Group functional template generated with BASC on cognitively normal elderly and mild cognitive impairment populations
Creators: name: Tam, Angela
name: Dansereau, Christian
name: AmanPreet Badhwar
name: Orban, Pierre
name: Belleville, Sylvie
name: Chertkow, Howard
name: Dagher, Alain
name: Hanganu, Alexandru
name: Oury Monchi
name: Rosa-Neto, Pedro
name: Shmuel, Amir
name: Seqian Wang
name: Breitner, John
name: Bellec, Pierre
name: Alzheimer's Disease Neuroimaging Initiative
Publisher: name: figshare
Year: 2015
Subjects: subject: 60102 Bioinformatics
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Neuroscience
subject: Biomarkers

description: <strong>### Content</strong> This work is derived from the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) and three samples from Montreal, Canada, as described in the following publication: Tam et al, Consistent inter-protocol differences in resting-state functional connectomes between normal aging and mild cognitive impairment (http://dx.doi.org/10.1101/019646). It includes group brain parcellations containing 33 clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment. The brain parcellations have been generated using a method called bootstrap analysis of stable clusters (BASC, Bellec et al., 2010) and a resolution of 33 clusters was selected using a data-driven method called MSTEPS (Bellec, 2013). This release more specifically contains the following files: * README.md: a markdown (text) description of the release. * brain_parcellation_mcinet_basc_33clusters_(sym,asym).(mnc,nii).gz: a 3D volume (either in .mnc or .nii format) at 3 mm isotropic resolution, in the MNI non-linear 2009a space (http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Region number I is filled with Is (background is filled with 0s). Note that four versions of the template are available, ending with either mnc_sym, mnc_asym, nii_sym or nii_asym. The mnc flavor contains files in the minc format, while the nii flavor has files in the nifti format. The asym flavor contains brain images that have been registered in the asymmetric version of the MNI brain template (reflecting that the brain is asymmetric), while with the sym flavor they have been registered in the symmetric version of the MNI template. The symmetric template has been forced to be symmetric anatomically, and is therefore ideally suited to study homotopic functional connections in fMRI: finding homotopic regions simply consists of flipping the x-axis of the template. * labels_brain_parcellation_mcinet_basc_33clusters_(sym,asym).csv: a comma-separated value file containing the numerical IDs, labels, and volumes of the clusters in the 3D volumes. <strong>### Preprocessing</strong> The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.12.18, 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 50 unscrubbed volumes per run 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. <strong>### Bootstrap Analysis of Stable Clusters</strong> Brain parcellations were derived using BASC (Bellec et al. 2010). A region growing algorithm was first applied to reduce the brain into regions of roughly equal size, set to 1000 mm3. The BASC used 100 replications of a hierarchical clustering with Ward's criterion on resampled individual time series, using circular block bootstrap. A consensus clustering (hierarchical with Ward's criterion) was generated across all the individual clustering replications pooled together, hence generating group clusters. The generation of group clusters was itself replicated by bootstraping subjects 500 times, and a (final) consensus clustering (hierarchical Ward's criterion) was generated on the replicated group clusters. The MSTEPS procedure (Bellec et al., 2013) was implemented to select a data-driven subset of scales in the range 5-200, approximating the group stability matrices up to 5% residual energy, through linear interpolation over selected scales. This resulted in 8 resolutions: 4, 6, 12, 22, 33, 65, 111, 208. Note that the number of resolutions was selected by the MSTEPS procedure in a data-driven fashion, and that the number of individual, group and final (consensus) number of clusters were not necessarily identical. Here we present a parcellation of 33 clusters because it was the lowest number of clusters for which the default mode network (that was of a priori interest) could be clearly decomposed into subnetworks. <strong>### References</strong> 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. 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 http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Bellec, P., Jun. 2013. Mining the Hierarchy of Resting-State Brain Networks: Selection of Representative Clusters in a Multiscale Structure. In: Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on. pp. 54–57. 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. 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 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 functional 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 <strong>### Other derivatives</strong> The datasets that were used to generate the parcellations are described in a publication, see the following link: https://github.com/SIMEXP/mcinet