A central goal of affective neuroscience is to understand the brain systems and mechanisms underlying the evaluation, experience, and expression of emotion. For example, a widely studied and hotly debated issue in the field is the manner in which biophysical responses to emotional stimuli can be characterized, whether by distinct categories or alternatively along dimensions of valence and arousal. Advances in the fields of psychology, neuroscience, and computer science have fostered significant progress in identifying brain regions involved in processing emotion generally;however, consistent and specific neural markers for distinct affective states have yet to be found. This proposal uses a cutting-edge approach to this core, unresolved question in the field by harnessing emerging pattern classification techniques that are capable of detecting subtle yet coordinated signals from an array of sources. The overarching goal is to identify multivariate patterns of behavioral and biological responding to specific affective states and determine whether these states are organized according to categorical or dimensional architectures. By combining psychophysiology (Aim 1) and functional magnetic resonance imaging (fMRI) (Aim 2), these studies will examine how humans respond to emotional stimuli that vary in duration, modality, and categorical nature. Study 1 focuses on distinct emotions elicited by instrumental music and movie clips whereas Study 2 focuses on those elicited by facial and vocal affect. Together, the aims will provide an integrative, computationally-rigorous method to identify biomarkers of specific emotions that are typically overlooked by conventional univariate statistical approaches. Applying machine learning algorithms in this innovative way could be fruitful for identifying how emotional representations are altered in affective disorders, with the potential for developing novel therapeutic targets. Moreover, identifying patterns of overlap between specific emotion categories may further aid efforts to understand comorbidity issues in anxiety and mood disorders.

Public Health Relevance

The proposed research will identify patterns of brain and bodily responses that are representative of unique emotional states, using analytical methods that examine changes in multiple biological signals in concert. Identifying neural and peripheral representations of emotional states and how they relate to self-reports of emotion will provide a better understanding of how emotions are organized and coded in the brain and body. These biomarkers of specific emotions can provide clues as to how emotional processing is altered in mood and anxiety disorders and can lead to novel treatment targets.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory/Developmental Grants (R21)
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Biobehavioral Mechanisms of Emotion, Stress and Health Study Section (MESH)
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Simmons, Janine M
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Duke University
Schools of Arts and Sciences
United States
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Kragel, Philip A; LaBar, Kevin S (2016) Somatosensory Representations Link the Perception of Emotional Expressions and Sensory Experience. eNeuro 3:
Kragel, Philip A; LaBar, Kevin S (2016) Decoding the Nature of Emotion in the Brain. Trends Cogn Sci 20:444-55
Kragel, Philip A; LaBar, Kevin S (2015) Multivariate neural biomarkers of emotional states are categorically distinct. Soc Cogn Affect Neurosci 10:1437-48
Kragel, Philip A; LaBar, Kevin S (2014) Advancing emotion theory with multivariate pattern classification. Emot Rev 6:160-174
Kragel, Philip A; Labar, Kevin S (2013) Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions. Emotion 13:681-90