Pipeline modules are self-contained image processing routines. An image processing pipeline is created by combining desired modules; modules are the building blocks of an image processing pipeline. Each module either (a) processes the main brain image (the analyte), for instance by filtering or denoising it, or (b) uses the analyte to produce another dataset, called a derivative. (It’s also possible for modules to use derivatives to produce further derivatives).
Functional image processing¶
Modules that process the analyte for functional data.
- prestats: Formerly an omnibus module for functional preprocessing. Its functionality is largely implemented in
FMRIPREPand additional functions have been moved to other modules.
- confound2: Generates a matrix of nuisance time series for confound regression. Supports most frequently used denoising models, including realignment parameters, tissue-based time series, PCA-derived time series, the global signal, and temporal expansions.
- regress: Executes confound regression using the matrix generated by the confound module. Censors any volumes flagged for poor data quality. Incorporates spatial and temporal filtering.
- Modules that generate derivative maps from functional data.
Anatomical image processing¶
Modules that process the analyte for anatomical data. Note that anatomical imaging processing using the struct module is NOT required unless desired, anatomic processing completed as part of fMRIPREP can be used instead.
- struc: Omnibus module for volumetric anatomical preprocessing. Leverages ANTs to execute N4 bias field correction, SyN diffeomorphic registration, Atropos segmentation (prior-driven or priorless), or the complete ANTs Cortical Thickness pipeline.
Modules that generate derivative maps from anatomical data.
- jlf:: Uses the ANTs Joint Label Fusion algorithm to produce a high-resolution anatomical segmentation of the subject’s anatomical data. Generates a subject-specific atlas of anatomical landmarks that can be used for regional quantification or network mapping.
- gmd: Computes voxelwise grey matter density.
ASL image processing¶
Modules that process the analyte for ASL image to produced CBF are .
- Modules that generate derivative maps from ASL data:
- scorescrub: detects and discards the cbf volumes that might contribute to artifact and
compute average cbf from cbf timeseries by using Bayesian inference method with the aim of removing the white noise.
Modules that generate transforms between different coordinate spaces, or that apply those transforms.
- struc: Computes transforms between a high-resolution anatomical image and a template image representing a standard coordinate space using the top-performing SyN algorithm.
- norm: Applies the requisite transforms (computed by
struc) to shift all derivative maps from subject native space to a standard coordinate space.
- roiquant: Uses provided brain atlases or parcellations to compute, for each voxelwise derivative, a value for each region of interest in each provided brain atlas or parcellation. Converts voxelwise derivatives to regional derivatives.
Connectomics and networks¶
Modules that map or analyse brain networks.
- fcon: Computes the functional connectivity between each pair of regions in each provided brain atlas or parcellation to produce an adjacency matrix for the functional connectome. Computes static FC using a Pearson correlation.
Modules that produce estimates of data quality.
- QCFC: Quality assessment for functional connectivity. Generates voxelwise plots, QC-FC measures, and QC-FC estimates of distance-dependence to facilitate diagnosis of motion-related contamination and assessment of denoising efficacy.