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