EAGER: Neural Behavioral Analysis (NBA) Pipeline for Behavior and Neural Activity Analysis in Autism

EAGER：用于自闭症行为和神经活动分析的神经行为分析 (NBA) 流程

• 批准号: 2035018
• 负责人: Qian Chen
• 金额: $ 20万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035018&HistoricalAwards=false
• 关键词: EAGER; Neural; Behavioral; Analysis; NBA

Naturalistic behaviors are external reflections of brain's internal integration of bottom up processes that mediate inputs sent to the brain, and top down processes that mediate appropriate responses determined by the brain. Measuring behavioral data without concurrent neuronal activity monitoring would only provide an incomplete picture of brain function. One bottleneck in the field is that these behavioral data and neuronal activity data are typically collected separately under different experimental paradigms and subsequently analyzed with different analytical pipelines. It is, therefore, impractical to infer mechanistic correlation between behavior and neural activity using these existing pipelines. A system that enables simultaneous collection of behavior and neuronal activity data followed by integrated decoding of these two types of data would be a breakthrough that offers unique opportunities to explore behavior and its governing neuron activity pattern. This project will take advantage of a clinically relevant mouse model of autism, to develop a novel machine learning based pipeline for simultaneous decoding of behavioral and neuronal activity data. By providing an novel and integrated data analytic toolkit which enables analysis of high-dimensional large data sets of behaviors and neural activities in an autistic mouse model, this project will delineate the temporal and spatial pattern of neural activities underlying social deficits and sensory abnormities which might provide a novel mechanistic link between those two keys symptoms of autism. Besides providing a powerful and versatile toolkit, this project will help fill in the critical knowledge gap between brain circuit functional changes and social behavioral deficits in autism, with the potential of strong impact on other psychiatry disorders, such as schizophrenia and bipolar disorders.By developing a novel machine learning based pipeline to enable simultaneous analysis of animal behaviors and neuronal activities, this project will focus on addressing the following three research challenges: (1) concurrently collecting large data sets of mouse social behaviors and neural activity in different brain areas using intravital calcium imaging, which will be used to establish and optimize the machine learning-based neural behavioral analysis pipeline, (2) validating the neural behavioral analysis pipeline in an autistic mouse model, (3) using the neural behavioral analysis pipeline to interrogate mouse behavior and calcium imaging data from different brain areas and infer causal relation between neural activity pattern and autism-like behavior traits. Using unbiased machine learning algorithms to extract videotaped behavioral and neural imaging data in a high-throughput manner, this project will be able to make sense of neural circuit data in the context of complex behavior deficits. Successful execution of the proposal will establish a general "computational behavior-neural function" framework capable of identifying the hierarchy of social deficits and sensory abnormalities in autistic mice, which will provide a powerful tool to the field to untangle complex animal behavior and neuronal activity pattern for mechanistic exploration.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自然主义行为是大脑内部整合的外部反映，自下而上的过程调解发送给大脑的输入，自上而下的过程调解由大脑决定的适当反应。在不同时监测神经元活动的情况下测量行为数据只能提供一个不完整的大脑功能图像。该领域的一个瓶颈是，这些行为数据和神经元活动数据通常是在不同的实验范式下分别收集的，随后用不同的分析管道进行分析。因此，利用这些现有的管道来推断行为和神经活动之间的机制相关性是不切实际的。一个系统能够同时收集行为和神经元活动数据，然后对这两种类型的数据进行综合解码，这将是一个突破，为探索行为及其控制神经元活动模式提供了独特的机会。该项目将利用临床相关的自闭症小鼠模型，开发一种新的基于机器学习的管道，用于同时解码行为和神经元活动数据。通过提供一种新颖的集成数据分析工具包，可以分析自闭症小鼠模型中行为和神经活动的高维大数据集，该项目将描绘社会缺陷和感觉异常背后的神经活动的时空模式，这可能为自闭症这两个关键症状之间提供一种新的机制联系。除了提供一个强大而多功能的工具包外，该项目将有助于填补自闭症患者大脑回路功能变化和社会行为缺陷之间的关键知识空白，并有可能对其他精神疾病，如精神分裂症和双相情感障碍产生重大影响。通过开发一种新的基于机器学习的管道来同时分析动物行为和神经元活动，该项目将专注于解决以下三个研究挑战：(1)利用活体钙成像技术同时收集小鼠不同脑区社交行为和神经活动的大数据集，用于建立和优化基于机器学习的神经行为分析管道；(2)在自闭症小鼠模型上验证神经行为分析管道；(3)利用神经行为分析管道查询小鼠不同脑区的行为和钙成像数据，推断神经活动模式与自闭症样行为特征之间的因果关系。使用无偏机器学习算法以高通量的方式提取视频行为和神经成像数据，该项目将能够在复杂行为缺陷的背景下理解神经回路数据。该建议的成功实施将建立一个通用的“计算行为-神经功能”框架，能够识别自闭症小鼠的社会缺陷和感觉异常的层次，这将为该领域解开复杂的动物行为和神经元活动模式提供一个强大的工具，用于机械探索。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。