MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

The temporal dynamics of Large‐Scale brain network changes in disorders of consciousness: A Microstate‐Based study

“…Source time courses were bandpass‐filtered in the 1–30 Hz frequency band. 18 For each scan, we randomly selected 5000 global field powers (GFP) to determine the optimal number of microstates. The microstate maps were identified from 420,000 GFP peaks; moreover, microstate labels were assigned to each sample of the full scan.…”

“…All other samples were assigned the same state label as that of the nearest GFP peak. 18 The kneedle algorithm showed that seven states were optimum; therefore, we proceeded to back‐fit the results of the seven‐state clustering to the full EEG scans. 22 …”

“…Finally, the sensor‐space EEG microstate maps underestimate the importance of non‐occipital or non‐alpha‐band networks given the dominance of alpha‐band occipital sources in the sensor‐space eyes‐closed resting‐state EEG, which could be attributed to the head shape and forward model. 18 …”

The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure

“…For all MEG analyses, we therefore used data from those 28 participants. The MEG dataset was used in previous studies for different analyses 17 , 58 , 59 .…”

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

“…However, conventional EEG microstate pipelines may not be suitable for source-reconstructed M/EEG data ( Tait and Zhang (2022) ). Since source-reconstruction allows for anatomical interpretation of the electrophysiological data on the cortical level ( He et al, 2018 ), generalization of the microstate pipeline to the source space is crucial for advancement of understanding the neural mechanisms underpinning brain microstates.…”