C-PAC Version 1.7.1 Beta

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.

  • https://doi.org/10.1016/j.neuroimage.2013.04.127
  • https://github.com/Washington-University/HCPpipelines/blob/master/PreFreeSurfer/scripts/ACPCAlignment.sh

• 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

C-PAC Version 1.7.0 Beta

New Features

• New Command Line Interface. The cpac pip-installable package is now available. This CLI allows you to use C-PAC from the command line without interacting via container-based (Docker, Singularity) CLI commands.

• Anatomical Longitudinal Pipeline. C-PAC can now explicitly handle longitudinal data. The dedicated anatomical pipeline generates robust participant-specific templates that improve the registration of longitudinal data and increase reliability.

  • [https://www.sciencedirect.com/science/article/pii/S1053811912002765?via%3Dihub]

• Prior-Based Segmentation. DCAN Labs ANTS prior-based tissue segmentation is now available. This approach is thought to be advantageous in the segmentation of the non-human primate brain.

  • DCAN Labs
  • nhp-ABCD-BIDS-pipeline

• Physiological Noise Filtering for Motion Estimates. Adapted from DCAN Labs, C-PAC can now apply a notch filter to mitigate respiratory artifacts that may present issues in motion estimation.

  • DCAN Labs
  • https://www.biorxiv.org/content/10.1101/337360v1.full.pdf

• Tissue Mask Ingress. In an increase of pipeline flexibility, C-PAC can now intake pre-existing anatomical tissue masks if desired, bypassing tissue segmentation. This is particularly important for researchers using manual or manually edited segmentations.

Improvements

• Pipeline speed increase: Motion correction using AFNI 3dvolreg can now be completed faster by dedicating multiple CPUs to each participant. This can provide a major speed increase in data with many TRs, as motion correction is often one of the processing time bottlenecks in a pipeline.

• C-PAC now writes out smoothed functional time series, when spatial smoothing is chosen (previously smoothing was only applied to derivatives).

Documentation Improvements

• The C-PAC User Guide now features versioned access! You can now view the user guide for any current or previous supported version (1.5+) of C-PAC.

Error Fixes

• A memory error triggered by aCompCor DetrendPC in nuisance regression has been resolved.

• An error that sometimes prevented the pipeline from starting when censoring was enabled has been resolved.

• An error that would prevent data configuration generation from finishing if hidden files were in the data directory
  has been resolved.

• The "SameFileError" crash has been resolved. This error did not have any impact, but it would cause many crash files to be generated.

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

C-PAC Version 1.6.2a Beta

Error Fixes

• Brain extraction in Singularity container: an error in the Singularity container with running AFNI 3dSkullStrip in the C-PAC pipeline (RuntimeError: Xvfb did not start) has been resolved. (#541, #1277)

• Censoring during nuisance regression: an error where a pipeline crash would result when the motion threshold was too high and no frames were marked for censoring has been resolved. (#1256)

• C-PAC Warm Restart: an error that would result when running C-PAC a second time with the same working directory (_csv.Error) has been resolved. (#1300, #1302)

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

C-PAC Version 1.6.2 Beta

New Features

• ANTs registration transformation levels and parameters are now fully customizable in the pipeline configuration.

• Expanded Registration Forking Capability. C-PAC can now fork and simultaneously run both T1-template-based and EPI-template-based registration.

• Expanded Motion Correction Options. FSL MCFLIRT is now available as a tool selection for motion estimation and correction, alongside AFNI 3dvolreg.

Improvements & Upgrades

• C-PAC now runs on Python 3. During this transition, some underlying software packages, such as AFNI and ANTs, have been upgraded. Based on our release testing, performance is mostly unchanged. However, small changes can occur due to improvements made to the tools.

• Motion estimation configurability. More motion estimation and correction parameters have been opened for configurability.

• Increased memory efficiency and speed: C-PAC's new default behavior for preparing time-series extraction is to realign the ROI atlases to the functional time series, instead of the other way around. This can be toggled back if desired. This change aims to cut down on memory limit errors that occur during execution with the combination of larger datasets and pipeline parallelization.

• Increased speed: Redundant realignments of ROI atlases and the time series have also been removed.

• The ingress of phase-difference field maps for distortion correction has been streamlined.

Error Fixes

• In the QC Interface, some of the template underlays would default to the MNI152 template, even if the pipeline was configured to use a custom template. This has been resolved.

• Resolved an issue that would prevent the pipeline from starting if ICA-AROMA and blip-up/down distortion correction were both enabled, for ANTs registration-based pipelines.

• Resolved an issue that was preventing Neurodata ndmg-f connectome graphs from being completed in some cases.

• Resolved the "Range parameter must be finite" QC image generation crash.

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

C-PAC Version 1.6.1a Beta

FIXES

• A hotfix has been made to correct an issue where a refined/processed version of the white matter segmentation mask was being sent to boundary-based functional-to-anatomical coregistration, instead of the raw white matter mask as intended.

• An issue where group-level analyses weren't pulling the required input data from the output directories of individual-level preprocessing runs has been fixed.

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

C-PAC Version 1.6.1 Beta

NEW FEATURES

• Pre-configured Pipeline Library. C-PAC can now be easily run with a growing selection of pre-configured pipelines with the --preconfig flag. See the updated User Documentation for the pipelines available.

• Time Series De-spiking. C-PAC can now run AFNI 3dDespike at the beginning of functional preprocessing, if enabled. https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dDespike.html

• Smooth to a Specific FWHM. C-PAC can now run AFNI BlurToFWHM as an option, alongside the original FSL-based smoothing option. https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dBlurToFWHM.html

IMPROVEMENTS

• Temporal Filtering Configurability. The order in which temporal filtering is performed in the pipeline can now be toggled either before or after nuisance regression. If set to before, nuisance regressors are also filtered.

• tCompCor Configurability. Users can now choose polynomial regression of the time series, as well as configure the degree of brain mask erosion for tCompCor nuisance regression.

• BOLD Masking Configurability. The FSL-BET option for BOLD masking now has full access to configurable parameters.

• CSF Regressor Configurability. Ventricle mask refinement of CSF masks for CSF-based nuisance regression can now be toggled.

• More informative messages for when someone provides an incorrect AWS S3 link for input data.

FIXES

• Anatomical in template space QC images are now displayed properly in the QC Interface.

• The 'scan' and 'selector' buttons now work properly again in the QC Interface.

• Translation and rotation motion plots are now labeled correctly.

• A bug where the input data configuration would be formatted incorrectly when no BIDS directory JSON was present has been fixed.

• The "start_idx" pipeline field, which allows users to select the first time point in the time series, now works properly again.

• Fixed an error that would report a duplicate node name when running a FNIRT-based pipeline and ICA-AROMA at the same time.

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

C-PAC Version 1.6.0 Beta

NEW FEATURES

• Anatomical-Refined BOLD Mask Generation. A new method for creating the BOLD mask is available, which uses the T1 brain mask to refine the boundaries of the generated BOLD mask.
• Carpet plots. The Quality Control interface now includes carpet plots, which are useful for rapid visual inspection for motion and other artifacts.

NEW FEATURES - Options that enhance reproducibility with fMRIPrep

• Cosine Filtering for CompCor. Users can now configure nuisance correction to perform cosine filtering on the time series data prior to CompCor calculation.

• Motion Estimation Before Slice Timing Correction. Users now have the option to calculate motion parameter estimation before slice timing correction, with actual motion correction still occurring after slice timing correction. The motion parameters go on to be used in nuisance regression and statistics reporting.

• N4 Correction for EPI. Users can now apply N4 Bias correction to the mean EPI image. This may help enhance coregistration quality.

NEW FEATURES - Options that facilitate nonhuman data processing

• EPI-Based Registration. Users can now register their BOLD data directly to an EPI template, foregoing structural-to-template registration, if desired.

• Template-Based Segmentation. Optimal for use with functional-only pipelines commonly used for rodent data, users can now employ a template-based tissue segmentation approach that applies inverse registration transforms to template-space tissue priors.

• Rodent Data compatibility. The pipeline now has an option to size-scale rodent brains to a larger size as an initial preprocessing step to prepare rodent data for processing tools.

• U-Net Brain Extraction Model. U-Net is a Fully Convolutional Network (FCN) that does image segmentation. Users can now select this option for brain extraction, especially optimal for non-human primate data.

IMPROVEMENTS

• Dramatically reducing working directory size. When running aCompCor: the space requirements of the working directory has been reduced to a sustainable size. A change in the previous version resulted in too-large intermediate files for aCompCor calculation.
  Some redundancies in the pipeline during time series extraction have been removed, and the pipeline now consumes less memory and disk space during processing.

BUG FIXES - Quality Control Interface

• The Quality Control interface is fully functional once again. We apologize for the instability in recent versions, in which QC images and metrics would be generated, but the HTML portal would not generate properly.

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

C-PAC Version 1.5.0 Beta

NEW FEATURES - GENERAL

• 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.

  • http://stnava.github.io/ANTs/
  • http://manpages.ubuntu.com/manpages/trusty/man1/N4BiasFieldCorrection.1.html

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

  • http://stnava.github.io/ANTs/
  • https://manpages.debian.org/experimental/ants/DenoiseImage.1.en.html

• 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.

  • https://github.com/ChildMindInstitute/PyPEER
  • 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
  • https://www.biorxiv.org/content/10.1101/347765v5

NEW FEATURES - CROSS-PIPELINE REPRODUCIBILITY

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).

• New BOLD Masking option.
  A BOLD masking strategy designed by the fmriprep team is now available. The method employs both BET and 3dAutomask for refined BOLD masks. See the User Guide for more information.

  • https://fmriprep.readthedocs.io/en/stable/
  • https://fmriprep.readthedocs.io/en/stable/workflows.html#bold-preprocessing

• ANTs Brain Extraction.
  ANTs Brain extraction has now been integrated as an option for brain extraction. Implementation by the fmriprep team. See the User Guide for more information.

  • http://stnava.github.io/ANTs/
  • https://github.com/ANTsX/ANTs/blob/master/Scripts/antsBrainExtraction.sh
  • https://fmriprep.readthedocs.io/en/stable/
  • https://fmriprep.readthedocs.io/en/stable/workflows.html#bold-preprocessing

• Increased Segmentation Configurability.
  Thresholding options have returned, and new erosion options for anatomical segmentation have been introduced. The erosion implementation was adapted from fmriprep.

  • https://fmriprep.readthedocs.io/en/stable/
  • https://fmriprep.readthedocs.io/en/stable/workflows.html#brain-extraction-brain-tissue-segmentation-and-spatial-normalization

IMPROVEMENTS

• 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).

  • https://github.com/neurodata/neuroparc
  • https://neurodata.io/

• 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.

BUG FIXES

• 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:
https://groups.google.com/forum/#!forum/cpax_forum

C-PAC Version 1.4.3 Beta

NEW FEATURES

• Quasi-Periodic Pattern (QPP) template generation. QPPs are spatial patterns of dynamic connectivity that may be useful in illustrating differences present in neurological or psychiatric disorders. C-PAC can now easily calculate a QPP template derived from a set of functional data.

Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. Majeed W, Magnuson M, Hasenkamp W, Schwarb H, Schumacher EH, Barsalou L, Keilholz SD. Neuroimage. 2011 Jan 15;54(2):1140-50.

Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity. Thompson GJ, Pan WJ, Magnuson ME, Jaeger D, Keilholz SD. Neuroimage. 2014 Jan 1;84:1018-31.

Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal. Yousefi B, Shin J, Schumacher EH, Keilholz SD. Neuroimage. 2018 Feb 15;167:297-308.

• Custom regressors for nuisance regression. Users can now provide custom regressors to C-PAC's nuisance regression suite. This could allow a user to regress out any time series from the data, such as physiological noise regressors, task regressors, or QPP time series prior to individual-level analysis. Refer to the updated User Guide for more information.

• Lesion masking for anatomical registration. For ANTs registration pipelines, users can now provide a lesion mask to improve anatomical registration quality in participant data containing lesions. Refer to the updated User Guide for more information. [http://stnava.github.io/ANTs/]

ERROR FIXES

• Fixed a bug where the working directory would not be deleted (when configured to do so) if the pipeline did not complete successfully.
• Fixed a bug where, in some cases, working directory settings in a custom pipeline would be over-ridden by defaults.

COMING SOON (v1.4.4/v1.5.0 - Summer 2019)

• New Group-Level Model Builder GUI
• Predictive Eye Estimation Regression (PEER)
• Non-human primate pipeline optimization
• Easy integration & analysis of other preprocessing pipeline results

C-PAC Version 1.4.2 Beta

ERROR FIXES

• Fixed a bug where ICA AROMA was not being forked correctly, possibly overriding previous preprocessing.