roiquant performs ROI-wise quantification of voxelwise metrics. For each provided atlas or parcellation of the brain, it produces a table of values for each region of that parcellation. While many modules include internal routines for ROI-wise statistics, this module centralises all ROI-wise measures in a single routine. It accepts any number of ROI maps or parcellations, then computes, for each voxelwise metric, the mean across all voxels in each ROI of each provided parcellation.


Brain atlas or parcellation.

Contains a comma-separated list of the names of the atlases over which regional values should be computed. The atlases should correspond to valid paths in $XCPEDIR/atlas or another appropriate $BRAINATLAS directory.:

# Use the Power 264-sphere parcellation only

# Use both the Power 264 atlas and the Gordon atlas

# Use the 400-node version of the Schaefer atlas

# Use all available resolutions of the Schaefer atlas

# Use all available atlases


Compute mean values over the brain and tissue compartments.

It is also possible to compute the average values over voxels in the entire brain and over voxels in each tissue compartment from a provided anatomical segmentation (e.g., white matter, grey matter, CSF). The flag roiquant_globals instructs the roiquant module whether these values should also be tabulated.:

# Include global means

# Do not include global means


Compute parcel volumes.

The volume of each parcel can be computed by registering the parcellation or atlas into the subject’s native space, counting the number of voxels in each parcel, and finally multiplying the number of voxels by the voxel dimension. This can be more useful for parcellations that are data-driven, such as those produced by atlas fusion techniques.:

# Compute volumes

# Do not compute volumes


Ordinarily, each module will detect whether a particular analysis has run to completion before beginning it. If re-running is disabled, then the module will immediately skip to the next stage of analysis. Otherwise, any completed analyses will be repeated.If you change the run parameters, you should rerun any modules downstream of the change.:

# Skip processing steps if the pipeline detects the expected output

# Repeat all processing steps


Modules often produce numerous intermediate temporary files and images during the course of an analysis. In many cases, these temporary files are undesirable and unnecessarily consume disk space. If cleanup is enabled, any files stamped as temporary will be deleted when a module successfully runs to completion. If a module fails to detect the output that it expects, then temporary files will be retained to facilitate error diagnosis.:

# Remove temporary files

# Retain temporary files