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) StopSignal 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.
Conclusion: 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.