Noisy network attractor models for transitions between EEG microstates

Abstract

The brain is intrinsically organized into large-scale networks that constantly re-organize on multiple timescales, even when the brain is at rest. The timing of these dynamics is crucial for sensation, perception, cognition, and ultimately consciousness, but the underlying dynamics governing the constant reorganization and switching between networks are not yet well understood. Electroencephalogram (EEG) microstates are brief periods of stable scalp topography that have been identified as the electrophysiological correlate of functional magnetic resonance imaging defined resting-state networks. Spatiotemporal microstate sequences maintain high temporal resolution and have been shown to be scale-free with long-range temporal correlations. Previous attempts to model EEG microstate sequences have failed to capture this crucial property and so cannot fully capture the dynamics; this paper answers the call for more sophisticated modeling approaches. We present a dynamical model that exhibits a noisy network attractor between nodes that represent the microstates. Using an excitable network between four nodes, we can reproduce the transition probabilities between microstates but not the heavy tailed residence time distributions. We present two extensions to this model: first, an additional hidden node at each state; second, an additional layer that controls the switching frequency in the original network. Introducing either extension to the network gives the flexibility to capture these heavy tails. We compare the model generated sequences to microstate sequences from EEG data collected from healthy subjects at rest. For the first extension, we show that the hidden nodes ‘trap’ the trajectories allowing the control of residence times at each node. For the second extension, we show that two nodes in the controlling layer are sufficient to model the long residence times. Finally, we show that in addition to capturing the residence time distributions and transition probabilities of the sequences, these two models capture additional properties of the sequences including having interspersed long and short residence times and long range temporal correlations in line with the data as measured by the Hurst exponent.

Spatio-temporal dynamics of resting-state brain networks are associated with migraine disability

“…Although we did not find abnormal VN through fMRI FC, the change of MsB indicates that this may be the reflection of abnormal neural activity and network abnormality in visual cortex on different time and spatial scales. After all, EEG microstate gives priority to fMRI by two orders of magnitude in terms of time process [ 45 ]. The change from fast EEG time to slow fMRI space can reflect the same potential physiological process of VN changes in migraine patients without aura from two scales [ 11 ].…”

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

“…This complexity in the microstates‐RSNs correspondence leads to identifying the functional significance of EEG microstates becoming challenging. From the very beginning of the emergence of the EEG microstates, the link between the microstates and RSNs was surprising because they are measured on different temporal scales (RSNs: 10–20 s and EEG microstates: 50–100 ms Britz et al, 2010; Creaser et al, 2021). One of the well‐established reasons for the microstates‐RSNs link is that the EEG microstates time‐series are scale‐free; namely, they likely have a similar representation in different temporal scales (Creaser et al, 2021; Van de Ville et al, 2010).…”

“…From the very beginning of the emergence of the EEG microstates, the link between the microstates and RSNs was surprising because they are measured on different temporal scales (RSNs: 10–20 s and EEG microstates: 50–100 ms Britz et al, 2010; Creaser et al, 2021). One of the well‐established reasons for the microstates‐RSNs link is that the EEG microstates time‐series are scale‐free; namely, they likely have a similar representation in different temporal scales (Creaser et al, 2021; Van de Ville et al, 2010). One of the ways to show the scale‐free behaviour of EEG microstates is long‐range temporal correlations (LRTC) (the same method used for investigating the scale‐free behaviour of oscillation in our previous publication Eqlimi et al, 2020).…”

Brain Responses to a Self-Compassion Induction in Trauma Survivors With and Without Post-traumatic Stress Disorder

“…The duration of a microstate is the length of time that it is active (consecutively assigned to the data) before transitioning to another microstate; a single duration of microstate B is shown in Figure 2C . Microstate sequences can be fully classified by their transition probabilities and the microstate durations ( Creaser et al, 2019 ). For each individual we computed the mean duration spent in each microstate for the baseline and each minute of the LKM-S. To mitigate for individual variation, we computed the duration response by subtracting the mean duration of each microstate at baseline from the mean duration at each minute of the LKM-S. We then computed the mean and standard error of the duration response for each group in each minute of the LKM-S.…”