Machine learning classification of resting state functional connectivity predicts smoking status

Abstract

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.

Figure 1

The 16 resting state networks and their corresponding node regions. Resting state networks were selected and thresholded from a 20-component ICA decomposition of task fMRI data from the BrainMap database and resting data from 36 participants carried out in a previous study (Smith et al., 2009) (DMN, Default Mode Network; ECN, Executive Control Network; HON, Higher Order Network).

Figure 2

Classification algorithm for predicting smoking status using SVM-Adaboost.

Figure 3

Features maximally contributing to SVM classification performance. Features that were utilized in the within-RSN classifier following 90% feature elimination on 15 or more runs of LOOCV were identified, and these consisted of circuits within the (A) ECN, (B) FP, (C) HON-2, and (D) HON-3. Red and blue lines indicate circuits in which connectivity was greater and lower, respectively, in smokers relative to controls. Thick lines indicate circuits that were individually statistically different between smokers and controls, as inferred from t-tests. Inset brains indicate the orientation of the larger configuration (ECN, Executive Control Network; FP, Frontoparietal Network; HON, Higher Order Network).