Natural stimuli are increasingly used in functional magnetic resonance imaging (fMRI) studies to imitate real-life situations. Consequently, challenges are created for novel analysis methods, including new machine learning tools. With natural stimuli it is no longer feasible to assume single features of the experimental design alone to account for the brain activity. Instead, relevant combinations of rich-enough stimulus features could explain the more complex activation patterns.
We propose a novel two-step approach, where independent component analysis is first used to identify spatially independent brain processes, which we refer to as functional patterns. As the second step, temporal dependencies between stimuli and functional patterns are detected using canonical correlation analysis. Our proposed method looks for combinations of stimulus features and the corresponding combinations of functional patterns.
This two-step approach was used to analyze measurements from a {fMRI} study during multi-modal stimulation. The detected complex activation patterns were explained as resulting from interactions of multiple brain processes. Our approach seems promising for analysis of data from studies with natural stimuli.
The authors1 belong to Helsinki Institute for Information Technology HIIT and the Adaptive Informatics Research Centre, a CoE of the Academy of Finland. The authors2 belong to Advanced Magnetic Imaging Centre and Brain Research Unit, Low Temperature Laboratory, Helsinki University of Technology. This work was in part supported by the PASCAL2 Network of Excellence.