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. We addressed this issue by using simultaneous MEG/EEG recordings at rest and comparing 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 differed both spatially and temporally. Microstates tend to exhibit spatio-temporal locality, 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.

MEG microstates: an investigation of underlying brain sources and potential neurophysiological processes

“…Interestingly, as reported in the supplementary materials, setting k = 4 in our group-level clustering analysis, we obtained microstate topographies visually similar to those of Coquelet et al (2022). Lateralized microstates are often not observed in EEG studies.…”

“…The main limitation of this study is the lack of simultaneous EEG recording, a condition that would have allowed a direct comparison between EEG microstates and MEG microstates. Coquelet et al (2022) found no discernible temporal correlation between the labeling of MEG and EEG data. However, the labeling was performed with EEG and MEG microstates that showed substantial differences in topography.…”

“…To date, the replication of EEG microstates in MEG data remains challenging. Coquelet et al (2022) investigated microstates and Hidden Markov Model states in simultaneous MEG-EEG recordings. However, the four MEG microstates they obtained did not match EEG microstates regarding topographies and signal labeling, besides a poor temporal correspondence with Hidden Markov Model states.…”

Exploring the limits of MEG spatial resolution with multipolar expansions

“…We considered MEG resting-state data of 14 healthy adult subjects used in previous studies (Coquelet et al, 2020(Coquelet et al, , 2022, to which we refer for details. Briefly, MEG signals were acquired at rest (5 min, 0.1-330 Hz analog bandpass, 1 kHz sampling rate) using a Neuromag Vectorview system (ME-GIN Oy, Helsinki, Finland) and denoised using signal-space separation (Maxfilter v2.2 with default parameters, MEGIN; Taulu et al, 2004Taulu et al, , 2005 and independent component analysis (Hyvärinen and Oja, 2000).…”

The dissociative role of bursting and non-bursting neural activity in the oscillatory nature of functional brain networks

“…Research on these brain networks has evolved tremendously since their first discovery using resting-state functional magnetic resonance imaging (fMRI) (Biswal et al, 1995; Fox et al, 2005) and the identification of coupled brain rhythms as their electrophysiological signature using resting-state magnetoencephalography (MEG) (Brookes et al, 2011; de Pasquale et al, 2010; Hipp et al, 2012). A wealth of knowledge has accumulated on diverse aspects of their electrophysiology, such as the spectral specificity of resting-state networks (Brookes et al, 2014, 2016; Tewarie et al, 2016; Wens et al, 2014a), the existence of supra-second-scale dynamics underlying versatile global network topology (de Pasquale et al, 2012, 2016; Della Penna et al, 2019) and metastable cross-network couplings (Wens et al, 2019), or their relationship to sub-second bursts of transient brain oscillations (Baker et al, 2014; Coquelet et al, 2022; Seedat et al, 2020; Vidaurre et al, 2018). Several studies also revealed their role in stimulus processing (Betti et al, 2018; Hawellek et al, 2013; Smith et al, 2009), task performance (O’Neill et al, 2015b; O’Neill et al, 2017; Quinn et al, 2018), learning and memory (Higgins et al, 2021; Mary et al, 2017; Roshchupkina et al, 2022; Van Dyck et al, 2021b), and in the pathophysiology of brain disorders (Brookes et al, 2018; Naeije et al, 2019; Puttaert et al, 2020; Sitnikova et al, 2018; Sjøgård et al, 2020; Van Dyck et al, 2021a; Van Schependom et al, 2019).…”