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

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.

Publication
In MICCAI (Oral)
Meenakshi Khosla
Meenakshi Khosla
Assistant Professor of Cognitive Science and Computer Science and Engineering

My research interests include computational neuroscience, artificial intelligence and large-scale data analysis.