Embracing Complexity in Neurodevelopment

This project aimed to understand barriers that affect children's learning, without the constraints of strict diagnoses.

Brain networks
Credit: Dr Duncan Astle, University of Cambridge

In a nutshell

Up to 30% of children and adolescents worldwide face cognitive or behavioural barriers to learning that vary widely in scope and impact. Strict diagnostic criteria have constrained our understanding of the cognitive barriers faced by children and young people and limited our theories about why these might occur. Research is needed that sheds light on these barriers to allow for the creation of better interventions and support. 

Rather than searching for what ‘causes’ any diagnosis, we expected that multiple brain pathways converge on common barriers to learning, irrespective of strict criteria. Moreover, we were interested to find any shared pathways that make children vulnerable to multiple barriers.

About the project

The proposal had three overarching aims, to:

  • apply a data-driven approach that breaks outside the constraints of standard diagnoses
  • identify neurocognitive pathways to barriers with the greatest impact on learning and everyday life, irrespective of diagnosis
  • develop models of brain development that combine cognition, neurophysiology and genetics

This study took place in two stages. The first was an analysis of existing large-scale data from community samples. The second was a data modelling project using artificial neural networks.

The study was led from the University of Cambridge by Prof Duncan Astle. 

Outputs

 

Funder

James S MacDonnell Foundation

 

Key contact

Professor Sue Fletcher Watson

 

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