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Creators/Authors contains: "Morey, Rajendra A."

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  1. ; ; ; ; ; ; ( , Proceedings of the National Academy of Sciences)

    Physical proximity to a traumatic event increases the severity of accompanying stress symptoms, an effect that is reminiscent of evolutionarily configured fear responses based on threat imminence. Despite being widely adopted as a model system for stress and anxiety disorders, fear-conditioning research has not yet characterized how threat proximity impacts the mechanisms of fear acquisition and extinction in the human brain. We used three-dimensional (3D) virtual reality technology to manipulate the egocentric distance of conspecific threats while healthy adult participants navigated virtual worlds during functional magnetic resonance imaging (fMRI). Consistent with theoretical predictions, proximal threats enhanced fear acquisition by shifting conditioned learning from cognitive to reactive fear circuits in the brain and reducing amygdala–cortical connectivity during both fear acquisition and extinction. With an analysis of representational pattern similarity between the acquisition and extinction phases, we further demonstrate that proximal threats impaired extinction efficacy via persistent multivariate representations of conditioned learning in the cerebellum, which predicted susceptibility to later fear reinstatement. These results show that conditioned threats encountered in close proximity are more resistant to extinction learning and suggest that the canonical neural circuitry typically associated with fear learning requires additional consideration of a more reactive neural fear system to fully account for this effect.

     
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  2. ; ; ; ; ; ( , Journal of Neuroimaging)
    ABSTRACT BACKGROUND AND PURPOSE

    Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here, we used a novel machine learning method – evolving partitions to improve classification (EPIC) – to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat‐exposed military veterans.

     METHODS

    We used EPIC with repeated cross‐validation (CV) to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n = 40) versus controls (n = 57), and classification of ELS within the PTSD (ELS+n = 16; ELS−n = 24) and control groups (ELS+n = 16; ELS−n = 41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression.

     RESULTS

    On average, EPIC classified PTSD with 69% accuracy (SD = 5%), and ELS with 64% accuracy in the PTSD group (SD = 10%), and 62% accuracy in controls (SD = 6%). EPIC selected unique sets of individual features that classified each group with 75‐85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas‐defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS.

     CONCLUSIONS

    EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat‐exposed military veterans, which may represent distinct biotypes of stress‐related neuropathology.

     
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  3. ; ; ; ; ; ; ; ; ; ; et al ( , Translational Psychiatry)
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