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.

项目概要： 要了解神经精神疾病，必须将行为表型与改变联系起来。 大脑功能和大脑回路。然而，大多数神经精神疾病表现出一系列 表型和高变异性使得与神经相关因素的关联变得困难。 自闭症等神经精神疾病动物模型的开发 通过连锁分析促进基因发现。药物治疗也模型 啮齿动物中这些疾病的各个方面。然而，综合的行为和生理 分析非常耗时。此外，常见的行为和生理测试是 在需要处理啮齿动物的专门设备中进行，这反过来又会影响 他们的表现并导致实验者和环境引起的变异。这是这个 很难观察同一动物的行为和神经活动等多种指标 不受干扰，可以捕获疾病进展的发展轨迹和/或 治疗干预。我们的目标是通过开发一种新的、灵活的解决方案来解决这个问题 可以在其家笼环境中持续评估小鼠的平台 一系列行为测试以及神经生理学测量（脑功能成像） 和光遗传学干预。 我们的系统将包括对这些区域内大脑活动的自主监测和控制 家庭笼子中的刺激用于评估听觉感觉处理。这 家庭笼子环境与听觉学习范式的结合提供了一个单一的 用于培训、评估和潜在治疗操作的平台。我们的战略将 合作建造每笼最多可容纳 10 只小鼠的家用笼子 持续长达 1 年。在这段时间里，动物们将开始自我引导的试验 大脑成像以及听觉处理评估。 我们系统的一个核心特征是使用占用空间相对较小的开源 Linux—— 基于使用单板 Raspberry Pi 和 Arduino 计算机的计算机工具。这些 系统将协同工作，实现灵活的家笼训练和评估系统。 第一个目标是完善硬件和软件以实现跨实验室协作 然后将这些工具更广泛地传播到更广泛的神经科学界。 我们将在先前展示过的特定小鼠品系上测试家庭笼子 模拟自闭症样行为并揭示行为、认知和行为的共同变化 各种模型内部和之间的生理缺陷。

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