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Research
Our research aims to provide a neurobiological framework for the psychological processes of the experience of our own emotions and evaluation of the emotions of others. Using multimodal neuroimaging tools that include task-based and resting state functional magnetic resonance imaging (fMRI) and diffusion magnetic resonance imaging (dMRI), our work examines the role of a corticolimbic neural circuitry that centers on the amygdala and the prefrontal cortex (PFC) in such affective processes and their implications for mental health (Kim et al., 2011, Behavioural Brain Research). Summarized below are some examples of research questions that the HumAN lab seeks to answer:
How Do We Decode Emotional Ambiguity in the Facial Expressions Others?
 
Evaluating the emotional state of another person involves some degree of ambiguity. Interpreting emotionally ambiguous facial expressions of others is one such instance. Surprised faces are a particularly potent example in this respect because of their inherent ambiguity – these expressions can signal either positive (e.g., I got the job!) or negative (e.g., My wallet is not in my pocket!) outcomes. One source of information that we may use to disambiguate surprised faces is embedded within the facial features per se. Using a machine learning classifier, we identified that the facial features in the mouth were driving the affective valence (i.e., positivity-negativity) of surprised faces – information that human perceivers were unable to explicitly report – and demonstrated that these features were eliciting the activity of the amygdala (Kim et al., 2017, Journal of  Neuroscience). 
How Do We Integrate Affective Information from Multiple Sources in the Brain?
 
When surprised faces are contextualized by a verbal description of a hypothetical scenario that could be either positive or negative, amygdala activity represented the valence values that were computed by considering a given context when judging a face on a trial-by-trial basis. Interestingly, only when a surprised face was viewed in a positive context, amygdala activity was functionally connected with the ventral striatum and the ventromedial PFC – a pair of brain regions best known for processing reward or positivity (Kim et al., in preparation). As no such interaction in brain activity was found when surprised faces were paired with a negative context, this served as evidence that supports the idea that extra neural computation is required when trying to view an emotionally ambiguous stimulus in a positive light (Neta & Whalen, 2010).
How Do We Understand the Individual Differences in the Tendency to Rely on Contextual Information versus Facial Features?
 
When a surprised face is presented with an accompanying social context – as is always the case in real world situations – perceivers will have varying degrees of sensitivity towards top-down (i.e., contextual cues) and bottom-up (i.e., facial features) information, when making a decision about how positive or negative the face seems to be. Adapting a computational approach, a behavioral model that considers the individual differences in the sensitivity to these contexts and features was constructed on a subject-by-subject basis to explain how social information from multiple sources are integrated. This measure of sensitivity showed adequate reliability (ICC > 0.8) and trait-like psychometric properties, and was subsequently found to be associated with the strength of amygdala-PFC connectivity (Kim et al., in preparation). We are investigating how the individual differences in sensitivity may be represented as distributed patterns of brain activity.
What Factors Modulate the Association Between Trait Anxiety and Corticolimbic Circuitry?
Neurobiological research on anxiety, or negative affect in general, has consistently implicated the amygdala and the PFC, leading to a hypothesis that an imbalance of the corticolimbic circuitry – reflecting an imbalance of top-down and bottom-up flow – may be key to understanding the generation and regulation of negative emotions. Using dMRI in conjunction with fMRI, we observed that the structural integrity of white matter pathways between the amygdala and the ventral PFC was weaker in trait-anxious individuals (Kim & Whalen, 2009, Journal of Neuroscience). We then found that this association was moderated by sex, such that this brain-anxiety correlation was substantially stronger in women compared to men (Kim et al., 2016, Frontiers in Systems Neuroscience). Both of these effects were replicated in a subsequent study, where we demonstrated that a functional polymorphism in the human BDNF gene (rs6265) further influenced the brain-anxiety association (Kim et al., 2017, Scientific Reports).  We are working on identifying other potential modulating factors, as it will allow us to better understand the neural underpinnings of trait anxiety, and inform clinical research on anxiety disorders.