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

N Coquelet,X De Tiège,L Roshchupkina,P Peigneux,S Goldman,M Woolrich,V Wens

doi:10.1016/j.neuroimage.2021.118850

N Coquelet, X De Tiège + Show 5 more

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https://doi.org/10.1016/j.neuroimage.2021.118850

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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.

Highlights

A fundamental part of human neural dynamics is the spontaneous emergence of brain rhythms, i.e., large-scale oscillations of neuroelectric activity (for a review, see, e.g., (Hari and Salmelin, 1997))
We started with a comparison of the spatial signature and temporal dynamics disclosed by microstates as classically obtained from EEG signals (AAHC with K = 4 applied to 200 Hz-downsampled sensor maps at time points of local global field power (GFP) maxima, with temporally smoothed microstate activation time courses) on the one hand, and MEG power envelope hidden Markov modeling (HMM) (K = 6 states inferred from 40 Hz-downsampled MNE source power envelopes) on the other hand
Microstates were sorted and labeled so as to match the denomination typically used in the literature (see, e.g., (Michel and Koenig, 2018)), and scalp topographies were normalized with respect to their GFP

Summary

Introduction

A fundamental part of human neural dynamics is the spontaneous emergence of brain rhythms, i.e., large-scale oscillations of neuroelectric activity (for a review, see, e.g., (Hari and Salmelin, 1997)) These rhythms play a critical role for human brain functions such as sensory, motor and cognitive processes ((Klimesch, 2012), for reviews, see (Klimesch et al, 2010; Pfurtscheller and Lopes da Silva, 1999)). Short-lived power bursts may correspond to the fast activation/deactivation of functional networks (Baker et al, 2014; Britz et al, 2010; Vidaurre et al, 2018) and their co-occurrence, to the intrinsic functional connectivity of these networks (Seedat et al, 2020) They might relate to the metastable cross-network interactions characteristic of functional integration at the supra-second timescale (de Pasquale et al, 2016, 2012; Della Penna et al, 2019; Wens et al, 2019). Exploring the spontaneous dynamics of MEG/EEG power bursts represents a fundamental step towards a better understanding of the functional architecture of the human brain at rest

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