Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10531473
• 负责人: BOOIL JO
• 金额: $ 15.72万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-06 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10531473
• 关键词: Age; Animal Genetics; Animal Model; Behavior; Behavioral; Behavioral Symptoms; Biological; Calibration; Chick; Child; Clinical; Cognition; Cognitive; Collection; Comparative Biology; Data; Data Analyses; Data Analytics; Data Set; Development; Diphenhydramine; Disease model; Double-Blind Method; Electroencephalography; Electrophysiology (science); Ensure; Gender; Genetic Diseases; Genetic Models; Heterogeneity; Human; Impairment; Leadership; Maintenance; Measures; Modeling; Mus; Neurobehavioral Manifestations; Noise; Participant; Pharmaceutical Preparations; Phenotype; Placebo Control; Polysomnography; Quality Control; Randomized; Reproducibility; Research Personnel; Rest; Role; Secure; Sleep; Sleep Architecture; Sleep Fragmentations; Source; Statistical Data Interpretation; Statistical Methods; Structure; Symptoms; Validation; Variant; Zebrafish; actigraphy; adolescent with autism spectrum disorder; aged; autism spectrum disorder; awake; circadian; cognitive function; comparative; data integration; data management; data visualization; design; experimental study; improved; mouse model; multimodal data; multimodality; programs; repetitive behavior; response; sleep physiology; social communication; zolpidem

ABSTRACT: DATA MANAGEMENT AND ANALYTIC CORE (DMAC) In our proposed ACE, we propose a multi-modal, cross-species program which encompasses four synergistic projects aimed at characterizing the role of sleep fragmentation and sleep physiology on the core symptoms, repetitive behaviors and cognitive function in Autism Spectrum Disorder (ASD). Leveraging comparative biology across humans with ASD and complementary genetic animal models of ASD, the overall aims of our ACE are to examine if multisystem sleep measurements across species a) converge on a common phenotype of sleep fragmentation and architecture in ASD; b) are differentially associated with the core, behavioral and cognitive symptoms of ASD; and c) if sleep normalization in humans with ASD and animal models of ASD, alleviate the core, behavioral and cognitive symptoms of ASD. The management and analysis of such large, multi-domain, comparative data sets include careful maintenance and tracking of the quality and eventual sources of heterogeneity, correct calibration and quality control of the data and control for the multiplicity of choices in the normalization and metrics. The DMAC will employ a reproducible approach to the development of the complete statistical analyses and record to attain the following objectives. Objective 1: Coordination, transfer, integration and management of data across all ACE Projects, and their different platforms; Objective 2: Integration, calibration and normalization of data sets from the different sources; Objective 3: Design, Analysis, and Power Estimation; and Objective 4: Integrating and analyzing comparative data across the RAC and Projects 1-4.

摘要：数据管理和分析核心（DMAC） 在我们提出的ACE中，我们提出了一个多模式的跨物种程序，该程序包括四个协同作用 旨在表征睡眠破碎和睡眠生理学对核心症状的作用的项目， 自闭症谱系障碍（ASD）中的重复行为和认知功能。利用比较 ASD的ASD和互补遗传动物模型的生物学，我们的ASD的整体目的 ACE要检查物种的多系统睡眠测量是否a）在常见表型上融合 ASD的睡眠破碎和建筑； b）与核心，行为和 ASD的认知症状； c）如果ASD的ASD和ASD动物模型在人类中的睡眠正常化， 减轻ASD的核心，行为和认知症状。如此大的管理和分析 多域比较数据集包括仔细维护和质量跟踪和最终 异质性，正确校准以及数据和控制的质量控制的来源 标准化和指标中的选择。 DMAC将采用可重现的方法来开发 完整的统计分析和记录以实现以下目标。 目标1：在所有ACE项目中的数据协调，转移，集成和管理及其 不同的平台；目标2：来自不同的数据集的集成，校准和归一化 来源；目标3：设计，分析和功率估计；和目标4：整合和分析 RAC和项目1-4的比较数据。