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Establishing brain states in neuroimaging data
The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience—from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge t...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602380/ https://www.ncbi.nlm.nih.gov/pubmed/37844124 http://dx.doi.org/10.1371/journal.pcbi.1011571 |
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author | Dezhina, Zalina Smallwood, Jonathan Xu, Ting Turkheimer, Federico E. Moran, Rosalyn J. Friston, Karl J. Leech, Robert Fagerholm, Erik D. |
author_facet | Dezhina, Zalina Smallwood, Jonathan Xu, Ting Turkheimer, Federico E. Moran, Rosalyn J. Friston, Karl J. Leech, Robert Fagerholm, Erik D. |
author_sort | Dezhina, Zalina |
collection | PubMed |
description | The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience—from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the ’state’ of a system—i.e., a specification of the system’s future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets. |
format | Online Article Text |
id | pubmed-10602380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106023802023-10-27 Establishing brain states in neuroimaging data Dezhina, Zalina Smallwood, Jonathan Xu, Ting Turkheimer, Federico E. Moran, Rosalyn J. Friston, Karl J. Leech, Robert Fagerholm, Erik D. PLoS Comput Biol Research Article The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience—from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the ’state’ of a system—i.e., a specification of the system’s future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets. Public Library of Science 2023-10-16 /pmc/articles/PMC10602380/ /pubmed/37844124 http://dx.doi.org/10.1371/journal.pcbi.1011571 Text en © 2023 Dezhina et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dezhina, Zalina Smallwood, Jonathan Xu, Ting Turkheimer, Federico E. Moran, Rosalyn J. Friston, Karl J. Leech, Robert Fagerholm, Erik D. Establishing brain states in neuroimaging data |
title | Establishing brain states in neuroimaging data |
title_full | Establishing brain states in neuroimaging data |
title_fullStr | Establishing brain states in neuroimaging data |
title_full_unstemmed | Establishing brain states in neuroimaging data |
title_short | Establishing brain states in neuroimaging data |
title_sort | establishing brain states in neuroimaging data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602380/ https://www.ncbi.nlm.nih.gov/pubmed/37844124 http://dx.doi.org/10.1371/journal.pcbi.1011571 |
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