title: Utility of Machine-Learning Approaches to Identify Behavioral Markers for Substance Use Disorders: Impulsivity Dimensions as Predictors of Current Cocaine Dependence.
abstract: BACKGROUND
  Identifying objective and accurate markers of cocaine dependence (CD) can innovate its prevention and treatment. Existing evidence suggests that CD is characterized by a wide range of cognitive deficits, most notably by increased impulsivity. Impulsivity is multidimensional and it is unclear which of its various dimensions would have the highest predictive utility for CD. The machine-learning approach is highly promising for discovering predictive markers of disease. Here, we used machine learning to identify multivariate predictive patterns of impulsivity phenotypes that can accurately classify individuals with CD.
  
  
  METHODS
  Current cocaine-dependent users (N = 31) and healthy controls (N = 23) completed the self-report Barratt Impulsiveness Scale-11 and five neurocognitive tasks indexing different dimensions of impulsivity: (1) Immediate Memory Task (IMT), (2) Stop-Signal Task, (3) Delay-Discounting Task (DDT), (4) Iowa Gambling Task (IGT), and (5) Probabilistic Reversal-Learning task. We applied a machine-learning algorithm to all impulsivity measures.
  
  
  RESULTS
  Machine learning accurately classified individuals with CD and predictions were generalizable to new samples (area under the curve of the receiver-operating characteristic curve was 0.912 in the test set). CD membership was predicted by higher scores on motor and non-planning trait impulsivity, poor response inhibition, and discriminability on the IMT, higher delay discounting on the DDT, and poor decision making on the IGT.
  
  
  CONCLUSIONS
  Our results suggest that multivariate behavioral impulsivity phenotypes can predict CD with high degree of accuracy, which can potentially be used to assess individuals' vulnerability to CD in clinical settings.
authors.affiliation: Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA; Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychology, The Ohio State University, Columbus, OH, USA.
authors.forename: Woo-Young
authors.initials: WY
authors.lastname: Ahn
authors.affiliation: School of Nursing, University of Connecticut , Storrs, CT , USA.
authors.forename: Divya
authors.initials: D
authors.lastname: Ramesh
authors.affiliation: Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA; Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA; Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Frederick Gerard
authors.initials: FG
authors.lastname: Moeller
authors.affiliation: Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA; Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Jasmin
authors.initials: J
authors.lastname: Vassileva
keywords: LASSO; addiction; cocaine; impulsivity; machine learning; substance dependence