Through their eyes: multi-subject Brain Decoding with simple alignment techniques

TL;DR

This study demonstrates that cross-subject brain decoding using simple alignment techniques is feasible with limited data, significantly reducing scan time while maintaining decoding accuracy, thus advancing neuroimaging efficiency.

Contribution

The paper introduces a practical alignment approach for cross-subject fMRI decoding, showing that functional alignment enables effective decoding with minimal data and reduced scan durations.

Findings

Cross-subject decoding is feasible with around 10% of data.

Ridge regression outperforms other alignment methods.

Potential to reduce scan time by up to 90%.

Abstract

Previous brain decoding research primarily involves single-subject studies, reconstructing stimuli via fMRI activity from the same subject. Our study aims to introduce a generalization technique for cross-subject brain decoding, facilitated by exploring data alignment methods. We utilized the NSD dataset, a comprehensive 7T fMRI vision experiment involving multiple subjects exposed to 9841 images, 982 of which were viewed by all. Our approach involved training a decoding model on one subject, aligning others' data to this space, and testing the decoding on the second subject. We compared ridge regression, hyper alignment, and anatomical alignment techniques for fMRI data alignment. We established that cross-subject brain decoding is feasible, even using around 10% of the total data, or 982 common images, with comparable performance to single-subject decoding. Ridge regression was the best method for functional alignment. Through subject alignment, we achieved superior brain decoding and a potential 90% reduction in scan time. This could pave the way for more efficient experiments and further advancements in the field, typically requiring an exorbitant 20-hour scan time per subject.