# Decoding ensemble dynamics from cortico-amygdalar circuits during social choice

> **NIH NIH K08** · YALE UNIVERSITY · 2023 · $192,402

## Abstract

Project Summary
Social relationships are a key component of human health and survival and impairments in social
behavior have a major impact in many psychiatric conditions. Yet despite the importance of social
context to health, there remains no FDA-approved medications that target social cognition and
behavior. Social context is defined by the social stimuli available to an animal, is a key mediator
of behavior in rodents, and impacts social choices. Yet little is known about how neuronal circuits
encode social context and choice.In rodents, circuits in ACC that project to the amygdala (ACC-
BLA) and Nucleus accumbens (ACC-Nac) have been shown to be necessary for different aspects
of social information transfer. However, how neural activity in these regions encode social context
and choice is not known. We developed a social choice paradigm in which mice choose access
to a novel or a familiar/cagemate mouse. In this novel paradigm, mice consistently show
preference for a social target over a novel object, but they show variable individual biases in social
choice between a novel and cagemate conspecific. Recording neural activity from the ACC-BLA
and ACC-Nac circuits during this behavioral paradigm will allow for a more nuanced
understanding of the neural mechanisms underlying social choice. In order to better understand
how activity recorded during this and traditional social behavioral paradigms we will leverage
recently developed statistical models and inference algorithms to cluster nonlinear dynamical
neural responses into an unspecified number of functional sub-groups called Functional Encoding
Units (FEUs). We will also apply deep learning tools for behavioral analysis to engage in joint
modeling of neural and behavioral data. This will enhance our ability to predict social context and
enrich encoding of social behavior. Lastly, given the impact of 3,4-Methylenedioxy
methamphetamine (MDMA) on social behavior and empathy and its recent clinical significance in
post traumatic stress disorder, we hypothesize that MDMA paired social exposure will bias social
choice in our paradigm. We will apply deep learning to behavioral analysis of our paradigm in
order to test this hypothesis. Through this K-Award we will define how social stimuli are encoded
in a context-specific manner within ACC-BLA and ACC-Nac circuits during social choice and how
MDMA biases these social choices. In parallel, intensive mentoring, directed readings, and
structured coursework will enhance my skills and toolkit in computational modeling and machine
learning-based analysis of both neural and behavioral data, and behavioral pharmacology, setting
the stage for independence.

## Key facts

- **NIH application ID:** 10723932
- **Project number:** 1K08MH134028-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** AZA Stephen Allsop
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $192,402
- **Award type:** 1
- **Project period:** 2023-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10723932

## Citation

> US National Institutes of Health, RePORTER application 10723932, Decoding ensemble dynamics from cortico-amygdalar circuits during social choice (1K08MH134028-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10723932. Licensed CC0.

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