Dr. Vanessa M Brown received her undergraduate degree in Psychology and German from St. Olaf College in 2009 and her PhD in Psychology (Clinical Science) from Virginia Tech in 2018. She completed a predoctoral clinical internship and postdoctoral research fellowship at Western Psychiatric Hospital and the University of Pittsburgh before joining the faculty at the University of Pittsburgh in 2020. She will join the Department of Psychology at Emory University in January 2024.
November 1, 2012 Publications
Amygdala Volume Changes in Posttraumatic Stress Disorder in a Large Case-Controlled Veterans GroupAmygdala Volume Changes in PTSD
Smaller hippocampal volumes are well established in posttraumatic stress disorder (PTSD), but the relatively few studies of amygdala volume in PTSD have produced equivocal results...
August 9, 2013 Publications
Altered Resting-State Functional Connectivity of Basolateral and Centromedial Amygdala Complexes in Posttraumatic Stress Disorder
The amygdala is a major structure that orchestrates defensive reactions to environmental threats and is implicated in hypervigilance and symptoms of heightened arousal in posttraumatic stress disorder (PTSD). The basolateral and centromedial amygdala (CMA) complexes are functionally heterogeneous, with distinct roles in learning and expressing fear behaviors. PTSD differences in amygdala-complex function and functional connectivity with cortical and subcortical structures remain unclear. Recent military veterans with PTSD (n= 20) and matched trauma-exposed controls (n= 22) underwent a resting-state fMRI scan to measure task-free synchronous blood-oxygen level dependent activity. Whole-brain voxel-wise functional connectivity of basolateral and CMA seeds was compared between groups. The PTSD group had stronger functional connectivity of the basolateral amygdala (BLA) complex with the …
December, 2015 Publications
Fear learning circuitry is biased toward generalization of fear associations in posttraumatic stress disorder
Fear conditioning is an established model for investigating posttraumatic stress disorder (PTSD). However, symptom triggers may vaguely resemble the initial traumatic event, differing on a variety of sensory and affective dimensions. We extended the fear-conditioning model to assess generalization of conditioned fear on fear processing neurocircuitry in PTSD. Military veterans (n= 67) consisting of PTSD (n= 32) and trauma-exposed comparison (n= 35) groups underwent functional magnetic resonance imaging during fear conditioning to a low fear-expressing face while a neutral face was explicitly unreinforced. Stimuli that varied along a neutral-to-fearful continuum were presented before conditioning to assess baseline responses, and after conditioning to assess experience-dependent changes in neural activity. Compared with trauma-exposed controls, PTSD patients exhibited greater post-study memory …
February, 2012 Publications
Trait empathy as a predictor of individual differences in perceived loneliness
Loneliness has been shown to be inversely correlated with empathy in younger adults. The present study extends previous research by investigating the association between empathy and loneliness across the adult lifespan and examining the role of relevant demographic and personality factors. 110 community-dwelling adults (18 to 81 years old) completed the UCLA Loneliness Scale and the Empathy Quotient. Empathy scores were inversely associated with rated loneliness and predicted 8.7% of variance in loneliness scores after accounting for sex, age, relationship status, education, and neuroticism. The Social Skills factor of the Empathy Quotient was the strongest predictor of the association between perceived empathy and loneliness. Previous research is extended by the finding that rated loneliness was inversely associated with empathy scores across the adult lifespan. Underlying this relationship may be …
June 1, 2020 Publications
Improving the reliability of computational analyses: Model-based planning and its relationship with compulsivity
Computational models show great promise in mapping latent decision-making processes onto dissociable neural substrates and clinical phenotypes. One prominent example in reinforcement learning is model-based planning, which specifically relates to transdiagnostic compulsivity. However, the reliability of computational model-derived measures such as model-based planning is unclear. Establishing reliability is necessary to ensure that such models measure stable, traitlike processes, as assumed in computational psychiatry. Although analysis approaches affect validity of reinforcement learning models and reliability of other task-based measures, their effect on reliability of reinforcement learning models of empirical data has not been systematically studied...
Anxiety, trauma, and depression; reinforcement learning and decision-making; neurocomputational modeling; mechanisms of psychopathology and treatment; cognitive and computational neuroscience
Research Areas
Dr. Brown studies reinforcement learning and decision-making in internalizing disorders. Work in her lab uses computational modeling, behavioral assessments and interventions, functional and structural neuroimaging, ecological assessment, and neuromodulation to understand mechanistic, treatment-relevant processes. The long-term goal of her research is to advance understanding and treatment of psychopathology to reduce suffering from mental illness.
Current projects focus on the following areas:
· Integrating computational learning theory into theories of altered learning in anxiety
· Neural systems underlying altered uncertainty learning in anxiety
· Using generative neurocomputational models to understand mechanisms of treatment for internalizing disorders
· Psychometrics and clinical applicability of computational approaches