seed performs seed-based correlation analyses given a seed region or set of seed regions. For each seed region, which may be provided either as a 3D volume (mask) in NIfTI format or coordinates. seed computes the pairwise connectivity between each voxel and the seed region, for instance using the Pearson correlation coefficient between timeseries (Biswal et al., 1995).

seed_points,seed_names and seed_radius

seed_names is three-letters to identify a seed for naming purpose and can be more than one.

If this field is left blank, then no seed-based correlation analysis will be performed. The seed_points are three coordnates (in mm) of the seed point in template space. seed_points and seed_names can be specify as shown below:

# for  one seed point correlation

seed_names[cxt]=PCC # for seed at PCC seed_points[cxt]=0,-62,24 # seed location of PCC seed_radius[cxt]=8 # 8mm radius, 5mm will de used as if radius is not specify

# for more than one seed loaction seed_names[cxt]=PCC#VMF#LOC # PCC, VMF and LOC seed_points[cxt]=0,-62,24#0,34,-14#-36,-52,-2 # seed locations


A 3D mask can also be supply the mask must be derived from the template. xcpEngine assumes that the mask in the same dimesnion as template:


seed_sptf and seed_smo

Spatial smoothing parameters.

Endemic noise, for instance due to physiological signals or scanner activity, can introduce spurious or artefactual results in single voxels. The effects of noise-related artefacts can be mitigated by spatially filtering the data, thus dramatically increasing the signal-to-noise ratio. However, spatial smoothing is not without its costs: it effectively reduces volumetric resolution by blurring signals from adjacent voxels. The spatial smoothing implemented in the seed module (i) keeps the unsmoothed analyte image for downstream use and (ii) creates a derivative image that is smoothed using the specified kernel. This allows either the smoothed or the unsmoothed version of the image to be used in any downstream modules as appropriate.:

# No smoothing

# Gaussian kernel (fslmaths) of FWHM 6 mm

# SUSAN kernel (FSL's SUSAN) of FWHM 4 mm

# Uniform kernel (AFNI's 3dBlurToFWHM) of FWHM 5 mm

seed_sptf specifies the type of spatial filter to apply for smoothing, while seed_smo specifies the full-width at half-maximum (FWHM) of the smoothing kernel in mm.

  • Gaussian smoothing applies the same Gaussian smoothing kernel across the entire volume.
  • SUSAN-based smoothing restricts mixing of signals from disparate tissue classes (Smith and Brady, 1997).
  • Uniform smoothing applies smoothing to all voxels until the smoothness computed at every voxel attains the target value.
  • Uniform smoothing may be used as a compensatory mechanism to reduce the effects of subject motion on the final processed image (Scheinost et al., 2014).


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

Expected outputs

A sub-directory of seed_names is created in seed directory. The directory constist of::
  • prefix_connectivity_{seed_name}_seed.nii.gz # seed mask in BOLD space
  • prefix_connectivity_{seed_name}_sm*.nii.gz # seed correlation map
  • prefix_connectivity_{seed_name}Z_sm*.nii.gz # Fisherz transfromed seed correlation map
  • prefix_connectivity_{seed_name}_ts.1D # time series of seed point