Altered entropy in the precuneus and posterior cingulate cortex in Alzheimer’s disease: A resting functional magnetic resonance imaging study

Aura C. Puche, J. Ochoa-Gómez, Yésika Alexandra Agudelo-Londoño, Jan Karlo Rodas-Marín, C. A. Tóbon-Quintero
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Abstract

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.
阿尔茨海默病楔前叶和后扣带皮层熵的改变:静息功能磁共振成像研究
人类的大脑被描述为一个复杂的系统。利用神经生理信号对其进行的研究揭示了线性和非线性相互作用的存在。在这种情况下,熵度量已被用于揭示存在和不存在神经紊乱的大脑行为。熵映射对于诸如阿尔茨海默病等进行性神经退行性疾病的研究具有重要意义。本研究的目的是利用从开放获取成像研究系列中提取的数据库,通过阿尔茨海默病患者和健康个体的默认网络和执行控制网络的Bold信号的低频振荡的熵和振幅来表征这种疾病的大脑振荡动力学。结果表明,与低频波动幅度和低频波动的分数幅度相比,排列熵的判别能力更高。在患者的默认网络和执行控制网络区域,通过排列获得熵增加。后扣带皮层和楔前叶在两组通过排列评估熵时表现出不同的特征。当将指标与临床量表相关联时,没有发现。结果表明,排列熵可以表征阿尔茨海默病患者的大脑功能,并且还揭示了非线性相互作用的信息,这些信息与通过计算低频振荡幅度获得的特征相辅相成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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