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-fMRIDetails: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 parcellationcfg.roiTS{2} = Power parcellationThere 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).

用于生成这些数据的代码可从以下链接获取：https://github.com/lindenmp/rs-fMRIDetails：本数据集包括对下述文稿中所述的四份数据集中的三份数据进行了全面处理和降噪的时间序列数据：1) 来自北京藏语数据集的健康对照组（http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html）；1) 来自神经精神表型学联盟数据集的健康对照组和精神性分裂症组（https://openfmri.org/dataset/ds000030/）。2) 来自可靠性及可重复性联盟（CoRR）纽约大学数据集的健康对照组的三个时间点（http://fcon_1000.projects.nitrc.org/indi/CoRR/html/）。对于每位参与者，每个降噪管道的结果都被保存在单独的、命名的子目录中。在这些子目录中，每个降噪管道的时间序列数据以cfg.mat的格式保存。当将这些数据加载到MATLAB中时：cfg.roiTS{1} = Gordon分区cfg.roiTS{2} = Power分区。此外，还包括文稿中未提及的额外分区时间序列（详细信息请参阅GitHub上的run_prepro.m）。对于CNP数据集，还包括每个管道比较健康对照组和精神性分裂症组的基于网络的统计量（Zalesky等，2010年，NeuroImage）。与QC代码（https://github.com/lindenmp/rs-fMRI）一起，这些数据允许重现下述文稿中展示的图表。若使用此代码，请引用：L. Parkes, B. D. Fulcher, M. Yucel, & A. Fornito. 对静息态功能性核磁共振运动校正策略的有效性、可靠性和敏感性的评估。NeuroImage（2017年）。