Pre-configured Pipelines

human

default: The Default Pipeline

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_default.yml

Note

C-PAC runs this pipeline by default, and it is not necessary to invoke the –preconfig flag to run it.

Note

Changed in version 1.8.5: This pipeline was modified during the v1.8.5 release cycle. See Version 1.8.5 Beta (May 24, 2023) for details. The previous default pipeline has been preserved as default-deprecated

C-PAC is packaged with a default processing pipeline so that you can get your data preprocessing and analysis started immediately. Just pull the C-PAC Docker container and kick off the container with your data, and you’re on your way.

The default processing pipeline performs fMRI processing using four strategies, with and without global signal regression, with and without bandpass filtering.

Anatomical processing begins with conforming the data to RPI orientation and removing orientation header information that will interfere with further processing. A non-linear transform between skull-on images and a 2mm MNI brain-only template are calculated using ANTs[1].

Changed in version 1.8.5: Images are them skull-stripped using FSL’s BET[2] (was using AFNI’s 3dSkullStrip[3] prior to v1.8.5. See Version 1.8.5 Beta (May 24, 2023) for details.) and subsequently segmented into WM, GM, and CSF using FSL’s FAST tool[4].

The resulting WM mask was multiplied by a WM prior map that was transformed into individual space using the inverse of the linear transforms previously calculated during the ANTs procedure. A CSF mask was multiplied by a ventricle map derived from the Harvard-Oxford atlas distributed with FSL[5]. Skull-stripped images and grey matter tissue maps are written into MNI space at 2mm resolution.

Functional preprocessing begins with resampling the data to RPI orientation, and slice timing correction. Next, motion correction is performed using a two-stage approach in which the images are first coregistered to the mean fMRI and then a new mean is calculated and used as the target for a second coregistration (AFNI 3dvolreg[6]). A 7 degree of freedom linear transform between the mean fMRI and the structural image is calculated using FSL’s implementation of boundary-based registration[4]. Nuisance variable regression (NVR) is performed on motion corrected data using a 2nd order polynomial, a 24-regressor model of motion[7], 5 nuisance signals, identified via principal components analysis of signals obtained from white matter (CompCor[8]), and mean CSF signal. WM and CSF signals were extracted using the previously described masks after transforming the fMRI data to match them in 2mm space using the inverse of the linear fMRI-sMRI transform. The NVR procedure is performed twice, with and without the inclusion of the global signal as a nuisance regressor. The residuals of the NVR procedure are processed with and without bandpass filtering (0.01Hz < f < 0.1Hz), written into MNI space at 3mm resolution and subsequently smoothed using a 6mm FWHM kernel.

Several different individual level analysis are performed on the fMRI data including:

• Amplitude of low frequency fluctuations (alff)[9]: the variance of each voxel is calculated after bandpass filtering in original space and subsequently written into MNI space at 2mm resolution and spatially smoothed using a 6mm FWHM kernel.

• Fractional amplitude of low frequency fluctuations (falff)[10]: Similar to alff except that the variance of the bandpassed signal is divided by the total variance (variance of non-bandpassed signal).

• Regional homogeneity (ReHo)[11]: a simultaneous Kendall rank correlation is calculated between each voxel’s time course and the time courses of the 27 voxels that are face, edge, and corner touching the voxel. ReHo is calculated in original space and subsequently written into MNI space at 2mm resolution and spatially smoothed using a 6mm FWHM kernel.

• Voxel mirrored homotopic connectivity (VMHC)[12]: an non-linear transform is calculated between the skull-on anatomical data and a symmetric brain template in 2mm space. Using this transform, processed fMRI data are written in to symmetric MNI space at 2mm and the correlation between each voxel and its analog in the contralateral hemisphere is calculated. The Fisher transform is applied to the resulting values, which are then spatially smoothed using a 6mm FWHM kernel.

• Weighted and binarized degree centrality (DC)[13]: fMRI data is written into MNI space at 2mm resolution and spatially smoothed using a 6mm FWHM kernel. The voxel x voxel similarity matrix is calculated by the correlation between every pair of voxel time courses and then thresholded so that only the top 5% of correlations remain. For each voxel, binarized DC is the number of connections that remain for the voxel after thresholding and weighted DC is the average correlation coefficient across the remaining connections.

• Eigenvector centrality (EC)[14]: fMRI data is written into MNI space at 2mm resolution and spatially smoothed using a 6mm FWHM kernel. The voxel x voxel similarity matrix is calculated by the correlation between every pair of voxel time courses and then thresholded so that only the top 5% of correlations remain. Weighted EC is calculated from the eigenvector corresponding to the largest eigenvalue from an eigenvector decomposition of the resulting similarity. Binarized EC is the first eigenvector of the similarity matrix after setting the non-zero values in the resulting matrix are set to 1.

• Local functional connectivity density (lFCD)[15]: fMRI data is written into MNI space at 2mm resolution and spatially smoothed using a 6mm FWHM kernel. For each voxel, lFCD corresponds to the number of contiguous voxels that are correlated with the voxel above 0.6 (r>0.6). This is similar to degree centrality, except it only includes the voxels that are directly connected to the seed voxel.

• 10 intrinsic connectivity networks (ICNs) from dual regression[16]: a template including 10 ICNs from a meta-analysis of resting state and task fMRI data[17] is spatially regressed against the processed fMRI data in MNI space. The resulting time courses are entered into a multiple regression with the voxel data in original space to calculate individual representations of the 10 ICNs. The resulting networks are written into MNI space at 2mm and then spatially smoothed using a 6mm FWHM kernel.

• Seed correlation analysis (SCA): preprocessed fMRI data is to match template that includes 160 regions of interest defined from a meta-analysis of different task results[18]. A time series is calculated for each region from the mean of all intra-ROI voxel time series. A separate functional connectivity map is calculated per ROI by correlating its time course with the time courses of every other voxel in the brain. Resulting values are Fisher transformed, written into MNI space at 2mm resolution, and then spatially smoothed using a 6mm FWHM kernel.

• Time series extraction: similar the procedure used for time series analysis, the preprocessed functional data is written into MNI space at 2mm and then time series for the various atlases are extracted by averaging within region voxel time courses. This procedure was used to generate summary time series for the automated anatomic labelling atlas[19], Eickhoff-Zilles atlas[20], Harvard-Oxford atlas[21], Talaraich and Tournoux atlas[22], 200 and 400 regions from the spatially constrained clustering voxel timeseries[23], and 160 ROIs from a meta-analysis of task results[18]. Time series for 10 ICNs were extracted using spatial regression.

References

[1]

Avants, B., Epstein, C., Grossman, M., and Gee, J. 2008 February. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis, 12 :1 , pp. 26–41 doi:10.1016/j.media.2007.06.004

[2]

Smith, S. M. 2002 November. Fast robust automated brain extraction. Human brain mapping, 17 :3 , pp. 143–155 doi:10.1002/hbm.10062

[3]

Cox, R. W. 1996 June. AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and biomedical research, 29 :3 , pp. 162–173 doi:10.1006/cbmr.1996.0014

[4] (1,2)

Zhang, Y., Brady, M., and Smith, S. Jan./2001. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. Ieee transactions on medical imaging, 20 :1 , pp. 45–57 doi:10.1109/42.906424

[5]

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E.J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M., and Matthews, P. M. 2004 January. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23 , pp. S208-S219 doi:10.1016/j.neuroimage.2004.07.051

[6]

Cox, R. W. and Jesmanowicz, A. 1999. Real-time 3D image registration for functional MRI. Magnetic resonance in medicine, 42 :6 , pp. 1014–1018 doi:10.1002/(SICI)1522-2594(199912)42:6<1014::AID-MRM4>3.0.CO;2-F

[7]

Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., and Turner, R. 1996 March. Movement-related effects in fMRI time-series. Magnetic resonance in medicine, 35 :3 , pp. 346–355 doi:10.1002/mrm.1910350312

[8]

Behzadi, Y., Restom, K., Liau, J., and Liu, T. T. 2007 August. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage, 37 :1 , pp. 90–101 doi:10.1016/j.neuroimage.2007.04.042

[9]

Zang, Y.-F., He, Y., Zhu, C.-Z., Cao, Q.-J., Sui, M.-Q., Liang, M., Tian, L.-X., Jiang, T.-Z., and Wang, Y.-F. 2007 March. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain & development, 29 :2 , pp. 83–91 doi:10.1016/j.braindev.2006.07.002

[10]

Zou, Q.-H., Zhu, C.-Z., Yang, Y., Zuo, X.-N., Long, X.-Y., Cao, Q.-J., Wang, Y.-F., and Zang, Y.-F. 2008 July. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of neuroscience methods, 172 :1 , pp. 137–141 doi:10.1016/j.jneumeth.2008.04.012

[11]

Zang, Y., Jiang, T., Lu, Y., He, Y., and Tian, L. 2004 May. Regional homogeneity approach to fMRI data analysis. Neuroimage, 22 :1 , pp. 394–400 doi:10.1016/j.neuroimage.2003.12.030

[12]

Stark, D. E., Margulies, D. S., Shehzad, Z. E., Reiss, P., Kelly, A. M. C., Uddin, L. Q., Gee, D. G., Roy, A. K., Banich, M. T., Castellanos, F. X., and Milham, M. P. 2008 December. Regional Variation in Interhemispheric Coordination of Intrinsic Hemodynamic Fluctuations. The journal of neuroscience, 28 :51 , pp. 13754–13764 doi:10.1523/JNEUROSCI.4544-08.2008

[13]

Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., and Johnson, K. A. 2009 February. Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease. The journal of neuroscience, 29 :6 , pp. 1860–1873 doi:10.1523/JNEUROSCI.5062-08.2009

[14]

Lohmann, G., Margulies, D. S., Horstmann, A., Pleger, B., Lepsien, J., Goldhahn, D., Schloegl, H., Stumvoll, M., Villringer, A., and Turner, R. 2010 April. Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain. Plos one, 5 :4 , pp. e10232 doi:10.1371/journal.pone.0010232

[15]

Tomasi, D. and Volkow, N. D. 2010 May. Functional connectivity density mapping. Proceedings of the national academy of sciences, 107 :21 , pp. 9885–9890 doi:10.1073/pnas.1001414107

[16]

Beckmann, C., Mackay, C., Filippini, N., and Smith, S. 2009 July. Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. Neuroimage, 47 , pp. S148 doi:10.1016/S1053-8119(09)71511-3

[17]

Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., and Beckmann, C. F. 2009 August. Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the national academy of sciences, 106 :31 , pp. 13040–13045 doi:10.1073/pnas.0905267106

[18] (1,2)

Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., Barnes, K. A., Dubis, J. W., Feczko, E., Coalson, R. S., Pruett, J. R., Barch, D. M., Petersen, S. E., and Schlaggar, B. L. 2010 September. Prediction of Individual Brain Maturity Using fMRI. Science, 329 :5997 , pp. 1358–1361 doi:10.1126/science.1194144

[19]

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., and Joliot, M. 2002 January. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. Neuroimage, 15 :1 , pp. 273–289 doi:10.1006/nimg.2001.0978

[20]

Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., and Zilles, K. 2005 May. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage, 25 :4 , pp. 1325–1335 doi:10.1016/j.neuroimage.2004.12.034

[21]

Harvard-Oxford cortical and subcortical structural atlases, Fslwiki: atlases.

[22]

Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas, C. S., Rainey, L., Kochunov, P. V., Nickerson, D., Mikiten, S. A., and Fox, P. T. 2000 July. Automated Talairach Atlas labels for functional brain mapping. Human brain mapping, 10 :3 , pp. 120–131 doi:10.1002/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8

[23]

Craddock, R. C., James, G.Andrew, Holtzheimer, P. E., Hu, X. P., and Mayberg, H. S. 2012 August. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human brain mapping, 33 :8 , pp. 1914–1928 doi:10.1002/hbm.21333

anat-only: Default with Anatomical Preprocessing Only

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_anat-only.yml

Based on the preprocessing decisions of the default pipeline, this preconfiguration allows you to immediately kick off a run with only anatomical preprocessing selected. This includes:

• Brain extraction (via AFNI 3dSkullStrip)

• Tissue segmentation (via FSL FAST)

• Registration to template (via ANTs/ITK)

preproc: Default without Derivatives

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_preproc.yml

Based on the preprocessing decisions of the default pipeline, this preconfiguration allows you to preprocess all of your data, without launching into calculation of outputs and data derivatives. This includes:

Anatomical:

• Brain extraction (via AFNI 3dSkullStrip)

• Tissue segmentation (via FSL FAST)

• Registration to template (via ANTs/ITK)

Functional:

• Slice-timing correction

• Motion estimation & correction

• Co-registration to structural

• Nuisance correction & filtering

• Registration to template (via ANTs/ITK)

fmriprep-options: fmriprep-Options Pipeline

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_fmriprep-options.yml

This pipeline is designed to increase reproducibility with the preprocessing results of the fmriprep pipeline package[24] produced by the Poldrack Lab at Stanford University.

References

• Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., and Gorgolewski, K. J. 2019 January. fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature methods, 16 :1 , pp. 111–116 doi:10.1038/s41592-018-0235-4

• NiPreps Developers. 2020 September. fMRIPrep: A Robust Preprocessing Pipeline for fMRI Data.

[24]

fMRIPrep developers. 2016. fMRIPrep: A Robust Preprocessing Pipeline for fMRI Data — fMRIPrep stable version documentation.

ndmg: Neurodata’s ‘ndmg-f’ Pipeline

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_ndmg.yml

This pipeline is the result of Neurodata’s study to converge upon the intersection of pipeline configuration decisions that maximizes discriminability between participants’ data, drawing from the connectome graphs produced (labeled ‘ndmg_graph’ in the C-PAC output directory). This pipeline invokes a minimal set of preprocessing.

Note, the ‘ndmg_graph’ connectome graph outputs are always produced by C-PAC. This pipeline configuration simply replicates the preprocessing methods described in the paper, linked below.

References

• Kiar, G., Bridgeford, E. W., Roncal, W. R. G., Consortium for Reliability and Reproducibility (CoRR), Chandrashekhar, V., Mhembere, D., Ryman, S., Zuo, X.-N., Margulies, D. S., Craddock, R. C., Priebe, C. E., Jung, R., Calhoun, V. D., Caffo, B., Burns, R., Milham, M. P., and Vogelstein, J. T. 2018 April. A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability. doi:10.1101/188706

• NeuroData. NeuroData's MRI to Graphs.

• NeuroData. 2018 January. ndmg v0.1.0.

rbc-options: ReproBrainChart Options Pipeline

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_rbc-options.yml

RBC-options pipeline was built and integrated in C-PAC based on the Reproducible Brain Charts initiative, which aims to aggregate and harmonize phenotypic and neuroimage data to delineate node mechanisms regarding developmental basis of psychopathology in youth and yield reproducible growth charts of brain development[25].

References

[25]

Hoffmann, M. S., Salum, G., Moore, T. M., Milham, M., and Satterthwaite, T. 2021 June. Reproducible Brain Charts initiative - Reliability and Validity of Bifactor Models of Dimensional Psychopathology in Youth. doi:10.17605/OSF.IO/UWY5N

non-human primate

monkey: Default with Monkey Preprocessing

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_monkey.yml

This pipeline is based on the work of Xu et al.[26] and nhp-ABCD-BIDS-pipeline.[27]

References

• Ramirez, J. S. B., Graham, A. M., Thompson, J. R., Zhu, J. Y., Sturgeon, D., Bagley, J. L., Thomas, E., Papadakis, S., Bah, M., Perrone, A., Earl, E., Miranda-Dominguez, O., Feczko, E., Fombonne, E. J., Amaral, D. G., Nigg, J. T., Sullivan, E. L., and Fair, D. A. 2020 March. Maternal Interleukin-6 Is Associated With Macaque Offspring Amygdala Development and Behavior. Cerebral cortex, 30 :3 , pp. 1573–1585 doi:10.1093/cercor/bhz188

• Wang, X., Li, X.-H., Cho, J. W., Russ, B. E., Rajamani, N., Omelchenko, A., Ai, L., Korchmaros, A., Sawiak, S., Benn, R. A., Garcia-Saldivar, P., Wang, Z., Kalin, N. H., Schroeder, C. E., Craddock, R. C., Fox, A. S., Evans, A. C., Messinger, A., Milham, M. P., and Xu, T. 2021 July. U-net model for brain extraction: Trained on humans for transfer to non-human primates. Neuroimage, 235 , pp. 118001 doi:10.1016/j.neuroimage.2021.118001

[26]

Xu, T., Sturgeon, D., Ramirez, J. S. B., Froudist-Walsh, S., Margulies, D. S., Schroeder, C. E., Fair, D. A., and Milham, M. P. 2019 June. Interindividual variability of functional connectivity in awake and anesthetized rhesus macaque monkeys. Biological psychiatry: cognitive neuroscience and neuroimaging, 4 :6 , pp. 543–553 doi:10.1016/j.bpsc.2019.02.005

[27]

Sturgeon, D., Earl, E., Snider, K., Perrone, A., Ramirez, J., Mitchell, A. J., and Fair, D. Zenodo, 2020 June. DCAN-Labs/nhp-abcd-bids-pipeline Version 0.1.0. doi:10.5281/zenodo.3888969#.Xw31IpNKjyU

Based on the preprocessing decisions of the default pipeline, this preconfiguration allows you to preprocess all of your macaque data, includes:

Anatomical:

• Brain extraction (via U-Net)

• Tissue segmentation (via ANTs-prior based)

• Registration to template (via ANTs/ITK)

Functional:

• Despike

• Slice-timing correction

• Motion estimation & correction

• EPI N4 Bias Correction

• Brain Extraction (Anatomical-refined)

• Co-registration to structural

• Nuisance correction & filtering

• Registration to template (via ANTs/ITK)

• spatial smoothing

testing

benchmark-ANTS: C-PAC Benchmark with ANTs Registration

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_benchmark-ANTS.yml

The benchmark pipeline has remained mostly unchanged since the project’s inception, and is used at the end of each release cycle to ensure the results of C-PAC’s key outputs have not changed. It is designed to test a wide range of pipeline options. This pipeline is based on registration-to-template using the ANTs/ITK toolset, as this decision impacts many other aspects of the pipeline further downstream.

benchmark-FNIRT: C-PAC Benchmark with FSL FNIRT Registration

Pipeline Configuration YAML: https://github.com/FCP-INDI/C-PAC/blob/main/CPAC/resources/configs/pipeline_config_benchmark-FNIRT.yml

The benchmark pipeline has remained mostly unchanged since the project’s inception, and is used at the end of each release cycle to ensure the results of C-PAC’s key outputs have not changed. It is designed to test a wide range of pipeline options. This pipeline is based on registration-to-template using the FSL FLIRT & FNIRT, as this decision impacts many other aspects of the pipeline further downstream.