Remapping the cognitive and neural profiles of children who struggle at school

作者：Duncan E. Astle, Joe Bathelt, The CALM Team, Joni Holmes · 发表于：Developmental Science · 年份：2018 · DOI：10.1111/desc.12747 · 被引用次数：114 · 研究领域：Attention Deficit Hyperactivity Disorder、Children's Physical and Motor Development、Cognitive and developmental aspects of mathematical skills

Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n = 530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behavior questionnaires. Within the network we could identify four groups of children: (a) children with broad cognitive difficulties, and severe reading, spelling and maths problems; (b) children with age-typical cognitive abilities and learning profiles; (c) children with working memory problems; and (d) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age-expected levels on all learning measures. Importantly a child's cognitive profile was not predicted by diagnosis or referral reason. ...