demo_niak cobre_lightweight20

pbellec@www.nitrc.org (Pierre Bellec) — Tue, 22 Nov 2016 5:35:00 GMT

### 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
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/
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:
* 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 symmetric space
(http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009).
* 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 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/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
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
kernel.

### References

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. The CIVET Image-Processing Environment: A Fully Automated Com-
prehensive Pipeline for Anatomical Neuroimaging Research. 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.
Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neu-
roImage 51 (3), 1126–1139.
URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082
Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlin-
ear registration-based segmentation. International Journal of Pattern Recog-
nition and Artificial Intelligence 11, 1271–1294.
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins,
D. L., Jan. 2011. Unbiased average age-appropriate atlases for pediatric stud-
ies. NeuroImage 54 (1), 313–327.
URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033
Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009.
Images-based suppression of unwanted global signals in resting-state func-
tional connectivity studies. 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. Spurious but systematic correlations in functional connectivity
MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154.
URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018

test_niak data test niak nifti version 1.1

pbellec@www.nitrc.org (Pierre Bellec) — Wed, 06 Jul 2016 1:06:00 GMT

NIFTI release of the test data, equivalent to the MINC1 1.1 release.

test_niak target_test_niak_mnc1-2015-10-13

surchs@www.nitrc.org (Sebastian Urchs) — Wed, 14 Oct 2015 1:10:00 GMT

test_niak target_test_niak_mnc1-2015-06-11

surchs@www.nitrc.org (Sebastian Urchs) — Thu, 11 Jun 2015 19:13:00 GMT

The new test target that includes scores

test_niak target_test_niak_mnc1-2015-05-15

pbellec@www.nitrc.org (Pierre Bellec) — Sat, 16 May 2015 3:48:00 GMT

Generated with the simexp/octave docker container
https://registry.hub.docker.com/u/simexp/octave/

with NIAK commit bdc332179c38139193c08b17a367a74eb42f9591 at https://github.com/SIMEXP/niak

niak niak "boss" version 0.13.0

pbellec@www.nitrc.org (Pierre Bellec) — Sun, 12 Apr 2015 5:10:00 GMT

T1-fMRI coregistration is now based on normalized mutual information.
A new function niak_wget to fetch datasets online using wget.
A couple of bug fixes.

test_niak target_test_niak_mnc1-0.13.0

pbellec@www.nitrc.org (Pierre Bellec) — Sat, 11 Apr 2015 18:32:00 GMT

Generated with docker container simexp/niak:0.13.0. See https://registry.hub.docker.com/u/simexp/niak/ for more info.

test_niak target_test_niak_mnc1-0.12.21

pbellec@www.nitrc.org (Pierre Bellec) — Sat, 03 Jan 2015 3:43:00 GMT

Generated with Ubuntu 12.04, minc tool-kit 1.9.2, octave 3.8.1, BCT 2014_04_05, psom 1.0.2, niak 0.12.20 (the tests are identical for 0.12.20, but the target was updated using ubuntu 12.04 to match the specs of Travis' test servers).

test_niak target_test_niak_mnc1-0.12.20

pbellec@www.nitrc.org (Pierre Bellec) — Wed, 17 Dec 2014 16:39:00 GMT

test_niak data test niak minc1 version 1.1

pbellec@www.nitrc.org (Pierre Bellec) — Fri, 12 Dec 2014 2:31:00 GMT

Leaner test dataset: less time points and just on functional run per subject.

NeuroImaging Analysis Kit (NIAK) Releases