FCP-INDI/C-PAC: C-PAC Version 1.7.1 Beta
Authors/Creators
- Steve Giavasis1
- Anibal Sólon2
- nrajamani3
- Daniel Clark3
- Ranjit
- Xinhui Li1
- Hecheng Jin
- Sharad Sikka4
- Jon Clucas5
- Zarrar Shehzad6
- John Pellman7
- Cameron Craddock8
- Caroline Froehlich
- Ranjit Khanuja
- Brian Cheung9
- Paul Novak
- Azeez Adebimpe
- Floris van Vugt
- James Kent10
- Sebastian Urchs
- Ilkay Isik11
- Yaroslav Halchenko12
- Daniel Lurie13
- Ross Lawrence
- Asier Erramuzpe
- joshua vogelstein14
- Rosalia Tungaraza
- Adam Liska
- Chris Gorgolewski15
- Dan Kessler16
- 1. Child Mind Institute
- 2. PhD Student in CS @ UTexas
- 3. @LiveRamp
- 4. CYAN INC
- 5. @ChildMindInstitute
- 6. Yale University
- 7. @ZuckermanBrain
- 8. Dell Medical School, University of Texas, Austin
- 9. UC Berkeley
- 10. @PsychoinformaticsLab
- 11. Max Planck Institute for Empirical Aesthetic
- 12. Dartmouth College, @Debian, @DataLad, @PyMVPA, @fail2ban
- 13. University of California, Berkeley
- 14. Johns Hopkins University
- 15. Google LLC
- 16. University of Michigan
Description
New Features
ACPC Alignment. Anterior and Posterior Commissure (ACPC) alignment for anatomical preprocessing is now available. This technique may be beneficial for registration quality, primarily in non-human primate data.
Low-pass Filter for Motion Estimates. The ability to run a low-pass filter instead of a notch filter for motion estimate filtering is now available. This filter is adapted from the DCAN Labs filter described in this publication.
Improvements
Speed Increase. The transformation of functional time series data to template space is now parallelizable. Assign multiple CPUs per participant to enable this speed-up.
Motion Estimate Filter Configurability. The filter design of the motion estimate notch and low-pass filters can now be directly configured, if the user wishes to design these filters manually.
Composite Transform. C-PAC now outputs the composite transform from functional (BOLD) space to template space as one warp file. Users can use this file to easily transform their native-space BOLD data to template as needed (if necessary beyond the transforms to template space C-PAC already automatically performs).
Error Fixes
- An error that would prevent users from running frequency bandpass filtering without any other nuisance regression strategies has been resolved.
Coming Soon
- Pipeline Dashboard
- Surface-Based Processing
- BIDS-Derivatives Compatibility
In addition, the C-PAC Docker and Singularity images, as well as the AWS AMI, have all been updated. These provide a quick way to get started.
And as always, you can contact us here for user support and discussion: https://groups.google.com/forum/#!forum/cpax_forum
Files
FCP-INDI/C-PAC-v1.7.1.zip
Files
(78.0 MB)
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Additional details
Related works
- Is supplement to
- https://github.com/FCP-INDI/C-PAC/tree/v1.7.1 (URL)