Identifying Core Affect in Individuals from fMRI Responses to Dynamic Naturalistic Audiovisual Stimuli.

Jongwan Kim,Svetlana V Shinkareva,Douglas H Wedell,Jing Wang,Andreas B Eder

doi:10.1371/journal.pone.0161589

Jongwan Kim, Svetlana V Shinkareva + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0161589

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Abstract

Recent research has demonstrated that affective states elicited by viewing pictures varying in valence and arousal are identifiable from whole brain activation patterns observed with functional magnetic resonance imaging (fMRI). Identification of affective states from more naturalistic stimuli has clinical relevance, but the feasibility of identifying these states on an individual trial basis from fMRI data elicited by dynamic multimodal stimuli is unclear. The goal of this study was to determine whether affective states can be similarly identified when participants view dynamic naturalistic audiovisual stimuli. Eleven participants viewed 5s audiovisual clips in a passive viewing task in the scanner. Valence and arousal for individual trials were identified both within and across participants based on distributed patterns of activity in areas selectively responsive to audiovisual naturalistic stimuli while controlling for lower level features of the stimuli. In addition, the brain regions identified by searchlight analyses to represent valence and arousal were consistent with previously identified regions associated with emotion processing. These findings extend previous results on the distributed representation of affect to multimodal dynamic stimuli.

Highlights

The two major components of core affect posited to underlie more complex emotions are valence, varying from negative to positive, and arousal, varying from low to high [1, 2]
The fact that the neural representation of affect was conveyed by brain areas selectively responsive to naturalistic dynamic multisensory information is consistent with the idea that valence and arousal are two fundamental properties that are readily accessed when processing dynamic multimodal stimuli
In addition to classifying valence and arousal within individuals, we found that the affective states of an individual could be reliably modeled by training the classifiers on data from other individuals

Summary

Introduction

The two major components of core affect posited to underlie more complex emotions are valence, varying from negative to positive, and arousal, varying from low to high [1, 2]. Neuroimaging studies have demonstrated a number of correspondences between different neural activation patterns and levels of valence and arousal for typical [6,7,8] and clinical [9] populations. These results support the idea that the neural representations of valence and arousal should be identifiable on a trial-bytrial basis for a variety of different types of stimuli. There has been growing interest in investigating the utility of multivariate pattern analysis (MVPA) approaches applied to fMRI data in clinical populations.

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