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 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.Author summary: There is a deceptively simple question that remains unasked at the heart of computational neuroscience—what exactly is a ’brain state’? This question is motivated by the various and seemingly unrelated definitions of brain states: ranging from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. There is, however, a precise definition of the state of a dynamical system that often remains overlooked: some piece of information that allow us to say what the system does next. Here, we show that this same definition can be used to quantify the information required to predict the future in neuroimaging timeseries. We demonstrate, with the aid of simulations, that this theoretical framework can be used to extract the characteristic features constituting dynamical system states in a range of scenarios. We then apply the same methodology to fMRI datasets and show that task conditions require more information about a neural system’s history to constitute a brain state, as compared with rest conditions.