COBRE preprocessed with NIAK 0.12.4

<strong>!!! WIP !!! Because of the 1Gb quota, I need to upload this dataset bits by bits. The upload is not currently complete !!</strong> <strong><br></strong> <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 reprocessed 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.<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 removed if tagged with excessive motion. See the _extra.mat file below for more info.<br><strong>* fmri_szxxxSUBJECT_session1_run1_extra</strong>.mat: 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. 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<br>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<br>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> NeuroImage 51 (3), 1126–1139.<br>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 studies</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 functional connectivity studies</em>. Magnetic resonance imaging 27 (8), 1058–1064.<br>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.,<br>Feb. 2012. <em>Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion</em>. NeuroImage 59 (3), 2142–2154.<br>URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018 ### Other derivatives This dataset was used in a publication, see the link below.<br>https://github.com/SIMEXP/glm_connectome