Poster No:
621
Submission Type:
Abstract Submission
Authors:
Xuan Yang1, Christian O'Reilly2, Svetlana Shinkareva1
Institutions:
1University of South Carolina, COLUMBIA, SC, 2University of South Carolina, Columbia, SC
First Author:
Xuan Yang
University of South Carolina
COLUMBIA, SC
Co-Author(s):
Introduction:
Affective processing is essential to all aspects of human psychological functioning. A deeper understanding of its neural mechanisms could provide a conceptual foundation for current psychopathological therapies and inspire new applications. Most existing fMRI research used controlled laboratory stimuli, such as emotion-evocative images, isolated words, and affective sounds. However, how valence is represented in the brain in real-life contexts is still understudied. Listening to narratives approximates some daily experiences and is well-suited for studying affective processing ecologically. In this study, we used a data-driven method to investigate the neural representations of valence during narrative listening in an fMRI scanner.
Methods:
We leveraged four datasets from the Narrative fMRI data collection (Nastase et al., 2021), consisting of preprocessed fMRI data of 64 participants (mean age = 22.67) listening to four 14-minute narratives. To capture the dynamics of affective experience within the narratives, we parsed the transcripts into segments adhering to general grammatical rules and natural speech transitions. A separate sample of participants (N = 156) listened to the story one segment at a time and rated their affective experience using a valence-by-arousal grid. We implemented four separate general linear regression models to examine the relationship between the blood-oxygen-level dependent (BOLD) signals and different types of valence measures: 1) Bipolar model (BOLD changes linearly with bipolar negative-positive ratings), 2) Bivariate model (BOLD changes linearly with either neutral-negative or neutral-positive ratings, 3) Valence-general absolute model (BOLD changes linearly with the absolute values of the bipolar ratings, and 4) Valence-general quadratic model (BOLD changes quadratically with bipolar ratings). Arousal ratings were included as a confounding variable. We used cross-validated Bayesian model selection (Soch et al., 2016) to determine which model is the most probable. We reported the significant brain activations (FWE corrected) within the brain regions that were selected by the winning model.
Results:
Our results revealed that the Bipolar model best explained BOLD signal changes in most brain regions. The valence-general absolute model was selected for a few clusters in the occipitoparietal and medial prefrontal cortices. The valence-general quadratic model and the bivariate valence models were not selected for any brain regions. Within the brain regions for which the bipolar model was selected, BOLD signals were positively correlated with bipolar valence ratings in the bilateral amygdala, hippocampus, superior temporal gyrus, and orbital frontal cortex, and negatively correlated with activity in the middle and posterior cingulate cortices and bilateral supramarginal gyrus. Within the regions for which the valence-general absolute model was selected, no significant results survived the FWE correction.
Conclusions:
We investigated the neural representation of valence using naturalistic stimuli. Our model selection results supported both the bipolarity and valence-general hypotheses. Our findings align best with the bipolarity hypothesis of valence representation in the brain, a proposition that valence is a single continuum from negativity to positivity (Russell & Barrett, 1999). Furthermore, our study underscores the importance of using statistical model selection to investigate the competing theories of valence representation.
Emotion, Motivation and Social Neuroscience:
Emotional Perception 1
Modeling and Analysis Methods:
Bayesian Modeling 2
Keywords:
Data analysis
Emotions
1|2Indicates the priority used for review
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Provide references using APA citation style.
Barrett, L. F., & Russell, J. A. (1999). The structure of current affect: Controversies and emerging consensus. Current directions in psychological science, 8(1), 10-14.
Nastase, S. A., Liu, Y. F., Hillman, H., Zadbood, A., Hasenfratz, L., Keshavarzian, N., ... & Hasson, U. (2021). The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension. Scientific data, 8(1), 250.
Soch, J., Haynes, J. D., & Allefeld, C. (2016). How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection. NeuroImage, 141, 469-489.
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