Dimitriadis Stavros I, López María E, Bruña Ricardo, Cuesta Pablo, Marcos Alberto, Maestú Fernando, Pereda Ernesto
Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom.
Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.
Front Neurosci. 2018 Jun 1;12:306. doi: 10.3389/fnins.2018.00306. eCollection 2018.
Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α:α and 94% for the iPLV in α. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.
我们的工作旨在展示机器学习和图论相结合的方法，用于利用闭眼神经磁记录为轻度认知障碍（MCI）受试者设计一种连接组学生物标志物。整个分析基于源重建的神经磁活动。作为感兴趣区域（ROI）的表示方法，我们采用了主成分分析（PCA）和质心方法。作为用于估计频率内和跨频率相互作用的代表性双变量连通性估计器，我们采用了锁相值（PLV）、虚部（iPLV）和包络相关性（CorrEnv）。频率内和跨频率相互作用（CFC）均已通过这三种连通性估计器在七个频段内（频率内）以及成对情况（CFC）下进行了估计。我们展示了不同版本的功能连通性图，即单层（SL-FCG）和多层（ML-FCG），如何能让我们对大脑区域间的功能相互作用有不同的认识。最后，我们应用机器学习技术，主要目的是通过在两种不同方式下分析SL-FCG和ML-FCG来构建一个可靠的连接组学生物标志物：一种是使用张量提取算法将其作为一个整体单元，另一种是作为单个成对耦合估计。我们得出结论，边加权特征选择策略优于对SL-FCG和ML-FCG的张量处理。在质心ROI表示和SL-FCG的边加权分析中，对于α频段的CorrEnv，分类性能最高达到98%，对于α频段的iPLV，分类性能达到94%。基于多层参与系数（一种多重性指标）的分类性能，对于iPLV和CorrEnv均达到52%。在边加权情况下构建多变量连接组学生物标志物的选定功能连接位于默认模式、额顶叶和扣带回-岛叶网络。我们的分析支持了在波束形成记录中，使用各种连通性估计器同时分析频率内和跨频率全脑相互作用时对功能连通性图（FCG）进行分析的观点。
