权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Decoding ensemble dynamics from cortico-amygdalar circuits during social choice

在社会选择过程中从皮质-杏仁核回路解码整体动态

基本信息

批准号：
10723932
负责人：
AZA Stephen Allsop
金额：
$ 19.24万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10723932
关键词：
Absence of pain sensation Acute Algorithms Amygdaloid structure Animals Anterior Bayesian Modeling Behavior Behavioral Behavioral Model Behavioral Paradigm Clinical Computer Models Computing Methodologies Data Decision Making Dimensions Empathy Environment FDA approved Feeling Goals Health Human Impairment Intervention Joints K-Series Research Career Programs Machine Learning Measures Mediator Mentors Methamphetamine Methods Mus Neurons Neurosciences Nonlinear Dynamics Nucleus Accumbens Pain Partner in relationship Pharmaceutical Preparations Post-Traumatic Stress Disorders Primates Research Research Personnel Rewards Rodent Role Social Behavior Social Change Social Environment Social Interaction Social Processes Space Models Statistical Models Stimulus Stress Structure Subgroup System Testing Training behavioral pharmacology career cingulate cortex clinically significant deep learning deep learning model ecstasy experience experimental study individual variation innovation neural neural circuit neural model neuromechanism neuronal circuitry neuropsychiatry neurotransmission novel optogenetics pharmacologic preference response skills social social anxiety social bias social cognition social learning social relationships tool vector

项目摘要

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.

项目总结