Project Summary
Depression is a complex heterogenous psychiatric disorder that impacts multiple brain systems. Human imaging studies have described aberrant spatiotemporal dynamics in specific brain networks across subjects with major depressive disorder. Furthermore, rodent studies have identified dysfunctional synchrony across cortical limbic circuits in genetic and stress-induced models of major depressive disorder. Nevertheless, it remains to be clarified whether these observed changes in neural dynamics play a causal role or simply reflect (i.e., correlate with) the behavioral state observed in major depressive disorder. Several major challenges to addressing this question exist. 1) The brain synchronizes dynamics across multiple timescales. Rodent studies classically monitor dynamics at the millisecond time scale (reflecting circuits), and human studies typically monitor brain dynamics at the seconds time scale (reflect circuit and network level activity). 2) Rodent studies are generally limited in their ability to monitor large-scale activity from many brain regions concurrently, while human imaging studies observe activity across the whole brain. 3) To our knowledge, few approaches/models integrate changes in cell-type specific gene expression implicated in depression to changes in circuit and network-specific brain dynamics. 4) Techniques which directly manipulate brain dynamics (neural synchrony and cross-frequency coupling) have yet to be largely implemented throughout the rodent research community.
Our current studies are utilizing multi-circuit in vivo neural recordings in the two widely used rodent models of depression. Using machine learning, we are determining the spatiotemporal dynamic alterations that are shared between the two models, and we are testing whether cellular-molecular manipulations implicated in major depressive disorder are sufficient to induce the same spatiotemporal dynamic alterations. Finally, we will verify that these spatiotemporal dynamics are causal by directly inducing and suppressing them and measuring their impact on behavior.
This strategy will yield an unprecedented understanding of how altered dynamics within specific brain circuits contribute to depression, and ultimately facilitate the next generation of antidepressant treatments based on direct brain stimulation.