A Transdiagnostic Examination of Cognitive Heterogeneity in Children and Adolescents with Neurodevelopmental Disorders

Abstract

Children and adolescents with neurodevelopmental disorders (NDDs) demonstrate extensive cognitive heterogeneity that is not adequately captured by traditional diagnostic systems. Using a transdiagnostic approach, a retrospective cohort study of cognitive functioning was conducted with a large heterogenous sample (n = 1529) of children and adolescents 7 to 18 years of age with NDDs. Measures of short-term memory, verbal ability, and reasoning were administered to participants with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), comorbid ADHD/ASD, and typically developing (TD) participants using a 12-item web-based neurocognitive testing battery. Unsupervised machine learning techniques were implemented to create a self-organizing map (SOM), an artificial neural network, in conjunction with k-means clustering algorithms to identify data-driven subgroups. Six clusters representing different cognitive profiles were identified, including participants with varying degrees of cognitive impairment. Diagnostic status did not correspond with cluster-membership, providing evidence for the application of transdiagnostic approaches to understanding cognitive heterogeneity in children and adolescents with NDDs. Additionally, the findings suggest that many TD participants may have undiagnosed learning difficulties, emphasizing the need for accessible cognitive assessment tools in school-based settings.