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
00.projects.nitrc.org/indi/retro/cobre.html), released under
Creative Commons -- Attribution Non-Commercial.. See the [webiste
of the COBRE preprocessed lightweight release](https://figshare.com/articles/
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
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 =
The COBRE preprocessed fMRI release more specifically contains the
* README.md: 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
* 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
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.
The datasets were analysed using the NeuroImaging Analysis Kit
(NIAK https://github.com/SIMEXP/niak) version 0.17,
under CentOS version 6.3 with Octave(http://gnu.octave.org) version 4.0.2 and the Minc toolkit
(http://www.bic.mni.mcgill.ca/ServicesSof...) 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
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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