Add options to subset atlases (PennLINC#1034)

README.rst

@@ -52,11 +52,13 @@ connectivity matrices.
 
 XCP-D picks up right where `fMRIprep <https://fmriprep.org>`_ ends, directly consuming the outputs
 of fMRIPrep.
-XCP-D leverages the BIDS and NiPrep frameworks to automatically generate denoised BOLD images,
+XCP-D leverages the BIDS and NiPreps frameworks to automatically generate denoised BOLD images,
 parcellated time series, functional connectivity matrices, and quality assessment reports.
-XCP-D can also process outputs from: `NiBabies <https://nibabies.readthedocs.io>`_ and
-`Minimally preprocessed HCP data <https://www.humanconnectome.org/study/hcp-lifespan-development/\
-data-releases>`_.
+XCP-D can also process outputs from: `NiBabies <https://nibabies.readthedocs.io>`_,
+`ABCD-BIDS <https://github.com/DCAN-Labs/abcd-hcp-pipeline>`_,
+`Minimally preprocessed HCP <https://www.humanconnectome.org/study/hcp-lifespan-development/\
+data-releases>`_,
+and `UK Biobank <https://doi.org/10.1016/j.neuroimage.2017.10.034>`_ data.
 
 *Please note that XCP is only compatible with HCP-YA versions downloaded c.a. Feb 2023 at the moment.*
 

docs/links.rst

@@ -4,6 +4,7 @@
 .. _ANTs: https://stnava.github.io/ANTs/
 .. _AFNI: https://afni.nimh.nih.gov/
 .. _`Connectome Workbench`: https://www.humanconnectome.org/software/connectome-workbench.html
+.. _`ABCD-BIDS`: https://github.com/DCAN-Labs/abcd-hcp-pipeline
 .. _`HCP Pipelines`: https://humanconnectome.org/software/hcp-mr-pipelines/
 .. _`Docker Engine`: https://www.docker.com/products/container-runtime
 .. _`Docker`: https://docs.docker.com

docs/outputs.rst

@@ -72,6 +72,14 @@ The 4S atlas is used in the same manner across three PennLINC BIDS Apps:
 XCP-D, QSIPrep_, and ASLPrep_, to produce synchronized outputs across modalities.
 For more information about the 4S atlas, please see https://github.com/PennLINC/AtlasPack.
 
+.. tip::
+
+   You can choose to only use a subset of the available atlases by using the ``--atlases``
+   parameter.
+
+   Alternatively, if you want to skip the parcellation step completely,
+   you can use the ``--skip-parcellation`` parameter.
+
 Atlases are written out to the ``atlases`` subfolder, following BEP038.
 
 .. code-block::

docs/usage.rst

@@ -15,8 +15,8 @@ Running XCP-D
 Execution and Input Formats
 ***************************
 
-The *XCP-D* workflow takes `fMRIPRep`, `NiBabies` and `HCP` outputs in the form of BIDS
-derivatives.
+The *XCP-D* workflow takes `fMRIPRep`_, `NiBabies`_, `HCP Pipelines`_, `ABCD-BIDS`_,
+and `UK Biobank` outputs in the form of BIDS derivatives.
 In these examples, we use an fmriprep output directory.
 
 The outputs are required to include at least anatomical and functional outputs with at least one

docs/workflows.rst

@@ -36,8 +36,8 @@ Surface normalization
 ---------------------
 :func:`~xcp_d.workflows.anatomical.init_warp_surfaces_to_template_wf`
 
-If the ``--warp-surfaces-native2std`` is used, then fsnative surface files from the preprocessing
-derivatives will be warped to fsLR-32k space.
+If the ``--warp-surfaces-native2std`` flag is used,
+then fsnative surface files from the preprocessing derivatives will be warped to fsLR-32k space.
 
 .. important::
 
@@ -504,15 +504,20 @@ ReHo
 :func:`~xcp_d.workflows.restingstate.init_reho_cifti_wf`
 
 
-Parcellation and functional connectivity estimation
-===================================================
+Parcellation and functional connectivity estimation [OPTIONAL]
+==============================================================
 :func:`~xcp_d.workflows.connectivity.init_functional_connectivity_nifti_wf`,
 :func:`~xcp_d.workflows.connectivity.init_functional_connectivity_cifti_wf`
 
-The ``filtered, denoised BOLD`` is fed into a functional connectivity workflow,
+If the user chooses,
+the ``filtered, denoised BOLD`` is fed into a functional connectivity workflow,
 which extracts parcel-wise time series from the BOLD using several atlases.
 These atlases are documented in :doc:`outputs`.
 
+Users can control which atlases are used with the ``--atlases`` parameter
+(by default, all atlases are used),
+or can skip this step entirely with ``--skip-parcellation``.
+
 The resulting parcellated time series for each atlas is then used to generate static functional
 connectivity matrices, as measured with Pearson correlation coefficients.