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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.
Highlights
Conventional univariate methods of fMRI analysis have been used to identify differences in neural processing between various diseased populations and healthy controls over a plethora of tasks
The betweenRSN classifier’s performance was not consistently above chance. This suggests that there is limited predictive information for nicotine addiction in the functional connectivity between resting state networks (RSNs), at least based on the current method of defining network nodes
We employed a machine learning-based approach to identify functional connectivity measures that are predictive of nicotine dependence
Summary
Conventional univariate methods of fMRI analysis have been used to identify differences in neural processing between various diseased populations and healthy controls over a plethora of tasks. Not all such group differences are guaranteed to be predictive; there may be significant overlap between the two group distributions of the pertinent metric. Traditional univariate approaches to fMRI analysis by definition overlook multivariate patterns in the data. Attempts to apply machine learning-based approaches to classify individuals based on various disease states has gained significant traction for screening and diagnosis (Vemuri et al, 2008; Stonnington et al, 2010), and monitoring disease trajectory (Hobbs et al, 2010). Depending on the specific method involved, and the neurobiological disease in question, such attempts have been met with moderate to high success, i.e., ranging from 60 to 100% classification accuracy (Orrù et al, 2012)
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