Keywords: fMRI Analysis, fMRI (task based)
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords