ABSTRACT: Chronic psychological stress triggers and exacerbates major depressive disorder (MDD) and many other psychiatric conditions – causing changes in sleep, eating habits, addictive behaviors, activity levels, circadian rhythms, mood and other domains. The rodent stress response shares many behavioral and physiologic alterations with that of humans. Chronic stress also has broad effects on the brain. But major gaps exist in our knowledge in regard to the integrated behavior and physiology as well as the corresponding brain circuit changes with chronic stress. Prior work has found many behavioral and physiologic phenotypes of stress, but we lack a cohesive sense of how these variables co-evolve over time. Our first aim is to delineate this co-evolution of stress response elements in stressed versus unstressed mice. We will accomplish this by examining mice under a chronic unpredictable stress (CUS) paradigm versus controls in our new naturalistic observation system the “Digital Homecage”. This system allows us to monitor over 50 behavioral measures simultaneously over weeks. Mice will live in these homecages for 8 weeks: 2 weeks baseline, 4 weeks CUS and 2 weeks of recovery. An exploratory element of that aim is to use machine learning to determine a coherent mouse stress biomarker for future quantitative studies. Our next goal is to determine electrophysiologic signatures of chronic stress. It is known that chronic stress alters brain circuit synaptic structure and neuromodulatory balance. It is known that the behavior is controlled by the electrophysiologic state of brain networks and that those networks operate both locally within regions and via coordinated multi-regional transmission. Therefore, we aim to study changes in electrophysiology both within and across regions. We focus on the medial prefrontal cortex, the ventral hippocampus and infralimbic medial prefrontal cortex given their strong involvement in chronic stress. We will implant tetrode arrays into these regions and will record over 8 weeks as above. In Aim 2, we will determine the effects of chronic stress on within-region spiking tendencies including spike rate variability and excitatory- inhibitory balance. In a second part of this aim we will use machine learning applied to a wider variety of within- region dynamical measures to determine a potentially more complete set of differences between CUS and control mice. In our final Aim, we will assess cross-regional coordination between these 3 regions. We will test the hypothesis that pairwise coupling between regions will be altered in a manner consistent with MDD by measuring coupling using both spiking and LFP. Again, we will then use machine learning methods on our large dataset to detect further inter-regional dynamics un-revealed in our hypothesis-driven testing. This mixture of behavior and electrophysiology is done to generate new understanding about chronic stress. We also have a long-term vision of creating large da...