Embracing Complexity in Neurodevelopment

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

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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 expect that multiple brain pathways converge on common barriers to learning, irrespective of strict criteria. Moreover, there may be shared pathways that make children vulnerable to multiple barriers.

About the project

The proposal has 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, genetics

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

The study is led from Cambridge by Dr Duncan Astle

Impact

We hope to challenge and expand the thinking around diagnostic categories and their ability – or not – to reflect underlying reality. We want to move beyond and the hunt for ‘core deficits’ to explain the real-world cognitive and behavioural challenges faced by individuals in the hope that this will advance the search for suitable support.

Funder

James S MacDonnell Foundation

Publications

Beyond the core deficit hypothesis in developmental disorders

 

Key contact

Professor Sue Fletcher Watson