Li Xuanyu, Zhu Zhaojun, Zhao Weina, Sun Yu, Wen Dong, Xie Yunyan, Liu Xiangyu, Niu Haijing, Han Ying
Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, 100053, China.
Xuanyu Li, Zhaojun Zhu, and Weina Zhao contributed equally to this research.
Biomed Opt Express. 2018 Mar 26;9(4):1916-1929. doi: 10.1364/BOE.9.001916. eCollection 2018 Apr 1.
Multiscale entropy (MSE) analysis is a novel entropy-based analysis method for quantifying the complexity of dynamic neural signals and physiological systems across multiple temporal scales. This approach may assist in elucidating the pathophysiologic mechanisms of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Using resting-state fNIRS imaging, we recorded spontaneous brain activity from 31 healthy controls (HC), 27 patients with aMCI, and 24 patients with AD. The quantitative analysis of MSE revealed that reduced brain signal complexity in AD patients in several networks, namely, the default, frontoparietal, ventral and dorsal attention networks. For the default and ventral attention networks, the MSE values also showed significant positive correlations with cognitive performances. These findings demonstrated that the MSE-based analysis method could serve as a novel tool for fNIRS study in characterizing and understanding the complexity of abnormal cortical signals in AD cohorts.
多尺度熵（MSE）分析是一种基于熵的新型分析方法，用于量化跨多个时间尺度的动态神经信号和生理系统的复杂性。这种方法可能有助于阐明遗忘型轻度认知障碍（aMCI）和阿尔茨海默病（AD）的病理生理机制。我们使用静息态功能近红外光谱（fNIRS）成像，记录了31名健康对照者（HC）、27名aMCI患者和24名AD患者的自发脑活动。MSE的定量分析显示，AD患者在几个网络中脑信号复杂性降低，即默认网络、额顶叶网络、腹侧和背侧注意网络。对于默认网络和腹侧注意网络，MSE值也与认知表现呈显著正相关。这些发现表明，基于MSE的分析方法可作为fNIRS研究中的一种新型工具，用于表征和理解AD队列中异常皮质信号的复杂性。
