HIGH THROUGHPUT HOME CAGE PLATFORMS FOR INVESTIGATING NEUROPSYCHIATRIC DISORDERS IN MICE

用于研究小鼠神经精神疾病的高通量家用笼式平台

• 批准号: 10325608
• 负责人: PATRICK O KANOLD
• 金额: $ 5.05万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-11 至 2023-09-10
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325608
• 关键词: THROUGHPUT; HOME; CAGE; PLATFORMS; INVESTIGATING

Project Summary: To understand neuropsychiatric disorders behavioral phenotypes have to be linked to altered brain function and brain circuits. However, most neuropsychiatric disorders show a range of phenotypes and high variability making the association with neurological correlates difficult. Development of animal models of neuropsychiatric disorders such as autism, have been facilitated by gene discovery through linkage analysis. Pharmacological treatments also model aspects of these disorders in rodents. However, comprehensive behavioral and physiological analysis is very time consuming. Moreover, common behavioral and physiological testing is performed in specialized apparatuses requiring handling of the rodents which in turn can affect their performance and lead to experimenter and environment induced variability. This it is difficult to observe multiple measures such as behavioral and neural activity in the same animal without perturbation and can capture developmental trajectories of disease progression and/or therapeutic intervention. We here aim to solve this problem by developing a new, flexible platform in which mice can be continually assessed within their home cage environments with a barrage of behavioral tests as well as neurophysiological measures (brain functional imaging) and optogenetic intervention. Our system will include autonomous monitoring and control of brain activity within these home cages in the context of stimuli used to assess auditory sensory processing. The combination of the home cage environments with auditory learning paradigms provides a single platform for both training, assessment, and potential therapeutic manipulation. Our strategy will employ a collaborative effort to build home cages for up to 10 mice/cage, which can be housed continually for up to a 1-year period. Over this time, the animals will initiate self-directed trials of brain imaging, as well as auditory processing assessments. A core feature of our system is the usage of relatively small footprint open source, Linux- based computer tools employing single board Raspberry Pi and Arduino computers. These systems will work together to enable the flexible home cage training and assessment system. The first goals are to refine the hardware and software to enable cross-laboratory collaboration and then wider dissemination of these tools to the broader neuroscience community. We will test the home cages on specific lines of mice which have previously been shown to model autistic-like behaviors and uncover co-variation of behavioral, cognitive, and physiological deficits within and across the various models.

项目摘要： 要了解神经精神疾病的行为表型必须与变化有关 大脑功能和大脑电路。但是，大多数神经精神疾病都显示 表型和高变异性使与神经系统的关联很难相关。 自闭症等神经精神疾病的动物模型的发展已是 通过链接分析，基因发现促进。药理治疗也模型 啮齿动物中这些疾病的各个方面。但是，全面的行为和生理 分析非常耗时。而且，常见的行为和生理测试是 在需要处理啮齿动物的专业设备中进行 它们的性能并导致实验者和环境引起的可变性。这是 很难观察到多种措施，例如同一动物的行为和神经活动 没有扰动，可以捕获疾病进展的发展轨迹和/或 治疗干预。我们在这里旨在通过开发一个新的灵活性来解决这个问题 可以在其家庭笼环境中不断评估小鼠的平台 行为测试的弹幕以及神经生理学措施（脑功能成像） 和光遗传学干预。 我们的系统将包括自主监测和控制这些内部的大脑活动 在用于评估听觉感觉处理的刺激的背景下，家用笼子。这 家庭笼环境与听觉学习范式的结合提供了一个 培训，评估和潜在治疗操作的平台。我们的策略将 采取协作努力来建造最多10只鼠标/笼子的房屋笼子，可以容纳 不断长达1年。在这段时间里，动物将开始进行自我指导的试验 大脑成像以及听觉处理评估。 我们系统的核心功能是使用相对较小的足迹开源，Linux- 采用单板Raspberry Pi和Arduino计算机的基于计算机工具。这些 系统将共同努力，使灵活的家居笼培训和评估系统。 第一个目标是完善硬件和软件以实现跨劳动协作 然后将这些工具更广泛地传播给更广泛的神经科学社区。 我们将在以前显示的特定小鼠上测试家用笼子 建模类似自闭症的行为并发现行为，认知和 各种模型内部和跨模型的生理缺陷。

项目成果

A three-dimensional virtual mouse generates synthetic training data for behavioral analysis.

• DOI: 10.1038/s41592-021-01103-9
• 发表时间: 2021-04
• 期刊: Nature methods
• 影响因子: 48
• 作者: Bolaños LA;Xiao D;Ford NL;LeDue JM;Gupta PK;Doebeli C;Hu H;Rhodin H;Murphy TH
• 通讯作者: Murphy TH

MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.

• DOI: 10.1038/s41467-021-26255-2
• 发表时间: 2021-10-13
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Xiao D;Forys BJ;Vanni MP;Murphy TH
• 通讯作者: Murphy TH