Merge remote-tracking branch 'upstream/main' into bidsuri-v2

docs/usage.rst

@@ -279,6 +279,10 @@ Again, the standard space is determined based on other files.
 There are additional keys that control how mesh and morphometry files are selected.
 Please refer to ``io_spec.yaml`` for more information on them.
 
+``atlas`` selects atlases for parcellation.
+This is primarily useful for specifying spaces or resolutions.
+This field is not reflected in the ``io_spec.yaml`` file.
+
 
 Example bids-filter-file
 ========================

docs/workflows.rst

@@ -580,54 +580,54 @@ Finally, there is the ``query`` field in each confound set.
 This defines the BIDS entities that will be used to find the confounds file associated with the
 preprocessed BOLD file that is being post-processed by XCP-D.
 
-```yaml
-name: aroma_gsr
-description: |
-   Nuisance regressors were selected according to the 'aroma_gsr' strategy.
-   AROMA motion-labeled components [@pruim2015ica], mean white matter signal,
-   mean cerebrospinal fluid signal, and mean global signal were selected as
-   nuisance regressors [@benchmarkp;@satterthwaite_2013].
-confounds:
-   preproc_confounds:
-      dataset: preprocessed
-      query:
-         space: null
-         cohort: null
-         res: null
-         den: null
-         desc: confounds
-         extension: .tsv
-         suffix: timeseries
-      columns:
-      - global_signal
-      - csf
-      - white_matter
-   aroma_confounds:
-      dataset: aroma
-      query:
-         space: null
-         cohort: null
-         res: null
-         den: null
-         desc: aroma
-         extension: .tsv
-         suffix: timeseries
-      columns:
-      - ^aroma_orth_motion_.*$
-```
+.. code-block:: yaml
+
+   name: aroma_gsr
+   description: |
+      Nuisance regressors were selected according to the 'aroma_gsr' strategy.
+      AROMA motion-labeled components [@pruim2015ica], mean white matter signal,
+      mean cerebrospinal fluid signal, and mean global signal were selected as
+      nuisance regressors [@benchmarkp;@satterthwaite_2013].
+   confounds:
+      preproc_confounds:
+         dataset: preprocessed
+         query:
+            space: null
+            cohort: null
+            res: null
+            den: null
+            desc: confounds
+            extension: .tsv
+            suffix: timeseries
+         columns:
+         - global_signal
+         - csf
+         - white_matter
+      aroma_confounds:
+         dataset: aroma
+         query:
+            space: null
+            cohort: null
+            res: null
+            den: null
+            desc: aroma
+            extension: .tsv
+            suffix: timeseries
+         columns:
+         - ^aroma_orth_motion_.*$
 
 In addition to TSV-based confound files, XCP-D also supports the use of voxel-wise confound files.
 Take this partial example:
 
-```yaml
-confounds:
-    rapidtide_slfo:
-        dataset: rapidtide
-        query:
-            desc: LFO
-            extension: .nii.gz
-            suffix: timeseries
-```
+.. code-block:: yaml
+
+   confounds:
+      rapidtide_slfo:
+         dataset: rapidtide
+         query:
+               desc: LFO
+               extension: .nii.gz
+               suffix: timeseries
 
 Note in this case that the confounds definition includes a query section, but not a columns section.
 This is because this query will return a 4D NIfTI file, rather than a TSV file.

pyproject.toml

@@ -42,7 +42,7 @@ dependencies = [
     "scikit-learn <= 1.5.2",
     "scipy >= 1.14.0,<= 1.14.1",  # nipype needs networkx, which needs scipy > 1.8.0
     "seaborn",  # for plots
-    "sentry-sdk ~= 2.14.0",  # for usage reports
+    "sentry-sdk ~= 2.15.0",  # for usage reports
     "templateflow ~= 24.2.0",
     "toml",
 ]

xcp_d/utils/atlas.py

@@ -74,6 +74,7 @@ def collect_atlases(datasets, atlases, file_format, bids_filters={}):
         The file format of the atlases.
     bids_filters : dict
         Additional filters to apply to the BIDS query.
+        Only the "atlas" key is used.
 
     Returns
     -------
@@ -101,6 +102,7 @@ def collect_atlases(datasets, atlases, file_format, bids_filters={}):
     bids_filters = bids_filters or {}
 
     atlas_filter = bids_filters.get("atlas", {})
+    atlas_filter["suffix"] = atlas_filter.get("suffix") or "dseg"  # XCP-D only supports dsegs
     atlas_filter["extension"] = [".nii.gz", ".nii"] if file_format == "nifti" else ".dlabel.nii"
     # Hardcoded spaces for now
     if file_format == "cifti":