Endogenous variation in ventromedial prefrontal cortex state dynamics during naturalistic viewing reflects affective experience

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Description: How we process ongoing experiences is shaped by our personal history, current needs, and future goals. Consequently, brain regions involved in generating these subjective appraisals, such as the vmPFC, often appear to be heterogeneous across individuals even in response to the same external information. To elucidate the role of the vmPFC in processing our ongoing experiences, we developed a computational framework and analysis pipeline to characterize the spatiotemporal dynamics of individual vmPFC responses as participants viewed a 45-minute television drama. Through a combination of functional magnetic resonance imaging, facial expression tracking, and self-reported emotional experiences across four studies, our data suggest that the vmPFC slowly transitions through a series of discretized states that broadly map onto affective experiences. Although these transitions typically occur at idiosyncratic times across people, participants exhibited a marked increase in state alignment during high affectively valenced events in the show. Our work suggests that the vmPFC ascribes affective meaning to our ongoing experiences. In this repository we share de-identified data from study 3 (Face Expression) and study 4 (Subjective Ratings). Unfortunately, we do not have permission to share the raw video data for Study3, instead we share the action unit predictions extracted using Facet developed by Emotient previously available from IMotions before being acquired by Apple. Data has been downsampled to 0.5Hz to correspond to neuroimaging sampling frequency. Data for Study 4 was acquired on mechanical turk using a sparse sampling strategy via our Moth Rating App (https://github.com/cosanlab/moth_app). We inferred the missing data using our collaborative filtering toolbox (https://github.com/cosanlab/emotionCF) using Non-Negative Matrix Factorization with Stochastic Gradient Descent with a 60 second smoothing kernel to a sampling resolution of 0.5Hz. For analysis code see our github repository: https://github.com/cosanlab/vmPFC_dynamics For Rating App for Study 4 data collection, see our github repository: https://github.com/cosanlab/moth_app For Neuroimaging data, see OpenNeuro: Study 1 - Study 2 -

License: MIT License

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