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The increasing popularity of naturalistic paradigms in fMRI (such as moviewatching) demands novel strategies for multi-subject data analysis, such as useof neural encoding models. In the present study, we propose a sharedconvolutional neural encoding method that accounts for individual-leveldifferences. Our method leverages multi-subject data to improve the predictionof subject-specific responses evoked by visual or auditory stimuli. We showcaseour approach on high-resolution 7T fMRI data from the Human Connectome Projectmovie-watching protocol and demonstrate significant improvement oversingle-subject encoding models. We further demonstrate the ability of theshared encoding model to successfully capture meaningful individual differencesin response to traditional task-based facial and scenes stimuli. Takentogether, our findings suggest that inter-subject knowledge transfer can bebeneficial to subject-specific predictive models.

A shared neural encoding model for the prediction of subject-specific fMRI response