The EEG microstate topography is predominantly determined by intracortical sources in the alpha band

Abstract

Human brain electric activity can be measured at high temporal and fairly good spatial resolution via electroencephalography (EEG). The EEG microstate analysis is an increasingly popular method used to investigate this activity at a millisecond resolution by segmenting it into quasi-stable states of approximately 100 ms duration. These so-called EEG microstates were postulated to represent atoms of thoughts and emotions and can be classified into four classes of topographies A through D, which explain up to 90% of the variance of continuous EEG. The present study investigated whether these topographies are primarily driven by alpha activity originating from the posterior cingulate cortex (all topographies), left and right posterior cortices, and the anterior cingulate cortex (topographies A, B, and C, respectively). We analyzed two 64-channel resting state EEG datasets (N = 61 and N = 78) of healthy participants. Sources of head-surface signals were determined via exact low resolution electromagnetic tomography (eLORETA). The Hilbert transformation was applied to identify instantaneous source strength of four EEG frequency bands (delta through beta). These source strength values were averaged for each participant across time periods belonging to a particular microstate. For each dataset, these averages of the different microstate classes were compared for each voxel. Consistent differences across datasets were identified via a conjunction analysis. The intracortical strength and spatial distribution of alpha band activity mainly determined whether a head-surface topography of EEG microstate class A, B, C, or D was induced. EEG microstate class C was characterized by stronger alpha activity compared to all other classes in large portions of the cortex. Class A was associated with stronger left posterior alpha activity than classes B and D, and class B was associated with stronger right posterior alpha activity than A and D. Previous results indicated that EEG microstate dynamics reflect a fundamental mechanism of the human brain that is altered in different mental states in health and disease. They are characterized by systematic transitions between four head-surface topographies, the EEG microstate classes. Our results show that intra-cortical alpha oscillations, which likely reflect decreased cortical excitability, primarily account for the emergence of these classes. We suggest that microstate class dynamics reflect transitions between four global attractor states that are characterized by selective inhibition of specific intra-cortical regions.

Within and Between-person Correlates of the Temporal Dynamics of Resting EEG Microstates

“…They found that these oscillatory indices, and their corresponding neural source estimations, were associated with memory span and working memory performance, as calculated from a switch-cost score from the Test of Attentional Performance. Alpha oscillatory power and LRTC quantify similar phenomena to those of microstate dynamics, given that oscillations in the alpha frequency largely determine the periodicity and spatial distribution of microstates in the broadband EEG (Javed et al, 2019;Milz et al, 2017;Wegner et al, 2017). It would be instructive for future studies to more clearly establish functional connections among these various methods.…”

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

“…However, within-subject fluctuations in the spatial and temporal distribution of dominant EEG spectral power have been systematically associated with the momentary presence of particular microstates (Milz et al, 2017).…”

“…To date, only two groups have utilized this direct approach (Custo et al, in press;Milz et al, 2017; In a recent study (Milz et al, 2017), the same group demonstrated that such activation primarily occurred within the alpha frequency range. However, in contrast to the EEGfMRI studies described earlier, (Pascual-Marqui et al, 2014) and (Milz et al, 2017) did not distinguish the microstate networks based on their temporal signature, but rather focused on the spatial characteristics of the potential fields. Custo and colleagues utilized a source localization approach that more closely resembled the GLM approach used in the EEG-fMRI study by Britz and colleagues .…”

Transient topographical dynamics of the electroencephalogram predict brain connectivity and behavioural responsiveness during drowsiness

“…The neural sources underlying microstates are still being 105 explored Milz et al, 2017;Pascual-Marqui et al, 2014). Still, the dynamics of the 106 sequence of microstates itself can be seen as a "syntax" of neural activity that is in and of itself an 107 .…”