Optimizing the performance of local canonical correlation analysis in fMRI using spatial constraints

The benefits of locally adaptive statistical methods for fMRI research have been shown in recent years, as these methods are more proficient in detecting brain activations in a noisy environment. One such method is local canonical correlation analysis (CCA), which investigates a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel for convenience. The method without constraints is prone to artifacts, especially in a region of localized strong activation. To compensate for these deficiencies, the impact of different spatial constraints in CCA on sensitivity and specificity are investigated. The ability of constrained CCA (cCCA) to detect activation patterns in an episodic memory task has been studied. This research shows how any arbitrary contrast of interest can be analyzed by cCCA and how accurate P-values optimized for the contrast of interest can be computed using nonparametric methods. Results indicate an increase of up to 20% in detecting activation patterns for some of the advanced cCCA methods, as measured by ROC curves derived from simulated and real fMRI data.