Sun Jie, Wang Bin, Niu Yan, Tan Yuan, Fan Chanjuan, Zhang Nan, Xue Jiayue, Wei Jing, Xiang Jie
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
Entropy (Basel). 2020 Feb 20;22(2):239. doi: 10.3390/e22020239.
Alzheimer's disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000-2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.
阿尔茨海默病（AD）是一种发病率高且不可逆转的退行性脑疾病。近年来，由于脑信号具有复杂的非线性动力学特性，对研究AD患者脑信号时间序列的复杂变化的兴趣日益浓厚。我们回顾了AD患者脑电图（EEG）、脑磁图（MEG）和功能磁共振成像（fMRI）单通道时间序列复杂性分析的研究，并确定了未来的研究方向。在科学网和PubMed数据库中对2000 - 2019年的文献进行了系统检索，共识别出126项研究。与健康个体相比，AD患者的信号复杂性较低，振荡更具可预测性，主要出现在左顶叶、枕叶、右额叶和颞叶区域。这种复杂性被认为是准确反映AD功能损害的潜在生物标志物。当前的综述有助于揭示AD患者大脑功能障碍的模式，并研究信号复杂性是否可作为生物标志物来准确反映AD的功能损害。我们建议进一步研究AD患者的信号复杂性，包括研究复杂性算法的可靠性和信号复杂性的空间模式。总之，当前的综述有助于更好地理解AD大脑异常的复杂性，并为AD诊断提供有用信息。
