Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Brain. 2022 Apr 18;145(2):744-753. doi: 10.1093/brain/awab442.
Since the first demonstrations of network hyperexcitability in scientific models of Alzheimer's disease, a growing body of clinical studies have identified subclinical epileptiform activity and associated cognitive decline in patients with Alzheimer's disease. An obvious problem presented in these studies is lack of sensitive measures to detect and quantify network hyperexcitability in human subjects. In this study we examined whether altered neuronal synchrony can be a surrogate marker to quantify network hyperexcitability in patients with Alzheimer's disease. Using magnetoencephalography (MEG) at rest, we studied 30 Alzheimer's disease patients without subclinical epileptiform activity, 20 Alzheimer's disease patients with subclinical epileptiform activity and 35 age-matched controls. Presence of subclinical epileptiform activity was assessed in patients with Alzheimer's disease by long-term video-EEG and a 1-h resting MEG with simultaneous EEG. Using the resting-state source-space reconstructed MEG signal, in patients and controls we computed the global imaginary coherence in alpha (8-12 Hz) and delta-theta (2-8 Hz) oscillatory frequencies. We found that Alzheimer's disease patients with subclinical epileptiform activity have greater reductions in alpha imaginary coherence and greater enhancements in delta-theta imaginary coherence than Alzheimer's disease patients without subclinical epileptiform activity, and that these changes can distinguish between Alzheimer's disease patients with subclinical epileptiform activity and Alzheimer's disease patients without subclinical epileptiform activity with high accuracy. Finally, a principal component regression analysis showed that the variance of frequency-specific neuronal synchrony predicts longitudinal changes in Mini-Mental State Examination in patients and controls. Our results demonstrate that quantitative neurophysiological measures are sensitive biomarkers of network hyperexcitability and can be used to improve diagnosis and to select appropriate patients for the right therapy in the next-generation clinical trials. The current results provide an integrative framework for investigating network hyperexcitability and network dysfunction together with cognitive and clinical correlates in patients with Alzheimer's disease.
自阿尔茨海默病科学模型中首次展示网络过度兴奋以来，越来越多的临床研究在阿尔茨海默病患者中发现了亚临床癫痫样活动和相关认知下降。这些研究中一个明显的问题是缺乏敏感的措施来检测和量化人类受试者的网络过度兴奋。在这项研究中，我们研究了改变的神经元同步是否可以作为量化阿尔茨海默病患者网络过度兴奋的替代标志物。我们使用静息态脑磁图（MEG）研究了 30 名无亚临床癫痫样活动的阿尔茨海默病患者、20 名有亚临床癫痫样活动的阿尔茨海默病患者和 35 名年龄匹配的对照组。通过长期视频脑电图和 1 小时静息 MEG 同时进行 EEG 评估阿尔茨海默病患者的亚临床癫痫样活动。我们使用静息状态源空间重建的 MEG 信号，在患者和对照组中计算了 alpha（8-12 Hz）和 delta-theta（2-8 Hz）振荡频率的全局想象相干性。我们发现，有亚临床癫痫样活动的阿尔茨海默病患者的 alpha 想象相干性降低更大，delta-theta 想象相干性增强更大，这些变化可以高精度地区分有亚临床癫痫样活动的阿尔茨海默病患者和无亚临床癫痫样活动的阿尔茨海默病患者。最后，主成分回归分析表明，特定频率的神经元同步变异性可以预测患者和对照组的简易精神状态检查的纵向变化。我们的结果表明，定量神经生理测量是网络过度兴奋的敏感生物标志物，可以用于改善诊断，并为下一代临床试验中选择合适的患者和合适的治疗方法提供依据。目前的结果为研究阿尔茨海默病患者的网络过度兴奋和网络功能障碍以及认知和临床相关性提供了一个综合框架。
