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

D. Van De Ville, J. Britz, C. M. Michel
2010 Proceedings of the National Academy of Sciences of the United States of America  
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
more » ... icantly 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 to the time-frequency uncertainty principle. To account for short-lasting fluctuations of neuronal activity, analysis methods in the time domain are required. Lehmann and coworkers proposed to consider the temporal evolution of the topography of the scalp electric field, because it represents the sum of all momentarily active sources in the brain, irrespective of their frequency. This way, one obtains a global measure of momentary brain activity with high temporal resolution. The topography does not change randomly and continuously over time, but remains stable for~80-120 ms; these periods of quasi-stability are termed "EEG microstates" (7, 8). Cognition (9) and perception (10, 11) have been found to vary as a direct function of the prestimulus microstate, and microstates can characterize qualitative aspects of spontaneous thoughts (12, 13) . This result indicates that they index different types of mental processes. Surprisingly, only four different microstates are consistently observed at rest (14) . They reproduce well across subjects and can be identified across the entire life span (15), indicating that they might be mediated by predetermined anatomical connections. Alterations of microstates have been reported in schizophrenia (16, 17), depression (18), and Alzheimer's disease (19, 20) and as a function of drug administration and hypnosis (21-23). Recent work (24, 25) revealed a link between the rapid changes in the time courses of EEG microstate sequences on the one hand and slow coherent changes in the blood oxygen-level-dependent (BOLD) signal obtained with fMRI during rest on the other hand. More precisely, we identified the four prototypical EEG microstates during rest that each could explain one large-scale restingstate network (RSN) obtained from BOLD fMRI (25). This finding indicates that the EEG microstates are strong candidates for the electrophysiological signatures of these RSNs. At first sight, this link is surprising due to the different timescales at which both signals are meaningful, i.e., 50-100 ms for EEG microstates vs. 5-10 s for BOLD fMRI. The connection between EEG microstates and fMRI RSNs was etablished by convolving the time courses of the occurrence of the different EEG microstates with the hemodynamic response function (HRF) and then using these as regressors in a general linear model for conventional fMRI analysis, as illustrated in Fig. 1A . Because the HRF acts as a strong temporal smoothing filter on the rapid EEG-based signal, it is remarkable that statistically significant correlations can be found. The fact that this smoothing did not remove any information-carrying signal from the microstate sequence and that furthermore the original microstate sequences and the regressors show the same relative behavior at temporal scales about two orders of magnitude apart suggests that the time courses of the EEG microstates are scale invariant. The working hypothesis of this paper is that the microstate dynamics have fractal properties. We investigated whether they show statistically self-similar, scale-free properties over a large time range, which preserves their information after smoothing with the hemodynamic filter as shown in Fig. 1B . Several complex structures in nature manifest fractal behavior: Statistically the object looks the same on a wide range of observation scales. Fractals are most commonly associated with 2D
doi:10.1073/pnas.1007841107 pmid:20921381 pmcid:PMC2964192 fatcat:croa35eyubg5jiblyaakfjfpia