Cambridge resting-state fMRI time series preprocessed with NIAK 0.12.4

### Content

This work is a derivative from the Cambridge sample found in the [1000 functional connectome project](http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for about 200 young healthy subjects. Time series are packaged in a series of matlab/octave files, one per subject. For each subject, an array featuring about 100 time points for about 500 brain regions is available. The Cambridge preprocessed time series release more specifically contains the following files:

* README.md: a markdown (text) description of the release.

* brain_rois.nii.gz: a 3D nifti volume at 3 mm isotropic resolution, in the MNI non-linear 2009a symmetric space (http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Region number I is filled with Is (background is filled with 0s).

* tseries_rois_SUBJECT_session1_rest.mat: a matlab/octave file for each subject.

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_suppressed: 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.

* time_frames: a Tx1 vector. Each entry is the time of acquisition (in s) of the corresponding volume.

* tseries: a TxN array, where each row is a time sample and each column a region (N=483, numbered as in brain_rois.nii.gz). Note that the number of time samples may vary, as some samples have been removed if tagged with excessive motion.

### Preprocessing

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 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. 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. A region growing algorithm was used to extract 483 regions, as described in (Bellec et al., 2010).

### References

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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.