Cognitive Assessment and Neuroimaging (CAN) Core E

摘要/摘要：认知评估和神经成像核心E 认知评估和神经成像(CAN)核心E的目标是为神经学数据提供 认知评估、核磁共振和颈动脉超声将直接支持项目1、2和4，导致 认知老化个体差异的关键神经学特征识别。认知评估 协议将包括内存、执行功能、处理速度、发病前的性能测量 功能和整体认知功能。认知测试源于实验性的认知老化文献 已被证明对跨年龄组和年龄组内的个人差异敏感，以及 标准化的临床神经心理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。我们将规范和协调英语和英语在线认知评估 西班牙语，直接支持项目1和项目2，并在项目2的四个诊所进行面对面评估 (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，我们计划获取1620名参与者的脑MRI和超声颈动脉图像。我们的核磁共振 协议将建立在ADNI 3的高级MRI协议的基础上，这些协议已经过优化和测试 我们的核磁共振平台。从获得的数据中，我们将产生大脑形态的定量测量，白色 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。所获取的成像数据将是 用元数据标注，支持近实时质量控制、健壮的挖掘、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 项目4中提出的程序和大规模分析。元数据管理和注释 该系统还将支持纳入分散的数据(来自公共领域)，有了这些数据， 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 在我们的XNAT服务器中协调和预处理管道(与硬币平台集成；HPC 节点；和CyVerse)，以简化成像数据的质量控制和定量分析。我们拟建的管道 解决现有软件包的局限性(例如，在健壮分割海马体方面的现有挑战 和其他跨年龄组的关键大脑结构)通过机器学习，并可以内在地协调 多模式MRI数据(例如，在最小失真坐标下的快速自旋回波MRI；以及 扭曲的坐标)，实现了流线型分析(例如，从海马区分割到记忆 网络连通性分析)，无需人工干预。

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.