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

ABSTRACT: 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 learnin

SUBMITTER: Astle DE 

PROVIDER: S-EPMC6808180 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature