Decoding Spontaneous Emotional States in the Human Brain.

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2016-09

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Abstract

Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems.

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10.1371/journal.pbio.2000106

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Kragel, PA, AR Knodt, AR Hariri and KS LaBar (2016). Decoding Spontaneous Emotional States in the Human Brain. PLoS Biol, 14(9). p. e2000106. 10.1371/journal.pbio.2000106 Retrieved from https://hdl.handle.net/10161/12775.

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LaBar

Kevin S. LaBar

Professor of Psychology and Neuroscience

My research focuses on understanding how emotional events modulate cognitive processes in the human brain. We aim to identify brain regions that encode the emotional properties of sensory stimuli, and to show how these regions interact with neural systems supporting social cognition, executive control, and learning and memory. To achieve this goal, we use a variety of cognitive neuroscience techniques in human subject populations. These include psychophysiological monitoring, functional magnetic resonance imaging (fMRI), machine learning,  and behavioral studies in healthy adults as well as psychiatric patients. This integrative approach capitalizes on recent advances in the field and may lead to new insights into cognitive-emotional interactions in the brain.


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