Reproducibility of graph measures derived from resting‐state MEG functional connectivity metrics in sensor and source spaces

Abstract

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.

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

“…More broadly, many alternative thresholding methods also exist, whether based on statistical significance 133 , percolation [87][88][89] , or shrinkage methods 107,134 or avoiding thresholding entirely, by using analytic methods that can deal with fully connected and signed networks 52 . Additionally, it remains to be determined how our results will generalise to the case of frequency-specific or even multilayer networks obtained from EEG or MEG 135 (although see Jiang et al 44 and Dimitriadis et al 136,137 for recent investigations of frequency bands for fMRI network construction); and time-varying ("dynamic") networks, an increasingly popular approach in fMRI functional connectivity, whereby edges change over time [138][139][140][141] .…”

Harmonized multi‐metric and multi‐centric assessment of EEG source space connectivity for dementia characterization

“…EEG source space connectivity analyses provide topographic representations of brain functional interactions 14 with increased test–retest reliability, compared to sensor‐space estimates. 44 FC analyses provide better classification performance of dementia in comparison with spectral descriptors. 45 , 46 Likewise, joint analyses of connectivity result in a more robust classification of dementia than single metric approaches.…”

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

“…Graph measures were computed on thresholded and binarized connectivity matrices 45 . Matrices were binarized by keeping the 20% strongest connections, as this threshold delivers fairly reproducible graph measures based on dwPLI and AEC connectivity 59 . Nevertheless, it is good practice to test the reliability of final results with different binarizing thresholds 60 .…”