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 major depressive disorder (MDD)-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 MDD vulnerability. Finally, we demonstrate that this vulnerability network is biologically distinct from the networks that encode dysfunction after stress. Thus, these findings reveal a convergent mechanism through which MDD vulnerability is mediated in the brain. Patterns of brain activity predict vulnerability versus resilience to depression in response to stress.
Bibliographical noteFunding Information:
We would like to thank C. Liston and H.S. Mayberg for comments and C. Choi for technical support. This work was supported by an NIH grant ( MH79201-03S1 to B.D.S.); an NIH grant ( MH79201 ) and the Lennon Family Foundation (to M.G.C.); NIH grants ( MH099192-05S1 to C.B.; MH099192-05S2 to D.E.C.; MH096890 to E.J.N.); and MH099192-05S2 and DARPA HIST Program managed by Dr. Jack Judy (to L.C.). K. Dzirasa received support from a One Mind Institute Rising Star Award, an NIH grant ( MH099192 ), and from Kerima L. Collier . Special thanks to Freeman Hrabowski, Robert and Jane Meyerhoff, and the Meyerhoff Scholarship Program.
© 2018 Elsevier Inc.
- spatiotemporal dynamics
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)