Cognitive Assessment and Neuroimaging (CAN) Core E

认知评估和神经影像 (CAN) 核心 E

• 批准号: 10270192
• 负责人: NAN-KUEI CHEN
• 金额: $ 91.1万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10270192
• 关键词: Address; Adult; Age-associated memory impairment; Aging; Algorithms; Baltimore; Blood Flow Velocity; Blood flow; Brain; Brain Mapping; Clinical; Cloud Service; Cognitive; Cognitive aging; Collaborations; Computer software; Computers; Data; Data Analyses; Data Collection; Data Management Resources; Data Science Core; Decentralization; Dementia; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Docking; Echo-Planar Imaging; Elderly; Engineering; Ensure; Goals; Hippocampus (Brain); Image; Individual Differences; Intervention; Lead; Learning Module; Literature; Longevity; Machine Learning; Magnetic Resonance Imaging; Manuals; Measures; Memory; Metadata; Methods; Mining; Morphologic artifacts; Morphology; Neurologic; Neurologist; Neuropsychological Tests; Participant; Performance; Perfusion; Persons; Physiologic pulse; Procedures; Process; Property; Protocols documentation; Psychometrics; Public Domains; Quality Control; Reproducibility; Research; Research Personnel; Seeds; Series; Site; Standardization; Structure; Support System; System; Testing; Time; Training; Translating; Ultrasonography; Validation; White Matter Hyperintensity; age group; annotation system; base; brain morphology; carotid intima-media thickness; clinical research site; cognitive function; cognitive testing; data harmonization; data management; data pipeline; design; executive function; healthspan; image processing; large scale data; machine learning algorithm; meetings; multimodality; neuroimaging; processing speed; relating to nervous system; repository; tool

SUMMARY/ABSTRACT: Cognitive Assessment and Neuroimaging Core E The goal of Cognitive Assessment and Neuroimaging (CAN) Core E, is to provide neurological data with cognitive assessment, MRI, and carotid ultrasound that will directly support Projects 1, 2 and 4, leading to the identification of key neurological signatures of individual differences in cognitive aging. Cognitive assessment protocols will include performance measures of memory, executive functions, processing speed, premorbid function, and overall cognitive function. Cognitive tests derive from the experimental cognitive aging literature that have been demonstrated to be sensitive to individual differences across and within age groups, as well as standardized clinical neuropsychological tests typically used to identify age-related cognitive impairment and potential early dementia. We will standardize and coordinate online cognitive assessments in both English and Spanish, directly supporting Projects 1 and 2, and in-person assessments at four clinical for Project 2 (Tucson, Baltimore, Atlanta, Miami) to ensure quality and consistency throughout the project duration. In addition, we plan to acquire brain MRI and ultrasound carotid images from 1620 participants. Our MRI protocols will be built upon the advanced MRI protocols of ADNI 3, which have been optimized and tested for our MRI platforms. From the acquired data we will produce quantitative measures of brain morphology, white matter hyperintensities, structural and functional connectivity, perfusion, microbleeds, carotid intima-media thickness, plaque presence, size and morphology, and blood flow velocities. The acquired imaging data will be annotated with meta-data that support near real-time quality control, robust mining, query and analyses of imaging data, meeting the meta-data requirements for various MRI software packages, machine learning procedures, and large scale analyses proposed in Project 4. The meta-data management and annotation system will also support incorporation of decentralized data (from the public domain), with which the training of our machine learning modules can be further enhanced. Furthermore, we plan to implement a series of data harmonization and pre-processing pipelines in our XNAT server (integrated with the COINS platform; HPC nodes; and CyVerse), to streamline imaging data QC and quantitative analysis. Our proposed pipelines address limitations of existing software packages (e.g., existing challenge in robustly segmenting hippocampus and other critical brain structures across age groups) through machine learning, and can inherently harmonize multi-modal MRI data (e.g., fast spin-echo MRI in minimally distorted coordinates; and echo-planar imaging in distorted coordinates), enabling streamlined analysis (e.g., from hippocampal segmentation to memory network connectivity analysis) without manual intervention.

摘要/摘要：认知评估和神经影像核心 E 认知评估和神经影像 (CAN) Core E 的目标是提供神经学数据 认知评估、MRI 和颈动脉超声将直接支持项目 1、2 和 4，从而实现 识别认知衰老个体差异的关键神经学特征。认知评估 协议将包括记忆性能测量、执行功能、处理速度、病前情况 功能和整体认知功能。认知测试源自实验性认知衰老文献 已被证明对年龄组之间和年龄组内的个体差异敏感，以及 标准化临床神经心理学测试通常用于识别与年龄相关的认知障碍和 潜在的早期痴呆症。我们将标准化和协调英语和英语的在线认知评估 西班牙语，直接支持项目 1 和 2，并对项目 2 的四个临床进行现场评估 （图森、巴尔的摩、亚特兰大、迈阿密）确保整个项目期间的质量和一致性。 此外，我们计划获取 1620 名参与者的脑部 MRI 和超声颈动脉图像。我们的核磁共振成像 协议将建立在 ADNI 3 的先进 MRI 协议之上，该协议已经过优化和测试 我们的 MRI 平台。根据获得的数据，我们将产生大脑形态的定量测量，白 物质高信号、结构和功能连接、灌注、微出血、颈动脉内膜中层 厚度、斑块的存在、大小和形态以及血流速度。获取的成像数据将是 用元数据注释，支持近乎实时的质量控制、强大的挖掘、查询和分析 成像数据，满足各种MRI软件包、机器学习的元数据要求 项目 4 中提出的程序和大规模分析。元数据管理和注释 系统还将支持纳入分散数据（来自公共领域），通过这些数据进行培训 我们的机器学习模块可以进一步增强。此外，我们计划实施一系列数据 我们的 XNAT 服务器中的协调和预处理管道（与 COINS 平台集成；HPC 节点；和 CyVerse），以简化成像数据质量控制和定量分析。我们建议的管道 解决现有软件包的局限性（例如，稳健分割海马体方面存在的挑战 和其他跨年龄段的关键大脑结构）通过机器学习，并且可以本质上协调 多模态 MRI 数据（例如，在最小扭曲坐标中的快速自旋回波 MRI；以及在 扭曲的坐标），实现简化的分析（例如，从海马分割到记忆 网络连接分析）无需人工干预。