Interpretable Classifiers for fMRI Improve Prediction of Purchases

Abstract

Despite growing interest in applying machine learning to neuroimaging analyses, few studies have gone beyond classifying sensory input to directly predicting behavioral output. With spatial resolution on the order of millimeters and temporal resolution on the order of seconds, functional magnetic resonance imaging (fMRI) is a promising technology for such applications. However, fMRI data's low signal-to-noise ratio, high dimensionality, and extensive spatiotemporal correlations present formidable analytic challenges. Here, we apply different machine-learning algorithms to previously acquired data to examine the ability of fMRI activation in three regions—the nucleus accumbens (NAcc), medial prefrontal cortex (MPFC), and insula—to predict purchasing. Our goal was to improve spatiotemporal interpretability as well as classification accuracy. To this end, sparse penalized discriminant analysis (SPDA) enabled automatic selection of correlated variables, yielding interpretable models that generalized well to new data. Relative to logistic regression, linear discriminant analysis, and linear support vector machines, SPDA not only increased interpretability but also improved classification accuracy. SPDA promises to allow more precise inferences about when specific brain regions contribute to purchasing decisions. More broadly, this approach provides a general framework for using neuroimaging data to build interpretable models, including those that predict choice.