Abstract
Many developmental disorders are associated with deficits in controlling and regulating behaviour. These difficulties are foremost associated with attention deficit hyperactivity disorder (ADHD), but are also frequently observed in other groups, including in children with diagnoses of specific learning difficulties, autistic spectrum disorder, or conduct disorder. The co-occurrence of these behavioural problems across disorders typically leads to comorbid diagnoses and can complicate intervention approaches. An alternative to classifying children on the basis of specific diagnostic criteria is to use a data-driven grouping that identifies dimensions of behaviour that meaningfully distinguish groups of children and become specific targets for intervention. The current study applies a novel data-driven clustering algorithm to group children by similarities in their ratings on a parent questionnaire that is commonly used to assess behavioural problems associated with ADHD. The sample consisted of 442 children identified by health and educational professionals as having difficulties in attention, learning and/or memory. The data-driven clustering yielded three distinct groups of children with symptoms of either: (1) elevated inattention, and hyperactivity/impulsivity, and poor executive function, (2) learning problems, and (3) aggressive behaviours and problems with peer relationships. These groups were associated with significant inter-individual variation in white matter connectivity of the prefrontal and anterior cingulate. In sum, data-driven classification of executive function difficulties identifies stable groups of children, provides a good account of inter-individual differences, and aligns closely with underlying neurobiological substrates.
