Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease.
Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease.
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DOI：
10.3389/fnimg.2022.924811
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Parra, Mario A
中科院分区：
文献类型：
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作者：
Clark, Ruaridh A;Smith, Keith;Escudero, Javier;Ibanez, Agustin;Parra, Mario A
包括阿尔茨海默病（AD）在内的痴呆症的患病率在全球范围内呈上升趋势，筛查和干预对那些获得医疗保健机会有限的人特别重要和有益。脑电图（EEG）是一种廉价、可扩展和便携式的脑成像技术，可以为那些没有当地三级医疗基础设施的人提供AD筛查。我们分别使用高密度和低密度EEG研究了散发性轻度认知障碍（MCI）和前驱家族性早发性AD受试者相同工作记忆任务的EEG记录。从EEG记录检测电生理变化的挑战是噪声和体积传导效应是常见的和破坏性的。已知相干性的虚部（iCOH）可以生成功能性连接网络，其减轻体积传导，同时还擦除真实的瞬时活动（零或π相）。我们的目标是暴露在这些iCOH连接网络的拓扑差异，使用全球网络措施，特征向量对齐（EA），是强大的网络改造，模仿iCOH的连接擦除。EA评估的对齐根据其连接模式的相似性捕获一对EEG通道之间的关系。显着的一致性，从随机空模型的比较，被认为是一致的频率范围内（三角洲，θ，α和β）的工作记忆任务，其中一致性的iCOH连接也注意到。对于高密度EEG记录，观察到对照组和散发性MCI结果的明显差异，对照组表现出更加一致的对齐。对照组和痴呆前组之间的差异检测到显着的相关性和iCOH连接性，但只有EA表明一个显着的网络拓扑结构的差异时，比较受试者与散发性MCI和前驱家族性AD。在整个频率范围内的一致性的路线，提供了一个衡量的信心EA的检测拓扑结构，一个重要的方面，标志着这种方法作为一个有前途的方向，为早发性AD的可靠的测试。
The prevalence of dementia, including Alzheimer's disease (AD), is on the rise globally with screening and intervention of particular importance and benefit to those with limited access to healthcare. Electroencephalogram (EEG) is an inexpensive, scalable, and portable brain imaging technology that could deliver AD screening to those without local tertiary healthcare infrastructure. We study EEG recordings of subjects with sporadic mild cognitive impairment (MCI) and prodromal familial, early-onset, AD for the same working memory tasks using high- and low-density EEG, respectively. A challenge in detecting electrophysiological changes from EEG recordings is that noise and volume conduction effects are common and disruptive. It is known that the imaginary part of coherency (iCOH) can generate functional connectivity networks that mitigate against volume conduction, while also erasing true instantaneous activity (zero or π-phase). We aim to expose topological differences in these iCOH connectivity networks using a global network measure, eigenvector alignment (EA), shown to be robust to network alterations that emulate the erasure of connectivities by iCOH. Alignments assessed by EA capture the relationship between a pair of EEG channels from the similarity of their connectivity patterns. Significant alignments—from comparison with random null models—are seen to be consistent across frequency ranges (delta, theta, alpha, and beta) for the working memory tasks, where consistency of iCOH connectivities is also noted. For high-density EEG recordings, stark differences in the control and sporadic MCI results are observed with the control group demonstrating far more consistent alignments. Differences between the control and pre-dementia groupings are detected for significant correlation and iCOH connectivities, but only EA suggests a notable difference in network topology when comparing between subjects with sporadic MCI and prodromal familial AD. The consistency of alignments, across frequency ranges, provides a measure of confidence in EA's detection of topological structure, an important aspect that marks this approach as a promising direction for developing a reliable test for early onset AD.