C-PAC 1.3: Movie-fMRI analysis, Brain Parcellation, Graphs and More

Learn more and download at: http://fcp-indi.github.io
Note: C-PAC is also available in a Docker container, on Amazon AWS and through OpenNeuro


  1. MOVIE-FMRI ANALYSIS. Inter-Subject Correlation (ISC) and Inter-Subject Functional Connectivity (ISFC) (Simony et al., 2016). Implementation adapted from BRAINIAK.
  1. 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. https://hal.inria.fr/hal-01824205
  1. 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.
  • Nikolaidis, A., Vogelstein, J., Bellec, P., Milham, M.P. (2018). Improving Corticostriatal Parcellation Through Multilevel Bagging with PyBASC. BioRxiv.
  • https://github.com/AkiNikolaidis/PyBASC
  1. 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
  1. CUSTOM BRAIN EXTRACTION MASKS. You can now provide brain masks created with your own preferred skull-stripping method.


  • 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).


  • 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