Version 1.4.1 Beta - 2019.03.13

NEW FEATURES

  • 36-Parameter Confound Regression Model. A new nuisance regression option has been introduced into C-PAC for confound regression using whole-brain motion parameters.

Satterthwaite TD, Elliott MA, Gerraty RT, et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage. 2012;64:240-56. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3811142/)

  • tCompCor: Temporal Standard Deviation Noise ROI Component-Based Noise Correction. tCompCor has also been introduced into C-PAC as a nuisance regression option, for the removal of physiological noise from the functional time series.

Yashar Behzadi, Khaled Restom, Joy Liau, Thomas T. Liu. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37(1):90-101. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2214855/)

  • Linear anatomical registration. You can now run linear-only registration-to-template using FSL FLIRT. This allows a much faster processing time for when very high-quality nonlinear anatomical registration is not as important for your analysis.

(https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT/UserGuide)

  • ndmg Mode. With ndmg-mode enabled, C-PAC runs a leaner preprocessing pipeline and produces connectome graphs using the pipeline configuration originally selected by the ndmg team and Neurodata’s pre-selected collection of atlases.

(https://neurodata.io/mri-cloud/)

IMPROVEMENTS

  • Nuisance Regression Expansion. Along with the new addition of the 36-parameter motion model and tCompCor, the already-existing nuisance regression options have been expanded to include greater degrees of configurability. Refer to our updated User Guide for more details.

ERROR FIXES

  • Fixed an error where C-PAC would not write outputs to an AWS S3 bucket when configured to do so.

  • Fixed the “thresh_and_sum” error in the Singularity container that would cause the workflow run to fail.

COMING SOON (v1.4.2 & v1.5.0 - Spring 2019)

  • Quasi-Periodic Patterns (QPP) template generation and regression

  • New Group-Level Model Builder GUI

  • Predictive Eye Estimation Regression (PEER)

  • Non-human primate pipeline optimization

  • Easy integration & analysis of other preprocessing pipeline results