BRAIN EAGER: Integrative Cross-Modal and Cross-Species Brain Models: Motivation and Reward

BRAIN EAGER：综合跨模式和跨物种大脑模型：动机和奖励

• 批准号: 1451017
• 负责人: Katherine Heller
• 金额: $ 30万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451017&HistoricalAwards=false
• 关键词: BRAIN; EAGER; Integrative; Cross; Modal

Motivation translates goals into action, and significantly impacts cognition along several dimensions. The motivation for reward biases attention, perception, and memory, and enhances learning, with effects evidenced behaviorally as well as in specific brain regions. However, given such broad effects of reward motivation, the question remains: how does reward motivation propagate throughout the brain and how does it dynamically change the greater neural circuitry to prime us to behave appropriately? In order to answer these questions we develop statistical models for the effect of motivation on neural circuitry, which combine data recorded using different instruments across a variety of species. A fuller understanding of the neural effects of motivation would elucidate how it impacts important cognitive processes, yielding insights that are useful for better performance in various arenas, from education to therapy. In order to gain a more complete understanding of the neural network dynamics underlying the behavioral and cognitive effects of motivation, it is necessary to integrate research in human subjects, and in animal models. While extensive and crucial research has been carried out on reward motivation separately in humans and animal models, there is a clear need for improved translation across species. An overarching analytical framework for translation is extremely important, due to the complexity of the problems being addressed, and to leverage the strengths offered by each species and available technology. The aim of this work is the development of dynamic, hierarchical Bayesian models to discover functional neural networks that can translate across species and data collection modalities. Bayesian models of human behavior, and Bayesian machine learning inspired methods for neural network modeling, have been extremely successful, making Bayesian methods fertile ground for explorations into translational neural network discovery.

动机将目标转化为行动，并在沿着几个维度显著影响认知。奖励的动机会使注意力、感知和记忆产生偏差，并增强学习能力，其效果在行为上和特定的大脑区域都有体现。 然而，考虑到奖励动机的广泛影响，问题仍然存在：奖励动机如何在整个大脑中传播，以及它如何动态地改变更大的神经回路，以引导我们做出适当的行为？为了回答这些问题，我们开发了动机对神经回路影响的统计模型，该模型结合了使用不同仪器记录的各种物种的联合收割机数据。更全面地了解动机的神经效应将阐明它如何影响重要的认知过程，从而产生有助于在从教育到治疗的各个领域取得更好表现的见解。为了更全面地了解动机的行为和认知效应背后的神经网络动力学，有必要整合人类受试者和动物模型的研究。虽然在人类和动物模型中分别对奖励动机进行了广泛而重要的研究，但显然需要改进跨物种的翻译。翻译的总体分析框架是非常重要的，因为要解决的问题的复杂性，并利用每个物种和可用技术提供的优势。这项工作的目的是开发动态的、分层的贝叶斯模型，以发现可以跨物种和数据收集模式翻译的功能神经网络。人类行为的贝叶斯模型和贝叶斯机器学习启发的神经网络建模方法非常成功，使贝叶斯方法成为探索转化神经网络发现的肥沃土壤。