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Get Free AccessThe 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.
Zalina Dezhina, Jonathan Smallwood, Ting Xu, Federico Turkheimer, Rosalyn Moran, Karl Friston, Robert Leech, Erik D. Fagerholm (2023). Establishing brain states in neuroimaging data. , 19(10), DOI: https://doi.org/10.1371/journal.pcbi.1011571.
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Type
Article
Year
2023
Authors
8
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1371/journal.pcbi.1011571
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