Cognitive Assessment and Neuroimaging (CAN) Core E

认知评估和神经影像 (CAN) 核心 E

• 批准号: 10491860
• 负责人: NAN-KUEI CHEN
• 金额: $ 103.06万
• 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/10491860
• 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 magnetic resonance imaging; 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; ultrasound

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学核心E 认知评估和神经影像学（CAN）核心E的目的是提供神经系统数据 认知评估，MRI和Carotid Ultrasound将直接支持项目1、2和4，导致 认知衰老中个体差异的关键神经系统特征的识别。认知评估 协议将包括记忆的绩效指标，执行功能，处理速度，病态 功能和总体认知功能。认知测试来自实验认知衰老文献 事实证明，这些对年龄组和内部的个体差异以及 标准化的临床神经心理学测试通常用于识别与年龄有关的认知障碍和 潜在的早期痴呆。我们将在英语和 西班牙语，直接支持项目1和2，以及项目2的四个临床评估 （图森，巴尔的摩，亚特兰大，迈阿密），以确保整个项目期间的质量和一致性。 此外，我们计划从1620名参与者中获取大脑MRI和超声颈动脉图像。我们的MRI 协议将基于ADNI 3的高级MRI协议，该协议已对其进行了优化和测试。 我们的MRI平台。从获得的数据中，我们将产生脑形态的定量度量，白色 物质高强度，结构和功能连通性，灌注，微孔，颈动脉内膜媒体 厚度，斑块的存在，大小和形态以及血流速度。获得的成像数据将是 用元数据注释，支持接近实时质量控制，强大的采矿，查询和分析 成像数据，满足各种MRI软件包的元数据要求，机器学习 程序以及项目4中提出的大规模分析。元数据管理和注释 系统还将支持分散数据（来自公共领域）的合并，并通过该数据进行培训 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（与硬币平台集成； HPC 节点；和cyverse），简化成像数据QC和定量分析。我们提出的管道 地址限制现有软件包的局限 以及跨年龄段的其他关键大脑结构）通过机器学习，并且可以天生的协调 多模式MRI数据（例如，在微型扭曲的坐标中快速旋转回波MRI；以及回声 - 平面成像 扭曲的坐标），启用了简化的分析（例如，从海马分割到内存 网络连接分析）无手动干预。