Clark Ruaridh A, Smith Keith, Escudero Javier, Ibáñez Agustín, Parra Mario A
Centre for Signal and Image Processing, University of Strathclyde, Glasgow, United Kingdom.
Department of Physics and Mathematics, Nottingham Trent University, Nottingham, United Kingdom.
Front Neuroimaging. 2022 Jul 11;1:924811. doi: 10.3389/fnimg.2022.924811. eCollection 2022.
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.
包括阿尔茨海默病（AD）在内的痴呆症患病率在全球范围内呈上升趋势，筛查和干预对那些难以获得医疗保健服务的人群尤为重要且有益。脑电图（EEG）是一种廉价、可扩展且便于携带的脑成像技术，可将AD筛查提供给那些当地没有三级医疗基础设施的人群。我们分别使用高密度和低密度脑电图，研究了散发性轻度认知障碍（MCI）患者以及前驱性家族性早发性AD患者在执行相同工作记忆任务时的脑电图记录。从脑电图记录中检测电生理变化的一个挑战是，噪声和容积传导效应很常见且具有干扰性。已知相干性的虚部（iCOH）可以生成功能连接网络，减轻容积传导的影响，同时也会消除真实的瞬时活动（零或π相位）。我们旨在使用一种全局网络测量方法——特征向量对齐（EA），揭示这些iCOH连接网络中的拓扑差异，该方法已被证明对模拟iCOH连接消除情况的网络改变具有鲁棒性。通过EA评估的对齐从其连接模式的相似性捕获一对脑电图通道之间的关系。与随机空模型比较得出的显著对齐在工作记忆任务的各个频率范围（δ、θ、α和β）中都是一致的，在这些频率范围内iCOH连接的一致性也很明显。对于高密度脑电图记录，在对照组和散发性MCI结果之间观察到明显差异，对照组显示出更为一致的对齐。在对照组和痴呆前期分组之间检测到显著相关性和iCOH连接的差异，但只有EA表明，在比较散发性MCI患者和前驱性家族性AD患者时，网络拓扑存在显著差异。跨频率范围的对齐一致性为EA检测拓扑结构提供了一种置信度度量，这一重要方面标志着该方法是开发可靠的早发性AD检测方法的一个有前途的方向。
