Using Virtual Reality to Understand Fear and Proximity

Traditional lab-based fear conditioning studies are often limited in how well they generalize to real-life threatening experiences. To address this limitation, we developed a 3-D virtual reality simulation that can be used during functional MRI to provide participants with a more contextually rich environment during fear acquisition and extinction. With virtual reality we can also investigate how spatial distance to a threat (near or far) influences the fear conditioning process, since proximity has been shown to exacerbate the development of traumatic stress disorders. With this research we hope to better characterize the basic neurobehavioral mechanisms relating proximity and fear learning, thereby isolating neural circuitry that contributes to extinction-resistant memory for threats that invade peri-personal space. This project has recieved funding by an National Science Foundation grant. Graduate student Leonard Faul is leading this project. .


Evaluating Mechanisms of Emotion Regulation Across the Lifespan and in Depression (EMERALD). This project investigates the role of emotional regulation and the impact of depression across the lifespan. The ability to regulate one’s emotional responses is critical for maintaining emotional health in the face of adverse events that cumulate over time. We believe that multiple factors, including, age, depression status, neurocognitive functioning, and social support will impact the success of emotion regulation using reappraisal and distraction strategies. We also believe that the combined effect of those variables on strategy use will predict depressive symptoms further into a person’s lifespan. Ultimately, our goal for this project is to gain insights into how maturational changes influence the ability of depressed adults to reduce negative affect. This project is supported by a National Institute of Health R01 grant.

Neural Representation of Conditioned Fear

Extinction learning is a primary means by which acquired fear associations are suppressed and is a model system for emotion dysregulation in anxiety disorders. In this project we’re applying multivariate analysis techniques to functional MRI data to look at changes in brain activation patterns associated with encoded fear as that fear is extinguished. We hope to better understand where and when these changes occur in the brain so we can recognize when they might be disrupted in anxiety-related disorders. This project has received funding from a National Science Foundation grant. This project is currently led by Post Doc John Graner.

Neurocomputational Approaches to Emotion Decoding

This NIH-funded project uses machine learning and computational modeling to decode emotions from task-based and resting-state fMRI and psychophysiological signals. The project expands the set of emotions from the lab’s prior work on this topic and uses Markov modeling to infer how individuals dynamically transition across different emotional states. We are interested in linking these emotion profiles and dynamics with individual differences in affect and cognitive appraisals..

Mood and Memory

This project explores how mood biases episodic memory behaviorally and in the brain. We are especially interested in characterizing how mood impacts memory consolidation and how sustained moods interact with more acute emotional memory processes. This project is being led by graduate student Leonard Faul , with support from an NSF graduate fellowship, in collaboration with Felipe de Brigard.

Psychosocial Factors that Contribute to Pandemic Anxiety

This project seeks to understand how cognitive appraisals, emotion regulation strategies, and other psychosocial variables impact anxiety about the COVID-19 pandemic. This study replicates and extends a similar survey that took place during the H1N1 pandemic. This on-line study is a lab-wide project, led by graduate student Rachael Wright, with support from the Charles Lafitte Foundation in Psychological Research.

Learning to Self-Regulate Using fMRI-based Neurofeedback

This project, funded by the Duke Institute for Brain Sciences seeks to understand how individuals learn to self-regulate through fMRI-based neurofeedback. This project extends the lab’s prior work that used interoceptive feedback signals as a basis for self-regulation. Graduate student Rachael Wright is leading this project as a joint effort between the LaBar and Adcock labs..

Neuromodulation of Emotions and Emotion Regulation

The lab is involved in several collaborative projects that use neuromodulation tools, such as TMS, in combination with structural or functional MRI, to modify emotional processing and regulation. The goal is to identify the critical nodes in the brain where neurostimulation impacts these processes as a potential therapeutic tool. Our initial work on this topic examined the role of the lateral parietal cortex in emotional distancing and was led by lab alumnus John Powers. Currently this line of work is being extended to mitigate emotion dysregulation in psychiatric disorders in collaboration with Andrada Neasciu and Greg Appelbaum in the Department of Psychiatry and Behavioral Sciences. These projects have received funding from the Duke Institute for Brain Sciences, the Charles Lafitte Foundation in Psychological Research, an NSF Graduate Fellowship, the Brain and Behavior Research Foundation, a KL2 professional development award, and the REAM foundation..

Neuroimaging of Emotions and Emotion-Cognition Interactions in PTSD

The lab has a long-standing collaboration with Rajendra Morey’s group at the Durham VA Hospital to examine the neural circuits involved in emotional processing and emotion-cognition interactions in PTSD. Our recent work has focused on characterizing how traumatic stress impacts the brain mechanisms for perceptual and conceptual fear generalization using conditioning-based paradigms. We are expanding our investigations to consider how PTSD affects the neural circuits of other emotions, such as shame and guilt, and their role in moral injury and moral decision-making. This work has been funded by the Department of Veterans Affairs.