Spatially adaptive multivariate methods based on local CCA have been used in fMRI data analysis to improve sensitivity of activation detection. To improve specificity, local CCA methods require spatial constraints. In the past, local CCA methods have been used exclusively in 2D applications because of limitations imposed by the computational time requirements for 3D neighborhoods. We have implemented an efficient algorithm to solve the 3D local constrained CCA problem and furthermore proposed a global kernel CCA method to analyze the time series of the whole brain simultaneously. Results show that global kernel CCA outperforms local CCA in detecting activations.
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