CRCNS:Predictability as a New Paradigm for Rodent Social Neurobiology

长期的社会依恋是在与另一个人积极互动的过程中建立起来的。 形成这样的社会纽带涉及认知过程，如感知显著的感官线索，学习他们的行为， 积极的价值观，并选择适当的亲社会行为。阐明这种机制， 复杂的自然行为是NIMH的战略目标。我们的建议将通过以下方式推进这一目标： 在社会过程、积极效价和认知的RDoC领域内贡献新知识 系统.我们的长期目标是能够更深入地了解分子机制 影响神经回路，这些神经回路是建立持久的社会关系所必需的。 债券我们的目标是使用刻板印象和可预测性的原则来阐明行为 和神经动力学的基础上的社会联系在草原田鼠（田鼠ochrogaster），一个总理 揭示社会依恋的遗传和神经内分泌机制的系统。我们将测量 社会行为和纹状体，皮层和杏仁核神经活动在长时间尺度和建立 社会动态的预测模型导致了一种联系。我们的中心假设是， 刻板的社会行为和相应的神经活动可以从潜在的内部状态预测， “成对结合”;并且对这种潜在状态的动态建模将有助于预测未来的社会 配对的草原田鼠之间的相互作用我们将量化这种相互作用的可预测性， 使用过去的社会行为（目标1）和行为特定的动态行为相关的社会背景 社会脑神经网络中这些关键节点之间的功能连接（目标2）。我们的研究将 通过验证一个新的，定量的框架来研究社会的“暗物质”， 神经科学，基于这样的想法，即在一个人的动态过程中存在可预测性， 通过刻板的社会互动的行为空间的轨迹。通过建立可预测性作为 啮齿动物社会神经生物学的新范式，我们的研究将因此推进一个全面的框架， 如何思考社会缺陷以及如何鼓励亲社会行为。

A long-lasting social attachment is built over a course of positive interactions with another individual. Forming such social bonds involves cognitive processes like perceiving salient sensory cues, learning their positive value, and selecting appropriately prosocial behavioral actions. Elucidating the mechanisms of such a complex natural behavior is a Strategic Objective of the NIMH. Our proposal will advance this Objective by contributing new knowledge within the RDoC domains of Social Processes, Positive Valence and Cognitive Systems. Our long-term goal is to enable a more vertical understanding of how molecular mechanisms influence neural circuits underlying moment-by-moment processes that must occur to build enduring social bonds. Our objective here is to use the principles of stereotypy and predictability to elucidate the behavioral and neural dynamics that underlie social bonding in the prairie vole (Microtus ochrogaster), a premier system for uncovering genetic and neuroendocrine mechanisms of social attachments. We will measure social behaviors and striatal, cortical and amygdalar neural activity over long time scales and build predictive models of the social dynamics leading to a bond. Our central hypothesis is that trajectories of stereotyped social behaviors and corresponding neural activity are predictable from a latent internal state of "pair-bondedness"; and that modeling the dynamics of this latent state will help predict future social interactions between pair-bonded prairie voles. We will quantify the predictability of such interactions in ethologically-relevant social contexts using past social behavior (Aim 1) and behavior-specific dynamic functional connectivity between those key nodes in the social brain neural network (Aim 2). Our research will have a positive impact by validating a novel, quantitative framework for studying the "dark matter" of social neuroscience, grounded in the idea that there is predictability in the dynamic processes that underlie one's trajectory through a behavioral space of stereotypical social interactions. By establishing predictability as a new paradigm for rodent social neurobiology, our studies will thus advance a comprehensive framework for how to think about social deficits and how to encourage prosocial behavior.