Font-Clos Francesc, Spelta Benedetta, D'Agostino Armando, Donati Francesco, Sarasso Simone, Canevini Maria Paola, Zapperi Stefano, La Porta Caterina A M
Center for Complexity and Biosystems, Department of Physics, University of Milan, Milano, Italy.
Department of Health Sciences, University of Milan, Milano, Italy.
Front Netw Physiol. 2021 Sep 28;1:746118. doi: 10.3389/fnetp.2021.746118. eCollection 2021.
High-density electroencephalography (hd-EEG) provides an accessible indirect method to record spatio-temporal brain activity with potential for disease diagnosis and monitoring. Due to their highly multidimensional nature, extracting useful information from hd-EEG recordings is a complex task. Network representations have been shown to provide an intuitive picture of the spatial connectivity underlying an electroencephalogram recording, although some information is lost in the projection. Here, we propose a method to construct multilayer network representations of hd-EEG recordings that maximize their information content and test it on sleep data recorded in individuals with mental health issues. We perform a series of statistical measurements on the multilayer networks obtained from patients and control subjects and detect significant differences between the groups in clustering coefficient, betwenness centrality, average shortest path length and parieto occipital edge presence. In particular, patients with a mood disorder display a increased edge presence in the parieto-occipital region with respect to healthy control subjects, indicating a highly correlated electrical activity in that region of the brain. We also show that multilayer networks at constant edge density perform better, since most network properties are correlated with the edge density itself which can act as a confounding factor. Our results show that it is possible to stratify patients through statistical measurements on a multilayer network representation of hd-EEG recordings. The analysis reveals that individuals with mental health issues display strongly correlated signals in the parieto-occipital region. Our methodology could be useful as a visualization and analysis tool for hd-EEG recordings in a variety of pathological conditions.
高密度脑电图（hd-EEG）提供了一种可获取的间接方法来记录脑电活动的时空信息，具有疾病诊断和监测的潜力。由于其高度多维的性质，从hd-EEG记录中提取有用信息是一项复杂的任务。网络表示已被证明能直观呈现脑电图记录背后的空间连通性，尽管在投影过程中会丢失一些信息。在此，我们提出一种构建hd-EEG记录多层网络表示的方法，以最大化其信息含量，并在有心理健康问题个体记录的睡眠数据上进行测试。我们对从患者和对照受试者获得的多层网络进行了一系列统计测量，检测到两组在聚类系数、介数中心性、平均最短路径长度和顶枕边缘存在方面存在显著差异。特别是，患有情绪障碍的患者相对于健康对照受试者在顶枕区域显示出边缘存在增加，表明该脑区存在高度相关的电活动。我们还表明，在恒定边缘密度下的多层网络表现更好，因为大多数网络属性与边缘密度本身相关，而边缘密度可能是一个混杂因素。我们的结果表明，通过对hd-EEG记录的多层网络表示进行统计测量，可以对患者进行分层。分析显示，有心理健康问题的个体在顶枕区域显示出强相关信号。我们的方法可作为hd-EEG记录在各种病理状况下的可视化和分析工具。
