Neuroimaging Core

神经成像核心摘要 关于“超级老人”的大脑，以及弹性因素对大脑的影响和影响方式，还有很多问题有待了解。 典型的大脑老化轨迹，并确定大脑储备的哪些方面与保存最相关 百岁老人的认知功能神经影像学中心负责监督这次收购 在四个参与的成像中心存储和分析成像数据：MGH（波士顿）、哥伦比亚大学 (NYC)、加州大学洛杉矶分校（洛杉矶）和格鲁吉亚州立大学（亚特兰大）。这些网站有相同的扫描仪， Siemens Prisma 3 T MRI，也是目前最先进的脉冲序列发生器。核心将优化 采集和分析方法，以确保跨中心的数据高度标准化， 等效的对比噪声比（CNR），以确保数据集成成功。核心的总体目标是 通过识别一组高分辨率脉冲序列来回答有关弹性的具体问题， 大脑结构和功能，在百岁老人可以忍受的时间间隔内，使用最先进的数据分析 技术. NIC的一般假设是，保留的结构和功能连接是必不可少的 到长期保存的认知。将通过生成最佳MRI数据并结合 旨在研究网络动态和纵向轨迹的分析工具，重点关注大脑网络 以及对认知和记忆最关键的区域。三个具体目标是：目标1：跨 西门子3 T Prisma平台上最先进的脉冲序列， 了解弹性大脑结构，包括方法：，导航仪启用高分辨率T1（多， 回波）和液体衰减反转恢复（FLAIR）成像，多波段静息态功能磁共振成像 共振成像，非均匀磁转移成像（ihMT），伪连续动脉自旋标记 （pCASL）和定量易感性作图（QSM）。目的2：添加10分钟扩散制备（DP）3D 梯度和自旋回波（GRASE）伪连续ASL（pCASL）序列，通过以下方式评估BBB功能： 在没有造影剂的情况下定量BBB水交换率（kw）。为了评估这一措施的可靠性， 极老年人，比较他们的MRI BBB结果与BBB的神经病理学评估（ Phenotyping & Biosample Core的神经病理学部分）。目标3：与研究中心和研究中心之间持续进行 质量保证，图像标准化和偏差校正的预处理，并将数据集中在 网站到成像数据存储库。目标4：进行图像分析，结合研究中心之间/研究中心内的数据 模态;为每种数据类型创建输出指标，并与项目一起，协助集成 将影像测量转化为项目假设。这些成像中心通过一个国家的 加州大学洛杉矶分校的数据分析中心成像统计专家与其他RADCO核心接口， 项目，以整合数据类型进行假设检验。

NEUROIMAGING CORE SUMMARY Much has yet to be understood about the brains of "superagers" and what and how resilience factors impact the typical brain-aging trajectory and identify which aspects of brain reserve are most associated with preserved cognition functioning in centenarian cognitive superagers. The Neuroimaging Core oversees the acquisition, storage, and analysis of imaging data at four participating imaging centers: MGH (Boston), Columbia University (NYC), UCLA (Los Angeles) and Georgia State University (Atlanta). These sites have identical scanners, the Siemens Prisma 3T MRI, that are also the most advanced pulse sequencers available. The Core will optimize acquisition and analysis approaches to ensure that the data across centers are highly standardized and achieve equivalent contrast to noise ratios (CNRs) to ensure that data integration is successful. The Core's broad goal is to answer specific questions about resilience by identifying a set of high-resolution pulse sequences that examine brain structure and function, in a time interval tolerable for centenarians, using state of the art data analysis techniques. The NIC’s general hypothesis is that preserved structural and functional connectivity are essential to long-term preserved cognition. This hypothesis will be tested by generating optimal MRI data combined with analysis tools designed to examine network dynamics and longitudinal trajectories, focusing on brain networks and regions most critical to cognition and memory. The three specific aims are: Aim 1: Test and deploy across four sites, state-of-the -art pulse sequences on the Siemens 3T Prisma platform that are essential for understanding the resilient brain structure including the methods:, navigator-enabled high-resolution T1 (multi- echo) and fluid-attenuated inversion recovery (FLAIR) imaging, multi-band resting-state functional magnetic resonance imaging, inhomogeneous magnetic transfer imaging (ihMT), pseudo-continuous arterial spin labeling (pCASL), and quantitative susceptibility mapping (QSM). Aim 2: Add a 10-minute diffusion prepared (DP) 3D gradient and spin-echo (GRASE) pseudo-continuous ASL (pCASL) sequence to assess BBB function by quantifying BBB water exchange rate (kw) without contrast agents. To assess the reliability of this measure in extremely old individuals, to compare their MRI BBB results with neuropathological assessment of the BBB (the neuropathology portion of the Phenotyping & Biosample Core). Aim 3: Perform ongoing with- and between-site quality assurance, pre-processing for image standardization and bias corrections, and centralize data across sites into an imaging data repository. Aim 4: Conduct image analysis that combines data across sites/within modality; create output metrics for each data type, and in conjunction with the projects, assist in integrating imaging measures into project hypotheses. The imaging centers are joined and further integrated by a state-of- the-art data analysis center at UCLA. Experts in imaging statistics interface with the other RADCO cores and projects to integrate data types for hypothesis testing.