A convergent depression vulnerability pathway encoded by emergent spatiotemporal dynamics
Fluctuations in brain local field potential (LFP) oscillations reflect emergent network-level signals that mediate behavior. Cracking the code whereby these LFP oscillations coordinate in time and space (spatiotemporal dynamics) to represent complex behaviors would provide fundamental insights into how the brain signals emotional processes at the network level. Here we use machine learning to integrate LFP activity acquired concurrently from seven cortical and subcortical brain regions into an analytical model that predicts the emergence of depression-related behavioral dysfunction across individual mice subjected to chronic social defeat stress. We uncover a spatiotemporal dynamic network in which activity originates in prefrontal cortex (PFC) and nucleus accumbens (NAc, ventral striatum), relays through amygdala and ventral tegmental area (VTA), and converges in ventral hippocampus (VHip). The activity of this network correlates with acute threat responses and brain-wide cellular firing, and it is enhanced in three independent molecular-, physiological-, and behavioral-based models of depression vulnerability. Finally, we use two antidepressant manipulations to demonstrate that this vulnerability network is biologically distinct from the networks that signal behavioral dysfunction after stress. Thus, corticostriatal to VHip-directed spatiotemporal dynamics organized at the network level are a novel convergent depression vulnerability pathway.