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Reality Resists Classification: A Transdiagnostic, Network-Based Approach to Behavioural and Neural Variation in Childhood



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Zdorovtsova, Natalia 


Childhood development is shaped, and characterised, by a variety of interacting processes that encompass biological and environmental phenomena. Key developmental outcomes, such as cognitive ability and behaviour, are closely associated with the structure and function of the brain. These features of neurobiology are shaped concurrently by genetic factors, individuals’ interactions with the environment, and stochastic effects that instantiate divergent trajectories of development over time. Some behavioural and cognitive profiles are recognised as being particularly divergent from the population-norm, such that they are included in clinical taxonomies of neurodevelopmental conditions. Research in developmental cognitive neuroscience, until relatively recently, has assumed that different neurodevelopmental conditions—such as ADHD, autism, and dyslexia—constitute fundamentally separate developmental trajectories, each with their own genetic, neurological, cognitive, and behavioural distinctions. However, there is considerable heterogeneity within, and overlap between, neurodevelopmental conditions, suggesting that diagnostic categories are not robust predictors of behavioural and cognitive differences between individuals. A growing number of researchers are therefore choosing to study neurodevelopmental heterogeneity in a manner that is agnostic to the presence, or absence, of formal diagnoses. This thesis builds on the current literature in developmental cognitive neuroscience by taking a transdiagnostic approach to studying the associations between neurology, cognition, and behaviour. We endeavoured to address these three questions:

  1. Do the topological features of structural brain network connectivity differentiate children with elevated inattention and hyperactivity?
  2. Do patterns of functional brain network connectivity differentiate the brains of children with elevated inattention and hyperactivity?
  3. How are resting-state neural dynamics related to individual differences in behaviour and cognition?

We addressed the first two questions by analysing the relationships between structural MRI, functional MRI, cognitive, and behavioural data from the Centre for Attention, Learning, and Memory, which included participants aged 6-17 (CALM; n=383). To address our third question, we analysed the spontaneous, transient dynamics of resting-state MEG data from a sample of children aged 8-13 (n = 46). Additionally, we worked with a multidisciplinary team of academic researchers, charity leaders, educators, policymakers, and neurodivergent community partners to develop a set of freely-available online resources that help schools create inclusive educational frameworks.

Exploratory factor analysis indicated that inattention and hyperactivity are best represented as one latent factor in the CALM sample. No single component of structural brain organisation predicted linear differences in inattention and hyperactivity in our sample. However, a further analysis that combined multidimensional scaling with k-means clustering revealed two structural neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by communicability—a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had similar 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. Further analyses that compared measures of localised functional connectivity between these clusters revealed no significant differences; however, between-cluster differences were found on measures of intra- and inter-network connectivity between global brain networks. In our third empirical study, we inferred a Hidden Markov Model using resting-state MEG data to investigate the relationships between neurodevelopmental features of interest and transient states of neural activity. The complexity of participants’ MEG time-courses was positively related to their cognitive ability. Higher probabilities of transitioning into certain states, particularly those involving the default-mode network, fronto-parietal networks, and sensory processing regions, also predicted individual differences in cognitive ability. Finally, we completed a large public engagement project centred around neurodiversity and inclusive practices in schools. After running a multi-stakeholder workshop about barriers to learning and wellbeing in the UK, we collected evidence-based recommendations for educational and social care policy change, which informed our comprehensive set of inclusion resources for schools.

This thesis represents an important advance towards the transdiagnostic understanding of neurodevelopmental differences. This work builds on previous research in cognitive neuroscience while placing brain network complexity and neural dynamics in a developmental context. We believe that the research and practical endeavours described here will help guide future efforts in the scientific study of neurodiversity, in addition to the creation of more equitable, evidence-based educational frameworks.





Astle, Duncan


Brain Networks, Cognitive Neuroscience, Connectomics, Development, Developmental Neuroscience, Neurodevelopment, Neurodiversity, Neuroscience


Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge