Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity

Abstract

Multi-voxel pattern analysis (MVPA) has been applied successfully to a variety of fMRI research questions in healthy participants. The full potential of applying MVPA to functional data from patient groups has yet to be fully explored. Our goal in this study was to investigate whether MVPA might yield a sensitive predictor of patient symptoms. We also sought to demonstrate that this benefit can be realized from existing datasets, even when they were not designed with MVPA in mind. We analyzed data from an fMRI study of the neural basis for face processing in individuals with an Autism Spectrum Disorder (ASD), who often show fusiform gyrus hypoactivation when presented with unfamiliar faces, compared to controls. We found reliable correlations between MVPA classification performance and standardized measures of symptom severity that exceeded those observed using a univariate measure; a relation that was robust across variations in ROI definition. A searchlight analysis across the ventral temporal lobes identified regions with relationships between classification performance and symptom severity that were not detected using mean activation. These analyses illustrate that MVPA has the potential to act as a sensitive functional biomarker of patient severity.