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

Abstract

State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.

Electrocorticographic Activation Patterns of Electroencephalographic Microstates

“…Custo et al (2017) observed activation in the right inferior parietal lobe and the right middle and superior frontal gyri in microstate class D. These results resemble those of our study, which indicated that right ventral frontal negative activation is associated with this microstate class. Britz et al (2010) performed a combined EEG-fMRI study and found a negative BOLD response in the right-lateralized dorsal and ventral areas of the frontal and parietal cortices in microstate class D. Furthermore, using minimum norm source estimation of a wideband EEG signal (4-30 Hz), larger-scale anatomical regions of activation and deactivation were observed, similar to our results (Coquelet et al 2021). During this analysis, deactivation in the parieto-frontal and temporal regions was observed in microstate class D, which was in accordance with our results…”

Transcranial Direct Current Stimulation Modulates EEG Microstates in Low-Functioning Autism: A Pilot Study

“…A microstate topology remains relatively stable over a period of 80-120 ms and then rapidly transforms into another topographical structure, resulting in a relatively stable topological pattern of microstates [30]. Researchers proposed that the stable period reflected simple information processing and was the basic unit of cognition, and they named this period the functional microstate [31]. Previous research indicated that four to eight microstate categories can explain approximately 64-83% of the variance in experimental data [32].…”

Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging

“…Here we propose a novel model for extracting dynamic FC that relies on discrete and discontinuous “state changes” in brain activity. Indeed, there is mounting evidence that the brain’s dynamics results from its cycling through a number of brain-states, i.e., the transient, patterned, quasi-stable states or patterns of the brain activity ( Coquelet et al, 2021 ; Croce et al, 2020 ; Michel and Koenig, 2018 ), separated by brain state switches, such that while the FC during brain states may be considered stationary, FC during the transitions between brain states are subject to discontinuous, abrupt or non-smooth events ( Li et al, 2013 ; Saper et al, 2010 ; Vidaurre et al, 2017 ). In addition to being more biologically realistic, this approach allows us to benefit from several constraints, especially the concept that the spatial features of brain activity might be stationary, while the coupling between these stationary structures might be temporally dynamic.…”