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

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 diff...

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Bibliographic Details
Main Authors Khosla, Meenakshi, Ngo, Gia H, Jamison, Keith, Kuceyeski, Amy, Sabuncu, Mert R
Format Journal Article
LanguageEnglish
Published 29.06.2020
Subjects
Computer Science - Learning
Quantitative Biology - Neurons and Cognition
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DOI10.48550/arxiv.2006.15802

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Summary: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.
DOI:10.48550/arxiv.2006.15802