Nonlinear kernel-based fMRI activation detection

Motivation: Kernel Canonical Correlation Analysis (KCCA) is an efficient way to detect brain activation globally with less computational complexity. However, the current KCCA is limited to the linear kernel, and the performance for other more general types of kernels is not completely understood due to a lack of inverse mapping. Goal(s): This study aims to expand the current KCCA method to arbitrary nonlinear kernels. Approach: Compute correlation vector r measures the importance of each voxel’s contributing to the signal in kernel space. Results: Our results suggest that nonlinear kernels, such as the Gaussian kernel, can increase the prediction robustness under voxel shuffling. Impact: The method proposed in this abstract allows us to get the activation pattern from fMRI for any type of linear or nonlinear kernel mapping.