Using high-stakes, real-world events to map the neural dynamics linked to internalizing disorders

About This Grant

PROJECT SUMMARY Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) affect approximately 300 million people annually worldwide, with profound public health and economic consequences—lifetime prevalence in the U.S. approaches 1 in 3, healthcare costs exceed $40 billion per year, and existing treatments provide only modest relief. These disorders are characterized by persistent and recurrent negative emotional states, particularly during significant, personally meaningful events. However, most research in human affective neuroscience has relied on artificial affective stimuli that fail to evoke the intensity and relevance needed to capture real-world emotional dynamics, and moreover, prior studies have struggled to disentangle anticipatory processes (e.g., the emotional buildup before an event) from reactive processes (e.g., the emotional response to the event), especially within naturalistic conditions. This limitation has hindered progress in understanding the distinct yet interrelated mechanisms underlying emotion dysregulation in GAD and MDD. This study, building upon R21MH125311 (Heller, PI), addresses these critical gaps by leveraging a highly goal- relevant, emotionally impactful real-world event: undergraduate students receiving grades on challenging Chemistry ‘weed-out’ exams. Using fMRI to scan 144 participants (72 with GAD/MDD, 72 controls) across five sessions (four exam-related, one baseline), we will precisely delineate anticipatory and reactive neural processes over time. Advanced Hidden Markov Models will be applied to identify and differentiate negative affective brain states during anticipation, reaction, and recovery, providing insight into how these states emerge and persist. Preliminary findings suggest that hippocampal activity patterns may drive the recurrence of these states, offering novel clues to the neural circuit dynamics underlying emotion dysregulation in GAD and MDD. By using real-world, goal-relevant stimuli and cutting-edge computational tools, this will project uniquely disentangle anticipatory and reactive processes, capturing the full neural dynamics of naturalistic emotion. These insights into emotional brain states will inform the development of novel, brain-based interventions, directly targeting the mechanisms of emotional dysregulation and offering a path forward for improving mental health treatments.