Feature-based representations of emotional facial expressions in the human amygdala.

The amygdala plays a central role in processing facial affect, responding to diverse expressions and features shared between expressions. Although speculation exists regarding the nature of relationships between expression- and feature-specific amygdala reactivity, this matter has not been fully explored. We used functional magnetic resonance imaging and principal component analysis (PCA) in a sample of 300 young adults, to investigate patterns related to expression- and feature-specific amygdala reactivity to faces displaying neutral, fearful, angry or surprised expressions. The PCA revealed a two-dimensional correlation structure that distinguished emotional categories. The first principal component separated neutral and surprised from fearful and angry expressions, whereas the second principal component separated neutral and angry from fearful and surprised expressions. This two-dimensional correlation structure of amygdala reactivity may represent specific feature-based cues conserved across discrete expressions. To delineate which feature-based cues characterized this pattern, face stimuli were averaged and then subtracted according to their principal component loadings. The first principal component corresponded to displacement of the eyebrows, whereas the second principal component corresponded to increased exposure of eye whites together with movement of the brow. Our results suggest a convergent representation of facial affect in the amygdala reflecting feature-based processing of discrete expressions.

Duke Scholars

Author Ahmad Hariri Psychology & Neuroscience