Gradient Hyperalignment for multi-subject fMRI data alignment

TL;DR

This paper introduces Gradient Hyperalignment, a gradient-based method for aligning multi-subject fMRI data efficiently, addressing high dimensionality and large sample size challenges, with improved speed and comparable accuracy.

Contribution

It proposes Gradient Hyperalignment, combining ICA and SGA, to improve functional alignment in large-scale multi-subject fMRI datasets, reducing computational complexity.

Findings

Less time complexity than existing methods

Comparable or better classification accuracy

Effective handling of high-dimensional data

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.