Zdorovtsova, N. | bioRxiv Science

bioRxiv2023

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

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.

neuroscience↗

bioRxiv2022

Exploring Neural Heterogeneity in Inattention and Hyperactivity

Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability--a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had indistinguishable behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge from multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and cognition. Research HighlightsO_LIWe investigated variability in structural brain network organisation and its relationship with cognition and behaviour in a sample of 383 children. C_LIO_LIWe did not find linear components of brain structure that explained continuous variations in inattention and hyperactivity across this heterogeneous sample. C_LIO_LIFollowing this, we explored different attributes of brain organisation in children with particularly elevated levels of inattention and hyperactivity (n = 232). C_LIO_LIAmong highly inattentive and hyperactive children, we found two profiles of structural brain organisation ( neurotypes), which were differentiated primarily by the communicability of nodes in frontal and occipital brain areas. C_LIO_LIThese subgroups did not differ on additional measures of behaviour. However, the lower-nodal-communicability group demonstrated weaker performance on cognitive assessments of executive function and visuospatial processing. C_LIO_LIWe discuss the implications that these findings have for our understanding of variability in neurodevelopmental difficulties and related conditions, such as ADHD C_LI

neuroscience↗