Evidence for modulation of EEG microstate sequence by vigilance level

Abstract

Highlights• EEG microstate parameters are strongly related to vigilance levels and can predict them • We find that vigilance Granger-causes changes in parameters of microstates • Duration and occurrence of EEG microstates are differentially modulated by vigilance level AbstractThe momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

“…Microstate activation is thus exclusive , i.e., two microstates cannot be simultaneously active, and complete , i.e., a microstate is active at any time. Importantly, this basic criterion is fairly close in spirit to the Viterbi algorithm used in the HMM (as explained below), but it is frequently altered in the microstate literature by using a temporally smoothed version of these binary time series (da Cruz et al, 2020; D’Croz-Baron et al, 2019; Krylova et al, 2020; Pascual-Marqui et al, 1995; Sikka et al, 2020). The Viterbi algorithm does not involve such ad-hoc temporal smoothing, so we primarily analyzed the raw microstate time series for better comparability with the HMM.…”

“…The Viterbi algorithm does not involve such ad-hoc temporal smoothing, so we primarily analyzed the raw microstate time series for better comparability with the HMM. Nevertheless, we assessed the effect of such smoothing on microstate temporal properties (see below) using the popular approach whereby microstate activation is determined as described above but at GFP peaks only and is then extended between these peaks by nearest-neighbor interpolation (Krylova et al, 2020; Sikka et al, 2020).…”