Neuroimaging Analysis Kit - user

<p>The user's guide of the release 0.6.4.1 of the Neuroimaging Analysis Kit (NIAK). This software includes multiple processing pipelines, mainly targeted at functional magnetic resonance imaging. </p

Deprecated.

<h3>This file collection is broken. Use the following file collection instead:</h3><div>https://figshare.com/articles/COBRE_preprocessed_with_NIAK_0_17_-_lightweight_release/4197885</div

COBRE preprocessed with NIAK 0.17 - lightweight release

<h3>Content</h3><p>This work is a derivative from the COBRE sample found in the <a href="http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html">International Neuroimaging Data-sharing Initiative (INDI)</a>, originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed 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 data processing as well as packaging was implemented by Pierre Bellec, CRIUGM, Department of Computer Science and Operations Research, University of Montreal, 2016.</p><p>The COBRE preprocessed fMRI release more specifically contains the following files:</p><ul><li><code>README.md</code>: a markdown (text) description of the release.</li><li><code>phenotypic_data.tsv.gz</code>: 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 one participant, except the first row which contains the names of the variables (see file below for a description).</li><li><code>keys_phenotypic_data.json</code>: a json file describing each variable found in <code>phenotypic_data.tsv.gz</code>.</li><li><code>fmri_XXXXXXX.tsv.gz</code>: A gzipped tabular-separated value file, with each column representing a confounding variable for the time series of participant XXXXXXX (which is the same participant ID found in <code>phenotypic_data.tsv.gz</code>). Each row corresponds to a time frame, except for the first row, which contains the names of the variables (see file below for a definition).</li><li><code>keys_confounds.json</code>: a json file describing each variable found in the files <code>fmri_XXXXXXX.tsv.gz</code>.</li><li><code>fmri_XXXXXXX.nii.gz</code>: a 3D+t nifti volume at 6 mm isotropic resolution, stored as short (16 bits) integers, in the MNI non-linear 2009a symmetric space (<a href="http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009">http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009</a>). Each fMRI data features 150 volumes.</li></ul><h3><a href="https://github.com/SIMEXP/Projects/blob/master/cobre/README.md#usage-recommendations"></a>Usage recommendations</h3><p>Individual analyses: You may want to remove some time frames with excessive motion for each subject, see the confounding variable called <code>scrub</code> in <code>fmri_XXXXXXX.tsv.gz</code>. Also, after removing these time frames there may not be enough usable data. We recommend a minimum number of 60 time frames. A fairly large number of confounds have been made available as part of the release (slow time drifts, motion paramaters, frame displacement, scrubbing, average WM/Vent signal, COMPCOR, global signal). We strongly recommend regression of slow time drifts. Everything else is optional.</p><p>Group analyses: There will also be some residuals effect of motion, which you may want to regress out from connectivity measures at the group level. The number of acceptable time frames as well as a measure of residual motion (called frame displacement, as described by Power et al., Neuroimage 2012), can be found in the variables <code>Frames OK</code> and <code>FD scrubbed</code> in <code>phenotypic_data.tsv.gz</code>. Finally, the simplest use case with these data is to predict the overall presence of a diagnosis of schizophrenia (values <code>Control</code> or <code>Patient</code> in the phenotypic variable <code>Subject Type</code>). You may want to try to match the control and patient samples in terms of amounts of motion, as well as age and sex. Note that more detailed diagnostic categories are available in the variable <code>Diagnosis</code>.</p><h3><a href="https://github.com/SIMEXP/Projects/blob/master/cobre/README.md#preprocessing"></a>Preprocessing</h3><p>The datasets were analysed using the NeuroImaging Analysis Kit (NIAK <a href="https://github.com/SIMEXP/niak">https://github.com/SIMEXP/niak</a>) version 0.17, under CentOS version 6.3 with Octave (<a href="http://gnu.octave.org/">http://gnu.octave.org</a>) version 4.0.2 and the Minc toolkit (<a href="http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit">http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit</a>) 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 6 mm isotropic resolution.</p><p>Note that a number of confounding variables were estimated and are made available as part of the release. WARNING: no confounds were actually regressed from the data, so it can be done interactively by the user who will be able to explore different analytical paths easily. The “scrubbing” method of (Power et al., 2012), was used to identify the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~120 s of acquisition, is recommended for further analysis. The following nuisance parameters were estimated: 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 six rigid-body motion parameters (Giove et al., 2009), anatomical COMPCOR signal in the ventricles and white matter (Chai et al., 2012), PCA-based estimator of the global signal (Carbonell et al., 2011). The fMRI volumes were not spatially smoothed.</p><h3><a href="https://github.com/SIMEXP/Projects/blob/master/cobre/README.md#references"></a>References</h3><p>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 Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy.</p><p>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. NeuroImage 51 (3), 1126–1139. URL <a href="http://dx.doi.org/10.1016/j.neuroimage.2010.02.082">http://dx.doi.org/10.1016/j.neuroimage.2010.02.082</a></p><p>F. Carbonell, P. Bellec, A. Shmuel. Validation of a superposition model of global and system-specific resting state activity reveals anti-correlated networks. Brain Connectivity 2011 1(6): 496-510. doi:10.1089/brain.2011.0065</p><p>Chai, X. J., Castan, A. N. N., Ongr, D., Whitfield-Gabrieli, S., Jan. 2012. Anticorrelations in resting state networks without global signal regression. NeuroImage 59 (2), 1420-1428. <a href="http://dx.doi.org/10.1016/j.neuroimage.2011.08.048">http://dx.doi.org/10.1016/j.neuroimage.2011.08.048</a> Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294.</p><p>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 studies. NeuroImage 54 (1), 313–327. URL<a href="http://dx.doi.org/10.1016/j.neuroimage.2010.07.033">http://dx.doi.org/10.1016/j.neuroimage.2010.07.033</a></p><p>Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. URL<a href="http://dx.doi.org/10.1016/j.mri.2009.06.004">http://dx.doi.org/10.1016/j.mri.2009.06.004</a></p><p>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<a href="http://dx.doi.org/10.1016/j.neuroimage.2011.10.018">http://dx.doi.org/10.1016/j.neuroimage.2011.10.018</a></p

Preprocessing of fMRI data

<p>This slideshow presents the main preprocessing steps for connectivity analysis of fMRI data, as implemented in [niak](http://simexp.github.io/niak/).</p

ICBM_aging_connectome

<p>Some region-level connectomes for the ICBM aging dataset preprocessed with NIAK</p

Bootstrap Analysis of Stable Clusters

<p>This slideshow presents the main steps for bootstrap analysis of stable clusters in functional magnetic resonance imaging, as implemented in [niak](http://simexp.github.io/niak/).</p

COBRE preprocessed with NIAK 0.12.4

<p><strong>### Content</strong></p> <p>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 preprocessed 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 ).</p> <p>The COBRE preprocessed fMRI release more specifically contains the following files:<br>* <strong>README.md</strong>: a markdown (text) description of the release.</p> <p>* <strong>cobre_model_group.csv </strong>A comma-separated value file, with the sz (1: patient with schizophrenia, 0: control), age, sex, and FD (frame displacement, as defined by Power et al. 2012) variables. Each column codes for one variable, starting with the label, and each line has the label of the corresponding subject.<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<br>removed if tagged with excessive motion. See the _extra.mat file below for more info.<br>* <strong>fmri_szxxxSUBJECT_session1_run1_extra.mat</strong>: a matlab/octave file for each subject.</p> <p>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.</p> <p><strong>### Preprocessing</strong></p> <p>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.<br>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 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.</p> <p><strong>### References</strong></p> <p>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 Organization. Neuroimage, Florence, Italy.</p> <p>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>. Neu-<br>roImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082</p> <p>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.</p> <p>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 stud</em><em>ies</em>. NeuroImage 54 (1), 313–327.<br>URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033</p> <p>Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. <em>Images-based suppression of unwanted global signals in resting-state func</em><em>tional connectivity studies</em>. Magnetic resonance imaging 27 (8), 1058–1064. URL http://dx.doi.org/10.1016/j.mri.2009.06.004</p> <p>Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. <em>Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion</em>. NeuroImage 59 (3), 2142–2154. URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018</p> <p><strong>### Other derivatives</strong></p> <p>This dataset was used in a publication, see the link below.<br>https://github.com/SIMEXP/glm_connectome</p> <p> </p

Cobre Connectomes

<p>Cobre Connectomes in three different flavors:</p> <p>* as gzipped pickles (python)</p> <p>* as .mat files (matlab)</p> <p>* as .npy save files (npy)</p> <p>There is one file per connectome that contains the 2D connectome with subjects stacked in the 3rd dimension. The order of the subjects is the same as in the file subjects.txt</p> <p> </p> <p>You can take a look at the script that was used to generate these files here: http://nbviewer.ipython.org/github/SIMEXP/Projects/blob/master/Misc/make_connectome_notebook.ipynb</p

Quality Control and assessment of the NIAK functional MRI preprocessing pipeline

<div>This Manual is specific to NIAK preprocessed images, it describe a quality control and assessment procedure of preprocessed fMRI data. </div><div><br></div

Simulation study.

<p>Computational time (top), adjusted Rand index (middle) and proportion of correct classifications (bottom) for <i>D</i> = 6 (left) and <i>D</i> = 10 (right).</p