Major depressive disorder (MDD) and associated anxiety disorders are the most prevalent and costly mental illnesses in the United States, with health spending on treatment recently exceeding $71 billion per year. It is now well established that MDD represents a spectrum of disorders, but current drug based approaches to treatment are temporally nonselective, and their efficacy varies highly across individuals. In this proposal, we explore a ?novel individualized intervention strategy,? wherein we aim to prevent and reverse MDD through closed-loop behavioral and neural circuit ?tuning?. While some individuals develop MDD as a result of a stressful life event, other individuals appear more resilient to stress-induced depression. Our goal in this proposal is to leverage recent advances in machine learning to identify and detect specific pro-resilient behaviors and patterns of activation in resilient individuals, and then use these data to ?steer? susceptible individuals into pro-resilient states. We will accomplish this in two phases. In the first phase, we will test whether modification of behavior alone can generate a pro-resilient state. We will take a novel quantitative approach to behavior analysis, using machine learning to identify specific micro behaviors that are unique to resilient individuals during a chronic social stress. Then, to test whether promoting these behaviors can provide depression-protective effects, we will then use a closed-loop strategy to detect ongoing behavior, and reinforce identified pro-resilient micro behaviors. Second, we will perform circuit-wide calcium recordings in the brain?s subcortical social behavior network and perform unsupervised detection of pro-resilience circuit motifs across the population. We will then use a novel closed-loop read-write strategy to optogentically ?tune? the circuit dynamics to mimic these pro-resilient states. We will further explore how these interventions can be accomplished at various time points relative to a stressful life event (before, during, and after) to test whether circuit intervention can potentially provide protective or restorative treatment. These data can potentially be used to develop novel behavior-based therapies for MDD, or to significantly refine the current use of deep-brain stimulation in order to generate pro-resilient states.

Public Health Relevance

Depression and associated anxiety disorders are the most prevalent and costly mental illnesses in the United States, and current drug-based approaches to treatment are expensive, temporally nonspecific, and often ineffective. Here, we propose to explore whether we can prevent and reverse outcomes associated with stress-induced depression through a novel individualized closed-loop intervention strategy. To do this, we will leverage recent advances in machine learning to longitudinally track the behaviors and underlying neural dynamics of ?super resilient? individuals who are resistant to stress-induced depression and reinforce these behavior-based patterns in depressed individuals in order to promote pro-resilience states.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
NIH Director’s New Innovator Awards (DP2)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Buhring, Bettina D
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Princeton University
United States
Zip Code