Dimitriadis Stavros I, López María Eugenia, Maestu Fernando, Pereda Ernesto
Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.
Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.
Front Neurosci. 2019 Jun 11;13:542. doi: 10.3389/fnins.2019.00542. eCollection 2019.
The need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI) is evident. MCI patients have a high risk of developing Alzheimer's disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings on the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys) function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings. The activity of different brain rhythms {δ, 𝜃, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine 90 anatomical regions of interest (ROIs). A DFCG was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 MCI patients and 22 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments, which further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. An estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTS). The NMTS preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (n μstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation. We succeeded in gaining a high classification accuracy on the blind dataset (85%), which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could properly manipulate the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.
设计和验证用于检测轻度认知障碍（MCI）的新型生物标志物的需求是显而易见的。MCI患者患阿尔茨海默病（AD）的风险很高，因此引入新型且可靠的生物标志物具有重要的临床意义。受关于动态功能连接图（DFCG）丰富的脑（功能障碍）功能信息的最新研究结果的启发，我们引入了一种基于脑磁图（MEG）静息状态记录识别MCI的新方法。首先在MEG数据中使用线性约束最小范数方差对不同脑节律{δ、θ、α1、α2、β1、β2、γ1、γ2}的活动进行波束形成，以确定90个感兴趣的解剖区域（ROI）。然后使用相位滞后值的虚部（iPLV）估计DFCG，用于频率内耦合（8种）和跨频率耦合对（28种）。我们分析了18例MCI患者和22例健康对照的神经磁静息状态记录的DFCG图谱。我们遵循我们的模型，识别跨MEG源和时间片段的主导内在耦合模式（DICM），这进一步导致构建综合DFCG（iDFCG）。然后，我们使用数据驱动的方法对iDFCG的每个快照进行统计和拓扑滤波。对iDFCG的每个时间片段的归一化拉普拉斯变换和相关特征值的估计基于特征值的网络度量时间序列（NMTS）创建了一个二维图。NMTS保留了每个受试者iDCFG波动同步性的非平稳性。使用最初的20名健康老年人和20名MCI患者作为训练集，我们构建了一个超完备的网络微状态（n μstates）字典集。之后，我们在另外一组20名受试者的盲测集中测试了整个过程以进行外部验证。我们在盲测数据集上成功获得了较高的分类准确率（85%），这进一步支持了所提出的脑状态演变的马尔可夫模型。结合先进信号处理和网络神经科学工具的适当神经信息学工具的应用，可以恰当地处理时间分辨功能连接模式的非平稳性，从而揭示一种用于MCI的强大生物标志物。
