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Deep learning is leading to major advances in the realm of brain decodingfrom functional Magnetic Resonance Imaging (fMRI). However, the largeinter-subject variability in brain characteristics has limited most studies totrain models on one subject at a time. Consequently, this approach hampers thetraining of deep learning models, which typically requires very large datasets.Here, we propose to boost brain decoding by aligning brain responses to videosand static images across subjects. Compared to the anatomically-alignedbaseline, our method improves out-of-subject decoding performance by up to 75%.Moreover, it also outperforms classical single-subject approaches when fewerthan 100 minutes of data is available for the tested subject. Furthermore, wepropose a new multi-subject alignment method, which obtains comparable resultsto that of classical single-subject approaches while improving out-of-subjectgeneralization. Finally, we show that this method aligns neural representationsin accordance with brain anatomy. Overall, this study lays the foundations forleveraging extensive neuroimaging datasets and enhancing the decoding ofindividuals with a limited amount of brain recordings.

Aligning brain functions boosts the decoding of visual semantics in novel subjects