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

• URL: http://arxiv.org/abs/2006.15802v2
• Date: Sat, 11 Jul 2020 03:10:46 GMT
• Title: A shared neural encoding model for the prediction of subject-specific fMRI response
• Authors: Meenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski and Mert R. Sabuncu
• Abstract summary: 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.
• Score: 17.020869686284165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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