An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI

Code used to generate these data can be found at:
https://github.com/lindenmp/rs-fMRI

Details:
These data are the fully processed and denoised time series from three of the four datasets presented in the manuscript listed below:
1) A healthy control cohort from the Beijing Zang dataset (http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html)
1) The healthy control and schizophrenia cohorts from the Consortium for Neuropsychiatric Phenomics dataset (https://openfmri.org/dataset/ds000030/).
2) The three time points for the healthy control cohort from the Consortium for Reliability and Reproducibility (CoRR) NYU dataset (http://fcon_1000.projects.nitrc.org/indi/CoRR/html/).

For each participant, the results for each denoising pipeline are saved into separate, named, subdirectories. Within each of these subdirectories, the time series for each denoising pipeline are saved in cfg.mat.
When loaded into matlab:
cfg.roiTS{1} = Gordon parcellation
cfg.roiTS{2} = Power parcellation
There are also additional parcellation time series not included in the manuscript (see run_prepro.m on GitHub for more details).

Also included for the CNP dataset are the Network Based Statistic (Zalesky et al. 2010. NeuroImage) outputs for each pipeline comparing healthy control and schizophrenia cohorts.

Together with the QC code (https://github.com/lindenmp/rs-fMRI), these data allow for the reproduction of the figures presented in the below manuscript.

if you use this code, please cite:

L. Parkes, B. D. Fulcher, M. Yucel, & A. Fornito. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage (2017).

生成本数据集所用的代码可从以下地址获取：
https://github.com/lindenmp/rs-fMRI

详细说明：
本数据集包含下述论文中提及的四项数据集里的三项的全预处理与去噪后的时间序列数据：
1) 北京臧数据集（Beijing Zang dataset）的健康对照队列（http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html）
1) 神经精神表型组学联盟（Consortium for Neuropsychiatric Phenomics, CNP）数据集内的健康对照与精神分裂症队列（https://openfmri.org/dataset/ds000030/）
2) 可靠性与可重复性联盟（Consortium for Reliability and Reproducibility, CoRR）纽约大学（NYU）数据集里健康对照队列的三个时间点数据（http://fcon_1000.projects.nitrc.org/indi/CoRR/html/）

针对每一位受试者，各去噪流程（denoising pipeline）的处理结果会被保存至独立命名的子目录中。在每个子目录内，对应去噪流程的时间序列数据均存储于cfg.mat文件内。
在Matlab中加载该文件时：
cfg.roiTS{1} = 戈登脑区分割（Gordon parcellation）
cfg.roiTS{2} = 鲍尔脑区分割（Power parcellation）
此外还包含论文未提及的其他脑区分割（parcellation）时间序列数据（详见GitHub仓库中的run_prepro.m文件）。

本数据集还为神经精神表型组学联盟（CNP）数据集提供了各去噪流程的基于网络的统计量（Network Based Statistic, NBS；Zalesky等，2010，NeuroImage）输出结果，用于对比健康对照与精神分裂症队列。

结合质量控制（Quality Control, QC）代码（https://github.com/lindenmp/rs-fMRI），本数据集可复现下述论文中的所有图表。

若使用本代码，请引用以下文献：
L. Parkes、B. D. Fulcher、M. Yucel 与 A. Fornito. 《静息态功能磁共振成像（resting-state functional MRI, rs-fMRI）运动校正策略的效能、可靠性与敏感性评估》. NeuroImage, 2017.