One-Year Predictions of Delayed Reward Discounting in the Adolescent Brain Cognitive Development Study

• Published in: Experimental and clinical psychopharmacology 2022-12, Vol.30 (6), p.928-946
• Main Authors: Owens, Max M., Hahn, Sage, Allgaier, Nicholas, MacKillop, James, Albaugh, Matthew, Yuan, Dekang, Juliano, Anthony, Potter, Alexandra, Garavan, Hugh
• Format: Article
• Language: English
• Subjects: Adolescent; Adolescent Development; Brain; Brain Development; Child; Cognition; Cognitive Development; Delay Discounting; Female; Human; Humans; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Imaging - methods; Male; Prediction; Psychopathology; Reward; Rewards; Substance-Related Disorders - psychology
• ISSN: 1064-1297; 1936-2293; 1936-2293
• DOI: 10.1037/pha0000532

Delayed reward discounting (DRD) refers to the extent to which an individual devalues a reward based on a temporal delay and is known to be elevated in individuals with substance use disorders and many mental illnesses. DRD has been linked previously with both features of brain structure and function, as well as various behavioral, psychological, and life-history factors. However, there has been little work on the neurobiological and behavioral antecedents of DRD in childhood. This is an important question, as understanding the antecedents of DRD can provide signs of mechanisms in the development of psychopathology. The present study used baseline data from the Adolescent Brain Cognitive Development Study (N = 4,042) to build machine learning models to predict DRD at the first follow-up visit, 1 year later. In separate machine learning models, we tested elastic net regression, random forest regression, light gradient boosting regression, and support vector regression. In five-fold cross-validation on the training set, models using an array of questionnaire/task variables were able to predict DRD, with these findings generalizing to a held-out (i.e., "lockbox") test set of 20% of the sample. Key predictive variables were neuropsychological test performance at baseline, socioeconomic status, screen media activity, psychopathology, parenting, and personality. However, models using magnetic resonance imaging (MRI)-derived brain variables did not reliably predict DRD in either the cross-validation or held-out test set. These results suggest a combination of questionnaire/task variables as antecedents of excessive DRD in late childhood, which may presage the development of problematic substance use in adolescence. Public Health Significance Steep discounting of delayed rewards is a factor in many behavioral problems and psychiatric disorders. The present study demonstrates that steepness of discounting can be reliably predicted 1 year in advance in 10/11-year-old children using a machine learning approach with an array of questionnaire and task data. Specific variables predictive of discounting include sociodemographic factors, cognitive ability, developmental history, screen media activity, impulsive personality traits, and social activities.