Abstract
ObjectiveExecutive functions (EF) are cognitive skills that are important for regulating behavior and for achieving goals. Executive function deficits are common in children who struggle in school and are associated with multiple neurodevelopmental disorders. However, there is also considerable heterogeneity across children, even within diagnostic categories. This study took a data-driven approach to identify distinct clusters of children with common profiles of EF-related difficulties, and then identified patterns of brain organization that distinguish these data-driven groups.MethodThe sample consisted of 442 children identified by health and educational professionals as having difficulties in attention, learning, and/or memory. We applied community clustering, a data-driven clustering algorithm, to group children by similarities on a commonly used rating scale of EF-associated behavioral difficulties, the Conners 3 questionnaire. We then investigated whether the groups identified by the algorithm could be distinguished on white matter connectivity using a structural connectomics approach combined with partial least squares analysis.ResultsThe data-driven clustering yielded 3 distinct groups of children with symptoms of one of the following: (1) elevated inattention and hyperactivity/impulsivity, and poor EF; (2) learning problems; or (3) aggressive behavior and problems with peer relationships. These groups were associated with significant interindividual variation in white matter connectivity of the prefrontal and anterior cingulate cortices.ConclusionIn sum, data-driven classification of EF-related behavioral difficulties identified stable groups of children, provided a good account of interindividual differences, and aligned closely with underlying neurobiological substrates.
Highlights
We explored differences in white matter connectivity between the groups identified through the community detection
We investigated the relationship between white matter connectivity and the groups defined through community clustering using partial least squares (PLS) regression
We used a data-driven clustering algorithm to group children according to their similarity on ratings of executive function (EF)À related behavioral problems
Summary
Executive functions (EF) are cognitive skills that are important for regulating behavior and for achieving goals. Our aim was to identify clusters in a large sample of children, according to the similarity of their EF-related behavioral problems, using a community detection approach based on the Conners questionnaire This scale is routinely administered in health care and educational settings in many clinics in the United Kingdom. We applied the data-driven clustering approach in a large sample of children (N 1⁄4 442) identified as having problems in attention, learning, and/or memory by educational and clinical professionals working in various specialist children’s services This sample includes common, complex, and comorbid cases of behavioral and cognitive difficulties. A high proportion of children in the sample had scored in this range on each of the subscales (Table 2)
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