Establishing brain states in neuroimaging data

• Published in: PLoS computational biology Vol. 19; no. 10; p. e1011571
• Main Authors: Dezhina, Zalina, Smallwood, Jonathan, Xu, Ting, Turkheimer, Federico E., Moran, Rosalyn J., Friston, Karl J., Leech, Robert, Fagerholm, Erik D.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.10.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Anesthesia; Approximation; Biology and Life Sciences; Blood flow; Brain; Brain - physiology; Brain Mapping - methods; Brain research; Computational neuroscience; Computer and Information Sciences; Datasets; Diagnostic imaging; Dynamic systems theory; Dynamical systems; Embedding; Engineering and Technology; Functional magnetic resonance imaging; Humans; Linear equations; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medicine and Health Sciences; Models, Theoretical; Neuroimaging; Neurons; Neurosciences; Physical Sciences; Research and Analysis Methods; Sleep and wakefulness; System theory; Wakefulness; United Kingdom
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011571

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.