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Cortical response to naturalistic stimuli is largely predictable with deep neural networks

Meenakshi Khosla, Gia H. Ngo, View ORCID ProfileKeith Jamison, View ORCID ProfileAmy Kuceyeski, View ORCID ProfileMert R. Sabuncu
doi: https://doi.org/10.1101/2020.09.11.293878
Meenakshi Khosla
1School of Electrical & Computer Engineering, Cornell University
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Gia H. Ngo
1School of Electrical & Computer Engineering, Cornell University
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Keith Jamison
2Radiology, Weill Cornell Medicine
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Amy Kuceyeski
2Radiology, Weill Cornell Medicine
3Brain and Mind Research Institute, Weill Cornell Medicine
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Mert R. Sabuncu
1School of Electrical & Computer Engineering, Cornell University
2Radiology, Weill Cornell Medicine
4Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University
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  • For correspondence: msabuncu@cornell.edu
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Abstract

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict the neural response to a given stimulus can be very useful for studying brain function. However, existing neural encoding models focus on limited aspects of naturalistic stimuli, ignoring the complex and dynamic interactions of modalities in this inherently context-rich paradigm. Using movie watching data from the Human Connectome Project (HCP, N = 158) database, we build group-level models of neural activity that incorporate several inductive biases about information processing in the brain, including hierarchical processing, assimilation over longer timescales and multi-sensory auditory-visual interactions. We demonstrate how incorporating this joint information leads to remarkable prediction performance across large areas of the cortex, well beyond the visual and auditory cortices into multi-sensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize remarkably well to alternate task-bound paradigms. Taken together, our findings underscore the potential of neural encoding models as a powerful tool for studying brain function in ecologically valid conditions.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 13, 2020.
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Cortical response to naturalistic stimuli is largely predictable with deep neural networks
Meenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
bioRxiv 2020.09.11.293878; doi: https://doi.org/10.1101/2020.09.11.293878
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Cortical response to naturalistic stimuli is largely predictable with deep neural networks
Meenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
bioRxiv 2020.09.11.293878; doi: https://doi.org/10.1101/2020.09.11.293878

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