Niu Yan, Wang Bin, Zhou Mengni, Xue Jiayue, Shapour Habib, Cao Rui, Cui Xiaohong, Wu Jinglong, Xiang Jie
College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China.
Front Neurosci. 2018 Oct 1;12:677. doi: 10.3389/fnins.2018.00677. eCollection 2018.
Alzheimer's disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of temporal scales. However, the study of the temporal dynamics of BOLD signals in subjects with AD and mild cognitive impairment (MCI) remains largely unexplored. Multiscale entropy (MSE) analysis is a method for estimating the complexity of finite time series over multiple time scales. In this research, we applied MSE analysis to investigate the abnormal complexity of BOLD signals using the rs-fMRI data from the Alzheimer's disease neuroimaging initiative (ADNI) database. There were 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients. Following preprocessing of the BOLD signals, whole-brain MSE maps across six time scales were generated using the Complexity Toolbox. One-way analysis of variance (ANOVA) analysis on the MSE maps of four groups revealed significant differences in the thalamus, insula, lingual gyrus and inferior occipital gyrus, superior frontal gyrus and olfactory cortex, supramarginal gyrus, superior temporal gyrus, and middle temporal gyrus on multiple time scales. Compared with the NC group, MCI and AD patients had significant reductions in the complexity of BOLD signals and AD patients demonstrated lower complexity than that of the MCI subjects. Additionally, the complexity of BOLD signals from the regions of interest (ROIs) was found to be significantly associated with cognitive decline in patient groups on multiple time scales. Consequently, the complexity or MSE of BOLD signals may provide an imaging biomarker of cognitive impairments in MCI and AD.
阿尔茨海默病（AD）的特征是老年人脑功能进行性衰退。研究表明，在静息态功能磁共振成像（rs-fMRI）中，自发血氧水平依赖（BOLD）信号在广泛的时间尺度上存在异常相关性。然而，AD和轻度认知障碍（MCI）患者BOLD信号的时间动态研究在很大程度上仍未得到探索。多尺度熵（MSE）分析是一种用于估计多个时间尺度上有限时间序列复杂性的方法。在本研究中，我们应用MSE分析，使用来自阿尔茨海默病神经影像倡议（ADNI）数据库的rs-fMRI数据，研究BOLD信号的异常复杂性。研究对象包括30名正常对照（NC）、33名早期MCI（EMCI）、32名晚期MCI（LMCI）和29名AD患者。对BOLD信号进行预处理后，使用复杂性工具箱生成六个时间尺度上的全脑MSE图。对四组MSE图进行单因素方差分析（ANOVA），结果显示在多个时间尺度上，丘脑、岛叶、舌回和枕下回、额上回和嗅皮质、缘上回、颞上回和颞中回存在显著差异。与NC组相比，MCI和AD患者的BOLD信号复杂性显著降低，且AD患者的复杂性低于MCI受试者。此外，在多个时间尺度上，发现感兴趣区域（ROI）的BOLD信号复杂性与患者组的认知衰退显著相关。因此，BOLD信号的复杂性或MSE可能为MCI和AD的认知障碍提供一种影像学生物标志物。
