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剑桥衰老与神经科学中心(Cam-CAN)数据存储库:来自成人横断面寿命样本的结构和功能磁共振成像、脑磁图及认知数据。

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample.

作者信息

Taylor Jason R, Williams Nitin, Cusack Rhodri, Auer Tibor, Shafto Meredith A, Dixon Marie, Tyler Lorraine K, Henson Richard N

机构信息

School of Psychological Sciences, The University of Manchester, Zochonis Building, Brunswick Street, Manchester M13 9PL, UK.

MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK.

出版信息

Neuroimage. 2017 Jan;144(Pt B):262-269. doi: 10.1016/j.neuroimage.2015.09.018. Epub 2015 Sep 12.

Abstract

This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N=700), cross-sectional adult lifespan (18-87years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing. The database contains raw and preprocessed structural MRI, functional MRI (active tasks and resting state), and MEG data (active tasks and resting state), as well as derived scores from cognitive behavioural experiments spanning five broad domains (attention, emotion, action, language, and memory), and demographic and neuropsychological data. The dataset thus provides a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition, and providing a testbed for novel analyses of multi-modal neuroimaging data.

摘要

本文介绍了剑桥衰老与神经科学中心(Cam-CAN)初始研究队列的数据存储库。Cam-CAN第二阶段存储库包含来自一个大规模(约N = 700)、基于横断面成人寿命(18 - 87岁)人群样本的多模态(MRI、MEG和认知行为)数据。该研究旨在表征与年龄相关的认知、脑结构和功能变化,并揭示支持健康认知衰老的神经认知机制。数据库包含原始和预处理的结构MRI、功能MRI(主动任务和静息状态)以及MEG数据(主动任务和静息状态),以及来自跨越五个广泛领域(注意力、情绪、动作、语言和记忆)的认知行为实验的派生分数,以及人口统计学和神经心理学数据。因此,该数据集提供了目前无与伦比的神经认知表型深度,能够对脑结构、脑功能和认知方面与年龄相关的变化进行综合分析,并为多模态神经影像数据的新颖分析提供了一个试验台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5725/5182075/a46edef0e46e/gr1.jpg

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