Nonlinear Canonical Correlation Analysis of fMRI Signals Using HDR Models

A nonlinear canonical correlation analysis (CCA) for detecting neural activation in fMRI data is proposed in this paper. We use the BOLD response based on the HDR models with various parameters as reference signals. Instead of characterizing the relationship between the paradigm and time series using the oversimplified linear model, we employ the kernel trick that maps the intensities of the voxels within a small cubic at each time point into a high-dimensional kernel space, where the linear combinations correspond to nonlinear ones in the original space. The experimental results show that the proposed nonlinear CCA can improve the detection performance of traditional linear CCA.