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 governin...
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doaj-54e87f1ee7c3429d9ceb804960b79a812021-01-10T12:55:41ZengSpringerOpenJournal of Mathematical Neuroscience2190-85672021-01-0111112510.1186/s13408-020-00100-0Noisy network attractor models for transitions between EEG microstatesJennifer Creaser0Peter Ashwin1Claire Postlethwaite2Juliane Britz3Department of Mathematics and EPSRC Centre for Predictive Modelling in Healthcare, University of ExeterDepartment of Mathematics and EPSRC Centre for Predictive Modelling in Healthcare, University of ExeterDepartment of Mathematics, University of AucklandDepartment of Psychology, University of FribourgAbstract 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.https://doi.org/10.1186/s13408-020-00100-0EEG microstatesExcitable network modelResidence timesTransition processNoisy network attractorLong range temporal correlations |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jennifer Creaser Peter Ashwin Claire Postlethwaite Juliane Britz |
spellingShingle |
Jennifer Creaser Peter Ashwin Claire Postlethwaite Juliane Britz Noisy network attractor models for transitions between EEG microstates Journal of Mathematical Neuroscience EEG microstates Excitable network model Residence times Transition process Noisy network attractor Long range temporal correlations |
author_facet |
Jennifer Creaser Peter Ashwin Claire Postlethwaite Juliane Britz |
author_sort |
Jennifer Creaser |
title |
Noisy network attractor models for transitions between EEG microstates |
title_short |
Noisy network attractor models for transitions between EEG microstates |
title_full |
Noisy network attractor models for transitions between EEG microstates |
title_fullStr |
Noisy network attractor models for transitions between EEG microstates |
title_full_unstemmed |
Noisy network attractor models for transitions between EEG microstates |
title_sort |
noisy network attractor models for transitions between eeg microstates |
publisher |
SpringerOpen |
series |
Journal of Mathematical Neuroscience |
issn |
2190-8567 |
publishDate |
2021-01-01 |
description |
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. |
topic |
EEG microstates Excitable network model Residence times Transition process Noisy network attractor Long range temporal correlations |
url |
https://doi.org/10.1186/s13408-020-00100-0 |
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