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3. Decoding Distinct Stress States From Brain-Wide Spatiotemporal Dynamics

      Fluctuations in brain-wide 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 in this network originates in prefrontal cortex and ventral striatum, relays through amygdala and ventral tegmental area, and converges 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, these findings reveal a convergent mechanism through which depression vulnerability is mediated in the brain.
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