Modeling short visual events through the BOLD moments video fMRI dataset and metadata

Benjamin Lahner (), Kshitij Dwivedi, Polina Iamshchinina, Monika Graumann, Alex Lascelles, Gemma Roig, Alessandro Thomas Gifford, Bowen Pan, SouYoung Jin, N. Apurva Ratan Murty, Kendrick Kay, Aude Oliva and Radoslaw Cichy
Additional contact information
Benjamin Lahner: MIT
Kshitij Dwivedi: Freie Universität Berlin
Polina Iamshchinina: Freie Universität Berlin
Monika Graumann: Freie Universität Berlin
Alex Lascelles: MIT
Gemma Roig: Goethe University Frankfurt
Alessandro Thomas Gifford: Freie Universität Berlin
Bowen Pan: MIT
SouYoung Jin: MIT
N. Apurva Ratan Murty: MIT
Kendrick Kay: University of Minnesota
Aude Oliva: MIT
Radoslaw Cichy: Freie Universität Berlin

Nature Communications, 2024, vol. 15, issue 1, 1-26

Abstract: Abstract Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1000 short (3 s) naturalistic video clips of visual events across ten human subjects. We use the videos’ extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex. Furthermore, we reveal a match in hierarchical processing between cortical regions of interest and video-computable deep neural networks, and we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. With its rich metadata, BMD offers new perspectives and accelerates research on the human brain basis of visual event perception.

Date: 2024
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DOI: 10.1038/s41467-024-50310-3

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