Dr. Bernat’s research focuses on brain mechanisms that underlie individual differences in cognitive and affective processing. This involves basic science work developing measures for critical mechanisms, and clinical-translational work assessing how these mechanisms relate to psychopathology and individual differences.

Currently funded work includes a focus on substance use among lower SES minority individuals in a inpatient DC residential drug treatment facility. Broadly, projects there focus on brain mechanisms underlying substance use and psychopathology, and change in these mechanisms during treatment. For example, one thread focuses on the relationship between trauma and substance use, and development of brief trauma interventions.  Another thread is focused on changes in drug and other cue-reactivity during the initial period of abstinence (cue-incubation), and how this can index vulnerability to relapse.  

Emerging transdiagnostic (dimensional) models of psychopathology play a prominent role in the inferences involved in this work. The most common model involves three primary factors: 1) impulse control (externalizing) problems such as substance dependence, antisocial behavior, and psychopathy, 2) internalizing problems involving anxiety and depression, and 3) the shared variance between internalizing and externalizing (referred to as a psychopathology factor; p-factor). This parsimonious model provides reduced complexity when relating psychopathology to brain mechanisms. More importantly, however, this offers empirically-based approaches to identifying potential neurobiological factors underlying multiple related or comorbid clinical problems. We have recently applied this modeling approach to clinical interview data from the DC-based residential drug treatment facility we are collaborating with, providing important information about the nature of psychopathology related to substance use.

A primary methodological focus is on advanced time-frequency decomposition techniques employed with EEG/ERP data. This includes amplitude measures that can delineate active brain regions and functional connectivity measures to characterize dynamic communication between brain regions. A major current methodological focus is on developing higher resolution time-frequency representations of event-related functional connectivity, dynamically as it unfolds. To bring better spatial resolution, further integration between these EEG/ERP decomposition approaches and MRI/fMRI neuroimaging data is currently being advanced, including a new simultaneous EEG/fMRI system we are employing at the Maryland Neuroimaiging Center (MNC).