Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards.

中文翻译：

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使用基于小波的神经标记物通过 MEG 功能连接检测轻度认知障碍

基于脑磁图 (MEG) 信号开发有效神经标记物的研究已引起神经科学界越来越多的关注。本研究探讨了使用基于源的幅度平方光谱相干性作为有效感兴趣区域（ROI）定位的空间指标的想法，随后将患有轻度认知障碍（MCI）的参与者与一组年龄匹配的健康对照区分开来（ HC) 老年参与者。我们发现皮质区域可以根据其一致性指数分为两个不同的组。与 HC 相比，一些 ROI 显示 MCI 参与者的连接性增加（超连接 ROI），而其余 ROI 则显示连接性降低（低连接 ROI）。基于这些发现，针对两个独特的 ROI 组，提出并探索了一系列用于 MCI 检测的基于小波的源级神经标记。研究发现，从超连接 ROI 中提取的神经标记物在 MCI 检测方面的表现明显优于从低连接 ROI 中提取的神经标记物。使用支持向量机 (SVM) 和 k-NN 分类器对神经标记进行分类，并通过蒙特卡罗交叉验证进行评估。使用来自超连接 ROI 组的源重建信号获得了 93.83% 的平均识别率。为了更好地符合临床实践设置，还采用了留一交叉验证（LOOCV）方法来确保测试数据来自分类器从未见过的参与者。使用 LOOCV，我们发现使用从具有功能性超连接的 ROI 组获得的同一组神经标记物，最佳平均分类准确度降低至 83.80%。该性能比使用基于小波的特征报告的结果高出约 15%。总的来说，我们的工作表明（1）某些 ROI 对于 MCI 检测特别有效，特别是当采用多分辨率小波生物标志物进行此类诊断时；(2)基于研究的实验设计与临床公认的评价标准在系统评价方面存在显着的性能差异。