Machine learning-based approaches are now able to examine functional magnetic resonance imaging data in a multivariate manner and extract features predictive of group membership. We applied support vector machine (SVM)-based classification to resting state functional connectivity (rsFC) data from nicotine-dependent smokers and healthy controls to identify brain-based features predictive of nicotine dependence. By employing a network-centered approach, we observed that within-network functional connectivity measures offered maximal information for predicting smoking status, as opposed to between-network connectivity, or the representativeness of each individual node with respect to its parent network. Further, our analysis suggests that connectivity measures within the executive control and frontoparietal networks are particularly informative in predicting smoking status. Our findings suggest that machine learning-based approaches to classifying rsFC data offer a valuable alternative technique to understanding large-scale differences in addiction-related neurobiology.
Keywords: biomarkers; fMRI; machine learning; nicotine addiction; support vector machines.