Understanding the emotional dynamics of everyday life: modeling brain state changes and their implications for mental health

PROJECT SUMMARY / ABSTRACT Research has shown that individuals with, or at risk for developing, mood disorders have abnormalities in temporal aspects of their emotions in everyday life. Particularly relevant for major depressive disorder (MDD) is emotional inertia, which indicates an emotional system that is rigid and cannot flexibly respond to changing environmental demands. Lab studies have confirmed attenuated responses to both positive and negative stimuli in MDD, leading to the Emotion Context Insensitivity theory. Despite the relevance of affect dynamics for MDD, few studies have assessed the neural mechanisms that underly changing emotional states in an ecologically-valid manner. Resting-state fMRI studies have shown that time-varying patterns within and between fronto-insular and cortical midline brain regions have implications for both affect in daily life and MDD severity, but with an unstructured task like rest, it is difficult to assess how these patterns reflect emotional experiences. This project addresses these limitations through 3 specific aims that use naturalistic, emotional stimuli: (1) Determine time-varying brain patterns associated with dynamics of emotional experience in response to films; (2) Assess the time-varying brain patterns associated with emotions dynamics in response to a novel experimentally-designed musical stimuli; (3) Relate individual differences in time-varying brain patterns with individual differences in a) emotional dynamics in everyday life and b) affective traits related to depression. The hypothesis is that time-varying activation patterns in fronto-insular, subcortical, and cortical- midline regions will reflect changing emotions induced by movies and music and that variation in these patterns will predict variation in positive and negative emotion dynamics in daily life (over the course of weeks) and depression severity. Such results will specify the neural mechanisms underlying the association between emotional inertia and depression, lending neurobehavioral support for one of several emotion theories of mood disorders. The PI’s long-term goal is to become an NIH-funded faculty member of an R1 university, with a research program focused on modelling brain systems involved in complex, socioemotional behaviors and leveraging results to test new tools for characterizing, assessing risk for, and treating mood disorders. The training objectives are to (1) acquire practical knowledge needed to apply dynamic computational models to BOLD signal in response to naturalistic stimuli; (2) learn how clinicians conceptualize and measure affective symptoms of mood disorders; (3) further skills in experience sampling data analysis to model affect dynamics and predict individual differences in brain patterns; (4) enhance academic/professional skills to successfully transition to independence. Career development will take place at Columbia University, a vibrant research environment with outstanding resources to support the proposal, including relevant courses/ seminars, state- of-the-art MRI facilities, and expert faculty in Computational Neuroscience, Biostatistics, and Psychiatry.