Between 14-30% of children and adolescents worldwide are living with a learning-related problem sufficiently severe to require additional support (Department for Education, 2019; National Center for Education Statistics, 2019). Their difficulties vary widely in scope and severity, and are often associated with cognitive and/or behavioural problems. In some cases, children who are struggling at school receive a formal diagnosis of a specific learning difficulty/disability, such as dyslexia, dyscalculia or developmental language disorder (DLD). Others may receive a diagnosis of a related neurodevelopmental disorder commonly associated with learning problems such as Attention Deficit and Hyperactivity Disorder (ADHD), dyspraxia, or Autism Spectrum Disorder (ASD). However, in many cases, children who are struggling have either no diagnosis or receive multiple diagnoses (Embracing Complexity in Diagnosis, 2019).
Understanding how the brain gives rise to these difficulties has been challenging, primarily for two reasons: i) the purity of the diagnostic categories has been overstated – in reality there is lots of overlap across supposed categories, and lots of variability within each diagnosis; and ii) most neuroimaging methods assume that there is just one route to a set of cognitive or behavioural difficulties, whereas in reality there are likely multiple pathways to the same set of symptoms.
In response to this, one approach we have taken is ‘transdiagnostic’. In short, to study symptoms that are important for children’s learning, regardless of their diagnosis. The CALM dataset has been invaluable in doing this. In many cases we have combined this with methods taken from data science that can handle complexity, like machine learning.
A second approach we have taken is to explore brain organisation in children with developmental disorders where the underlying cause is known. For example, in children with single gene mutations.
If you would like to find out more about our work with developmental disorders then check out some of the papers appearing in the slider, or contact us via the website.
Dr Kate Baker (University of Cambridge)
Dr Joni Holmes (University of Cambridge)
Dr Sue Fletcher-Watson (University of Edinburgh)
Professor Susan Gathercole (University of Cambridge)