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Neural Signatures of Emotion Regulation

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Emotional experience is central to a fulfilling life. Although exposure to negative experiences is inevitable, an individual’s emotion regulation response may buffer against psychopathology. Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences. Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response. We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature. We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.5% across three classes. Our results suggest that emotional regulation produces a unique signature that is differentiable from passive viewing of negative imagery.
Title: Neural Signatures of Emotion Regulation
Description:
Emotional experience is central to a fulfilling life.
Although exposure to negative experiences is inevitable, an individual’s emotion regulation response may buffer against psychopathology.
Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences.
Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response.
We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature.
We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.
5% across three classes.
Our results suggest that emotional regulation produces a unique signature that is differentiable from passive viewing of negative imagery.

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