Improve custom confound documentation (PennLINC#1280)

README.rst

@@ -99,10 +99,10 @@ Citing XCP-D
 If you use XCP-D in your research, please use the boilerplate generated by the workflow.
 If you need an immediate citations, please cite the following preprint:
 
-   Mehta, K., Salo, T., Madison, T., Adebimpe, A., Bassett, D. S., Bertolero, M., ... & Satterthwaite, T. D.
-   (2023).
+   Mehta, K., Salo, T., Madison, T. J., Adebimpe, A., Bassett, D. S., Bertolero, M., ... & Satterthwaite, T. D.
+   (2024).
    XCP-D: A Robust Pipeline for the post-processing of fMRI data.
-   *bioRxiv*.
-   doi:10.1101/2023.11.20.567926.
+   *Imaging Neuroscience*, 2, 1-26.
+   doi:10.1162/imag_a_00257.
 
 Please also cite the Zenodo DOI for the version you're referencing.

docs/index.rst

@@ -11,7 +11,7 @@ Contents
 ********
 
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 1
 
    installation
    usage

docs/usage.rst

@@ -438,13 +438,17 @@ plot_design_matrix.html#create-design-matrices>`_.
 
 .. code-block:: python
 
+   import json
+   import os
+
    import numpy as np
+   import pandas as pd
    from nilearn.glm.first_level import make_first_level_design_matrix
 
    N_VOLUMES = 200
    TR = 0.8
    frame_times = np.arange(N_VOLUMES) * TR
-   events_df = pd.read_table("sub-X_ses-Y_task-Z_run-01_events.tsv")
+   events_df = pd.read_table("sub-X_task-Z_events.tsv")
 
    task_confounds = make_first_level_design_matrix(
       frame_times,
@@ -457,11 +461,24 @@ plot_design_matrix.html#create-design-matrices>`_.
    # The design matrix will include a constant column, which we should drop
    task_confounds = task_confounds.drop(columns="constant")
 
+   # Prepare the derivative dataset
+   os.makedirs("/my/project/directory/custom_confounds/sub-X/func", exist_ok=True)
+   # Include a dataset_description.json file
+   with open("/my/project/directory/custom_confounds/dataset_description.json", "w") as fo:
+      json.dump(
+         {
+            "Name": "Custom Confounds",
+            "BIDSVersion": "1.6.0",
+            "DatasetType": "derivative"
+         },
+         fo,
+      )
+
    # Assuming that the fMRIPrep confounds file is named
-   # "sub-X_ses-Y_task-Z_run-01_desc-confounds_timeseries.tsv",
+   # "sub-X_task-Z_desc-confounds_timeseries.tsv",
    # we will name the custom confounds file the same thing, in a separate folder.
    task_confounds.to_csv(
-      "/my/project/directory/custom_confounds/sub-X_ses-Y_task-Z_run-01_desc-confounds_timeseries.tsv",
+      "/my/project/directory/custom_confounds/sub-X/func/sub-X_task-Z_desc-confounds_timeseries.tsv",
       sep="\t",
       index=False,
    )
@@ -502,7 +519,7 @@ Something like this should work:
             desc: confounds
             extension: .tsv
             suffix: timeseries
-         columns:
+         columns:  # Assume the task regressors are called "condition1" and "condition2"
          - condition1
          - condition2
 

xcp_d/data/boilerplate.bib

@@ -1,3 +1,14 @@
+@article{mehta2024xcp,
+  title={XCP-D: A Robust Pipeline for the post-processing of fMRI data},
+  author={Mehta, Kahini and Salo, Taylor and Madison, Thomas J and Adebimpe, Azeez and Bassett, Danielle S and Bertolero, Max and Cieslak, Matthew and Covitz, Sydney and Houghton, Audrey and Keller, Arielle S and others},
+  journal={Imaging Neuroscience},
+  volume={2},
+  pages={1--26},
+  year={2024},
+  publisher={MIT Press},
+  url={https://doi.org/10.1162/imag_a_00257},
+  doi={10.1162/imag_a_00257}
+}
 
 @article{satterthwaite_2013,
 	title = {An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data},

xcp_d/workflows/base.py

@@ -211,7 +211,9 @@ def init_single_subject_wf(subject_id: str):
 
     workflow.__desc__ = f"""
 ### Post-processing of {config.workflow.input_type} outputs
-The eXtensible Connectivity Pipeline- DCAN (XCP-D) [@mitigating_2018;@satterthwaite_2013]
+
+The eXtensible Connectivity Pipeline- DCAN (XCP-D)
+[@mehta2024xcp;@mitigating_2018;@satterthwaite_2013]
 was used to post-process the outputs of *{info_dict["name"]}* version {info_dict["version"]}
 {info_dict["references"]}.
 XCP-D was built with *Nipype* version {nipype_ver} [@nipype1, RRID:SCR_002502].

xcp_d/workflows/bold/cifti.py

@@ -166,9 +166,8 @@ def init_postprocess_cifti_wf(
     inputnode.inputs.confounds_files = run_data["confounds"]
     inputnode.inputs.dummy_scans = dummy_scans
 
-    workflow = Workflow(name=name)
-
     workflow.__desc__ = f"""
+
 #### Functional data
 
 For each of the {num2words(n_runs)} BOLD runs found per subject (across all tasks and sessions),

xcp_d/workflows/bold/nifti.py

@@ -179,9 +179,8 @@ def init_postprocess_nifti_wf(
     inputnode.inputs.confounds_files = run_data["confounds"]
     inputnode.inputs.dummy_scans = dummy_scans
 
-    # Load confounds according to the config
-
     workflow.__desc__ = f"""
+
 #### Functional data
 
 For each of the {num2words(n_runs)} BOLD runs found per subject (across all tasks and sessions),