Abstract
Depression has been linked to cortico-limbic brain regions, and ketamine is known for its rapid antidepressant effects. However, how these brain regions encode depression collaboratively and how ketamine regulates these regions to exert its prompt antidepressant effects through mesoscale brain-wide fluctuations remain elusive. In this study, we used a multidisciplinary approach, including multi-region in vivo recordings in mice, chronic social defeat stress (CSDS), and machine learning, to construct a Mesoscale Brain-Wide Fluctuation Analysis platform (MBFA-platform). This platform analyzes the mesoscale brain-wide fluctuations of multiple brain regions from the perspective of local field potential oscillations and network dynamics. The decoder results demonstrate that our MBFA platform can accurately classify the Control/CSDS and ketamine/saline-treated groups based on neural oscillation and network activities among the eight brain regions. We found that multiple-region LFPs patterns are disrupted in CSDS-induced social avoidance, with the basolateral amygdala playing a key role. Ketamine primarily exerts the compensatory effects through network dynamics, contributing to its rapid antidepressant effect. These findings highlight the MBFA platform as an interdisciplinary tool for revealing mesoscale brain-wide fluctuations underlying complex emotional pathologies, providing insights into the etiology of psychiatry. Furthermore, the platform's evaluation capabilities present a novel approach for psychiatric therapeutic interventions.