FCP-INDI/C-PAC: C-PAC Version 1.4.1 Beta
Authors/Creators
- Steve Giavasis1
- Anibal Sólon1
- Daniel Clark2
- nrajamani3
- Ranjit
- Sharad Sikka3
- Zarrar Shehzad4
- John Pellman5
- Caroline Froehlich
- Ranjit Khanuja
- Brian Cheung6
- Cameron Craddock7
- Floris van Vugt
- Sebastian Urchs
- James Kent8
- Ilkay Isik9
- Daniel Lurie10
- Yaroslav Halchenko11
- Adam Liska
- Rosalia Tungaraza
- joshua vogelstein12
- Asier Erramuzpe
- Aimi Watanabe
- Dan Kessler13
- Chris Gorgolewski14
- 1. Child Mind Institute
- 2. @LiveRamp
- 3. CYAN INC
- 4. Yale University
- 5. Columbia University Libraries @cul
- 6. UC Berkeley
- 7. Dell Medical School, University of Texas, Austin
- 8. @HBClab
- 9. Max Planck Institute for Empirical Aesthetic
- 10. University of California, Berkeley
- 11. Dartmouth College, @Debian, @DataLad, @PyMVPA, @fail2ban
- 12. johns hopkins university
- 13. University of Michigan
- 14. Stanford University
Description
New Features
36-Parameter Confound Regression Model. A new nuisance regression option has been introduced into C-PAC for confound regression using whole-brain motion parameters.
- Satterthwaite TD, Elliott MA, Gerraty RT, et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage. 2012;64:240-56.
- (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3811142/)
tCompCor: Temporal Standard Deviation Noise ROI Component-Based Noise Correction. tCompCor has also been introduced into C-PAC as a nuisance regression option, for the removal of physiological noise from the functional time series.
- Yashar Behzadi, Khaled Restom, Joy Liau, Thomas T. Liu. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37(1):90-101
- (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2214855/)
Linear anatomical registration. You can now run linear-only registration-to-template using FSL FLIRT. This allows a much faster processing time for when very high-quality nonlinear anatomical registration is not as important for your analysis.
ndmg Mode. With ndmg-mode enabled, C-PAC runs a leaner preprocessing pipeline and produces connectome graphs using the pipeline configuration originally selected by the ndmg team and Neurodata's pre-selected collection of atlases.
Improvements
- Nuisance Regression Expansion. Along with the new addition of the 36-parameter motion model and tCompCor, the already-existing nuisance regression options have been expanded to include greater degrees of configurability. Refer to our updated User Guide for more details.
Error Fixes
- Fixed an error where C-PAC would not write outputs to an AWS S3 bucket when configured to do so.
- Fixed the "thresh_and_sum" error in the Singularity container that would cause the workflow run to fail.
Coming Soon (v1.4.2 & v1.5.0 - Spring 2019)
- Quasi-Periodic Patterns (QPP) template generation and regression
- New Group-Level Model Builder GUI
- Predictive Eye Estimation Regression (PEER)
- Non-human primate pipeline optimization
- Easy integration & analysis of other preprocessing pipeline results
Files
FCP-INDI/C-PAC-v1.4.1.zip
Files
(29.1 MB)
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Additional details
Related works
- Is supplement to
- https://github.com/FCP-INDI/C-PAC/tree/v1.4.1 (URL)