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、跨物种计划，其中包括四个协同作用 旨在描述睡眠碎片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 患有自闭症谱系障碍的人类的生物学和自闭症谱系障碍的互补遗传动物模型，我们的总体目标 ACE 将检查跨物种的多系统睡眠测量是否 a) 收敛于共同表型 自闭症谱系障碍 (ASD) 中的睡眠碎片化和结构； b) 与核心、行为和 自闭症谱系障碍（ASD）的认知症状； c) 如果 ASD 人类和 ASD 动物模型的睡眠正常化， 缓解 ASD 的核心行为和认知症状。如此大的管理和分析， 多领域、比较数据集包括仔细维护和跟踪质量和最终结果 异质性来源、数据的正确校准和质量控制以及多重性的控制 标准化和指标的选择。 DMAC 将采用可重复的方法进行开发 完整的统计分析和记录以实现以下目标。 目标 1：协调、传输、集成和管理所有 ACE 项目及其项目的数据 不同的平台；目标 2：来自不同领域的数据集的集成、校准和标准化 来源；目标 3：设计、分析和功耗估算；目标 4：整合和分析 RAC 和项目 1-4 的比较数据。