Han Xiaodong, Gong Shuting, Gong Jin, Wang Pin, Li Ruina, Chen Runqi, Xu Chang, Sun Wenxian, Li Shaoqi, Chen Yufei, Yang Yuting, Luan Heya, Wen Boye, Guo Jinxuan, Lv Sirong, Wei Cuibai
Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.
School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Psychiatry Clin Neurosci. 2025 Jun;79(6):336-343. doi: 10.1111/pcn.13812. Epub 2025 Mar 31.
Effective intervention for mild cognitive impairment (MCI) is key for preventing dementia. As a neuroprotective agent, butylphthalide has the potential to treat MCI due to Alzheimer disease (AD). However, the pharmacological mechanism of butylphthalide from the brain network perspective is not clear. Therefore, we aimed to investigate the multimodal brain network changes associated with butylphthalide treatment in MCI due to AD.
A total of 270 patients with MCI due to AD received either butylphthalide or placebo at a ratio of 1:1 for 1 year. Effective treatment was defined as a decrease in the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog) > 2.5. Brain networks were constructed using T1-magnetic resonance imaging and fluorodeoxyglucose positron emission tomography. A support vector machine was applied to develop predictive models.
Both treatment (drug vs. placebo)-time interactions and efficacy (effective vs. ineffective)-time interactions were detected on some overlapping structural network metrics. Simple effects analyses revealed a significantly increased global efficiency in the structural network under both treatment and effective treatment of butylphthalide. Among the overlapping metrics, an increased degree centrality of left paracentral lobule was significantly related to poorer cognitive improvement. The predictive model based on baseline multimodal network metrics exhibited high accuracy (88.93%) of predicting butylphthalide's efficacy.
Butylphthalide may restore abnormal organization in structural networks of patients with MCI due to AD, and baseline network metrics could be predictive markers for therapeutic efficacy of butylphthalide.
This study was registered in the Chinese Clinical Trial Registry (Registration Number: ChiCTR1800018362, Registration Date: 2018-09-13).
对轻度认知障碍（MCI）进行有效干预是预防痴呆的关键。丁苯酞作为一种神经保护剂，具有治疗阿尔茨海默病（AD）所致MCI的潜力。然而，从脑网络角度来看，丁苯酞的药理机制尚不清楚。因此，我们旨在研究丁苯酞治疗AD所致MCI时相关的多模态脑网络变化。
共有270例AD所致MCI患者，按照1:1的比例接受丁苯酞或安慰剂治疗，为期1年。有效治疗定义为阿尔茨海默病评估量表认知分量表（ADAS-cog）下降>2.5。使用T1磁共振成像和氟脱氧葡萄糖正电子发射断层扫描构建脑网络。应用支持向量机建立预测模型。
在一些重叠的结构网络指标上检测到治疗（药物与安慰剂）-时间交互作用和疗效（有效与无效）-时间交互作用。简单效应分析显示，在丁苯酞治疗和有效治疗下，结构网络的全局效率均显著提高。在重叠指标中，左侧中央旁小叶的度中心性增加与较差的认知改善显著相关。基于基线多模态网络指标的预测模型对丁苯酞疗效的预测准确率较高（88.93%）。
丁苯酞可能恢复AD所致MCI患者结构网络的异常组织，基线网络指标可能是丁苯酞治疗疗效的预测标志物。
本研究已在中国临床试验注册中心注册（注册号：ChiCTR1800018362，注册日期：2018年9月13日）。
