Statistics Core

统计核心

• 批准号: 10489295
• 负责人: Kumar B. Rajan
• 金额: $ 119.54万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 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/10489295
• 关键词: Area; Biological Models; Biomechanics; Biophysics; Characteristics; Clinical; 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; Qualifying; Quality Control; Reporting; Research; Research Design; Research Personnel; Resources; Risk; Risk Factors; Role; Secure; Site; Source; Standardization; Statistical Data Interpretation; Stratification; System; Testing; Work; analytical method; biological systems; 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 reduction; 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

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 通过开发创新的方法， 用于数据收集和管理的工具，协调临床研究中心的数据，开发深度学习和 白色物质损伤的生物力学神经成像量化，进行统计分析 调查员，并通过研究门户与内部和外部调查员共享数据。SC将 为实现这一目标，我们承诺实现5个具体目标：1）开发一个数据管理系统， 跨核心，提供一个报告系统来跟踪研究进展并描述特征 参与者，并支持用于数据共享和数据查询的资源门户; 2）提供 神经成像数据，以实现跨站点的数据池; 3）开发更好地捕获频谱的方法 白色物质损伤及其生物力学后果; 4）测试总体部分中提出的关键假设 5）开发和验证风险评分。SC汇集了一个合格的团队， 数据管理、MRI协调、成像指标和数字模型开发方面的专业知识， 生物系统，分析与认知能力下降和痴呆相关的风险因素，以及 制定风险评分以实现这些目标。创新包括一个能够动态 数据库的报告和查询，MRI协调的尖端方法，深度学习和 更好地表征白色损伤的多变量方法，以及多模态风险评分的发展。 SC将整合来自存储库核心的数据，为招聘提供跟踪信息， 保留核心，并在行政核心内的委员会任职。核心的工作将支持 使用精确医学预测进行性WM损伤对认知的影响的总体目标 在一个庞大而多样化的临床人群中。