Rotational Invariance in Adaptive fMRI Data Analysis

It has previously been shown that canonical correlation analysis (CCA) works well for detecting neural activity in fMRI data. This is due to the ability of CCA to perform simultaneous temporal modeling and adaptive spatial filtering of the data. In this paper, we demonstrate that our previously proposed method for CCA-based fMRI data analysis does not provide rotationally invariant detection of activated regions. We propose a modification of the previous method and show that it resolves the rotational invariance issue, thereby further improving the analysis method.