  The increasing popularity of naturalistic paradigms in fMRI (such as movie
watching) demands novel strategies for multi-subject data analysis, such as use
of neural encoding models. In the present study, we propose a shared
convolutional neural encoding method that accounts for individual-level
differences. Our method leverages multi-subject data to improve the prediction
of subject-specific responses evoked by visual or auditory stimuli. We showcase
our approach on high-resolution 7T fMRI data from the Human Connectome Project
movie-watching protocol and demonstrate significant improvement over
single-subject encoding models. We further demonstrate the ability of the
shared encoding model to successfully capture meaningful individual differences
in response to traditional task-based facial and scenes stimuli. Taken
together, our findings suggest that inter-subject knowledge transfer can be
beneficial to subject-specific predictive models.
