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).