Tuesday, October 16, 2018 - 4:00pm to 5:00pm
Greene Science Center, 9th Floor, 3227 Broadway, New York
Abstract: Brain-wide fluctuations in local field potential oscillations reflect emergent network-level signals that mediate behavior. Cracking the code whereby these oscillations coordinate in time and space (spatiotemporal dynamics) to represent complex behaviors would provide fundamental insights into how the brain signals emotional pathology. Using machine learning, we discover a spatiotemporal dynamic network that predicts the emergence of depression-related behavioral dysfunction in mice subjected to chronic social defeat stress. Activity patterns in this network originate in prefrontal cortex and ventral striatum, relay through amygdala and ventral tegmental area, and converge in ventral hippocampus. This network is increased by acute threat, and it is also enhanced in three independent models of depression vulnerability. Finally, we demonstrate that this vulnerability network is biologically distinct from the networks that encode dysfunction after stress. Thus, we reveal a convergent mechanism through which depression vulnerability is mediated in the brain. We also demonstrate a novel strategy for linking mesoscale brain states to emotional behavior.
Kafui Dzirasa, MD, PhD
Duke University Medical Center