# Generating pro-resilient states through individualized circuit read-write therapeutics

> **NIH NIH DP2** · PRINCETON UNIVERSITY · 2020 · $2,430,000

## Abstract

Project Summary
 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.

## Key facts

- **NIH application ID:** 10002574
- **Project number:** 1DP2MH126375-01
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Annegret Lea Falkner
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $2,430,000
- **Award type:** 1
- **Project period:** 2020-09-07 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002574

## Citation

> US National Institutes of Health, RePORTER application 10002574, Generating pro-resilient states through individualized circuit read-write therapeutics (1DP2MH126375-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10002574. Licensed CC0.

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