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.