Generalizable prediction of childhood ADHD symptoms from neurocognitive testing and youth characteristics

• Type of Material: Article
• Authors: Alexander Weigard, University of Michigan, Ann Arbor; Katherine L McCurry, University of Michigan, Ann Arbor; Zvi Shapiro, Emory University; Meghan E Martz, University of Michigan, Ann Arbor; Mike Angstadt, University of Michigan, Ann Arbor; Mary M Heitzeg, University of Michigan, Ann Arbor; Ivo D Dinov, University of Michigan, Ann Arbor; Chandra Sripada, University of Michigan, Ann Arbor
• Language: English
• Date: 2023-06-24
• Publisher: Nature Publishing Group
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41398-023-02502-6
• Title of Journal or Parent Work: Translational Psychiatry
• Volume: 13
• Grant/Funding Information: AW was supported by K23 DA051561. KM was supported by T32 AA007477 (awarded to Dr. Frederic Blow). MEM was supported by K01 AA027558. IDD was supported by NSF grants 1916425 and 1734853 and NIH grants R01 MH121079, R01 MH126137, and T32 GM141746. MMH was supported by R01 AA025790 and U01 DA041106. CS was supported by R01 MH123458 and U01DA041106.
• Keywords: Learning and memory; Human behaviour; Predictive markers; ADHD

Abstract

Childhood attention-deficit/hyperactivity disorder (ADHD) symptoms are believed to result from disrupted neurocognitive development. However, evidence for the clinical and predictive value of neurocognitive assessments in this context has been mixed, and there have been no large-scale efforts to quantify their potential for use in generalizable models that predict individuals’ ADHD symptoms in new data. Using data drawn from the Adolescent Brain Cognitive Development Study (ABCD), a consortium that recruited a diverse sample of over 10,000 youth (ages 9–10 at baseline) across 21 U.S. sites, we develop and test cross-validated machine learning models for predicting youths’ ADHD symptoms using neurocognitive abilities, demographics, and child and family characteristics. Models used baseline demographic and biometric measures, geocoded neighborhood data, youth reports of child and family characteristics, and neurocognitive tests to predict parent- and teacher-reported ADHD symptoms at the 1-year and 2-year follow-up time points. Predictive models explained 15–20% of the variance in 1-year ADHD symptoms for ABCD Study sites that were left out of the model-fitting process and 12–13% of the variance in 2-year ADHD symptoms. Models displayed high generalizability across study sites and trivial loss of predictive power when transferred from training data to left-out data. Features from multiple domains contributed meaningfully to prediction, including neurocognition, sex, self-reported impulsivity, parental monitoring, and screen time. This work quantifies the information value of neurocognitive abilities and other child characteristics for predicting ADHD symptoms and provides a foundational method for predicting individual youths’ symptoms in new data across contexts.