Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.
Magnetoencephalography Laboratory, Dell Children's Medical Center, Austin, Texas, USA.
Hum Brain Mapp. 2022 Mar;43(4):1342-1357. doi: 10.1002/hbm.25726. Epub 2022 Jan 12.
Prior studies have used graph analysis of resting-state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test-retest reliability and sensor versus source association of global graph measures. Atlas-based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage-corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony.
先前的研究已经使用静息态脑磁图（MEG）的图分析来描述神经障碍中的异常大脑网络。然而，研究人员目前面临的一个挑战是缺乏关于应采用哪种网络构建策略的指导。图度量的可重复性对于将其用作临床生物标志物非常重要。此外，全局图度量理想情况下不应取决于分析是在传感器还是源空间中进行。因此，使用人类连接组计划的 89 名健康受试者的 MEG 数据来研究全局图度量的测试-重测可靠性和传感器与源的关联。基于图谱的波束形成用于源重建，并且使用无偏加权相位滞后指数（dwPLI）、幅度包络相关（AEC）和校正泄漏的 AEC 在六个频带中对传感器和源信号估计功能连接（FC）。在使用比例权重和正交最小生成树阈值实现的多个网络密度水平上检查可靠性。在 100%密度下，大多数 FC 指标和频带的图度量具有良好到优秀的可靠性和显著的传感器与源关联。当使用幅度指标时，获得了最大的可靠性和传感器与源关联。当使用幅度指标时，传感器和源空间之间的可靠性相似，但当使用 dwPLI 时，在较高的频带中，源空间的可靠性大于传感器空间。这些结果表明，图度量是有用的生物标志物，特别是用于研究基于幅度同步的功能网络。
