A latent variable model for quantifying social behavior in rodents

用于量化啮齿类动物社会行为的潜变量模型

• 批准号: 10535865
• 负责人: Iris Stone
• 金额: $ 3.08万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-17 至 2025-09-16
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10535865
• 关键词: Accounting; Address; Affect; Aggressive behavior; Animal Behavior; Animals; Automobile Driving; Behavior; Behavioral; Calcium; Categories; Cells; Chronic; Complex; Computer Models; Computing Methodologies; Corpus striatum structure; Cues; Data; Data Set; Disease Marker; Environment; Fiber; Foundations; Genetic; Glutamates; Goals; Habenula; Human; Hybrids; Individual; Intervention; Joints; Label; Lateral; Learning; Link; Location; Measures; Mental Depression; Mental disorders; Methods; Modeling; Motor; Mus; National Institute of Mental Health; Nature; Neurologic; Neurons; Nucleus Accumbens; Output; Pattern; Photometry; Population; Positioning Attribute; Posture; Prevention; Process; Research; Resolution; Rewards; Rodent; Running; Schizophrenia; Sensory; Shapes; Social Behavior; Social Environment; Social Interaction; Statistical Models; Stimulus; Stochastic Processes; Stress; Structure; Tail; Techniques; Time; Work; autism spectrum disorder; base; behavioral response; body position; computational neuroscience; computerized tools; experience; flexibility; high dimensionality; improved; interest; markov model; mental state; neural circuit; neural correlate; nonlinear regression; novel; programs; relating to nervous system; response; sensory input; social; social defeat; unsupervised learning

PROJECT SUMMARY Computational methods for quantifying mammalian natural behavior, including social interactions, are crucial for developing a sophisticated understanding of the neural basis of behavior. Yet a full description of behavior consists of much more than an animal’s actions. External cues (such as the actions of a social partner) drive our behavioral responses, and our responses to those cues can depend on context, our internal mental state, and prior experience. We may approach an individual when we feel safe, or attack that same individual when we feel threatened. The resulting complexity makes natural behavior — and social interactions in particular — challenging to study. To overcome this barrier, I propose to develop broadly applicable models to predict natural and social behavioral dynamics in mice based on changing external cues and internal states. These models will use unsupervised learning techniques to quantify and predict complex patterns of behavior in an interpretable manner while linking social behaviors to changes in neural activity across multiple timescales. Together, these models will provide an unprecedented view of how different neural populations encode the internal states that shape social behaviors as they unfold over time. The first aim is to fit a set of increasingly complex datasets with flexible latent-state models that describe how natural and social behaviors arise in response to factors such as external cues and time-varying internal states. In the second aim, I will apply this modeling framework to calcium recordings in dopaminergic projections to the Nucleus Accumbens and Tail of the Striatum as well as glutamatergic cell bodies in the Lateral Habenula — all neural populations shown to respond in social contexts. I will determine how these neural populations differentially encode sensory inputs, internal states, and behavioral outputs. I will also examine how the activity in each neural population correlates with transitions between different behaviors and internal states and how these representations change with experience. The proposed work will break new ground by applying novel computational tools and sophisticated, unsupervised behavioral quantification methods to discover the internal and external variables that shape natural behaviors as well as the underlying neural correlates of social interactions. Together, the new computational modeling techniques that I am proposing will advance several goals of the NIMH Theoretical and Computational Neuroscience Program: they (1) contain distinct levels of analysis, (2) link neuronal and behavioral processes, (3) enhance predictions of high-resolution behavioral data along with neural units of analysis, and (4) provide effective explanatory techniques and methods of interpretation for their results.

项目摘要 量化哺乳动物自然行为（包括社会互动）的计算方法对于 发展对行为的神经基础的复杂理解。但对行为的完整描述 不仅仅是动物的行为外部线索（如社交伙伴的行为）驱动我们的行为。 行为反应，我们对这些线索的反应可能取决于上下文，我们的内部心理状态， 以前的经验。当我们感到安全时，我们可能会接近一个人，或者当我们感到安全时，我们可能会攻击同一个人。 威胁。由此产生的复杂性使得自然行为--特别是社会互动-- 挑战学习。为了克服这一障碍，我建议开发广泛适用的模型来预测自然 以及基于变化的外部线索和内部状态的小鼠社会行为动力学。这些模型将 使用无监督学习技术来量化和预测复杂的行为模式， 同时将社交行为与多个时间尺度上的神经活动变化联系起来。所有这些 模型将提供一个前所未有的观点，不同的神经群体如何编码的内部状态， 随着时间的推移塑造社会行为。第一个目标是将一组日益复杂的数据集与 灵活的潜在状态模型，描述自然和社会行为如何响应诸如 外部线索和随时间变化的内部状态。在第二个目标中，我将把这个建模框架应用于钙 记录多巴胺能投射到伏隔核和纹状体的尾部，以及 外侧缰核中的神经元能细胞体--所有的神经元群体都表现出在社会环境中的反应。 我将确定这些神经群体如何对感觉输入、内部状态和行为进行差异编码。 产出我还将研究每个神经群的活动如何与不同神经元之间的转换相关。 行为和内部状态，以及这些表征如何随着经验而变化。拟议的工作将 通过应用新颖的计算工具和复杂的，无监督的行为， 量化方法，以发现塑造自然行为的内部和外部变量，以及 社会互动的潜在神经关联。总之，新的计算建模技术，我 我的建议将推进NIMH理论和计算神经科学计划的几个目标： 它们（1）包含不同层次的分析，（2）连接神经元和行为过程，（3）增强预测 高分辨率行为数据沿着神经分析单元，以及（4）提供有效的解释 解释其结果的技术和方法。