The Lifespan Human Connectome Project in Aging: An overview.
The Lifespan Human Connectome Project in Aging: An overview.
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DOI：
10.1016/j.neuroimage.2018.10.009
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发表时间：
2019-01-15
期刊：
影响因子：
5.7
通讯作者：
Yacoub E
中科院分区：
文献类型：
--
作者：
Bookheimer SY;Salat DH;Terpstra M;Ances BM;Barch DM;Buckner RL;Burgess GC;Curtiss SW;Diaz-Santos M;Elam JS;Fischl B;Greve DN;Hagy HA;Harms MP;Hatch OM;Hedden T;Hodge C;Japardi KC;Kuhn TP;Ly TK;Smith SM;Somerville LH;Uğurbil K;van der Kouwe A;Van Essen D;Woods RP;Yacoub E
最初的人类连接组项目使用改进的数据采集、分析和共享方法，在健康年轻人的大样本中产生了丰富的结构和功能连接数据集。最近的努力正在将这种方法扩展到包括婴儿，儿童，老年人和大脑疾病。本文介绍并描述了人类连接组老龄化项目（HCP-A），该项目目前正在招募1200多名年龄在36岁至100岁以上的健康成年人，其中600多名参与者返回进行纵向评估。四个采集中心使用匹配的Siemens Prisma 3 T MRI扫描仪进行集中质量控制和数据分析，正在招募参与者。数据采集跨多模态成像和行为领域，重点是已知在高级老化中改变的因素。MRI采集包括结构（全脑和高分辨率海马）加多频带静息状态功能（rfMRI）、任务fMRI（tfMRI）、弥散MRI（dMRI）和动脉自旋标记（ASL）。行为表征包括认知（如处理速度和情景记忆）、精神、代谢和社会经济指标以及全身健康评估（通过激素测定关注绝经期）。该数据集将提供一个独特的资源，用于研究大脑组织和连接性在典型衰老过程中如何变化，以及这些差异如何与衰老的关键特征相关，包括荷尔蒙状态的改变、记忆力和一般认知能力的下降。HCP-A的主要目标是让科学界免费获得这些数据，由Connectome Coordination Facility（CCF）平台提供支持，用于数据质量保证，预处理和基本分析，并通过NIMH数据档案（NDA）共享。在这里，我们提供了我们的研究设计的基本原理和足够的资源细节，供科学家计划未来对这些数据的分析。一篇配套论文描述了相关的人类连接组开发项目（HCP-D），以及两项研究共同的图像采集协议。
The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D,), and the image acquisition protocol common to both studies.
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