FCP-INDI/C-PAC: C-PAC Version 1.3.0 Beta
Creators
- Steve Giavasis1
- Daniel Clark2
- Ranjit
- nrajamani3
- Sharad Sikka3
- Anibal Sólon1
- 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 Liska12
- Rosalia Tungaraza
- joshua vogelstein13
- Asier Erramuzpe
- Aimi Watanabe
- Dan Kessler14
- Chris Filo Gorgolewski15
- 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. University of Trento
- 13. johns hopkins university
- 14. University of Michigan
- 15. Stanford University
Description
Dear Colleagues,
We are happy to inform you that we have released CPAC Version 1.3.0 Beta.
New Features 1) MOVIE-FMRI ANALYSIS. Inter-Subject Correlation (ISC) and Inter-Subject Functional Connectivity (ISFC) (Simony et al., 2016). Implementation adapted from BRAINIAK. Dynamic reconfiguration of the default mode network during narrative comprehension. Erez Simony, Christopher J Honey, Janice Chen, Olga Lositsky, Yaara Yeshurun, Ami Wiesel & Uri Hasson. Nature Communications, 7(May 2015), 12141. (https://doi.org/10.1038/ncomms12141) BRAINIAK - https://github.com/brainiak/brainiak 2) GRAPH GENERATION. Users can now generate functional connectivity matrices for any parcellation set using Pearson Correlation, Partial Correlation and Tangent Embedding. Implementation based on Dadi et al (2018). Dadi K., Rahim M., Abraham A., Chyzhyk D., Milham M., Thirion B., Varoquaux G. (2018) Benchmarking functional connectome-based predictive models for resting-state fMRI. <hal-01824205> https://hal.inria.fr/hal-01824205 3) BOOTSTRAP-BASED FUNCTIONAL CONNECTIVITY-BASED PARCELLATION. Users can now seamlessly run Bootstrap Analysis of Stable Clusters (BASC) (Bellec et al., 2008, 2010) on data preprocessed by C-PAC. This is accomplished using PyBASC - a Python implementation of BASC, by Aki Nikolaidis - which is now integrated in C-PAC. Bellec, P., Marrelec, G., & Benali, H. (2008). A bootstrap test to investigate changes in brain connectivity for functional MRI. Statistica Sinica, 1253-1268. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neuroimage, 51(3), 1126-1139. Garcia-Garcia, M., Nikolaidis, A., Bellec, P., Craddock, R. C., Cheung, B., Castellanos, F. X., & Milham, M. P. (2017). Detecting stable individual differences in the functional organization of the human basal ganglia. NeuroImage. https://github.com/AkiNikolaidis/PyBASC Nikolaidis, A., Vogelstein, J., Bellec, P., Milham, M.P. (2018). Improving Corticostriatal Parcellation Through Multilevel Bagging with PyBASC. BioRxiv. 4) ICA-AROMA. Robust de-noising using the ICA-AROMA implementation of Independent Components Analysis for the removal of motion artifacts, as implemented by Maarten Mennes. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. Neuroimage. 2015 May 15;112:267-277. https://github.com/maartenmennes/ICA-AROMA 5) CUSTOM BRAIN EXTRACTION MASKS. You can now provide brain masks created with your own preferred skull-stripping method.
Improvements
- AWS S3 links can now be provided for all ROI and mask inputs in the pipeline configuration. This makes it easier to kick off runs without needing to gather or transfer ROI and mask files to a local disk.
- C-PAC is now compatible with Nipype version 1.1.2 (latest).
Error Fixes
- AWS S3 bucket credentials fixed to allow for anonymous connections.
- An error causing the Visual Quality Control interface to print warnings has been fixed.
- An error where the FSL FEAT Model Preset GUI dialogs would sometimes clear fields prematurely has been fixed.
Coming Soon (Releases 1.4 and 1.5 this winter)
- More ICA denoising options
- FSL Randomise
- Supervised Learning
- A new Graphical User Interface (GUI) Upgrade
Updated user documentation for this release can be found here: http://fcp-indi.github.io/docs/user/index.html
And as always, you can contact us here for user support and discussion: https://groups.google.com/forum/#!forum/cpax_forum
Regards, The CPAC development team.
Files
FCP-INDI/C-PAC-v1.3.0.zip
Files
(29.0 MB)
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Additional details
Related works
- Is supplement to
- https://github.com/FCP-INDI/C-PAC/tree/v1.3.0 (URL)