A shared neural encoding model for the prediction of subject-specific fMRI response
This work addresses the need for better multi-subject analysis in fMRI for researchers, though it is incremental as it builds on existing neural encoding models.
The study tackled the problem of predicting subject-specific fMRI responses in naturalistic paradigms by proposing a shared convolutional neural encoding method that accounts for individual-level differences, resulting in significant improvement over single-subject models as demonstrated on 7T fMRI data from the Human Connectome Project.
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.