Title: Gradient Hyperalignment for multi-subject fMRI data alignment,
Abstract: Multi-subject fMRI data analysis is an interesting and challenging problem in
human brain decoding studies. The inherent anatomical and functional
variability across subjects make it necessary to do both anatomical and
functional alignment before classification analysis. Besides, when it comes to
big data, time complexity becomes a problem that cannot be ignored. This paper
proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional
alignment method that is suitable for multi-subject fMRI datasets with large
amounts of samples and voxels. The advantage of Gradient-HA is that it can
solve independence and high dimension problems by using Independent Component
Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using
multi-classification tasks on big data demonstrates that Gradient-HA method has
less time complexity and better or comparable performance compared with other
state-of-the-art functional alignment methods.