pearson correlation analysis showed a close relationship between the Trail Making Test B (TMT-B) and DTI/NODDI metrics in the splenium of the CC and PCT and between the Montreal Cognitive Assessment (MoCA) and MAP metrics in the splenium of the CC in the SDBUD group (P=0.008-0.040).
Our findings provide evidence for the neurobiological mechanism of benzodiazepine addiction and a novel method for the clinical diagnosis of BUDs.
苯二氮䓬类药物使用障碍(BUDs)已成为一个不容忽视的公共卫生问题。我们旨在证明,BUDs患者可能会出现白质(WM)完整性变化,这与认知功能受损有关。
我们使用扩散张量成像(DTI)、扩散峰度成像(DKI)、神经突方向离散度和密度成像(NODDI)以及平均表观传播子(MAP)来观察29例患有BUD的睡眠障碍患者(SDBUD)、33例患有非BUD的睡眠障碍患者(SDNBUD)和25名健康参与者的WM结构变化。我们还比较了扩散指标和模型在预测BUDs状态方面的诊断性能,并评估了WM变化与认知障碍之间的关系。
BUD与胼胝体(CC)和脑桥交叉束(PCT)中的WM损伤密切相关。有14个主要扩散指标可用于预测BUD状态(P = 0.001 - 0.023)。DTI、DKI、NODDI和MAP在预测BUD状态方面具有相似的良好性能(P = 0.001 - 0.021)。Pearson相关分析显示,在SDBUD组中,CC和PCT的压部的连线测验B(TMT - B)与DTI/NODDI指标之间以及CC压部的蒙特利尔认知评估(MoCA)与MAP指标之间存在密切关系(P = 0.008 - 0.040)。
我们的研究结果为苯二氮䓬类药物成瘾的神经生物学机制提供了证据,并为BUDs的临床诊断提供了一种新方法。