The project goals are to use functional MRI along with functional/effective connectivity analysis tools to better characterize neural networks involved in emotion regulation (ER) and to identify ER network abnormalities in adolescent Major Depressive Disorder (MDD) - a highly prevalent, recurrent psychiatric disorder whose debilitating and typically chronic nature is highly relevant to public health. The knowledge provided by the proposed detailed analysis of ER network brain region interactions will provide important neuroscientific understanding about how affective and cognitive brain systems interact to modulate emotion in various ways. This will be useful not only for depression, but also to research of numerous other psychiatric disorders.
Dozens of previous functional neuroimaging studies have identified which brain regions are reliably engaged during deliberate, conscious attempts to modify an already-elicited emotional response by cognitively re- appraising the emotional significance of a stimulus to either minimize negative, or maximize positive reactions. However, there is surprisingly little understanding of how they work together in a coordinated neural system, or how abnormalities in network connectivity underlie the problems regulating emotion often found in Major Depressive Disorder (MDD). In this project, we will use functional magnetic resonance imaging (fMRI) and a series of 'effective connectivity' fMRI timeseries analyses to characterize how the interactions among all the major emotion regulation (ER) brain regions are modulated during different types of cognitive reappraisal ER. We not only will identify which among many plausible possible models of 'top-down' control best describes ER network architecture, we also will determine which specific PFC amygdala connections within that model most strongly predict ER success. The detailed understanding of typical ER network function gained by these analyses then will be used to help describe ER network dysfunction in MDD. Numerous lines of research clearly show the relevance of ER to mood disorder risk and brain dysfunction, as well as the potential of ER as a treatment target. While a few functional neuroimaging studies have begun to characterize ER brain region activity abnormalities, no study has yet examined ER network connectivity dysfunction in MDD, which ultimately will likely prove more informative to advance understanding than analysis of simple activation levels. Finally, we will take some initial steps towards validating ER network connectivity weaknesses as a possible biomarker that denotes depression risk. Overall, the project has the potential to make several important theoretical advances in understanding a commonly-used, typically-successful form of ER, as well as make translational advances in understanding the nature of neurobiological dysfunction in psychiatric disorders involving disordered emotional control. Upon completion, we will provide the field a validated neural network model of reappraisal ER which can be used to inform the future study of numerous psychiatric disorders that have been reliably linked to impaired ER. This project's central focus on depicting complex network function and network conceptualizations of psychiatric pathophysiology represents a major paradigm shift in understanding ER brain function and dysfunction.
|Stevens, Michael C (2016) The contributions of resting state and task-based functional connectivity studies to our understanding of adolescent brain network maturation. Neurosci Biobehav Rev 70:13-32|