Remapping the cognitive and neural profiles of children who struggle at school.
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Astle, D., Bathelt, J., CALM Team,, & Holmes, J. (2019). Remapping the cognitive and neural profiles of children who struggle at school.. Developmental science, 22 (1), e12747. https://doi.org/10.1111/desc.12747
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 behaviour questionnaires. Within the network we could identify four groups of children: i) children with broad cognitive difficulties, and severe reading, spelling and maths problems; ii) children with age-typical cognitive abilities and learning profiles; iii) children with working memory problems; and iv) 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. We also constructed whole-brain structural connectomes for children from these four groupings (n=184), alongside an additional group of typically developing children (n=36), and identified distinct patterns of brain organisation for each group. This study represents a novel move towards identifying data-driven neurocognitive dimensions underlying learning-related difficulties in a representative sample of poor learners. This article is protected by copyright. All rights reserved.
CALM Team, Humans, Language Development Disorders, Brain Mapping, Language, Memory, Short-Term, Attention, Cognition Disorders, Mathematics, Reading, Linguistics, Child, Female, Male, Learning Disabilities
External DOI: https://doi.org/10.1111/desc.12747
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285344
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/