Noisy network attractor models for transitions between EEG microstates
dc.contributor.author | Creaser, J | |
dc.contributor.author | Ashwin, P | |
dc.contributor.author | Postlethwaite, C | |
dc.contributor.author | Britz, J | |
dc.date.accessioned | 2020-12-08T14:16:52Z | |
dc.date.issued | 2021-01-04 | |
dc.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. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide anatomical and temporal information about the resting-state networks (RSNs), respectively. EEG microstates are brief periods of stable scalp topography, and four distinct configurations with characteristic switching patterns between them are reliably identified at rest. Microstates have been identified as the electrophysiological correlate of fMRI-defined RSNs, this link could be established because EEG microstate sequences are scale-free and have long-range temporal correlations. This property is crucial for any approach to model EEG microstates. This paper proposes a novel modeling approach for microstates: we consider nonlinear stochastic differential equations (SDEs) that exhibit a noisy network attractor between nodes that represent the microstates. Using a single layer network between four states, we can reproduce the transition probabilities between microstates but not the heavy tailed residence time distributions. Introducing a two layer network with a hidden layer gives the flexibility to capture these heavy tails and their long-range temporal correlations. We fit these models to capture the statistical properties of microstate sequences from EEG data recorded inside and outside the MRI scanner and show that the processing required to separate the EEG signal from the fMRI machine noise results in a loss of information which is reflected in differences in the long tail of the dwell-time distributions. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | Medical Research Council (MRC) | en_GB |
dc.description.sponsorship | Marsden Fund, Royal Society of New Zealand | en_GB |
dc.identifier.citation | Vol. 11, article 1 | en_GB |
dc.identifier.doi | 10.1186/s13408-020-00100-0 | |
dc.identifier.grantnumber | EP/N014391/1 | en_GB |
dc.identifier.grantnumber | MR/S019499/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/123959 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |
dc.subject | EEG microstates | en_GB |
dc.subject | excitable network model | en_GB |
dc.subject | residence times | en_GB |
dc.subject | transition process | en_GB |
dc.subject | noisy network attractor | en_GB |
dc.subject | long range temporal correlations | en_GB |
dc.title | Noisy network attractor models for transitions between EEG microstates | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-12-08T14:16:52Z | |
dc.identifier.issn | 2190-8567 | |
dc.description | This is the final version. Available on open access from Springer via the DOI in this record | en_GB |
dc.description | Availability of data and material: The data used in this paper are published in [18] and are available from JB on reasonable request. | en_GB |
dc.identifier.journal | Journal of Mathematical Neuroscience | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-12-08 | |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-12-08 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-12-08T13:31:09Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2021-01-22T15:23:43Z | |
refterms.panel | B | en_GB |
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to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by
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copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.