Lena Chan1, Cortney Simmons1, Scott Tillem2, May Conley1, Inti A Brazil3, Arielle Baskin-Sommers4. 1. Department of Psychology, Yale University, New Haven, Connecticut. 2. Department of Psychology, University of Michigan Ann Arbor, Ann Arbor, Michigan. 3. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Forensic Psychiatric Centre Pompestichting, Nijmegen, the Netherlands. 4. Department of Psychology, Yale University, New Haven, Connecticut. Electronic address: arielle.baskin-sommers@yale.edu.
Abstract
BACKGROUND: Conduct disorder (CD) is a common syndrome with far-reaching effects. Risk factors for the development of CD span social, psychological, and biological domains. Researchers note that predictive models of CD are limited if the focus is on a single risk factor or even a single domain. Machine learning methods are optimized for the extraction of trends across multidomain data but have yet to be implemented in predicting the development of CD. METHODS: Social (e.g., family, income), psychological (e.g., psychiatric, neuropsychological), and biological (e.g., resting-state graph metrics) risk factors were measured using data from the baseline visit of the Adolescent Brain Cognitive Development Study when youth were 9 to 10 years old (N = 2368). Applying a feed-forward neural network machine learning method, risk factors were used to predict CD diagnoses 2 years later. RESULTS: A model with factors that included social, psychological, and biological domains outperformed models representing factors within any single domain, predicting the presence of a CD diagnosis with 91.18% accuracy. Within each domain, certain factors stood out in terms of their relationship to CD (social: lower parental monitoring, more aggression in the household, lower income; psychological: greater attention-deficit/hyperactivity disorder and oppositional defiant disorder symptoms, worse crystallized cognition and card sorting performance; biological: disruptions in the topology of subcortical and frontoparietal networks). CONCLUSIONS: The development of an accurate, sensitive, and specific predictive model of CD has the potential to aid in prevention and intervention efforts. Key risk factors for CD appear best characterized as reflecting unpredictable, impulsive, deprived, and emotional external and internal contexts.
BACKGROUND: Conduct disorder (CD) is a common syndrome with far-reaching effects. Risk factors for the development of CD span social, psychological, and biological domains. Researchers note that predictive models of CD are limited if the focus is on a single risk factor or even a single domain. Machine learning methods are optimized for the extraction of trends across multidomain data but have yet to be implemented in predicting the development of CD. METHODS: Social (e.g., family, income), psychological (e.g., psychiatric, neuropsychological), and biological (e.g., resting-state graph metrics) risk factors were measured using data from the baseline visit of the Adolescent Brain Cognitive Development Study when youth were 9 to 10 years old (N = 2368). Applying a feed-forward neural network machine learning method, risk factors were used to predict CD diagnoses 2 years later. RESULTS: A model with factors that included social, psychological, and biological domains outperformed models representing factors within any single domain, predicting the presence of a CD diagnosis with 91.18% accuracy. Within each domain, certain factors stood out in terms of their relationship to CD (social: lower parental monitoring, more aggression in the household, lower income; psychological: greater attention-deficit/hyperactivity disorder and oppositional defiant disorder symptoms, worse crystallized cognition and card sorting performance; biological: disruptions in the topology of subcortical and frontoparietal networks). CONCLUSIONS: The development of an accurate, sensitive, and specific predictive model of CD has the potential to aid in prevention and intervention efforts. Key risk factors for CD appear best characterized as reflecting unpredictable, impulsive, deprived, and emotional external and internal contexts.
Authors: Julia Kim-Cohen; Louise Arseneault; Avshalom Caspi; Mónica Polo Tomás; Alan Taylor; Terrie E Moffitt Journal: Am J Psychiatry Date: 2005-06 Impact factor: 18.112
Authors: Luca Passamonti; Graeme Fairchild; Alex Fornito; Ian M Goodyer; Ian Nimmo-Smith; Cindy C Hagan; Andrew J Calder Journal: PLoS One Date: 2012-11-07 Impact factor: 3.240