DOI: 10.6084/m9.figshare.1160600
title: COBRE preprocessed with NIAK 0.12.4

description: <strong>### Content</strong> This work is a derivative from the COBRE sample found in the [International Neuroimaging Data-sharing Initiative (INDI)](http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 72 patients diagnosed with schizophrenia (58 males, age range = 18-65 yrs) and 74 healthy controls (51 males, age range = 18-65 yrs). The fMRI dataset for each subject are single nifti files (.nii.gz), featuring 150 EPI blood-oxygenation level dependent (BOLD) volumes were obtained in 5 mns (TR = 2 s, TE = 29 ms, FA = 75°, 32 slices, voxel size = 3x3x4 mm3 , matrix size = 64x64, FOV = mm2 ). The COBRE preprocessed fMRI release more specifically contains the following files:<br>* <strong>README.md</strong>: a markdown (text) description of the release. * <strong>cobre_model_group.csv </strong>A comma-separated value file, with the sz (1: patient with schizophrenia, 0: control), age, sex, and FD (frame displacement, as defined by Power et al. 2012) variables. Each column codes for one variable, starting with the label, and each line has the label of the corresponding subject.<br>* <strong>fmri_szxxxSUBJECT_session1_run1.nii.gz</strong>: a 3D+t nifti volume at 3 mm isotropic resolution, in the MNI non-linear 2009a symmetric space<br>(http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Note that the number of time samples may vary, as some samples have been<br>removed if tagged with excessive motion. See the _extra.mat file below for more info.<br>* <strong>fmri_szxxxSUBJECT_session1_run1_extra.mat</strong>: a matlab/octave file for each subject. Each .mat file contains the following variables:<br>* <strong>confounds</strong>: a TxK array. Each row corresponds to a time sample, and each column to one confound that was regressed out from the time series during preprocessing.<br>* <strong>labels_confounds</strong>: cell of strings. Each entry is the label of a confound that was regressed out from the time series.<br>* <strong>mask_suppressed</strong>: a T2x1 vector. T2 is the number of time samples in the raw time series (before preprocessing), T2=119. Each entry corresponds to a time sample, and is 1 if the corresponding sample was removed due to excessive motion (or to wait for magnetic equilibrium at the beginning of the series). Samples that were kept are tagged with 0s.<br>* <strong>time_frames</strong>: a Tx1 vector. Each entry is the time of acquisition (in s) of the corresponding volume. <strong>### Preprocessing</strong> The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.12.14, under CentOS version 6.3 with Octave(http://gnu.octave.org) version 3.8.1 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18.<br>Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-body<br>transform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of (Power et al., 2012), was used to remove the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~180 s of acquisition, was then required for further analysis. For this reason, 16 controls and 29 schizophrenia patients were rejected from the subsequent analyses. The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were finally spatially smoothed with a 6 mm isotropic Gaussian blurring kernel. <strong>### References</strong> Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C., 2006. <em>The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research</em>. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. <em>Multi-level bootstrap analysis of stable clusters in resting-state fMRI</em>. Neu-<br>roImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Collins, D. L., Evans, A. C., 1997. <em>Animal: validation and applications of nonlinear registration-based segmentation</em>. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011. <em>Unbiased average age-appropriate atlases for pediatric stud</em><em>ies</em>. NeuroImage 54 (1), 313–327.<br>URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. <em>Images-based suppression of unwanted global signals in resting-state func</em><em>tional connectivity studies</em>. Magnetic resonance imaging 27 (8), 1058–1064. URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. <em>Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion</em>. NeuroImage 59 (3), 2142–2154. URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018 <strong>### Other derivatives</strong> This dataset was used in a publication, see the link below.<br>https://github.com/SIMEXP/glm_connectome