EEG microstate sequences in healthy humans at rest reveal scale-free dynamics

Abstract

Recent findings identified electroencephalography (EEG) microstates as the electrophysiological correlates of fMRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks. Surprisingly, these rapidly changing EEG microstates correlate significantly with activity in fMRI resting-state networks after convolution with the hemodynamic response function that constitutes a strong temporal smoothing filter. We postulate here that microstate sequences should reveal scale-free, self-similar dynamics to explain this remarkable effect and thus that microstate time series show dependencies over long time ranges. To that aim, we deploy wavelet-based fractal analysis that allows determining scale-free behavior. We find strong statistical evidence that microstate sequences are scale free over six dyadic scales covering the 256-ms to 16-s range. The degree of long-range dependency is maintained when shuffling the local microstate labels but becomes indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. These results advance the understanding of temporal dynamics of brain-scale neuronal network models such as the global workspace model. Whereas microstates can be considered the "atoms of thoughts," the shortest constituting elements of cognition, they carry a dynamic signature that is reminiscent at characteristic timescales up to multiple seconds. The scale-free dynamics of the microstates might be the basis for the rapid reorganization and adaptation of the functional networks of the brain.critical state | microstates | resting-state networks | self-similar processes | wavelet fractal analysis T he human brain is intrinsically organized into interconnected neuronal clusters that form large-scale neurocognitive networks (1, 2). These networks have to dynamically and rapidly reorganize and coordinate on subsecond temporal scales to allow the execution of mental processes in a timely fashion (3, 4). Precise timing is crucial for the government of the continuous information flow from multiple sources to ensure perception, cognition, and action and ultimately consciousness. The anatomical architecture of several large-scale networks is well known and has been studied with different methods ranging from tracer studies to resting-state fMRI (5, 6). However, much less is known about their underlying temporal dynamics.Multichannel electroencephalography (EEG) is a key method to access real-time information about the function of large-scale neuronal networks with high temporal resolution. Traditionally, spontaneous EEG analysis relies mainly on the power variation in different frequency bands at a subset of electrodes; however, observing this variation inherently sacrifices temporal accuracy due t...

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

“…Here, we report accuracies well above 80% for windows as short as 22.5 s (Fig. 3), which approaches the upper limit of temporal scales (256 ms to 16 s) for which EEG microstate (32) sequences show scale-free behavior (33). Previous research already had limited success matching temporal sequences of EEG microstates to rs-fMRI networks, despite the contrast between their dynamic and stationary definitions (34)(35)(36).…”

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

“…Fractal behavior has been observed in many physiological systems and has been hypothesized to aid systems in coping with a constantly changing environment (Goldberger et al, 2002). Interestingly, mono-fractal behavior has been observed in EEG microstate time series (Dinov et al, 2016;Gschwind et al, 2015;van de Ville et al, 2010) (see further details in Section 6). Jirsa and colleagues have proposed a model that explicitly discusses the different time scales of brain network organization (Huys et al, 2014;Perdikis et al, 2011).…”

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

“…Further concurrent EEG-fMRI studies will be crucial in clarifying the relationship between these fluctuations and neural dynamics, as well as how they relate to attentional states. Along those lines, the scale-free dynamics of EEG microstate sequences have been shown to reach timescales of fMRI resting-state fluctuations ( Van de Ville et al, 2010) and the link between EEG microstates and dynamic fMRI FC is an intriguing future research question.…”