Badhwar AmanPreet, Collin-Verreault Yannik, Lussier Desiree, Sharmarke Hanad, Orban Pierre, Urchs Sebastian, Chouinard Isabelle, Vogel Jacob, Potvin Olivier, Duchesne Simon, Bellec Pierre
Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada.
Université de Montréal, Montréal, Canada.
Data Brief. 2020 May 16;31:105699. doi: 10.1016/j.dib.2020.105699. eCollection 2020 Aug.
The impact of multisite acquisition on resting-state functional MRI (rsfMRI) connectivity has recently gained attention. We provide consistency values (Pearson's correlation) between rsfMRI connectivity maps of an adult volunteer (Csub) scanned 25 times over 3.5 years at 13 sites using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). This dataset was generated as part of the following article: Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors [1]. Acquired on three 3T scanner vendors (GE, Siemens and Philips), the Csub dataset is part of an ongoing effort to monitor the quality and comparability of MRI data collected across the Canadian Consortium on Neurodegeneration in Aging (CCNA) imaging network. The participant was scanned 25 times in the above-mentioned article: multiple times at six sites over a period of 2.5 years, and once at the remaining seven sites. Since then the participant was scanned an additional 45 times, allowing us to extend the dataset to 70 rsfMRI scans over a period of >4 years. In addition, we provide intra- and inter-subject consistency values of rsfMRI connectivity maps derived from 26 adult participants belonging to the publicly released Hangzhou Normal University dataset (HNU1). All HNU1 participants underwent 10 rsfMRI scans over one month on a single 3T scanner (GE). Connectivity maps of seven canonical networks were generated for each scan in the two datasets (Csub and HNU1). All consistency values, along with the scripts used to preprocess the rsfMRI data and generate connectivity maps and pairwise consistency values, have been made available on two public repositories, Github and Zenodo. We have also made available four Jupyter notebooks that use the provided consistency values to (a) generate interactive graphical summaries - 1 notebook, (b) perform statistical analyses - 2 notebooks, and (c) perform data-driven cluster analysis for the recovery of subject identity (i.e. rsfMRI fingerprinting) - 1 notebook. In addition, we provide two interactive dashboards that allow visualization of individual connectivity maps from the two datasets. Finally, we also provide minimally preprocessed rsfMRI data in Brain Imaging Data Standard (BIDS) format on all 70 scans in the extended dataset.
多站点采集对静息态功能磁共振成像(rsfMRI)连接性的影响最近受到了关注。我们提供了一名成年志愿者(Csub)的rsfMRI连接性图谱之间的一致性值(皮尔逊相关性),该志愿者在3.5年的时间里,使用加拿大痴呆症成像协议(CDIP,www.cdip-pcid.ca)在13个站点接受了25次扫描。该数据集是以下文章的一部分:在2.5年、13个站点和3个供应商处对单个个体采集的静息态fMRI连接性图谱的多变量一致性[1]。Csub数据集是在三个3T扫描仪供应商(通用电气、西门子和飞利浦)上采集的,是正在进行的一项工作的一部分,旨在监测加拿大衰老神经退行性疾病联盟(CCNA)成像网络中收集的MRI数据的质量和可比性。在上述文章中,该参与者被扫描了25次:在2.5年的时间里,在六个站点进行了多次扫描,在其余七个站点各进行了一次扫描。从那时起,该参与者又被额外扫描了45次,使我们能够将数据集扩展到在超过4年的时间里进行70次rsfMRI扫描。此外,我们还提供了来自公开发布的杭州师范大学数据集(HNU1)的26名成年参与者的rsfMRI连接性图谱的组内和组间一致性值。所有HNU1参与者在一台3T扫描仪(通用电气)上,在一个月的时间里接受了10次rsfMRI扫描。在两个数据集(Csub和HNU1)中,为每次扫描生成了七个典型网络的连接性图谱。所有一致性值,以及用于预处理rsfMRI数据、生成连接性图谱和成对一致性值的脚本,都已在两个公共存储库Github和Zenodo上提供。我们还提供了四个Jupyter笔记本,它们使用提供的一致性值来(a)生成交互式图形摘要——1个笔记本,(b)进行统计分析——2个笔记本,以及(c)进行数据驱动的聚类分析以恢复个体身份(即rsfMRI指纹识别)——1个笔记本。此外,我们还提供了两个交互式仪表盘,用于可视化来自两个数据集的个体连接性图谱。最后,我们还以脑成像数据标准(BIDS)格式提供了扩展数据集中所有70次扫描的最小预处理rsfMRI数据。