adrose / xcpengine

Official public repository for the XCP Engine

new home directory for the XCP Engine

Adding modules

The primary output of any module must be an image whose name matches the primary subject identifier.

Change log

v0.5.0 2016.07

• Operational modules (16): asl, alff, confound, coreg, dico, locreg, net, norm, prestats, regress, reho, roiquant, seed, task
• Modules added (6): benchmark, dico, dti2xcp, locreg, roiquant, task
• Multimodal processing potential is extended to task-constrained activation analysis (and internally DTI). Task activation analysis is currently performed through FSL's FEAT, and the XCP Engine can now process any individual-level FEAT design file.
• ROI-wise quantification is centralised into the roiquant module.
• Group-level modules are enabled. The first of these is benchmark.g.mod, which computes motion-related quality metrics for the pipeline that has been run on a cohort.
• The XCP Engine has been restructured to support group-level modules. Interfacing with clusters outside of chead is still unlikely to work.

v0.4.0 and fixes (2016.03)

• RSPIPE deprecated. XCP Engine implemented to call modules and deploy pipelines as needed.
• Operational modules (10): prestats, coreg, confound, regress, net, seed, alff, reho, norm, asl
• Modules added (3): alff, reho, asl
• The XCP Engine now supports multimodal neuroimage processing. For now, this is limited to perfusion (arterial spin labelling) and is a BBL internal-only feature.
• Dependencies are checked at engine launch, and versions are tracked and logged.
• xcpConfig is now configured under the assumption that structural processing has been completed using an ANTs-like pipeline.
• Spatial and temporal filtering now have wrapper scripts that support a variety of approaches (FSL, AFNI, and utility R scripts).
• Help for module configuration options substantially expanded.

v0.3.0 and fixes (2015.10)

• Operational modules (7): prestats, coreg, confound, regress, sca, net, norm
• Modules added (4): confound, regress, sca, net
• Design file creation UI implemented.
• Design file localiser implemented.
• Master subject-level script (restpipe.sh) implemented.
• Confound matrix assembly implemented.
• Confound regression implemented.
• Confound regression module moves masks to the appropriate space.
• Matrix assembly and regression split into separate modules (anticipating future support for voxelwise regressors).
• Modules modified to conform to new paradigm.
• Power et al. (2014) iterative censoring pipeline is nearly implementable.
• Relative RMS displacement can now be included in the confound model.
• Mask erosion implemented.
• Temporal filters will now also filter the confound matrix prior to the regression step.

v0.2.0 and fixes (2015.10)

• Operational modules (3): prestats, coreg, norm
• Modules added (3): prestats, coreg, norm
• Design architecture implemented.
• Pipeline broken into modules.
• Prestats module implemented.
• Coregistration implemented.
• Normalisation implemented.
• Demeaning/detrending added to prestats.
• Extended filter options.

v0.1.0 and fixes (2015.09)

• Original RSPIPE deFEATed
• major debugging

devnotes

Modules implemented or partially implemented

• alff : computes amplitude of low-frequency fluctuations
• aroma : ICA-AROMA-like denoising
• confound : creates a matrix of global confound variables/nuisance regressors
• coreg : computes coregistration of structural and functional images
• locreg : generates localised confound variables/nuisance regressors
• net : extracts an a priori network from 4D timeseries and computes basic network metrics
• norm : moves all images from subject space into template space
• prestats : encompasses numerous preprocessing strategies
• regress : removes effects of global and local nuisance variables from data
• reho : computes regional homogeneity
• sca : seed-based correlation analysis

Planned/in preparation:

• astate : identifies activity states from network or 4D timeseries
• concat : performs all future analyses on subject-concatenated images/nets
• context : runs contextual connectivity pipeline on dynamic FC data
• cspace : computes trajectories through connectomic or activity space
• cstate : identifies connectivity states from dynamic FC
• infomax : computes a data-driven network using the Infomax ICA algorithm
• metacon : computes a metaconnectivity matrix from dynamic FC
• mtd : dynamic FC using the multiplication of temporal derivatives
• mtdregrec : alternative confound regression procedure with imputation
• qavars : obtains variables related to data and analysis quality
• sliwico : dynamic FC using sliding window correlations
• streg : spatiotemporal regression for simple back-reconstruction
• tica : temporal ICA on network timeseries to obtain TFMs
• time : obtains a time-by-time adjacency matrix and computes basic metrics