Multivariate fMRI Analysis using Canonical Correlation Analysis instead of Classifiers, Comment on Todd et al
2013 (English)In: figshare.comArticle in journal, Editorial material (Other academic) Published
Multivariate pattern analysis (MVPA) is a popular method for making inference about functional magnetic resonance imaging (fMRI) data. One approach is to train a classifier with voxels within a certain radius from the center voxel, to classify between different brain states. This approach is commonly known as the searchlight algorithm. As recently pointed out by Todd and colleagues, inference at the group level can however be confounded by the fact that the direction of the effect is lost if the per subject classification performance is used to generate group results. Here we show that canonical correlation analysis (CCA) can in some aspects be a better approach to multivariate fMRI analysis, than classification based analysis (CBA).
Place, publisher, year, edition, pages
Signal Processing Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-95668DOI: 10.6084/m9.figshare.787696OAI: oai:DiVA.org:liu-95668DiVA: diva2:637194
FunderLinnaeus research environment CADICS