Exploring Phenotypic and Sociodemographic Influences on Cognitive-Adaptive Functioning Gap in Neurodivergent Children
Authors: Wan Eric , Lerch Jason P. , Anagnostou Evdokia , Penner Melanie , Kelley Elizabeth , Schachar Russell , Nicolson Rob , Kushki Azadeh 
Published: February 2025

Abstract

Background: In the general population, adaptive functioning typically aligns with cognitive abilities; however, this relationship appears more complex among neurodivergent individuals. Neurodevelopmental conditions (NDCs), including autism, attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), are associated with significant differences between cognitive and adaptive functioning, described as the cognitive-adaptive functioning gap. While this gap has been examined primarily in autistic individuals, it has also been observed across other NDCs. In mixed neurotypical and neurodivergent samples, individuals exhibit a range of discrepancies, reflecting the heterogeneity of cognitive-adaptive functioning gap profiles. Although the gap tends to be larger in NDCs compared to neurotypical populations, there is limited understanding of the phenotypic and sociodemographic factors linked to these discrepancies in neurodivergent children. The present study explores the features associated with cognitive-adaptive functioning gap in a sample of children and adolescents, including both neurodivergent and neurotypical individuals.
Methods: The study used data from the Province of Ontario Neurodevelopmental Disorders Network (POND), comprising 902 participants (autism = 409, ADHD = 210, OCD = 36, neurotypical = 214, other = 33) aged 6-21 years. Cognitive functioning was measured with full-scale IQ (FSIQ) from the Wechsler family of tests, and adaptive functioning was measured with the Adaptive Behavior Assessment System-II (ABAS-II), specifically the General Adaptive Composite (GAC) score. The cognitive-adaptive functioning gap was calculated as the difference between FSIQ and ABAS-II GAC scores. Phenotypic measures included social communication (Social Communication Questionnaire, or SCQ), ADHD symptoms (Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Scale, or SWAN), OCD symptoms (Toronto Obsessive-Compulsive Scale, or TOCS), and mental health symptoms (Child Behavior Checklist, or CBCL). Sociodemographic data encompassed sex, age, race, household income, and caregiver education. Nine computational models were used to estimate the cognitive-adaptive functioning gap. SHapley Additive exPlanations (SHAP) analysis was used to interpret the contributions of individual features to the cognitive-adaptive functioning gap model.
Results: The random forest model demonstrated the highest predictive accuracy (R² = 0.88, p < 0.001), performing better than other models with a mean absolute error of 4.14. SHAP analysis indicated that FSIQ, SCQ, and inattentive traits were the most influential features in estimating cognitive-adaptive functioning gap. Higher FSIQ (FSIQ ≥ 97.0) was linked to larger cognitive-adaptive functioning gaps across the combined sample. Similarly, social communication differences (SCQ ≥ 10.7) and inattentive traits (SWAN ≥ 3.4) were associated with larger gaps. Sociodemographic factors showed smaller but statistically significant associations, with sex (p < 0.001) and age (p < 0.001) showing relations to the cognitive-adaptive functioning gap (male sex and younger age were linked to smaller gaps).
Limitations: Key limitations include the use of broad sociodemographic categories, the cross-sectional design limiting insights into developmental changes in the cognitive-adaptive functioning gap, and the absence of an independent test set, which may affect generalizability. Future longitudinal studies and larger sample size are needed to address these limitations.
Conclusions: This study identifies the cognitive-adaptive functioning gap as associated with higher FSIQ, social-communication challenges, and inattentive traits, with phenotypic features showing stronger connections than sociodemographic factors. These findings suggest that focusing support on these key factors may help reduce the gap. Further longitudinal research is needed to explore how these gaps evolve and assess potential intervention strategies.