Release Name: cobre_lightweight20

### Content
This work is a subsample of the "COBRE preprcessed lightweight" release, which is a derivative from the COBRE sample found in the [International Neuroimaging Data-sharing Initiative (INDI)](http://fcon_10, released under Creative Commons -- Attribution Non-Commercial.. See the [webiste of the COBRE preprocessed lightweight release](
COBRE_preprocessed_with_NIAK_0_17_-_lightweight_release/4197885) for more informations. This subrelease includes 20 subjects, 10 controls and 10 patients suffering from schizophrenia, selected from the fu
ll sample. All subjects included in this release are male and right-handed. They also have been roughly matched in age and motion level, which were chosen to be low. This subset of 20 subjects was prepare
d for the purpose of supporting tutorials, only. 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:
* a markdown (text) description of the release.
* fmri_SUBJECT.nii.gz: a 3D+t nifti volume at 6 mm isotropic resolution, 16 bit integer encoding, in the MNI non-linear 2009a symmetric space
* fmri_SUBJECT_extra.mat: a matlab/octave file for each subject.
* 'phenotypic_data.tsv.gz': A gzipped tabular-separated value file, with each column representing a phenotypic variable as well as measures of data quality (related to motions). Each row corresponds to o
ne participant, except the first row which contains the names of the variables (see file below for a description).
* 'keys_phenotypic_data.json': a json file describing each variable found in 'phenotypic_data.tsv.gz'.
Each .mat file contains the following variables:
* confounds: 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.
* labels_confounds: cell of strings. Each entry is the label of a confound that was regressed out from the time series.
* mask_scrubbing: a Tx1 vector. Each entry corresponds to a time sample, and is 1 if the corresponding sample should be
removed due to excessive motion (or to wait for magnetic equilibrium at the beginning of the series). Samples that should be kept are tagged with 0s.
* time_frames: a Tx1 vector. Each entry is the time of acquisition (in s) of the corresponding volume.
### Preprocessing
The datasets were analysed using the NeuroImaging Analysis Kit (NIAK version 0.17,
under CentOS version 6.3 with Octave( version 4.0.2 and the Minc toolkit
( 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
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). 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

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