阿尔茨海默病的静息状态皮质脑电图节律:脑电图标记物的临床应用综述。

Fabrizio Vecchio, Claudio Babiloni, Roberta Lizio, Fabrizio De Vico Fallani, Katarzyna Blinowska, Giulio Verrienti, Giovanni Frisoni, Paolo M Rossini
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引用次数: 121

摘要

人类的大脑包含一个由大约1000亿个神经元组成的复杂网络。大脑衰老的特点是突触修剪,皮质-皮质连接的丧失和神经元凋亡的结合,引起认知功能的年龄依赖性下降。神经/突触的冗余和大脑网络的可塑性重塑,也继发于心理和身体训练,促进维持大脑活动和认知状态的健康老年人的日常生活。然而,年龄是影响认知的神经退行性疾病(如阿尔茨海默病(AD))的主要危险因素。越来越多的证据支持这样一种观点,即阿尔茨海默病的目标是特定的、功能相连的神经网络,而振荡的电磁脑活动可能是该疾病的一个标志。在这方面,数字脑电图(EEG)允许对皮层神经元同步进行无创分析,如静息状态脑节律所显示的那样。本文综述了健忘症轻度认知障碍(MCI)和AD患者静息状态闭眼脑电图节律的研究进展。一些研究支持这样一种观点,即这些脑电图节律的频谱标记,如功率密度、频谱一致性和其他定量特征,在正常老年人、轻度认知障碍和AD受试者之间存在差异,至少在组水平上是如此。关于个体水平上对这些受试者的分类,大多数先前的研究表明,相对于正常和AD受试者,EEG标记物的分类准确率为中等(70-80%)。总之,静息状态脑电图仪有望用于大规模、低成本、完全无创的老年AD风险筛查。
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Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review.

The human brain contains an intricate network of about 100 billion neurons. Aging of the brain is characterized by a combination of synaptic pruning, loss of cortico-cortical connections, and neuronal apoptosis that provoke an age-dependent decline of cognitive functions. Neural/synaptic redundancy and plastic remodeling of brain networking, also secondary to mental and physical training, promote maintenance of brain activity and cognitive status in healthy elderly subjects for everyday life. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Growing evidence supports the idea that AD targets specific and functionally connected neuronal networks and that oscillatory electromagnetic brain activity might be a hallmark of the disease. In this line, digital electroencephalography (EEG) allows noninvasive analysis of cortical neuronal synchronization, as revealed by resting state brain rhythms. This review provides an overview of the studies on resting state eyes-closed EEG rhythms recorded in amnesic mild cognitive impairment (MCI) and AD subjects. Several studies support the idea that spectral markers of these EEG rhythms, such as power density, spectral coherence, and other quantitative features, differ among normal elderly, MCI, and AD subjects, at least at group level. Regarding the classification of these subjects at individual level, the most previous studies showed a moderate accuracy (70-80%) in the classification of EEG markers relative to normal and AD subjects. In conclusion, resting state EEG makers are promising for large-scale, low-cost, fully noninvasive screening of elderly subjects at risk of AD.

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