Abstract: One of the major challenges in functional magnetic resonance imaging(fMRI) research is the heterogeneity of fMRI data across different subjects.On the one hand,analyzing multi-subject data is crucial for determining the generalizability and effectiveness of the generated results across subjects.On the other hand,analyzing multi-subject fMRI data requires accurate anatomical and functional alignment among the neural activities of different subjects to enhance the performance of the final results.However,most existing functional alignment studies employ shallow models to handle the complex relationships among multiple subjects,severely limiting the modeling capacity for multi-subject information.To solve this problem,this paper proposes a multi-view auto-encoder functional alignment(MAFA) method based on multi-view auto-encoders.Specifically,our method learns node embedding by reconstructing the response spaces of different subjects,capturing shared feature representations among subjects,and creating a common response space.We also introduce the graph clustering process by introducing self-training clustering objectives using high-confidence nodes as soft labels.Experimental results on four datasets demonstrate that the proposed method achieves the best decoding accuracy compared to other multi-subject fMRI functional alignment methods.

Key words: Functional magnetic resonance imaging, Functional alignment, Multi-view representation learning, Multi-subject analysis, Brain decoding