The entropy of resting-state neural dynamics is a marker of general cognitive ability in childhood

Abstract

1Resting-state network activity has been associated with the emergence of individual differences across childhood development. However, due to the limitations of time-averaged representations of neural activity, little is known about how cognitive and behavioural variability relates to the rapid spatiotemporal dynamics of these networks. Magnetoencephalography (MEG), which records neural activity at a millisecond timescale, can be combined with Hidden Markov Modelling (HMM) to track the spatial and temporal characteristics of transient neural states. We applied HMMs to resting-state MEG data from (n = 46) children aged 8-13, who were also assessed on their cognitive ability and across multiple parent-report measures of behaviour. We found that entropy-related properties of participants resting-state time-courses were positively associated with cognitive ability. Additionally, cognitive ability was positively correlated with the probability of transitioning into HMM states involving fronto-parietal and somatomotor activation, and negatively associated with a state distinguished by default-mode network suppression. We discuss how using dynamical measures to characterise rapid, spontaneous patterns of brain activity can shed new light on neurodevelopmental processes implicated in the emergence of cognitive differences in childhood. Significance StatementThere is increasing evidence that the function of resting-state brain networks contributes to individual differences in cognition and behaviour across development. However, the relationship between dynamic, transient patterns of switching between resting-state networks and neurodevelopmental diversity is largely unknown. Here, we show that cognitive ability in childhood is related to the complexity of resting-state brain dynamics. Additionally, we demonstrate that the probability of transitioning into and remaining in certain states of brain network activity predicts individual differences in cognitive ability.