Information Geometry Approach to Analyzing Simulated EEG Signals of Alzheimer's Disease Patients and Healthy Control Subjects
Information Geometry Approach to Analyzing Simulated EEG Signals of Alzheimer's Disease Patients and Healthy Control Subjects
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DOI：
10.1109/bibm58861.2023.10385583
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Jia-Chen Hua;Eun-jin Kim;Fei He
中科院分区：
文献类型：
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作者：
Jia-Chen Hua;Eun-jin Kim;Fei He
在这项工作中，我们探索信息几何理论的方法来分析脑电信号的随机非线性耦合振子模型模拟健康受试者和阿尔茨海默病（AD）患者的闭眼和睁眼条件。特别是，我们采用信息速率来量化模拟EEG信号的概率密度函数的时间演化，并采用因果信息速率来量化一个信号对另一个信号的信息速率的瞬时影响。这两项措施帮助我们发现健康受试者和AD患者在改变眼睛的睁开/闭合状态时的显著和有趣的区别。这些区别可能还与相应脑区的神经信息处理活动的差异以及这些脑区之间的连通性差异有关。特别是，一个突出的区别是，当健康的受试者睁开眼睛，有一个方向性的变化，净因果关系的连接在大脑区域（产生随机非线性耦合振荡器建模的EEG信号），净因果信息率测量，而这种方向性的变化，净因果关系不存在当AD患者睁开眼睛。由于这些信息几何理论测度可以以无模型的方式应用于实验EEG信号，并且它们能够量化真实世界EEG信号中呈现的非平稳时变效应、非线性和非高斯随机性，我们相信它们可以成为一个重要而有力的工具，用于理解大脑中的神经信息处理和诊断阿尔茨海默氏症等神经系统疾病。
In this work, we explore information geometry theoretic approach to analyzing EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer’s Disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal’s instantaneous influence on another signal’s information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they change their eyes’ open/closed status. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. In particular, a prominent distinction is that, when healthy subjects open their eyes, there is a directional change in net causal connectivities among brain regions (that generate EEG signals modeled by stochastic nonlinear coupled oscillators), as measured by net causal information rates, whereas this directional change in net causality does not present when AD patients open their eyes. Since these information geometry theoretic measures can be applied to experimental EEG signals in a modelfree manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and diagnosis of neurological disorders such as Alzheimer’s Disease.