Locality and low-dimensions in the prediction of natural experience from fMRI

Functional Magnetic Resonance Imaging (fMRI) provides an unprecedented window into the complex functioning of the human brain, typically detailing the activity of thousands of voxels during hundreds of sequential time points. Unfortunately, the interpretation of fMRI is complicated due both to the relatively unknown connection between the hemodynamic response and neural activity and the unknown spatiotemporal characteristics of the cognitive patterns themselves. Here, we use data from the Experience Based Cognition competition to explore the combination of local methods of prediction with various techniques of dimensionality reduction. We find effective, low-dimensional models based on the principal components of cognitive activity in classically-defined anatomical regions, the Brodmann Areas. For some of the stimuli, the top predictive regions were stable across subjects and episodes, including Wernickes area for verbal instructions, visual cortex for facial and body features, and visual-temporal regions (Brodmann Area 7) for velocity. These interpretations and the relative simplicity of our approach provide a transparent and conceptual basis upon which to build more sophisticated techniques for fMRI decoding. To our knowledge, this is the first time that classical areas have been used in fMRI for an effective prediction of complex natural experience.