Statistics Core

统计核心

基本信息

批准号：
10266151
负责人：
Kumar B. Rajan
金额：
$ 72.36万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-30 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10266151
关键词：
Area Biological Models Biomechanics Biophysics Characteristics Clinical Clinical assessments Cognition Cognitive Collection Complex Country Data Data Collection Data Management Resources Data Pooling Data Security Data Set Database Management Systems Databases Dementia Development Disease Progression Enrollment Genetic Goals Image Impaired cognition Injury Internet Lesion Machine Learning Magnetic Resonance Imaging Measures Methods Modeling Outcome Participant Performance Plasma Population Population Heterogeneity Process Protocols documentation Quality Control Reporting Research Research Design Research Personnel Resources Risk Risk Factors Role Secure Site Source Standardization Statistical Data Interpretation Stratification System Systems Analysis Testing Work analytical method base clinical research site clinically significant cognitive change cohort computer framework computerized data processing data access data collection site data de-identification data harmonization data integration data management data pipeline data portal data repository data sharing data tools database query deep learning design follow-up innovation member multimodality neuroimaging participant enrollment precision medicine programs recruit repository risk prediction risk prediction model statistics user-friendly web app web based interface white matter white matter injury

项目摘要

PROJECT SUMMARY – Statistics Core The purpose of the Statistics Core (SC) is to support and advance the Clinical significance of INciDEntal white matter lEsions on MRI in a Diverse population with cognitive complaints (INDEED) by developing innovative tools for data collection and management, harmonizing data across clinical sites, developing deep learning and biomechanical neuroimaging quantification of white matter injury, performing statistical analysis for investigators, and sharing data through a research portal with internal and external investigators. The SC will undertake 5 specific aims to fulfill this purpose: 1) develop a data management system that will integrate data across the cores, provide a reporting system for tracking of study progress and describing characteristics of participants, and support a resource portal for data sharing and data interrogation; 2) provide harmonization of neuroimaging data to enable pooling of data across sites; 3) develop methods to better capture the spectrum of white matter injury and its biomechanical consequences; 4) test key hypotheses posed in the Overall section of the application; and 5) develop and validate risk scores. The SC brings together a qualified team with expertise in data management, MRI harmonization, development of imaging metrics and numerical models of biological systems, analysis of risk factors associated with cognitive decline and incident dementia, and development of risk scores to accomplish these aims. Innovations include a database capable of dynamic reporting and querying of the database, cutting-edge approaches to MRI harmonization, deep learning and multivariate approaches to better characterize white matter injury, and development of multi-modal risk scores. The SC will integrate data from the Repository Core, provide tracking information for the Recruitment and Retention Core, and serve on committees within the Administrative Core. The work of the Core will support the overall goal of predicting the impact of progressive WM injury on cognition using a precision medicine approach in a large and diverse clinical population.

项目摘要 - 统计核心统计核心 (SC) 的目的是支持和推进 INciDEntal 白色的临床意义通过开发创新技术，在具有认知问题的不同人群中（确实）进行 MRI 的重大病变用于数据收集和管理、协调临床站点数据、开发深度学习和白质损伤的生物力学神经影像量化，进行统计分析 SC 将通过研究门户与内部和外部调查人员共享数据。为实现这一目的，采取 5 个具体目标： 1) 开发一个能够集成数据的数据管理系统跨核心，提供一个报告系统来跟踪研究进度并描述研究的特征参与者，并支持数据共享和数据查询的资源门户 2) 提供协调；神经影像数据，以便跨站点汇集数据；3) 开发更好地捕获频谱的方法；白质损伤及其生物力学后果；4）测试总体部分提出的关键假设 5) SC 汇集了合格的团队来制定和验证风险评分。数据管理、MRI 协调、成像指标开发和数值模型方面的专业知识生物系统，与认知能力下降和痴呆症相关的风险因素分析，以及开发风险评分以实现这些目标的创新包括能够动态的数据库。数据库的报告和查询、MRI 协调的尖端方法、深度学习和多变量方法可以更好地表征白质损伤，并开发多模式风险评分。 SC 将整合来自存储库核心的数据，提供招聘的跟踪信息和保留核心，并在行政核心内的委员会中任职。核心的工作将支持使用精准医学预测进行性 WM 损伤对认知的影响的总体目标方法适用于大量且多样化的临床人群。