C-PAC 1.5: Preprocessing Expansion

Dear Colleagues,

The C-PAC team has released C-PAC v1.5 Beta. (See: http://fcp-indi.github.io/docs/user/index.html). The updates include:


  • Phase-Encoding Polarity Distortion Correction (Blip-Up / Blip-Down).
    A new option for distortion correction is available! Phase-Encoding Polarity (commonly known as blip-up/blip-down) employs phase-encoding direction-specific EPI field maps to correct for distortion in the direction of the phase-encoding.

  • N4 Bias Field Correction.
    The ability to run N4 Bias Field Correction in the anatomical preprocessing pipeline has been added, via ANTs’ N4BiasFieldCorrection.

  • Non-Local Means (NLM) filtering.
    NLM has been integrated into the anatomical preprocessing pipeline, via ANTs DenoiseImage.

  • Increased Configurability of Output Resolution.
    Users can now select write-out resolutions with a finer granularity of values, also allowing for native voxel dimension write-outs.

  • Increased Interpolation Configurability.
    Introduced the ability to select a full range of interpolation options for transform application and resampling. LanczosWindowedSinc has been set as the new default for ANTs operations and Sinc for FSL operations.

  • PyPEER Integration.
    C-PAC can now prepare your pipeline results directly for Predictive Eye Estimation. PEER is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye’s orbit.

Evaluating fMRI-Based Estimation of Eye Gaze during Naturalistic Viewing. Jake Son, Lei Ai, Ryan Lim, Ting Xu, Stanley Colcombe, Alexandre Rosa Franco, Jessica Cloud, Stephen LaConte, Jonathan Lisinski, Arno Klein, R. Cameron Craddock, Michael Milham. https://doi.org/10.1101/347765


Several new preprocessing features have been added to C-PAC’s pipeline choices, in an ongoing effort to incrementally expand C-PAC’s configurability. These methods have been adapted from the niworkflows and fmriprep packages (see appropriate links below).


  • Added a selection of Neurodata’s Neuroparc atlases to the C-PAC container, and C-PAC now also performs time-series extraction on these atlases by default (in addition to the original defaults).

  • Improved parallelization for ISC and ISFC runs.

  • Users can now employ the -monkey option for AFNI 3dSkullStrip for brain extraction, for non-human primate data.


  • Fixed an issue where BIDS-format slice timing information was not being read into 3dTshift properly in some cases.

  • Fixed an error preventing Seed-Based Correlation Analysis from running to completion.

  • Fixed an error that would cause the pipeline to crash at the smoothing stage if the write-out resolution for functional preprocessed data and the resolution for functional-derived data were different.

  • Fixed an issue that would prevent output files from being written to the output directory if nuisance regression was disabled.

  • Fixed an issue where ISC and ISFC would not write out the results to the output directory.

In addition, the C-PAC Docker image and AWS AMI have both been updated. These provide a quick way to get started without needing to go through the install process.

And as always, you can contact us here for user support and discussion:

Thank you!