Localizing Brain Function Based on Full Multivariate Activity Patterns: The Case of Visual Perception and Emotion Decoding

Abstract

Multivariate statistics and machine learning methods have become a common tool to extract information represented in the brain. What is less recognized is that, in the process, it has become more difficult to perform data-driven discovery and functional localization. This is because multivariate pattern analysis (MVPA) studies tend to restrict themselves to a subset of the available data, or because sound inference to map model parameters back to brain anatomy is lacking. Here, we present a high-dimensional (including brain-wide) multivariate classification pipeline for the detection and localization of brain functions during tasks. In particular, we probe it at visual and socio-affective states in a task-oriented functional magnetic resonance imaging (fMRI) experiment. Classification models for a group of human participants and existing rigorous cluster inference methods are used to construct group anatomical-statistical parametric maps, which correspond to the most likely neural correlates of each psychological state. This led to the discovery of a multidimensional pattern of macroscale brain activity which reliably encodes for the perception of happiness in the visual cortex, lingual gyri and the posterior perivermian cerebellum. We failed to find similar evidence for sadness and anger. Anatomical consistency of discriminating features across subjects and contrasts despite the high number of dimensions suggests MVPA is a viable tool for a complete functional mapping pipeline, and not just the prediction of psychological states. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/438425v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1961forg.highwire.dtl.DTLVardef@26d25aorg.highwire.dtl.DTLVardef@bc4b35org.highwire.dtl.DTLVardef@1eda0a6_HPS_FORMAT_FIGEXP M_FIG C_FIG