A Projection CCA Method for Effective fMRI Data Analysis.
Muhammad Ali Qadar, Abd-Krim Seghouane
IEEE Transactions on Biomedical Engineering | Institute of Electrical and Electronics Engineers | Published : 2019
OBJECTIVE: Canonical correlation analysis (CCA) is a data-driven method that has been successfully used in functional magnetic resonance imaging (fMRI) data analysis. Standard CCA extracts meaningful information from a pair of data sets by seeking pairs of linear combinations from two sets of variables with maximum pairwise correlation. So far, however, this method has been used without incorporating prior information available for fMRI data. In this paper, we address this issue by proposing a new CCA method named pCCA (for projection CCA). METHODS: The proposed method is obtained by projection onto a set of basis vectors that better characterize temporal information in the fMRI data set. A ..View full abstract
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Awarded by Australian Research Council
This work was supported by the Australian Research Council under Grant FT130101394