From 673efa3e2b056ca04b0fb36ab095c5322d985e96 Mon Sep 17 00:00:00 2001
From: Taylor Salo <salot@pennmedicine.upenn.edu>
Date: Fri, 19 Jan 2024 16:38:39 -0500
Subject: [PATCH] Add options to subset atlases (#1034)

---
 README.rst                                    |  10 +-
 docs/links.rst                                |   1 +
 docs/outputs.rst                              |   8 +
 docs/usage.rst                                |   4 +-
 docs/workflows.rst                            |  15 +-
 xcp_d/cli/run.py                              | 257 +++++---
 xcp_d/interfaces/concatenation.py             |  10 +-
 xcp_d/interfaces/connectivity.py              |  15 +-
 .../data/test_ds001419_cifti_outputs.txt      |  28 +
 .../data/test_ds001419_nifti_outputs.txt      | 617 ------------------
 xcp_d/tests/data/test_pnc_cifti_outputs.txt   | 276 --------
 .../data/test_pnc_cifti_t2wonly_outputs.txt   | 270 --------
 xcp_d/tests/data/test_ukbiobank_outputs.txt   |   6 +-
 xcp_d/tests/test_cli.py                       |  18 +-
 xcp_d/tests/test_cli_run.py                   |  27 +
 xcp_d/tests/test_utils_atlas.py               |  21 +-
 xcp_d/tests/test_workflows_connectivity.py    |  10 +-
 xcp_d/utils/atlas.py                          |  70 +-
 xcp_d/utils/doc.py                            |   9 +-
 xcp_d/workflows/base.py                       | 119 ++--
 xcp_d/workflows/bold.py                       | 130 ++--
 xcp_d/workflows/cifti.py                      | 144 ++--
 xcp_d/workflows/concatenation.py              | 312 +++++----
 xcp_d/workflows/connectivity.py               |  70 +-
 xcp_d/workflows/outputs.py                    | 601 +++++++++--------
 25 files changed, 977 insertions(+), 2071 deletions(-)

diff --git a/README.rst b/README.rst
index e6ff534c9..bc69d4f3a 100644
--- a/README.rst
+++ b/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.*
 
diff --git a/docs/links.rst b/docs/links.rst
index 6f5222391..4e77a2c61 100644
--- a/docs/links.rst
+++ b/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
diff --git a/docs/outputs.rst b/docs/outputs.rst
index e9459bd41..94e8ccdf7 100644
--- a/docs/outputs.rst
+++ b/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::
diff --git a/docs/usage.rst b/docs/usage.rst
index 1031a4459..86f106bec 100644
--- a/docs/usage.rst
+++ b/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
diff --git a/docs/workflows.rst b/docs/workflows.rst
index 6344a4e72..e29e9a60b 100644
--- a/docs/workflows.rst
+++ b/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.
 
diff --git a/xcp_d/cli/run.py b/xcp_d/cli/run.py
index ffe2108d9..eab596c73 100644
--- a/xcp_d/cli/run.py
+++ b/xcp_d/cli/run.py
@@ -25,6 +25,7 @@
     check_deps,
     json_file,
 )
+from xcp_d.utils.atlas import select_atlases
 
 warnings.filterwarnings("ignore")
 
@@ -106,15 +107,8 @@ def get_parser():
         metavar="FILE",
         help="A JSON file defining BIDS input filters using PyBIDS.",
     )
-    g_bids.add_argument(
-        "-m",
-        "--combineruns",
-        action="store_true",
-        default=False,
-        help="After denoising, concatenate each derivative from each task across runs.",
-    )
 
-    g_surfx = parser.add_argument_group("Options for cifti processing")
+    g_surfx = parser.add_argument_group("Options for CIFTI processing")
     g_surfx.add_argument(
         "-s",
         "--cifti",
@@ -186,14 +180,16 @@ def get_parser():
 
     g_param = parser.add_argument_group("Postprocessing parameters")
     g_param.add_argument(
-        "--smoothing",
-        default=6,
-        action="store",
-        type=float,
+        "--dummy-scans",
+        "--dummy_scans",
+        dest="dummy_scans",
+        default=0,
+        type=_int_or_auto,
+        metavar="{{auto,INT}}",
         help=(
-            "FWHM, in millimeters, of the Gaussian smoothing kernel to apply to the denoised BOLD "
-            "data. "
-            "This may be set to 0."
+            "Number of volumes to remove from the beginning of each run. "
+            "If set to 'auto', xcp_d will extract non-steady-state volume indices from the "
+            "preprocessing derivatives' confounds file."
         ),
     )
     g_param.add_argument(
@@ -243,102 +239,34 @@ def get_parser():
         ),
     )
     g_param.add_argument(
-        "--min_coverage",
-        "--min-coverage",
-        required=False,
-        default=0.5,
-        type=_restricted_float,
-        help=(
-            "Coverage threshold to apply to parcels in each atlas. "
-            "Any parcels with lower coverage than the threshold will be replaced with NaNs. "
-            "Must be a value between zero and one, indicating proportion of the parcel. "
-            "Default is 0.5."
-        ),
-    )
-    g_param.add_argument(
-        "--min_time",
-        "--min-time",
-        required=False,
-        default=100,
+        "--smoothing",
+        default=6,
+        action="store",
         type=float,
         help=(
-            "Post-scrubbing threshold to apply to individual runs in the dataset. "
-            "This threshold determines the minimum amount of time, in seconds, "
-            "needed to post-process a given run, once high-motion outlier volumes are removed. "
-            "This will have no impact if scrubbing is disabled "
-            "(i.e., if the FD threshold is zero or negative). "
-            "This parameter can be disabled by providing a zero or a negative value."
-        ),
-    )
-    g_param.add_argument(
-        "--dummy-scans",
-        "--dummy_scans",
-        dest="dummy_scans",
-        default=0,
-        type=_int_or_auto,
-        metavar="{{auto,INT}}",
-        help=(
-            "Number of volumes to remove from the beginning of each run. "
-            "If set to 'auto', xcp_d will extract non-steady-state volume indices from the "
-            "preprocessing derivatives' confounds file."
+            "FWHM, in millimeters, of the Gaussian smoothing kernel to apply to the denoised BOLD "
+            "data. "
+            "This may be set to 0."
         ),
     )
-
     g_param.add_argument(
-        "--random-seed",
-        "--random_seed",
-        dest="random_seed",
-        default=None,
-        type=int,
-        metavar="_RANDOM_SEED",
-        help="Initialize the random seed for the workflow.",
+        "-m",
+        "--combineruns",
+        action="store_true",
+        default=False,
+        help="After denoising, concatenate each derivative from each task across runs.",
     )
 
-    g_filter = parser.add_argument_group("Filtering parameters")
-
-    g_filter.add_argument(
-        "--disable-bandpass-filter",
-        "--disable_bandpass_filter",
-        dest="bandpass_filter",
-        action="store_false",
-        help=(
-            "Disable bandpass filtering. "
-            "If bandpass filtering is disabled, then ALFF derivatives will not be calculated."
+    g_motion_filter = parser.add_argument_group(
+        title="Motion filtering parameters",
+        description=(
+            "These parameters enable and control a filter that will be applied to motion "
+            "parameters. "
+            "Motion parameters may be contaminated by non-motion noise, and applying a filter "
+            "may reduce the impact of that contamination."
         ),
     )
-    g_filter.add_argument(
-        "--lower-bpf",
-        "--lower_bpf",
-        action="store",
-        default=0.01,
-        type=float,
-        help=(
-            "Lower cut-off frequency (Hz) for the Butterworth bandpass filter to be applied to "
-            "the denoised BOLD data. Set to 0.0 or negative to disable high-pass filtering. "
-            "See Satterthwaite et al. (2013)."
-        ),
-    )
-    g_filter.add_argument(
-        "--upper-bpf",
-        "--upper_bpf",
-        action="store",
-        default=0.08,
-        type=float,
-        help=(
-            "Upper cut-off frequency (Hz) for the Butterworth bandpass filter to be applied to "
-            "the denoised BOLD data. Set to 0.0 or negative to disable low-pass filtering. "
-            "See Satterthwaite et al. (2013)."
-        ),
-    )
-    g_filter.add_argument(
-        "--bpf-order",
-        "--bpf_order",
-        action="store",
-        default=2,
-        type=int,
-        help="Number of filter coefficients for the Butterworth bandpass filter.",
-    )
-    g_filter.add_argument(
+    g_motion_filter.add_argument(
         "--motion-filter-type",
         "--motion_filter_type",
         action="store",
@@ -354,7 +282,7 @@ def get_parser():
 If the filter type is set to "lp", then only ``band-stop-min`` must be defined.
 """,
     )
-    g_filter.add_argument(
+    g_motion_filter.add_argument(
         "--band-stop-min",
         "--band_stop_min",
         default=None,
@@ -371,7 +299,7 @@ def get_parser():
 this parameter is 6 BPM (equivalent to 0.1 Hertz), based on Gratton et al. (2020).
 """,
     )
-    g_filter.add_argument(
+    g_motion_filter.add_argument(
         "--band-stop-max",
         "--band_stop_max",
         default=None,
@@ -384,7 +312,7 @@ def get_parser():
 This parameter is used in conjunction with ``motion-filter-order`` and ``band-stop-min``.
 """,
     )
-    g_filter.add_argument(
+    g_motion_filter.add_argument(
         "--motion-filter-order",
         "--motion_filter_order",
         default=4,
@@ -421,6 +349,108 @@ def get_parser():
         ),
     )
     g_censor.add_argument(
+        "--min_time",
+        "--min-time",
+        required=False,
+        default=100,
+        type=float,
+        help=(
+            "Post-scrubbing threshold to apply to individual runs in the dataset. "
+            "This threshold determines the minimum amount of time, in seconds, "
+            "needed to post-process a given run, once high-motion outlier volumes are removed. "
+            "This will have no impact if scrubbing is disabled "
+            "(i.e., if the FD threshold is zero or negative). "
+            "This parameter can be disabled by providing a zero or a negative value."
+        ),
+    )
+
+    g_temporal_filter = parser.add_argument_group(
+        title="Data filtering parameters",
+        description=(
+            "These parameters determine whether a bandpass filter will be applied to the BOLD "
+            "data, after the censoring, denoising, and interpolation steps of the pipeline, "
+            "but before recensoring."
+        ),
+    )
+    g_temporal_filter.add_argument(
+        "--disable-bandpass-filter",
+        "--disable_bandpass_filter",
+        dest="bandpass_filter",
+        action="store_false",
+        help=(
+            "Disable bandpass filtering. "
+            "If bandpass filtering is disabled, then ALFF derivatives will not be calculated."
+        ),
+    )
+    g_temporal_filter.add_argument(
+        "--lower-bpf",
+        "--lower_bpf",
+        action="store",
+        default=0.01,
+        type=float,
+        help=(
+            "Lower cut-off frequency (Hz) for the Butterworth bandpass filter to be applied to "
+            "the denoised BOLD data. Set to 0.0 or negative to disable high-pass filtering. "
+            "See Satterthwaite et al. (2013)."
+        ),
+    )
+    g_temporal_filter.add_argument(
+        "--upper-bpf",
+        "--upper_bpf",
+        action="store",
+        default=0.08,
+        type=float,
+        help=(
+            "Upper cut-off frequency (Hz) for the Butterworth bandpass filter to be applied to "
+            "the denoised BOLD data. Set to 0.0 or negative to disable low-pass filtering. "
+            "See Satterthwaite et al. (2013)."
+        ),
+    )
+    g_temporal_filter.add_argument(
+        "--bpf-order",
+        "--bpf_order",
+        action="store",
+        default=2,
+        type=int,
+        help="Number of filter coefficients for the Butterworth bandpass filter.",
+    )
+
+    g_parcellation = parser.add_argument_group("Parcellation options")
+
+    g_atlases = g_parcellation.add_mutually_exclusive_group(required=False)
+    all_atlases = select_atlases(atlases=None, subset="all")
+    g_atlases.add_argument(
+        "--atlases",
+        action="store",
+        nargs="+",
+        choices=all_atlases,
+        default=all_atlases,
+        dest="atlases",
+        help="Selection of atlases to apply to the data. All are used by default.",
+    )
+    g_atlases.add_argument(
+        "--skip-parcellation",
+        "--skip_parcellation",
+        action="store_const",
+        const=[],
+        dest="atlases",
+        help="Skip parcellation and correlation steps.",
+    )
+
+    g_parcellation.add_argument(
+        "--min_coverage",
+        "--min-coverage",
+        required=False,
+        default=0.5,
+        type=_restricted_float,
+        help=(
+            "Coverage threshold to apply to parcels in each atlas. "
+            "Any parcels with lower coverage than the threshold will be replaced with NaNs. "
+            "Must be a value between zero and one, indicating proportion of the parcel. "
+            "Default is 0.5."
+        ),
+    )
+    g_parcellation.add_argument(
         "--exact-time",
         "--exact_time",
         required=False,
@@ -439,6 +469,15 @@ def get_parser():
     )
 
     g_other = parser.add_argument_group("Other options")
+    g_other.add_argument(
+        "--random-seed",
+        "--random_seed",
+        dest="random_seed",
+        default=None,
+        type=int,
+        metavar="_RANDOM_SEED",
+        help="Initialize the random seed for the workflow.",
+    )
     g_other.add_argument(
         "-w",
         "--work_dir",
@@ -783,6 +822,13 @@ def _validate_parameters(opts, build_log):
             "is not set."
         )
 
+    # Parcellation parameters
+    if not opts.atlases and opts.min_coverage != 0.5:
+        build_log.warning(
+            "When no atlases are selected or parcellation is explicitly skipped "
+            "('--skip-parcellation'), '--min-coverage' will have no effect."
+        )
+
     # Some parameters are automatically set depending on the input type.
     if opts.input_type in ("dcan", "hcp"):
         if not opts.cifti:
@@ -1023,6 +1069,7 @@ def build_workflow(opts, retval):
         process_surfaces=opts.process_surfaces,
         dcan_qc=opts.dcan_qc,
         input_type=opts.input_type,
+        atlases=opts.atlases,
         min_coverage=opts.min_coverage,
         min_time=opts.min_time,
         exact_time=opts.exact_time,
diff --git a/xcp_d/interfaces/concatenation.py b/xcp_d/interfaces/concatenation.py
index f991ff3b9..4e00a4ca8 100644
--- a/xcp_d/interfaces/concatenation.py
+++ b/xcp_d/interfaces/concatenation.py
@@ -190,7 +190,10 @@ class _FilterOutFailedRunsOutputSpec(TraitedSpec):
         desc="Smoothed, denoised BOLD data.",
     )
     timeseries = traits.List(
-        traits.List(File(exists=True)),
+        traits.Either(
+            traits.List(File(exists=True)),
+            Undefined,
+        ),
         desc="List of lists of parcellated time series TSV files.",
     )
     timeseries_ciftis = traits.List(
@@ -293,7 +296,10 @@ class _ConcatenateInputsInputSpec(BaseInterfaceInputSpec):
         desc="Smoothed, denoised BOLD data. Optional.",
     )
     timeseries = traits.List(
-        traits.List(File(exists=True)),
+        traits.Either(
+            traits.List(File(exists=True)),
+            Undefined,
+        ),
         desc="List of lists of parcellated time series TSV files.",
     )
     timeseries_ciftis = traits.List(
diff --git a/xcp_d/interfaces/connectivity.py b/xcp_d/interfaces/connectivity.py
index d89ebb163..b76c1c3e4 100644
--- a/xcp_d/interfaces/connectivity.py
+++ b/xcp_d/interfaces/connectivity.py
@@ -688,7 +688,7 @@ def _run_interface(self, runtime):
 
 
 class _ConnectPlotInputSpec(BaseInterfaceInputSpec):
-    atlas_names = InputMultiObject(
+    atlases = InputMultiObject(
         traits.Str,
         mandatory=True,
         desc="List of atlases. Aligned with the list of time series in time_series_tsv.",
@@ -703,7 +703,7 @@ class _ConnectPlotInputSpec(BaseInterfaceInputSpec):
         mandatory=True,
         desc=(
             "List of TSV file with correlation matrices. "
-            "Aligned with the list of atlases in atlas_names"
+            "Aligned with the list of atlases in 'atlases'."
         ),
     )
 
@@ -809,16 +809,19 @@ def _run_interface(self, runtime):
         }
 
         fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 20))
-        for atlas_name, subdict in ATLAS_LOOKUP.items():
-            atlas_idx = self.inputs.atlas_names.index(atlas_name)
+        for atlas, subdict in ATLAS_LOOKUP.items():
+            if atlas not in self.inputs.atlases:
+                continue
+
+            atlas_idx = self.inputs.atlases.index(atlas)
             atlas_file = self.inputs.correlations_tsv[atlas_idx]
             dseg_file = self.inputs.atlas_tsvs[atlas_idx]
 
-            column_name = COMMUNITY_LOOKUP[atlas_name]
+            column_name = COMMUNITY_LOOKUP[atlas]
             dseg_df = pd.read_table(dseg_file)
             corrs_df = pd.read_table(atlas_file, index_col="Node")
 
-            if atlas_name.startswith("4S"):
+            if atlas.startswith("4S"):
                 atlas_mapper = {
                     "CIT168Subcortical": "Subcortical",
                     "ThalamusHCP": "Thalamus",
diff --git a/xcp_d/tests/data/test_ds001419_cifti_outputs.txt b/xcp_d/tests/data/test_ds001419_cifti_outputs.txt
index d5e9b0d65..af20bb045 100644
--- a/xcp_d/tests/data/test_ds001419_cifti_outputs.txt
+++ b/xcp_d/tests/data/test_ds001419_cifti_outputs.txt
@@ -89,6 +89,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1056Parcels_den-
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1056Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1056Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1056Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1056Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1056Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1056Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1056Parcels_timeseries.json
@@ -105,6 +106,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S156Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S156Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S156Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S156Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S156Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S156Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S156Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S156Parcels_timeseries.json
@@ -121,6 +123,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S256Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S256Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S256Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S256Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S256Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S256Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S256Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S256Parcels_timeseries.json
@@ -137,6 +140,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S356Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S356Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S356Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S356Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S356Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S356Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S356Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S356Parcels_timeseries.json
@@ -153,6 +157,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S456Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S456Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S456Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S456Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S456Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S456Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S456Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S456Parcels_timeseries.json
@@ -169,6 +174,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S556Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S556Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S556Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S556Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S556Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S556Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S556Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S556Parcels_timeseries.json
@@ -185,6 +191,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S656Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S656Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S656Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S656Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S656Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S656Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S656Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S656Parcels_timeseries.json
@@ -201,6 +208,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S756Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S756Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S756Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S756Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S756Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S756Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S756Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S756Parcels_timeseries.json
@@ -217,6 +225,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S856Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S856Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S856Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S856Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S856Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S856Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S856Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S856Parcels_timeseries.json
@@ -233,6 +242,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S956Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S956Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S956Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S956Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S956Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S956Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S956Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S956Parcels_timeseries.json
@@ -249,6 +259,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Glasser_den-91k_ti
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Glasser_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Glasser_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Glasser_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Glasser_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Glasser_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Glasser_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Glasser_timeseries.json
@@ -265,6 +276,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Gordon_den-91k_tim
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Gordon_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Gordon_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Gordon_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Gordon_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Gordon_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Gordon_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Gordon_timeseries.json
@@ -281,6 +293,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-HCP_den-91k_timese
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-HCP_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-HCP_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-HCP_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-HCP_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-HCP_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-HCP_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-HCP_timeseries.json
@@ -297,6 +310,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Tian_den-91k_times
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Tian_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Tian_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Tian_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Tian_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Tian_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Tian_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-Tian_timeseries.json
@@ -333,6 +347,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S1056Parcels_den-
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S1056Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S1056Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S1056Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S1056Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S1056Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S1056Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S1056Parcels_timeseries.json
@@ -349,6 +364,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S156Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S156Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S156Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S156Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S156Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S156Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S156Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S156Parcels_timeseries.json
@@ -365,6 +381,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S256Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S256Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S256Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S256Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S256Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S256Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S256Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S256Parcels_timeseries.json
@@ -381,6 +398,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S356Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S356Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S356Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S356Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S356Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S356Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S356Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S356Parcels_timeseries.json
@@ -397,6 +415,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S456Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S456Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S456Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S456Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S456Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S456Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S456Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S456Parcels_timeseries.json
@@ -413,6 +432,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S556Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S556Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S556Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S556Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S556Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S556Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S556Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S556Parcels_timeseries.json
@@ -429,6 +449,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S656Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S656Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S656Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S656Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S656Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S656Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S656Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S656Parcels_timeseries.json
@@ -445,6 +466,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S756Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S756Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S756Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S756Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S756Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S756Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S756Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S756Parcels_timeseries.json
@@ -461,6 +483,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S856Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S856Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S856Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S856Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S856Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S856Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S856Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S856Parcels_timeseries.json
@@ -477,6 +500,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S956Parcels_den-9
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S956Parcels_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S956Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S956Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S956Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S956Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S956Parcels_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-4S956Parcels_timeseries.json
@@ -493,6 +517,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Glasser_den-91k_ti
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Glasser_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Glasser_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Glasser_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Glasser_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Glasser_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Glasser_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Glasser_timeseries.json
@@ -509,6 +534,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Gordon_den-91k_tim
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Gordon_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Gordon_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Gordon_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Gordon_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Gordon_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Gordon_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Gordon_timeseries.json
@@ -525,6 +551,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-HCP_den-91k_timese
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-HCP_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-HCP_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-HCP_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-HCP_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-HCP_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-HCP_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-HCP_timeseries.json
@@ -541,6 +568,7 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Tian_den-91k_times
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Tian_den-91k_timeseries.ptseries.nii
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Tian_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Tian_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Tian_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Tian_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Tian_reho.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-02_space-fsLR_atlas-Tian_timeseries.json
diff --git a/xcp_d/tests/data/test_ds001419_nifti_outputs.txt b/xcp_d/tests/data/test_ds001419_nifti_outputs.txt
index 21005045e..9a5fdf3b3 100644
--- a/xcp_d/tests/data/test_ds001419_nifti_outputs.txt
+++ b/xcp_d/tests/data/test_ds001419_nifti_outputs.txt
@@ -1,60 +1,3 @@
-xcp_d/atlases
-xcp_d/atlases/atlas-4S1056Parcels
-xcp_d/atlases/atlas-4S1056Parcels/atlas-4S1056Parcels_dseg.json
-xcp_d/atlases/atlas-4S1056Parcels/atlas-4S1056Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S1056Parcels/space-MNI152NLin2009cAsym_atlas-4S1056Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S156Parcels
-xcp_d/atlases/atlas-4S156Parcels/atlas-4S156Parcels_dseg.json
-xcp_d/atlases/atlas-4S156Parcels/atlas-4S156Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S156Parcels/space-MNI152NLin2009cAsym_atlas-4S156Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S256Parcels
-xcp_d/atlases/atlas-4S256Parcels/atlas-4S256Parcels_dseg.json
-xcp_d/atlases/atlas-4S256Parcels/atlas-4S256Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S256Parcels/space-MNI152NLin2009cAsym_atlas-4S256Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S356Parcels
-xcp_d/atlases/atlas-4S356Parcels/atlas-4S356Parcels_dseg.json
-xcp_d/atlases/atlas-4S356Parcels/atlas-4S356Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S356Parcels/space-MNI152NLin2009cAsym_atlas-4S356Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S456Parcels
-xcp_d/atlases/atlas-4S456Parcels/atlas-4S456Parcels_dseg.json
-xcp_d/atlases/atlas-4S456Parcels/atlas-4S456Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S456Parcels/space-MNI152NLin2009cAsym_atlas-4S456Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S556Parcels
-xcp_d/atlases/atlas-4S556Parcels/atlas-4S556Parcels_dseg.json
-xcp_d/atlases/atlas-4S556Parcels/atlas-4S556Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S556Parcels/space-MNI152NLin2009cAsym_atlas-4S556Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S656Parcels
-xcp_d/atlases/atlas-4S656Parcels/atlas-4S656Parcels_dseg.json
-xcp_d/atlases/atlas-4S656Parcels/atlas-4S656Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S656Parcels/space-MNI152NLin2009cAsym_atlas-4S656Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S756Parcels
-xcp_d/atlases/atlas-4S756Parcels/atlas-4S756Parcels_dseg.json
-xcp_d/atlases/atlas-4S756Parcels/atlas-4S756Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S756Parcels/space-MNI152NLin2009cAsym_atlas-4S756Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S856Parcels
-xcp_d/atlases/atlas-4S856Parcels/atlas-4S856Parcels_dseg.json
-xcp_d/atlases/atlas-4S856Parcels/atlas-4S856Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S856Parcels/space-MNI152NLin2009cAsym_atlas-4S856Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-4S956Parcels
-xcp_d/atlases/atlas-4S956Parcels/atlas-4S956Parcels_dseg.json
-xcp_d/atlases/atlas-4S956Parcels/atlas-4S956Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S956Parcels/space-MNI152NLin2009cAsym_atlas-4S956Parcels_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-Glasser
-xcp_d/atlases/atlas-Glasser/atlas-Glasser_dseg.json
-xcp_d/atlases/atlas-Glasser/atlas-Glasser_dseg.tsv
-xcp_d/atlases/atlas-Glasser/space-MNI152NLin2009cAsym_atlas-Glasser_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-Gordon
-xcp_d/atlases/atlas-Gordon/atlas-Gordon_dseg.json
-xcp_d/atlases/atlas-Gordon/atlas-Gordon_dseg.tsv
-xcp_d/atlases/atlas-Gordon/space-MNI152NLin2009cAsym_atlas-Gordon_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-HCP
-xcp_d/atlases/atlas-HCP/atlas-HCP_dseg.json
-xcp_d/atlases/atlas-HCP/atlas-HCP_dseg.tsv
-xcp_d/atlases/atlas-HCP/space-MNI152NLin2009cAsym_atlas-HCP_res-2_dseg.nii.gz
-xcp_d/atlases/atlas-Tian
-xcp_d/atlases/atlas-Tian/atlas-Tian_dseg.json
-xcp_d/atlases/atlas-Tian/atlas-Tian_dseg.tsv
-xcp_d/atlases/atlas-Tian/space-MNI152NLin2009cAsym_atlas-Tian_res-2_dseg.nii.gz
 xcp_d/dataset_description.json
 xcp_d/desc-linc_qc.json
 xcp_d/logs
@@ -75,174 +18,6 @@ xcp_d/sub-01/func/sub-01_task-imagery_run-01_desc-preproc_design.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_desc-preproc_design.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_outliers.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_outliers.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_alff.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_alff.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_coverage.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_reho.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_reho.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_timeseries.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S1056Parcels_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_alff.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_alff.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_coverage.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_reho.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_reho.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_timeseries.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S156Parcels_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_alff.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_alff.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_coverage.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_reho.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_reho.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_timeseries.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S256Parcels_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_alff.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_alff.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_coverage.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_reho.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_reho.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_timeseries.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S356Parcels_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_alff.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_alff.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_coverage.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_reho.json
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_reho.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-MNI152NLin2009cAsym_atlas-4S456Parcels_timeseries.json
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 xcp_d/sub-01/func/sub-01_task-rest_outliers.json
 xcp_d/sub-01/func/sub-01_task-rest_outliers.tsv
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-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S856Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S856Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S856Parcels_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S856Parcels_reho.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S856Parcels_reho.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S856Parcels_timeseries.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S856Parcels_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_alff.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_alff.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_coverage.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_reho.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_reho.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_timeseries.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-4S956Parcels_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_alff.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_alff.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_coverage.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_reho.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_reho.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_timeseries.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Glasser_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_alff.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_alff.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_coverage.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_reho.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_reho.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_timeseries.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Gordon_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_alff.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_alff.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_coverage.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_reho.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_reho.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_timeseries.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-HCP_timeseries.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_alff.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_alff.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_coverage.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_coverage.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_reho.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_reho.tsv
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_timeseries.json
-xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_atlas-Tian_timeseries.tsv
 xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_desc-linc_qc.csv
 xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_res-2_alff.json
 xcp_d/sub-01/func/sub-01_task-rest_space-MNI152NLin2009cAsym_res-2_alff.nii.gz
diff --git a/xcp_d/tests/data/test_pnc_cifti_outputs.txt b/xcp_d/tests/data/test_pnc_cifti_outputs.txt
index 78ef3a595..6fe8bbe90 100644
--- a/xcp_d/tests/data/test_pnc_cifti_outputs.txt
+++ b/xcp_d/tests/data/test_pnc_cifti_outputs.txt
@@ -1,52 +1,4 @@
 xcp_d/atlases
-xcp_d/atlases/atlas-4S1056Parcels
-xcp_d/atlases/atlas-4S1056Parcels/atlas-4S1056Parcels_dseg.json
-xcp_d/atlases/atlas-4S1056Parcels/atlas-4S1056Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S1056Parcels/space-fsLR_atlas-4S1056Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S156Parcels
-xcp_d/atlases/atlas-4S156Parcels/atlas-4S156Parcels_dseg.json
-xcp_d/atlases/atlas-4S156Parcels/atlas-4S156Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S156Parcels/space-fsLR_atlas-4S156Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S256Parcels
-xcp_d/atlases/atlas-4S256Parcels/atlas-4S256Parcels_dseg.json
-xcp_d/atlases/atlas-4S256Parcels/atlas-4S256Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S256Parcels/space-fsLR_atlas-4S256Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S356Parcels
-xcp_d/atlases/atlas-4S356Parcels/atlas-4S356Parcels_dseg.json
-xcp_d/atlases/atlas-4S356Parcels/atlas-4S356Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S356Parcels/space-fsLR_atlas-4S356Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S456Parcels
-xcp_d/atlases/atlas-4S456Parcels/atlas-4S456Parcels_dseg.json
-xcp_d/atlases/atlas-4S456Parcels/atlas-4S456Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S456Parcels/space-fsLR_atlas-4S456Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S556Parcels
-xcp_d/atlases/atlas-4S556Parcels/atlas-4S556Parcels_dseg.json
-xcp_d/atlases/atlas-4S556Parcels/atlas-4S556Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S556Parcels/space-fsLR_atlas-4S556Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S656Parcels
-xcp_d/atlases/atlas-4S656Parcels/atlas-4S656Parcels_dseg.json
-xcp_d/atlases/atlas-4S656Parcels/atlas-4S656Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S656Parcels/space-fsLR_atlas-4S656Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S756Parcels
-xcp_d/atlases/atlas-4S756Parcels/atlas-4S756Parcels_dseg.json
-xcp_d/atlases/atlas-4S756Parcels/atlas-4S756Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S756Parcels/space-fsLR_atlas-4S756Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S856Parcels
-xcp_d/atlases/atlas-4S856Parcels/atlas-4S856Parcels_dseg.json
-xcp_d/atlases/atlas-4S856Parcels/atlas-4S856Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S856Parcels/space-fsLR_atlas-4S856Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S956Parcels
-xcp_d/atlases/atlas-4S956Parcels/atlas-4S956Parcels_dseg.json
-xcp_d/atlases/atlas-4S956Parcels/atlas-4S956Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S956Parcels/space-fsLR_atlas-4S956Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-Glasser
-xcp_d/atlases/atlas-Glasser/atlas-Glasser_dseg.json
-xcp_d/atlases/atlas-Glasser/atlas-Glasser_dseg.tsv
-xcp_d/atlases/atlas-Glasser/space-fsLR_atlas-Glasser_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-Gordon
-xcp_d/atlases/atlas-Gordon/atlas-Gordon_dseg.json
-xcp_d/atlases/atlas-Gordon/atlas-Gordon_dseg.tsv
-xcp_d/atlases/atlas-Gordon/space-fsLR_atlas-Gordon_den-91k_dseg.dlabel.nii
 xcp_d/atlases/atlas-HCP
 xcp_d/atlases/atlas-HCP/atlas-HCP_dseg.json
 xcp_d/atlases/atlas-HCP/atlas-HCP_dseg.tsv
@@ -65,42 +17,6 @@ xcp_d/sub-1648798153/ses-PNC1
 xcp_d/sub-1648798153/ses-PNC1/anat
 xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-MNI152NLin6Asym_desc-preproc_T1w.nii.gz
 xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-MNI152NLin6Asym_dseg.nii.gz
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S1056Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S1056Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S1056Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S156Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S156Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S156Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S256Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S256Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S256Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S356Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S356Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S356Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S456Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S456Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S456Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S556Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S556Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S556Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S656Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S656Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S656Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S756Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S756Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S756Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S856Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S856Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S856Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S956Parcels_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S956Parcels_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S956Parcels_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-Glasser_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-Glasser_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-Glasser_den-91k_desc-thickness_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-Gordon_den-91k_desc-curv_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-Gordon_den-91k_desc-sulc_morph.tsv
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-Gordon_den-91k_desc-thickness_morph.tsv
 xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_den-32k_hemi-L_desc-hcp_inflated.surf.gii
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-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S656Parcels_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S656Parcels_timeseries.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_alff.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_alff.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_coverage.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_den-91k_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_den-91k_coverage.pscalar.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_den-91k_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_den-91k_measure-pearsoncorrelation_conmat.pconn.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_den-91k_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_den-91k_timeseries.ptseries.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_reho.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_reho.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S756Parcels_timeseries.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_alff.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_alff.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_coverage.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_den-91k_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_den-91k_coverage.pscalar.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_den-91k_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_den-91k_measure-pearsoncorrelation_conmat.pconn.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_den-91k_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_den-91k_timeseries.ptseries.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_reho.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_reho.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S856Parcels_timeseries.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_alff.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_alff.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_coverage.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_den-91k_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_den-91k_coverage.pscalar.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_den-91k_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_den-91k_measure-pearsoncorrelation_conmat.pconn.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_den-91k_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_den-91k_timeseries.ptseries.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_reho.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_reho.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-4S956Parcels_timeseries.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_alff.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_alff.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_coverage.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_den-91k_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_den-91k_coverage.pscalar.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_den-91k_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_den-91k_measure-pearsoncorrelation_conmat.pconn.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_den-91k_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_den-91k_timeseries.ptseries.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_reho.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_reho.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Glasser_timeseries.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_alff.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_alff.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_coverage.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_den-91k_coverage.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_den-91k_coverage.pscalar.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_den-91k_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_den-91k_measure-pearsoncorrelation_conmat.pconn.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_den-91k_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_den-91k_timeseries.ptseries.nii
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_measure-pearsoncorrelation_conmat.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_reho.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_reho.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_timeseries.json
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-Gordon_timeseries.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-HCP_alff.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-HCP_alff.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-fsLR_atlas-HCP_coverage.json
diff --git a/xcp_d/tests/data/test_pnc_cifti_t2wonly_outputs.txt b/xcp_d/tests/data/test_pnc_cifti_t2wonly_outputs.txt
index 4e611fc91..de150fcb4 100644
--- a/xcp_d/tests/data/test_pnc_cifti_t2wonly_outputs.txt
+++ b/xcp_d/tests/data/test_pnc_cifti_t2wonly_outputs.txt
@@ -1,60 +1,12 @@
 xcp_d/atlases
-xcp_d/atlases/atlas-4S1056Parcels
-xcp_d/atlases/atlas-4S1056Parcels/atlas-4S1056Parcels_dseg.json
-xcp_d/atlases/atlas-4S1056Parcels/atlas-4S1056Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S1056Parcels/space-fsLR_atlas-4S1056Parcels_den-91k_dseg.dlabel.nii
 xcp_d/atlases/atlas-4S156Parcels
 xcp_d/atlases/atlas-4S156Parcels/atlas-4S156Parcels_dseg.json
 xcp_d/atlases/atlas-4S156Parcels/atlas-4S156Parcels_dseg.tsv
 xcp_d/atlases/atlas-4S156Parcels/space-fsLR_atlas-4S156Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S256Parcels
-xcp_d/atlases/atlas-4S256Parcels/atlas-4S256Parcels_dseg.json
-xcp_d/atlases/atlas-4S256Parcels/atlas-4S256Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S256Parcels/space-fsLR_atlas-4S256Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S356Parcels
-xcp_d/atlases/atlas-4S356Parcels/atlas-4S356Parcels_dseg.json
-xcp_d/atlases/atlas-4S356Parcels/atlas-4S356Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S356Parcels/space-fsLR_atlas-4S356Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S456Parcels
-xcp_d/atlases/atlas-4S456Parcels/atlas-4S456Parcels_dseg.json
-xcp_d/atlases/atlas-4S456Parcels/atlas-4S456Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S456Parcels/space-fsLR_atlas-4S456Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S556Parcels
-xcp_d/atlases/atlas-4S556Parcels/atlas-4S556Parcels_dseg.json
-xcp_d/atlases/atlas-4S556Parcels/atlas-4S556Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S556Parcels/space-fsLR_atlas-4S556Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S656Parcels
-xcp_d/atlases/atlas-4S656Parcels/atlas-4S656Parcels_dseg.json
-xcp_d/atlases/atlas-4S656Parcels/atlas-4S656Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S656Parcels/space-fsLR_atlas-4S656Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S756Parcels
-xcp_d/atlases/atlas-4S756Parcels/atlas-4S756Parcels_dseg.json
-xcp_d/atlases/atlas-4S756Parcels/atlas-4S756Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S756Parcels/space-fsLR_atlas-4S756Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S856Parcels
-xcp_d/atlases/atlas-4S856Parcels/atlas-4S856Parcels_dseg.json
-xcp_d/atlases/atlas-4S856Parcels/atlas-4S856Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S856Parcels/space-fsLR_atlas-4S856Parcels_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-4S956Parcels
-xcp_d/atlases/atlas-4S956Parcels/atlas-4S956Parcels_dseg.json
-xcp_d/atlases/atlas-4S956Parcels/atlas-4S956Parcels_dseg.tsv
-xcp_d/atlases/atlas-4S956Parcels/space-fsLR_atlas-4S956Parcels_den-91k_dseg.dlabel.nii
 xcp_d/atlases/atlas-Glasser
 xcp_d/atlases/atlas-Glasser/atlas-Glasser_dseg.json
 xcp_d/atlases/atlas-Glasser/atlas-Glasser_dseg.tsv
 xcp_d/atlases/atlas-Glasser/space-fsLR_atlas-Glasser_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-Gordon
-xcp_d/atlases/atlas-Gordon/atlas-Gordon_dseg.json
-xcp_d/atlases/atlas-Gordon/atlas-Gordon_dseg.tsv
-xcp_d/atlases/atlas-Gordon/space-fsLR_atlas-Gordon_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-HCP
-xcp_d/atlases/atlas-HCP/atlas-HCP_dseg.json
-xcp_d/atlases/atlas-HCP/atlas-HCP_dseg.tsv
-xcp_d/atlases/atlas-HCP/space-fsLR_atlas-HCP_den-91k_dseg.dlabel.nii
-xcp_d/atlases/atlas-Tian
-xcp_d/atlases/atlas-Tian/atlas-Tian_dseg.json
-xcp_d/atlases/atlas-Tian/atlas-Tian_dseg.tsv
-xcp_d/atlases/atlas-Tian/space-fsLR_atlas-Tian_den-91k_dseg.dlabel.nii
 xcp_d/dataset_description.json
 xcp_d/desc-linc_qc.json
 xcp_d/logs
@@ -65,42 +17,12 @@ xcp_d/sub-1648798153/ses-PNC1
 xcp_d/sub-1648798153/ses-PNC1/anat
 xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-MNI152NLin6Asym_desc-preproc_T2w.nii.gz
 xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-MNI152NLin6Asym_dseg.nii.gz
-xcp_d/sub-1648798153/ses-PNC1/anat/sub-1648798153_ses-PNC1_acq-refaced_space-fsLR_atlas-4S1056Parcels_den-91k_desc-curv_morph.tsv
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@@ -120,22 +42,6 @@ xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-idemo_desc-filte
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@@ -152,134 +58,6 @@ xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-idemo_space-fsLR
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@@ -296,54 +74,6 @@ xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-idemo_space-fsLR
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 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-idemo_space-fsLR_den-91k_alff.dscalar.nii
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-idemo_space-fsLR_den-91k_alff.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-idemo_space-fsLR_den-91k_desc-denoisedSmoothed_bold.dtseries.nii
diff --git a/xcp_d/tests/data/test_ukbiobank_outputs.txt b/xcp_d/tests/data/test_ukbiobank_outputs.txt
index 693c7890b..f87b48f3f 100644
--- a/xcp_d/tests/data/test_ukbiobank_outputs.txt
+++ b/xcp_d/tests/data/test_ukbiobank_outputs.txt
@@ -72,6 +72,8 @@ xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_desc-preproc_design.j
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_desc-preproc_design.tsv
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_outliers.json
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_outliers.tsv
+xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_alff.json
+xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_alff.nii.gz
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_atlas-4S1056Parcels_alff.json
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_atlas-4S1056Parcels_alff.tsv
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_atlas-4S1056Parcels_coverage.json
@@ -212,13 +214,11 @@ xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_atlas-Tian_reho.tsv
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_atlas-Tian_timeseries.json
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_atlas-Tian_timeseries.tsv
-xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_desc-linc_qc.csv
-xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_alff.json
-xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_alff.nii.gz
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_desc-denoisedSmoothed_bold.json
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_desc-denoisedSmoothed_bold.nii.gz
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_desc-denoised_bold.json
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_desc-denoised_bold.nii.gz
+xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_desc-linc_qc.csv
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_desc-smooth_alff.json
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_desc-smooth_alff.nii.gz
 xcp_d/sub-0000001/ses-01/func/sub-0000001_ses-01_task-rest_space-MNI152NLin6Asym_reho.json
diff --git a/xcp_d/tests/test_cli.py b/xcp_d/tests/test_cli.py
index a85283bc7..1e7569b26 100644
--- a/xcp_d/tests/test_cli.py
+++ b/xcp_d/tests/test_cli.py
@@ -49,11 +49,7 @@ def test_ds001419_nifti(data_dir, output_dir, working_dir):
         "--smoothing=6",
         "--motion-filter-type=lp",
         "--band-stop-min=6",
-        "--min-coverage=1",
-        "--exact-time",
-        "80",
-        "100",
-        "200",
+        "--skip-parcellation",
         "--random-seed=8675309",
     ]
     opts = run.get_parser().parse_args(parameters)
@@ -115,6 +111,9 @@ def test_ds001419_cifti(data_dir, output_dir, working_dir):
         "--dummy-scans=auto",
         "--fd-thresh=0.3",
         "--upper-bpf=0.0",
+        "--exact-time",
+        "80",
+        "200",
         f"--fs-license-file={fs_license_file}",
     ]
     opts = run.get_parser().parse_args(parameters)
@@ -233,8 +232,7 @@ def test_pnc_cifti(data_dir, output_dir, working_dir):
         f"--bids-filter-file={filter_file}",
         "--min-time=60",
         "--nuisance-regressors=acompcor_gsr",
-        "--despike",
-        "--head_radius=40",
+        "--head-radius=40",
         "--smoothing=6",
         "--motion-filter-type=notch",
         "--band-stop-min=12",
@@ -246,6 +244,9 @@ def test_pnc_cifti(data_dir, output_dir, working_dir):
         "--dummy-scans=auto",
         "--fd-thresh=0.3",
         "--upper-bpf=0.0",
+        "--atlases",
+        "Tian",
+        "HCP",
     ]
     opts = run.get_parser().parse_args(parameters)
     retval = {}
@@ -328,6 +329,9 @@ def test_pnc_cifti_t2wonly(data_dir, output_dir, working_dir):
         "--dummy-scans=auto",
         "--fd-thresh=0.3",
         "--lower-bpf=0.0",
+        "--atlases",
+        "4S156Parcels",
+        "Glasser",
     ]
     opts = run.get_parser().parse_args(parameters)
     retval = {}
diff --git a/xcp_d/tests/test_cli_run.py b/xcp_d/tests/test_cli_run.py
index a3c43fad5..a9a67dcfa 100644
--- a/xcp_d/tests/test_cli_run.py
+++ b/xcp_d/tests/test_cli_run.py
@@ -40,6 +40,7 @@ def base_opts():
         "cifti": True,
         "process_surfaces": True,
         "fs_license_file": Path(os.environ["FS_LICENSE"]),
+        "atlases": ["Glasser"],
     }
     opts = FakeOptions(**opts_dict)
     return opts
@@ -303,3 +304,29 @@ def test_validate_parameters_19(base_opts, caplog):
 
     assert "Freesurfer license DNE" in caplog.text
     assert return_code == 1
+
+
+def test_validate_parameters_20(base_opts, caplog):
+    """Test run._validate_parameters."""
+    opts = deepcopy(base_opts)
+    opts.atlases = []
+    opts.min_coverage = 0.1
+
+    _, return_code = run._validate_parameters(deepcopy(opts), build_log)
+
+    assert "When no atlases are selected" in caplog.text
+    assert return_code == 0
+
+
+def test_validate_parameters_21(base_opts, caplog):
+    """Test run._validate_parameters."""
+    opts = deepcopy(base_opts)
+    opts.input_type = "ukb"
+    opts.cifti = True
+    opts.process_surfaces = True
+
+    _, return_code = run._validate_parameters(deepcopy(opts), build_log)
+
+    assert "cifti processing (--cifti) will be disabled automatically." in caplog.text
+    assert "(--warp-surfaces-native2std) will be disabled automatically." in caplog.text
+    assert return_code == 0
diff --git a/xcp_d/tests/test_utils_atlas.py b/xcp_d/tests/test_utils_atlas.py
index cbff69091..c6ffeab58 100644
--- a/xcp_d/tests/test_utils_atlas.py
+++ b/xcp_d/tests/test_utils_atlas.py
@@ -7,17 +7,18 @@
 
 
 def test_get_atlas_names():
-    """Test xcp_d.utils.atlas.get_atlas_names."""
-    atlas_names = atlas.get_atlas_names("all")
-    assert isinstance(atlas_names, list)
-    assert all(isinstance(name, str) for name in atlas_names)
+    """Test xcp_d.utils.atlas.select_atlases."""
+    selected_atlases = atlas.select_atlases(atlases=["4S156Parcels", "4S256Parcels"], subset="all")
+    assert isinstance(selected_atlases, list)
+    assert all(isinstance(name, str) for name in selected_atlases)
+    assert len(selected_atlases) == 2
 
 
 def test_get_atlas_nifti():
     """Test xcp_d.utils.atlas.get_atlas_nifti."""
-    atlas_names = atlas.get_atlas_names("all")
-    for atlas_name in atlas_names:
-        atlas_file, atlas_labels_file, metadata_file = atlas.get_atlas_nifti(atlas_name)
+    selected_atlases = atlas.select_atlases(atlases=["4S156Parcels", "4S256Parcels"], subset="all")
+    for selected_atlas in selected_atlases:
+        atlas_file, atlas_labels_file, metadata_file = atlas.get_atlas_nifti(selected_atlas)
         assert isinstance(atlas_file, str)
         assert isinstance(atlas_labels_file, str)
         assert isinstance(metadata_file, str)
@@ -31,9 +32,9 @@ def test_get_atlas_nifti():
 
 def test_get_atlas_cifti():
     """Test xcp_d.utils.atlas.get_atlas_cifti."""
-    atlas_names = atlas.get_atlas_names("all")
-    for atlas_name in atlas_names:
-        atlas_file, atlas_labels_file, metadata_file = atlas.get_atlas_cifti(atlas_name)
+    selected_atlases = atlas.select_atlases(atlases=["4S156Parcels", "4S256Parcels"], subset="all")
+    for selected_atlas in selected_atlases:
+        atlas_file, atlas_labels_file, metadata_file = atlas.get_atlas_cifti(selected_atlas)
         assert isinstance(atlas_file, str)
         assert isinstance(atlas_labels_file, str)
         assert isinstance(metadata_file, str)
diff --git a/xcp_d/tests/test_workflows_connectivity.py b/xcp_d/tests/test_workflows_connectivity.py
index 75ae25971..f38bff27e 100644
--- a/xcp_d/tests/test_workflows_connectivity.py
+++ b/xcp_d/tests/test_workflows_connectivity.py
@@ -33,6 +33,7 @@ def test_init_load_atlases_wf_nifti(ds001419_data, tmp_path_factory):
     bold_file = ds001419_data["nifti_file"]
 
     load_atlases_wf = init_load_atlases_wf(
+        atlases=["4S156Parcels", "Glasser"],
         output_dir=tmpdir,
         cifti=False,
         mem_gb=1,
@@ -45,7 +46,7 @@ def test_init_load_atlases_wf_nifti(ds001419_data, tmp_path_factory):
     load_atlases_wf_res = load_atlases_wf.run()
     nodes = get_nodes(load_atlases_wf_res)
     atlas_names = nodes["load_atlases_wf.warp_atlases_to_bold_space"].get_output("output_image")
-    assert len(atlas_names) == 14
+    assert len(atlas_names) == 2
 
 
 def test_init_load_atlases_wf_cifti(ds001419_data, tmp_path_factory):
@@ -55,6 +56,7 @@ def test_init_load_atlases_wf_cifti(ds001419_data, tmp_path_factory):
     bold_file = ds001419_data["cifti_file"]
 
     load_atlases_wf = init_load_atlases_wf(
+        atlases=["4S156Parcels", "Glasser"],
         output_dir=tmpdir,
         cifti=True,
         mem_gb=1,
@@ -67,7 +69,7 @@ def test_init_load_atlases_wf_cifti(ds001419_data, tmp_path_factory):
     load_atlases_wf_res = load_atlases_wf.run()
     nodes = get_nodes(load_atlases_wf_res)
     atlas_names = nodes["load_atlases_wf.ds_atlas"].get_output("out_file")
-    assert len(atlas_names) == 14
+    assert len(atlas_names) == 2
 
 
 def test_init_functional_connectivity_nifti_wf(ds001419_data, tmp_path_factory):
@@ -139,7 +141,7 @@ def test_init_functional_connectivity_nifti_wf(ds001419_data, tmp_path_factory):
     connectivity_wf.inputs.inputnode.name_source = bold_file
     connectivity_wf.inputs.inputnode.bold_mask = bold_mask
     connectivity_wf.inputs.inputnode.reho = fake_bold_file
-    connectivity_wf.inputs.inputnode.atlas_names = atlas_names
+    connectivity_wf.inputs.inputnode.atlases = atlas_names
     connectivity_wf.inputs.inputnode.atlas_files = warped_atlases
     connectivity_wf.inputs.inputnode.atlas_labels_files = atlas_labels_files
     connectivity_wf.base_dir = tmpdir
@@ -279,7 +281,7 @@ def test_init_functional_connectivity_cifti_wf(ds001419_data, tmp_path_factory):
     connectivity_wf.inputs.inputnode.temporal_mask = temporal_mask
     connectivity_wf.inputs.inputnode.name_source = bold_file
     connectivity_wf.inputs.inputnode.reho = fake_bold_file
-    connectivity_wf.inputs.inputnode.atlas_names = atlas_names
+    connectivity_wf.inputs.inputnode.atlases = atlas_names
     connectivity_wf.inputs.inputnode.atlas_files = atlas_files
     connectivity_wf.inputs.inputnode.atlas_labels_files = atlas_labels_files
     connectivity_wf.inputs.inputnode.parcellated_atlas_files = parcellated_atlases
diff --git a/xcp_d/utils/atlas.py b/xcp_d/utils/atlas.py
index 57fe7f3a2..cb035f4a6 100644
--- a/xcp_d/utils/atlas.py
+++ b/xcp_d/utils/atlas.py
@@ -1,7 +1,7 @@
 """Functions for working with atlases."""
 
 
-def get_atlas_names(subset):
+def select_atlases(atlases, subset):
     """Get a list of atlases to be used for parcellation and functional connectivity analyses.
 
     The actual list of files for the atlases is loaded from a different function.
@@ -10,7 +10,8 @@ def get_atlas_names(subset):
 
     Parameters
     ----------
-    subset = {"all", "subcortical", "cortical"}
+    atlases : None or list of str
+    subset : {"all", "subcortical", "cortical"}
         Description of the subset of atlases to collect.
 
     Returns
@@ -18,7 +19,7 @@ def get_atlas_names(subset):
     :obj:`list` of :obj:`str`
         List of atlases.
     """
-    atlases = {
+    BUILTIN_ATLASES = {
         "cortical": [
             "4S156Parcels",
             "4S256Parcels",
@@ -38,11 +39,20 @@ def get_atlas_names(subset):
             "HCP",
         ],
     }
-    atlases["all"] = sorted(list(set(atlases["cortical"] + atlases["subcortical"])))
-    return atlases[subset]
+    BUILTIN_ATLASES["all"] = sorted(
+        list(set(BUILTIN_ATLASES["cortical"] + BUILTIN_ATLASES["subcortical"]))
+    )
+    subset_atlases = BUILTIN_ATLASES[subset]
+    if atlases:
+        assert all([atlas in BUILTIN_ATLASES["all"] for atlas in atlases])
+        selected_atlases = [atlas for atlas in atlases if atlas in subset_atlases]
+    else:
+        selected_atlases = subset_atlases
+
+    return selected_atlases
 
 
-def get_atlas_nifti(atlas_name):
+def get_atlas_nifti(atlas):
     """Select atlas by name from xcp_d/data using pkgrf.
 
     All atlases are in MNI space.
@@ -51,10 +61,10 @@ def get_atlas_nifti(atlas_name):
 
     Parameters
     ----------
-    atlas_name : {"4S156Parcels", "4S256Parcels", "4S356Parcels", "4S456Parcels", \
-                  "4S556Parcels", "4S656Parcels", "4S756Parcels", "4S856Parcels", \
-                  "4S956Parcels", "4S1056Parcels", "Glasser", "Gordon", \
-                  "Tian", "HCP"}
+    atlas : {"4S156Parcels", "4S256Parcels", "4S356Parcels", "4S456Parcels", \
+             "4S556Parcels", "4S656Parcels", "4S756Parcels", "4S856Parcels", \
+             "4S956Parcels", "4S1056Parcels", "Glasser", "Gordon", \
+             "Tian", "HCP"}
         The name of the NIFTI atlas to fetch.
 
     Returns
@@ -70,25 +80,25 @@ def get_atlas_nifti(atlas_name):
 
     from pkg_resources import resource_filename as pkgrf
 
-    if "4S" in atlas_name or atlas_name in ("Glasser", "Gordon"):
+    if "4S" in atlas or atlas in ("Glasser", "Gordon"):
         # 1 mm3 atlases
-        atlas_fname = f"tpl-MNI152NLin6Asym_atlas-{atlas_name}_res-01_dseg.nii.gz"
-        tsv_fname = f"atlas-{atlas_name}_dseg.tsv"
+        atlas_fname = f"tpl-MNI152NLin6Asym_atlas-{atlas}_res-01_dseg.nii.gz"
+        tsv_fname = f"atlas-{atlas}_dseg.tsv"
     else:
         # 2 mm3 atlases
-        atlas_fname = f"tpl-MNI152NLin6Asym_atlas-{atlas_name}_res-02_dseg.nii.gz"
-        tsv_fname = f"atlas-{atlas_name}_dseg.tsv"
+        atlas_fname = f"tpl-MNI152NLin6Asym_atlas-{atlas}_res-02_dseg.nii.gz"
+        tsv_fname = f"atlas-{atlas}_dseg.tsv"
 
-    if "4S" in atlas_name:
+    if "4S" in atlas:
         atlas_file = join("/AtlasPack", atlas_fname)
         atlas_labels_file = join("/AtlasPack", tsv_fname)
-        atlas_metadata_file = f"/AtlasPack/tpl-MNI152NLin6Asym_atlas-{atlas_name}_dseg.json"
+        atlas_metadata_file = f"/AtlasPack/tpl-MNI152NLin6Asym_atlas-{atlas}_dseg.json"
     else:
         atlas_file = pkgrf("xcp_d", f"data/atlases/{atlas_fname}")
         atlas_labels_file = pkgrf("xcp_d", f"data/atlases/{tsv_fname}")
         atlas_metadata_file = pkgrf(
             "xcp_d",
-            f"data/atlases/tpl-MNI152NLin6Asym_atlas-{atlas_name}_dseg.json",
+            f"data/atlases/tpl-MNI152NLin6Asym_atlas-{atlas}_dseg.json",
         )
 
     if not (isfile(atlas_file) and isfile(atlas_labels_file) and isfile(atlas_metadata_file)):
@@ -99,7 +109,7 @@ def get_atlas_nifti(atlas_name):
     return atlas_file, atlas_labels_file, atlas_metadata_file
 
 
-def get_atlas_cifti(atlas_name):
+def get_atlas_cifti(atlas):
     """Select atlas by name from xcp_d/data.
 
     All atlases are in 91K space.
@@ -108,10 +118,10 @@ def get_atlas_cifti(atlas_name):
 
     Parameters
     ----------
-    atlas_name : {"4S156Parcels", "4S256Parcels", "4S356Parcels", "4S456Parcels", \
-                  "4S556Parcels", "4S656Parcels", "4S756Parcels", "4S856Parcels", \
-                  "4S956Parcels", "4S1056Parcels", "Glasser", "Gordon", \
-                  "Tian", "HCP"}
+    atlas : {"4S156Parcels", "4S256Parcels", "4S356Parcels", "4S456Parcels", \
+             "4S556Parcels", "4S656Parcels", "4S756Parcels", "4S856Parcels", \
+             "4S956Parcels", "4S1056Parcels", "Glasser", "Gordon", \
+             "Tian", "HCP"}
         The name of the CIFTI atlas to fetch.
 
     Returns
@@ -127,17 +137,17 @@ def get_atlas_cifti(atlas_name):
 
     from pkg_resources import resource_filename as pkgrf
 
-    if "4S" in atlas_name:
-        atlas_file = f"/AtlasPack/tpl-fsLR_atlas-{atlas_name}_den-91k_dseg.dlabel.nii"
-        atlas_labels_file = f"/AtlasPack/atlas-{atlas_name}_dseg.tsv"
-        atlas_metadata_file = f"/AtlasPack/tpl-fsLR_atlas-{atlas_name}_dseg.json"
+    if "4S" in atlas:
+        atlas_file = f"/AtlasPack/tpl-fsLR_atlas-{atlas}_den-91k_dseg.dlabel.nii"
+        atlas_labels_file = f"/AtlasPack/atlas-{atlas}_dseg.tsv"
+        atlas_metadata_file = f"/AtlasPack/tpl-fsLR_atlas-{atlas}_dseg.json"
     else:
         atlas_file = pkgrf(
             "xcp_d",
-            f"data/atlases/tpl-fsLR_atlas-{atlas_name}_den-32k_dseg.dlabel.nii",
+            f"data/atlases/tpl-fsLR_atlas-{atlas}_den-32k_dseg.dlabel.nii",
         )
-        atlas_labels_file = pkgrf("xcp_d", f"data/atlases/atlas-{atlas_name}_dseg.tsv")
-        atlas_metadata_file = pkgrf("xcp_d", f"data/atlases/tpl-fsLR_atlas-{atlas_name}_dseg.json")
+        atlas_labels_file = pkgrf("xcp_d", f"data/atlases/atlas-{atlas}_dseg.tsv")
+        atlas_metadata_file = pkgrf("xcp_d", f"data/atlases/tpl-fsLR_atlas-{atlas}_dseg.json")
 
     if not (isfile(atlas_file) and isfile(atlas_labels_file) and isfile(atlas_metadata_file)):
         raise FileNotFoundError(
diff --git a/xcp_d/utils/doc.py b/xcp_d/utils/doc.py
index 3ce623a1a..5e1875460 100644
--- a/xcp_d/utils/doc.py
+++ b/xcp_d/utils/doc.py
@@ -497,14 +497,11 @@
 """
 
 docdict[
-    "atlas_names"
+    "atlases"
 ] = """
-atlas_names : :obj:`list` of :obj:`str`
+atlases : :obj:`list` of :obj:`str`
     A list of atlases used for parcellating the BOLD data.
-    The list of atlas names is generated by :func:`xcp_d.utils.atlas.get_atlas_names`.
-    The atlases include: "4S156Parcels", "4S256Parcels", "4S356Parcels", "4S456Parcels",
-    "4S556Parcels", "4S656Parcels", "4S756Parcels", "4S856Parcels", "4S956Parcels",
-    "4S1056Parcels", "Glasser", "Gordon", "Tian", and "HCP".
+    The set of atlases to use is defined by the user.
 """
 
 docdict[
diff --git a/xcp_d/workflows/base.py b/xcp_d/workflows/base.py
index 793de70b8..1963b182f 100644
--- a/xcp_d/workflows/base.py
+++ b/xcp_d/workflows/base.py
@@ -75,6 +75,7 @@ def init_xcpd_wf(
     custom_confounds_folder,
     dummy_scans,
     random_seed,
+    atlases,
     exact_time,
     cifti,
     omp_nthreads,
@@ -132,15 +133,16 @@ def init_xcpd_wf(
                 custom_confounds_folder=None,
                 dummy_scans=0,
                 random_seed=None,
-                exact_time=[],
                 cifti=False,
                 omp_nthreads=1,
                 layout=None,
                 process_surfaces=False,
                 dcan_qc=False,
                 input_type="fmriprep",
-                min_coverage=0.5,
                 min_time=100,
+                atlases=["Glasser"],
+                min_coverage=0.5,
+                exact_time=[],
                 combineruns=False,
                 name="xcpd_wf",
             )
@@ -179,8 +181,9 @@ def init_xcpd_wf(
     %(process_surfaces)s
     %(dcan_qc)s
     %(input_type)s
-    %(min_coverage)s
     %(min_time)s
+    %(atlases)s
+    %(min_coverage)s
     %(exact_time)s
     combineruns
     %(name)s
@@ -224,8 +227,9 @@ def init_xcpd_wf(
             process_surfaces=process_surfaces,
             dcan_qc=dcan_qc,
             input_type=input_type,
-            min_coverage=min_coverage,
             min_time=min_time,
+            atlases=atlases,
+            min_coverage=min_coverage,
             exact_time=exact_time,
             combineruns=combineruns,
             name=f"single_subject_{subject_id}_wf",
@@ -273,8 +277,9 @@ def init_subject_wf(
     fd_thresh,
     despike,
     dcan_qc,
-    min_coverage,
     min_time,
+    atlases,
+    min_coverage,
     exact_time,
     omp_nthreads,
     layout,
@@ -298,6 +303,7 @@ def init_subject_wf(
                 input_type="fmriprep",
                 process_surfaces=False,
                 combineruns=False,
+                atlases=["Glasser"],
                 cifti=False,
                 task_id="imagery",
                 bids_filters=None,
@@ -334,6 +340,7 @@ def init_subject_wf(
     %(input_type)s
     %(process_surfaces)s
     combineruns
+    atlases
     %(cifti)s
     task_id : :obj:`str` or None
         Task ID of BOLD  series to be selected for postprocess , or ``None`` to postprocess all
@@ -524,7 +531,6 @@ def init_subject_wf(
         name="postprocess_anat_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, postprocess_anat_wf, [
             ("t1w", "inputnode.t1w"),
@@ -532,19 +538,20 @@ def init_subject_wf(
             ("anat_dseg", "inputnode.anat_dseg"),
             ("anat_to_template_xfm", "inputnode.anat_to_template_xfm"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     # Load the atlases, warping to the same space as the BOLD data if necessary.
-    load_atlases_wf = init_load_atlases_wf(
-        output_dir=output_dir,
-        cifti=cifti,
-        mem_gb=1,
-        omp_nthreads=omp_nthreads,
-        name="load_atlases_wf",
-    )
-    load_atlases_wf.inputs.inputnode.name_source = preproc_files[0]
-    load_atlases_wf.inputs.inputnode.bold_file = preproc_files[0]
+    if atlases:
+        load_atlases_wf = init_load_atlases_wf(
+            atlases=atlases,
+            output_dir=output_dir,
+            cifti=cifti,
+            mem_gb=1,
+            omp_nthreads=omp_nthreads,
+            name="load_atlases_wf",
+        )
+        load_atlases_wf.inputs.inputnode.name_source = preproc_files[0]
+        load_atlases_wf.inputs.inputnode.bold_file = preproc_files[0]
 
     if process_surfaces or (dcan_qc and mesh_available):
         # Run surface post-processing workflow if we want to warp meshes to standard space *or*
@@ -565,7 +572,6 @@ def init_subject_wf(
             name="postprocess_surfaces_wf",
         )
 
-        # fmt:off
         workflow.connect([
             (inputnode, postprocess_surfaces_wf, [
                 ("lh_pial_surf", "inputnode.lh_pial_surf"),
@@ -575,42 +581,36 @@ def init_subject_wf(
                 ("anat_to_template_xfm", "inputnode.anat_to_template_xfm"),
                 ("template_to_anat_xfm", "inputnode.template_to_anat_xfm"),
             ]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
 
         for morph_file in morph_file_types:
-            # fmt:off
             workflow.connect([
                 (inputnode, postprocess_surfaces_wf, [(morph_file, f"inputnode.{morph_file}")]),
-            ])
-            # fmt:on
+            ])  # fmt:skip
 
         if process_surfaces or standard_space_mesh:
             # Use standard-space structurals
-            # fmt:off
             workflow.connect([
                 (postprocess_anat_wf, postprocess_surfaces_wf, [
                     ("outputnode.t1w", "inputnode.t1w"),
                     ("outputnode.t2w", "inputnode.t2w"),
                 ]),
-            ])
-            # fmt:on
+            ])  # fmt:skip
 
         else:
             # Use native-space structurals
-            # fmt:off
             workflow.connect([
                 (inputnode, postprocess_surfaces_wf, [
                     ("t1w", "inputnode.t1w"),
                     ("t2w", "inputnode.t2w"),
                 ]),
-            ])
-            # fmt:on
+            ])  # fmt:skip
 
-        if morph_file_types:
+        if morph_file_types and atlases:
             # Parcellate the morphometry files
             parcellate_surfaces_wf = init_parcellate_surfaces_wf(
                 output_dir=output_dir,
+                atlases=atlases,
                 files_to_parcellate=morph_file_types,
                 min_coverage=min_coverage,
                 mem_gb=1,
@@ -619,13 +619,11 @@ def init_subject_wf(
             )
 
             for morph_file_type in morph_file_types:
-                # fmt:off
                 workflow.connect([
                     (inputnode, parcellate_surfaces_wf, [
                         (morph_file_type, f"inputnode.{morph_file_type}"),
                     ]),
-                ])
-                # fmt:on
+                ])  # fmt:skip
 
     # Estimate head radius, if necessary
     head_radius = estimate_brain_radius(
@@ -728,6 +726,7 @@ def init_subject_wf(
                 custom_confounds_folder=custom_confounds_folder,
                 dummy_scans=dummy_scans,
                 random_seed=random_seed,
+                atlases=atlases,
                 fd_thresh=fd_thresh,
                 despike=despike,
                 dcan_qc=dcan_qc,
@@ -743,48 +742,44 @@ def init_subject_wf(
             )
             run_counter += 1
 
-            # fmt:off
             workflow.connect([
                 (postprocess_anat_wf, postprocess_bold_wf, [
                     ("outputnode.t1w", "inputnode.t1w"),
                     ("outputnode.t2w", "inputnode.t2w"),
                 ]),
-                (load_atlases_wf, postprocess_bold_wf, [
-                    ("outputnode.atlas_names", "inputnode.atlas_names"),
-                    ("outputnode.atlas_files", "inputnode.atlas_files"),
-                    ("outputnode.atlas_labels_files", "inputnode.atlas_labels_files"),
-                ]),
-            ])
-            # fmt:on
+            ])  # fmt:skip
 
-            if cifti:
-                # fmt:off
+            if atlases:
                 workflow.connect([
                     (load_atlases_wf, postprocess_bold_wf, [
-                        (
-                            "outputnode.parcellated_atlas_files",
-                            "inputnode.parcellated_atlas_files",
-                        ),
+                        ("outputnode.atlas_files", "inputnode.atlas_files"),
+                        ("outputnode.atlas_labels_files", "inputnode.atlas_labels_files"),
                     ]),
-                ])
-                # fmt:on
-            else:
-                # fmt:off
+                ])  # fmt:skip
+
+                if cifti:
+                    workflow.connect([
+                        (load_atlases_wf, postprocess_bold_wf, [
+                            (
+                                "outputnode.parcellated_atlas_files",
+                                "inputnode.parcellated_atlas_files",
+                            ),
+                        ]),
+                    ])  # fmt:skip
+
+            if not cifti:
                 workflow.connect([
                     (inputnode, postprocess_bold_wf, [
                         ("anat_brainmask", "inputnode.anat_brainmask"),
                         ("template_to_anat_xfm", "inputnode.template_to_anat_xfm"),
                     ]),
-                ])
-                # fmt:on
+                ])  # fmt:skip
 
             if combineruns and (n_task_runs > 1):
                 for io_name, node in merge_dict.items():
-                    # fmt:off
                     workflow.connect([
                         (postprocess_bold_wf, node, [(f"outputnode.{io_name}", f"in{j_run + 1}")]),
-                    ])
-                    # fmt:on
+                    ])  # fmt:skip
 
         if combineruns and (n_task_runs > 1):
             concatenate_data_wf = init_concatenate_data_wf(
@@ -797,32 +792,26 @@ def init_subject_wf(
                 cifti=cifti,
                 dcan_qc=dcan_qc,
                 fd_thresh=fd_thresh,
+                atlases=atlases,
                 mem_gb=1,
                 omp_nthreads=omp_nthreads,
                 name=f"concatenate_entity_set_{ent_set}_wf",
             )
 
-            # fmt:off
             workflow.connect([
                 (inputnode, concatenate_data_wf, [
                     ("anat_brainmask", "inputnode.anat_brainmask"),
                     ("template_to_anat_xfm", "inputnode.template_to_anat_xfm"),
                 ]),
-                (load_atlases_wf, concatenate_data_wf, [
-                    ("outputnode.atlas_names", "inputnode.atlas_names"),
-                ]),
-            ])
-            # fmt:on
+            ])  # fmt:skip
 
             for io_name, node in merge_dict.items():
                 workflow.connect([(node, concatenate_data_wf, [("out", f"inputnode.{io_name}")])])
 
-    # fmt:off
     workflow.connect([
         (summary, ds_report_summary, [("out_report", "in_file")]),
         (about, ds_report_about, [("out_report", "in_file")]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     for node in workflow.list_node_names():
         if node.split(".")[-1].startswith("ds_"):
diff --git a/xcp_d/workflows/bold.py b/xcp_d/workflows/bold.py
index 7798dff24..478ce9e41 100644
--- a/xcp_d/workflows/bold.py
+++ b/xcp_d/workflows/bold.py
@@ -52,6 +52,7 @@ def init_postprocess_nifti_wf(
     t1w_available,
     t2w_available,
     n_runs,
+    atlases,
     min_coverage,
     exact_scans,
     random_seed,
@@ -118,6 +119,7 @@ def init_postprocess_nifti_wf(
                 t1w_available=True,
                 t2w_available=True,
                 n_runs=1,
+                atlases=["Glasser"],
                 min_coverage=0.5,
                 exact_scans=[],
                 random_seed=None,
@@ -156,6 +158,7 @@ def init_postprocess_nifti_wf(
     n_runs
         Number of runs being postprocessed by XCP-D.
         This is just used for the boilerplate, as this workflow only posprocesses one run.
+    %(atlases)s
     %(min_coverage)s
     %(exact_scans)s
     %(random_seed)s
@@ -203,7 +206,6 @@ def init_postprocess_nifti_wf(
     %(smoothed_denoised_bold)s
     %(boldref)s
     bold_mask
-    %(atlas_names)s
     %(timeseries)s
     %(timeseries_ciftis)s
         This will not be defined.
@@ -230,7 +232,8 @@ def init_postprocess_nifti_wf(
                 "fmriprep_confounds_file",
                 "fmriprep_confounds_json",
                 "dummy_scans",
-                "atlas_names",
+                # if parcellation is performed
+                "atlases",
                 "atlas_files",
                 "atlas_labels_files",
             ],
@@ -244,6 +247,7 @@ def init_postprocess_nifti_wf(
     inputnode.inputs.fmriprep_confounds_file = run_data["confounds"]
     inputnode.inputs.fmriprep_confounds_json = run_data["confounds_json"]
     inputnode.inputs.dummy_scans = dummy_scans
+    inputnode.inputs.atlases = atlases
 
     # Load custom confounds
     # We need to run this function directly to access information in the confounds that is
@@ -272,6 +276,7 @@ def init_postprocess_nifti_wf(
                 "smoothed_denoised_bold",
                 "boldref",
                 "bold_mask",
+                # if parcellation is performed
                 "timeseries",
                 "timeseries_ciftis",  # will not be defined
             ],
@@ -288,7 +293,6 @@ def init_postprocess_nifti_wf(
         n_procs=omp_nthreads,
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, outputnode, [("bold_file", "name_source")]),
         (inputnode, downcast_data, [
@@ -300,8 +304,7 @@ def init_postprocess_nifti_wf(
             ("bold_mask", "bold_mask"),
             ("boldref", "boldref"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     prepare_confounds_wf = init_prepare_confounds_wf(
         output_dir=output_dir,
@@ -322,7 +325,6 @@ def init_postprocess_nifti_wf(
         name="prepare_confounds_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, prepare_confounds_wf, [
             ("bold_file", "inputnode.name_source"),
@@ -334,8 +336,7 @@ def init_postprocess_nifti_wf(
             ("outputnode.fmriprep_confounds_file", "fmriprep_confounds_file"),
             ("outputnode.preprocessed_bold", "preprocessed_bold"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     denoise_bold_wf = init_denoise_bold_wf(
         TR=TR,
@@ -350,7 +351,6 @@ def init_postprocess_nifti_wf(
         name="denoise_bold_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (downcast_data, denoise_bold_wf, [("bold_mask", "inputnode.mask")]),
         (prepare_confounds_wf, denoise_bold_wf, [
@@ -360,8 +360,7 @@ def init_postprocess_nifti_wf(
         (denoise_bold_wf, outputnode, [
             ("outputnode.uncensored_denoised_bold", "uncensored_denoised_bold"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     if despike:
         despike_wf = init_despike_wf(
@@ -372,7 +371,6 @@ def init_postprocess_nifti_wf(
             name="despike_wf",
         )
 
-        # fmt:off
         workflow.connect([
             (prepare_confounds_wf, despike_wf, [
                 ("outputnode.preprocessed_bold", "inputnode.bold_file"),
@@ -380,43 +378,14 @@ def init_postprocess_nifti_wf(
             (despike_wf, denoise_bold_wf, [
                 ("outputnode.bold_file", "inputnode.preprocessed_bold"),
             ]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
 
     else:
-        # fmt:off
         workflow.connect([
             (prepare_confounds_wf, denoise_bold_wf, [
                 ("outputnode.preprocessed_bold", "inputnode.preprocessed_bold"),
             ]),
-        ])
-        # fmt:on
-
-    connectivity_wf = init_functional_connectivity_nifti_wf(
-        output_dir=output_dir,
-        min_coverage=min_coverage,
-        alff_available=bandpass_filter,
-        mem_gb=mem_gbx["timeseries"],
-        name="connectivity_wf",
-    )
-
-    # fmt:off
-    workflow.connect([
-        (inputnode, connectivity_wf, [
-            ("bold_file", "inputnode.name_source"),
-            ("atlas_names", "inputnode.atlas_names"),
-            ("atlas_files", "inputnode.atlas_files"),
-            ("atlas_labels_files", "inputnode.atlas_labels_files"),
-        ]),
-        (downcast_data, connectivity_wf, [("bold_mask", "inputnode.bold_mask")]),
-        (prepare_confounds_wf, connectivity_wf, [
-            ("outputnode.temporal_mask", "inputnode.temporal_mask"),
-        ]),
-        (denoise_bold_wf, connectivity_wf, [
-            ("outputnode.censored_denoised_bold", "inputnode.denoised_bold"),
-        ]),
-    ])
-    # fmt:on
+        ])  # fmt:skip
 
     if bandpass_filter:
         alff_wf = init_alff_wf(
@@ -433,7 +402,6 @@ def init_postprocess_nifti_wf(
             name="alff_wf",
         )
 
-        # fmt:off
         workflow.connect([
             (downcast_data, alff_wf, [("bold_mask", "inputnode.bold_mask")]),
             (prepare_confounds_wf, alff_wf, [
@@ -442,9 +410,7 @@ def init_postprocess_nifti_wf(
             (denoise_bold_wf, alff_wf, [
                 ("outputnode.interpolated_filtered_bold", "inputnode.denoised_bold"),
             ]),
-            (alff_wf, connectivity_wf, [("outputnode.alff", "inputnode.alff")]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
 
     reho_wf = init_reho_nifti_wf(
         name_source=bold_file,
@@ -454,15 +420,12 @@ def init_postprocess_nifti_wf(
         name="reho_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (downcast_data, reho_wf, [("bold_mask", "inputnode.bold_mask")]),
         (denoise_bold_wf, reho_wf, [
             ("outputnode.censored_denoised_bold", "inputnode.denoised_bold"),
         ]),
-        (reho_wf, connectivity_wf, [("outputnode.reho", "inputnode.reho")]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     qc_report_wf = init_qc_report_wf(
         output_dir=output_dir,
@@ -476,7 +439,6 @@ def init_postprocess_nifti_wf(
         name="qc_report_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, qc_report_wf, [
             ("bold_file", "inputnode.name_source"),
@@ -497,8 +459,7 @@ def init_postprocess_nifti_wf(
             ("outputnode.interpolated_filtered_bold", "inputnode.interpolated_filtered_bold"),
             ("outputnode.censored_denoised_bold", "inputnode.censored_denoised_bold"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     postproc_derivatives_wf = init_postproc_derivatives_wf(
         name_source=bold_file,
@@ -513,6 +474,7 @@ def init_postprocess_nifti_wf(
         smoothing=smoothing,
         params=params,
         exact_scans=exact_scans,
+        atlases=atlases,
         cifti=False,
         dcan_qc=dcan_qc,
         output_dir=output_dir,
@@ -520,11 +482,9 @@ def init_postprocess_nifti_wf(
         name="postproc_derivatives_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, postproc_derivatives_wf, [
             ("fmriprep_confounds_file", "inputnode.fmriprep_confounds_file"),
-            ("atlas_names", "inputnode.atlas_names"),
             ("atlas_files", "inputnode.atlas_files"),
         ]),
         (prepare_confounds_wf, postproc_derivatives_wf, [
@@ -542,13 +502,6 @@ def init_postprocess_nifti_wf(
         ]),
         (qc_report_wf, postproc_derivatives_wf, [("outputnode.qc_file", "inputnode.qc_file")]),
         (reho_wf, postproc_derivatives_wf, [("outputnode.reho", "inputnode.reho")]),
-        (connectivity_wf, postproc_derivatives_wf, [
-            ("outputnode.coverage", "inputnode.coverage"),
-            ("outputnode.timeseries", "inputnode.timeseries"),
-            ("outputnode.correlations", "inputnode.correlations"),
-            ("outputnode.correlations_exact", "inputnode.correlations_exact"),
-            ("outputnode.parcellated_reho", "inputnode.parcellated_reho"),
-        ]),
         (postproc_derivatives_wf, outputnode, [
             ("outputnode.filtered_motion", "filtered_motion"),
             ("outputnode.temporal_mask", "temporal_mask"),
@@ -557,21 +510,56 @@ def init_postprocess_nifti_wf(
             ("outputnode.smoothed_denoised_bold", "smoothed_denoised_bold"),
             ("outputnode.timeseries", "timeseries"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     if bandpass_filter:
-        # fmt:off
         workflow.connect([
             (alff_wf, postproc_derivatives_wf, [
                 ("outputnode.alff", "inputnode.alff"),
                 ("outputnode.smoothed_alff", "inputnode.smoothed_alff"),
             ]),
+        ])  # fmt:skip
+
+    if atlases:
+        connectivity_wf = init_functional_connectivity_nifti_wf(
+            output_dir=output_dir,
+            min_coverage=min_coverage,
+            alff_available=bandpass_filter,
+            mem_gb=mem_gbx["timeseries"],
+            name="connectivity_wf",
+        )
+
+        workflow.connect([
+            (inputnode, connectivity_wf, [
+                ("bold_file", "inputnode.name_source"),
+                ("atlases", "inputnode.atlases"),
+                ("atlas_files", "inputnode.atlas_files"),
+                ("atlas_labels_files", "inputnode.atlas_labels_files"),
+            ]),
+            (downcast_data, connectivity_wf, [("bold_mask", "inputnode.bold_mask")]),
+            (prepare_confounds_wf, connectivity_wf, [
+                ("outputnode.temporal_mask", "inputnode.temporal_mask"),
+            ]),
+            (denoise_bold_wf, connectivity_wf, [
+                ("outputnode.censored_denoised_bold", "inputnode.denoised_bold"),
+            ]),
+            (reho_wf, connectivity_wf, [("outputnode.reho", "inputnode.reho")]),
             (connectivity_wf, postproc_derivatives_wf, [
-                ("outputnode.parcellated_alff", "inputnode.parcellated_alff"),
+                ("outputnode.coverage", "inputnode.coverage"),
+                ("outputnode.timeseries", "inputnode.timeseries"),
+                ("outputnode.correlations", "inputnode.correlations"),
+                ("outputnode.correlations_exact", "inputnode.correlations_exact"),
+                ("outputnode.parcellated_reho", "inputnode.parcellated_reho"),
             ]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
+
+        if bandpass_filter:
+            workflow.connect([
+                (alff_wf, connectivity_wf, [("outputnode.alff", "inputnode.alff")]),
+                (connectivity_wf, postproc_derivatives_wf, [
+                    ("outputnode.parcellated_alff", "inputnode.parcellated_alff"),
+                ]),
+            ])  # fmt:skip
 
     # executive summary workflow
     execsummary_functional_plots_wf = init_execsummary_functional_plots_wf(
@@ -583,7 +571,6 @@ def init_postprocess_nifti_wf(
         name="execsummary_functional_plots_wf",
     )
 
-    # fmt:off
     workflow.connect([
         # Use inputnode for executive summary instead of downcast_data
         # because T1w is used as name source.
@@ -592,8 +579,7 @@ def init_postprocess_nifti_wf(
             ("t1w", "inputnode.t1w"),
             ("t2w", "inputnode.t2w"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     return workflow
 
diff --git a/xcp_d/workflows/cifti.py b/xcp_d/workflows/cifti.py
index 66b4cb5d5..204fa357f 100644
--- a/xcp_d/workflows/cifti.py
+++ b/xcp_d/workflows/cifti.py
@@ -52,6 +52,7 @@ def init_postprocess_cifti_wf(
     t1w_available,
     t2w_available,
     n_runs,
+    atlases,
     min_coverage,
     exact_scans,
     random_seed,
@@ -116,6 +117,7 @@ def init_postprocess_cifti_wf(
                 t1w_available=True,
                 t2w_available=True,
                 n_runs=1,
+                atlases=["Glasser"],
                 min_coverage=0.5,
                 exact_scans=[],
                 random_seed=None,
@@ -152,6 +154,7 @@ def init_postprocess_cifti_wf(
     n_runs
         Number of runs being postprocessed by XCP-D.
         This is just used for the boilerplate, as this workflow only posprocesses one run.
+    %(atlases)s
     %(min_coverage)s
     %(random_seed)s
     %(exact_scans)s
@@ -192,7 +195,6 @@ def init_postprocess_cifti_wf(
     %(boldref)s
     bold_mask
         This will not be defined.
-    %(atlas_names)s
     %(timeseries)s
     %(timeseries_ciftis)s
 
@@ -215,7 +217,8 @@ def init_postprocess_cifti_wf(
                 "fmriprep_confounds_file",
                 "fmriprep_confounds_json",
                 "dummy_scans",
-                "atlas_names",
+                # if parcellation is performed
+                "atlases",
                 "atlas_files",
                 "atlas_labels_files",
                 "parcellated_atlas_files",
@@ -229,6 +232,7 @@ def init_postprocess_cifti_wf(
     inputnode.inputs.fmriprep_confounds_file = run_data["confounds"]
     inputnode.inputs.fmriprep_confounds_json = run_data["confounds_json"]
     inputnode.inputs.dummy_scans = dummy_scans
+    inputnode.inputs.atlases = atlases
 
     # Load custom confounds
     # We need to run this function directly to access information in the confounds that is
@@ -259,7 +263,7 @@ def init_postprocess_cifti_wf(
                 "smoothed_denoised_bold",
                 "boldref",
                 "bold_mask",  # will not be defined
-                "atlas_names",
+                # if parcellation is performed
                 "timeseries",
                 "timeseries_ciftis",
             ],
@@ -276,15 +280,13 @@ def init_postprocess_cifti_wf(
         n_procs=omp_nthreads,
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, outputnode, [
             ("bold_file", "name_source"),
             ("boldref", "boldref"),
         ]),
         (inputnode, downcast_data, [("bold_file", "bold_file")]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     prepare_confounds_wf = init_prepare_confounds_wf(
         output_dir=output_dir,
@@ -305,7 +307,6 @@ def init_postprocess_cifti_wf(
         name="prepare_confounds_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, prepare_confounds_wf, [
             ("bold_file", "inputnode.name_source"),
@@ -319,8 +320,7 @@ def init_postprocess_cifti_wf(
             ("outputnode.fmriprep_confounds_file", "fmriprep_confounds_file"),
             ("outputnode.preprocessed_bold", "preprocessed_bold"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     denoise_bold_wf = init_denoise_bold_wf(
         TR=TR,
@@ -335,7 +335,6 @@ def init_postprocess_cifti_wf(
         name="denoise_bold_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (prepare_confounds_wf, denoise_bold_wf, [
             ("outputnode.temporal_mask", "inputnode.temporal_mask"),
@@ -344,8 +343,7 @@ def init_postprocess_cifti_wf(
         (denoise_bold_wf, outputnode, [
             ("outputnode.uncensored_denoised_bold", "uncensored_denoised_bold"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     if despike:
         despike_wf = init_despike_wf(
@@ -356,7 +354,6 @@ def init_postprocess_cifti_wf(
             name="despike_wf",
         )
 
-        # fmt:off
         workflow.connect([
             (prepare_confounds_wf, despike_wf, [
                 ("outputnode.preprocessed_bold", "inputnode.bold_file"),
@@ -364,44 +361,14 @@ def init_postprocess_cifti_wf(
             (despike_wf, denoise_bold_wf, [
                 ("outputnode.bold_file", "inputnode.preprocessed_bold"),
             ]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
 
     else:
-        # fmt:off
         workflow.connect([
             (prepare_confounds_wf, denoise_bold_wf, [
                 ("outputnode.preprocessed_bold", "inputnode.preprocessed_bold"),
             ]),
-        ])
-        # fmt:on
-
-    connectivity_wf = init_functional_connectivity_cifti_wf(
-        min_coverage=min_coverage,
-        alff_available=bandpass_filter,
-        output_dir=output_dir,
-        mem_gb=mem_gbx["timeseries"],
-        omp_nthreads=omp_nthreads,
-        name="connectivity_wf",
-    )
-
-    # fmt:off
-    workflow.connect([
-        (inputnode, connectivity_wf, [
-            ("bold_file", "inputnode.name_source"),
-            ("atlas_names", "inputnode.atlas_names"),
-            ("atlas_files", "inputnode.atlas_files"),
-            ("atlas_labels_files", "inputnode.atlas_labels_files"),
-            ("parcellated_atlas_files", "inputnode.parcellated_atlas_files"),
-        ]),
-        (prepare_confounds_wf, connectivity_wf, [
-            ("outputnode.temporal_mask", "inputnode.temporal_mask"),
-        ]),
-        (denoise_bold_wf, connectivity_wf, [
-            ("outputnode.censored_denoised_bold", "inputnode.denoised_bold"),
-        ]),
-    ])
-    # fmt:on
+        ])  # fmt:skip
 
     if bandpass_filter:
         alff_wf = init_alff_wf(
@@ -418,7 +385,6 @@ def init_postprocess_cifti_wf(
             name="alff_wf",
         )
 
-        # fmt:off
         workflow.connect([
             (prepare_confounds_wf, alff_wf, [
                 ("outputnode.temporal_mask", "inputnode.temporal_mask"),
@@ -426,9 +392,7 @@ def init_postprocess_cifti_wf(
             (denoise_bold_wf, alff_wf, [
                 ("outputnode.interpolated_filtered_bold", "inputnode.denoised_bold"),
             ]),
-            (alff_wf, connectivity_wf, [("outputnode.alff", "inputnode.alff")]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
 
     reho_wf = init_reho_cifti_wf(
         name_source=bold_file,
@@ -438,14 +402,11 @@ def init_postprocess_cifti_wf(
         name="reho_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (denoise_bold_wf, reho_wf, [
             ("outputnode.censored_denoised_bold", "inputnode.denoised_bold"),
         ]),
-        (reho_wf, connectivity_wf, [("outputnode.reho", "inputnode.reho")]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     qc_report_wf = init_qc_report_wf(
         output_dir=output_dir,
@@ -459,7 +420,6 @@ def init_postprocess_cifti_wf(
         name="qc_report_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, qc_report_wf, [("bold_file", "inputnode.name_source")]),
         (prepare_confounds_wf, qc_report_wf, [
@@ -474,8 +434,7 @@ def init_postprocess_cifti_wf(
             ("outputnode.interpolated_filtered_bold", "inputnode.interpolated_filtered_bold"),
             ("outputnode.censored_denoised_bold", "inputnode.censored_denoised_bold"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     postproc_derivatives_wf = init_postproc_derivatives_wf(
         name_source=bold_file,
@@ -490,6 +449,7 @@ def init_postprocess_cifti_wf(
         smoothing=smoothing,
         params=params,
         exact_scans=exact_scans,
+        atlases=atlases,
         cifti=True,
         dcan_qc=dcan_qc,
         output_dir=output_dir,
@@ -497,11 +457,9 @@ def init_postprocess_cifti_wf(
         name="postproc_derivatives_wf",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, postproc_derivatives_wf, [
             ("fmriprep_confounds_file", "inputnode.fmriprep_confounds_file"),
-            ("atlas_names", "inputnode.atlas_names"),
             ("atlas_files", "inputnode.atlas_files"),
         ]),
         (denoise_bold_wf, postproc_derivatives_wf, [
@@ -519,17 +477,6 @@ def init_postprocess_cifti_wf(
             ("outputnode.temporal_mask_metadata", "inputnode.temporal_mask_metadata"),
         ]),
         (reho_wf, postproc_derivatives_wf, [("outputnode.reho", "inputnode.reho")]),
-        (connectivity_wf, postproc_derivatives_wf, [
-            ("outputnode.coverage_ciftis", "inputnode.coverage_ciftis"),
-            ("outputnode.timeseries_ciftis", "inputnode.timeseries_ciftis"),
-            ("outputnode.correlation_ciftis", "inputnode.correlation_ciftis"),
-            ("outputnode.correlation_ciftis_exact", "inputnode.correlation_ciftis_exact"),
-            ("outputnode.coverage", "inputnode.coverage"),
-            ("outputnode.timeseries", "inputnode.timeseries"),
-            ("outputnode.correlations", "inputnode.correlations"),
-            ("outputnode.correlations_exact", "inputnode.correlations_exact"),
-            ("outputnode.parcellated_reho", "inputnode.parcellated_reho"),
-        ]),
         (postproc_derivatives_wf, outputnode, [
             ("outputnode.filtered_motion", "filtered_motion"),
             ("outputnode.temporal_mask", "temporal_mask"),
@@ -539,7 +486,7 @@ def init_postprocess_cifti_wf(
             ("outputnode.timeseries", "timeseries"),
             ("outputnode.timeseries_ciftis", "timeseries_ciftis"),
         ]),
-    ])
+    ])  # fmt:skip
 
     if bandpass_filter:
         workflow.connect([
@@ -547,11 +494,53 @@ def init_postprocess_cifti_wf(
                 ("outputnode.alff", "inputnode.alff"),
                 ("outputnode.smoothed_alff", "inputnode.smoothed_alff"),
             ]),
+        ])  # fmt:skip
+
+    if atlases:
+        connectivity_wf = init_functional_connectivity_cifti_wf(
+            min_coverage=min_coverage,
+            alff_available=bandpass_filter,
+            output_dir=output_dir,
+            mem_gb=mem_gbx["timeseries"],
+            omp_nthreads=omp_nthreads,
+            name="connectivity_wf",
+        )
+
+        workflow.connect([
+            (inputnode, connectivity_wf, [
+                ("bold_file", "inputnode.name_source"),
+                ("atlases", "inputnode.atlases"),
+                ("atlas_files", "inputnode.atlas_files"),
+                ("atlas_labels_files", "inputnode.atlas_labels_files"),
+                ("parcellated_atlas_files", "inputnode.parcellated_atlas_files"),
+            ]),
+            (prepare_confounds_wf, connectivity_wf, [
+                ("outputnode.temporal_mask", "inputnode.temporal_mask"),
+            ]),
+            (denoise_bold_wf, connectivity_wf, [
+                ("outputnode.censored_denoised_bold", "inputnode.denoised_bold"),
+            ]),
+            (reho_wf, connectivity_wf, [("outputnode.reho", "inputnode.reho")]),
             (connectivity_wf, postproc_derivatives_wf, [
-                ("outputnode.parcellated_alff", "inputnode.parcellated_alff"),
+                ("outputnode.coverage_ciftis", "inputnode.coverage_ciftis"),
+                ("outputnode.timeseries_ciftis", "inputnode.timeseries_ciftis"),
+                ("outputnode.correlation_ciftis", "inputnode.correlation_ciftis"),
+                ("outputnode.correlation_ciftis_exact", "inputnode.correlation_ciftis_exact"),
+                ("outputnode.coverage", "inputnode.coverage"),
+                ("outputnode.timeseries", "inputnode.timeseries"),
+                ("outputnode.correlations", "inputnode.correlations"),
+                ("outputnode.correlations_exact", "inputnode.correlations_exact"),
+                ("outputnode.parcellated_reho", "inputnode.parcellated_reho"),
             ]),
-        ])
-    # fmt:on
+        ])  # fmt:skip
+
+        if bandpass_filter:
+            workflow.connect([
+                (alff_wf, connectivity_wf, [("outputnode.alff", "inputnode.alff")]),
+                (connectivity_wf, postproc_derivatives_wf, [
+                    ("outputnode.parcellated_alff", "inputnode.parcellated_alff"),
+                ]),
+            ])  # fmt:skip
 
     # executive summary workflow
     execsummary_functional_plots_wf = init_execsummary_functional_plots_wf(
@@ -563,19 +552,14 @@ def init_postprocess_cifti_wf(
         name="execsummary_functional_plots_wf",
     )
 
-    # Use inputnode for executive summary instead of downcast_data
-    # because T1w is used as name source.
-    # fmt:off
     workflow.connect([
-        # Use inputnode for executive summary instead of downcast_data
-        # because T1w is used as name source.
+        # Use inputnode for executive summary instead of downcast_data because T1w is name source.
         (inputnode, execsummary_functional_plots_wf, [
             ("boldref", "inputnode.boldref"),
             ("t1w", "inputnode.t1w"),
             ("t2w", "inputnode.t2w"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     return workflow
 
diff --git a/xcp_d/workflows/concatenation.py b/xcp_d/workflows/concatenation.py
index c53d89287..ad9395c4f 100644
--- a/xcp_d/workflows/concatenation.py
+++ b/xcp_d/workflows/concatenation.py
@@ -27,6 +27,7 @@ def init_concatenate_data_wf(
     cifti,
     dcan_qc,
     fd_thresh,
+    atlases,
     mem_gb,
     omp_nthreads,
     name="concatenate_data_wf",
@@ -50,6 +51,7 @@ def init_concatenate_data_wf(
                 cifti=False,
                 dcan_qc=True,
                 fd_thresh=0.3,
+                atlases=[],
                 mem_gb=0.1,
                 omp_nthreads=1,
                 name="concatenate_data_wf",
@@ -65,7 +67,8 @@ def init_concatenate_data_wf(
     %(smoothing)s
     %(cifti)s
     %(dcan_qc)s
-    fd_thresh
+    %(fd_thresh)s
+    %(atlases)s
     %(mem_gb)s
     %(omp_nthreads)s
     %(name)s
@@ -94,8 +97,6 @@ def init_concatenate_data_wf(
     anat_brainmask : :obj:`str`
     %(template_to_anat_xfm)s
     %(boldref)s
-    %(atlas_names)s
-        This will be a list of strings.
     %(timeseries)s
         This will be a list of lists, with one sublist for each run.
     %(timeseries_ciftis)s
@@ -123,7 +124,6 @@ def init_concatenate_data_wf(
                 "boldref",  # only for niftis, from postproc workflows
                 "anat_brainmask",  # only for niftis, from data collection
                 "template_to_anat_xfm",  # only for niftis, from data collection
-                "atlas_names",
                 "timeseries",
                 "timeseries_ciftis",  # only for ciftis, from postproc workflows
             ],
@@ -142,7 +142,6 @@ def init_concatenate_data_wf(
         name="filter_runs",
     )
 
-    # fmt:off
     workflow.connect([
         (inputnode, filter_runs, [
             ("preprocessed_bold", "preprocessed_bold"),
@@ -158,15 +157,13 @@ def init_concatenate_data_wf(
             ("timeseries", "timeseries"),
             ("timeseries_ciftis", "timeseries_ciftis"),
         ])
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     concatenate_inputs = pe.Node(
         ConcatenateInputs(),
         name="concatenate_inputs",
     )
 
-    # fmt:off
     workflow.connect([
         (filter_runs, concatenate_inputs, [
             ("preprocessed_bold", "preprocessed_bold"),
@@ -180,8 +177,7 @@ def init_concatenate_data_wf(
             ("timeseries", "timeseries"),
             ("timeseries_ciftis", "timeseries_ciftis"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     # Now, run the QC report workflow on the concatenated BOLD file.
     qc_report_wf = init_qc_report_wf(
@@ -197,7 +193,6 @@ def init_concatenate_data_wf(
     )
     qc_report_wf.inputs.inputnode.dummy_scans = 0
 
-    # fmt:off
     workflow.connect([
         (inputnode, qc_report_wf, [
             ("template_to_anat_xfm", "inputnode.template_to_anat_xfm"),
@@ -219,8 +214,7 @@ def init_concatenate_data_wf(
             ("temporal_mask", "inputnode.temporal_mask"),
             ("run_index", "inputnode.run_index"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     ds_filtered_motion = pe.Node(
         DerivativesDataSink(
@@ -234,16 +228,13 @@ def init_concatenate_data_wf(
         run_without_submitting=True,
         mem_gb=1,
     )
-
-    # fmt:off
     workflow.connect([
         (clean_name_source, ds_filtered_motion, [("name_source", "source_file")]),
         (concatenate_inputs, ds_filtered_motion, [("filtered_motion", "in_file")]),
         (filter_runs, ds_filtered_motion, [
             (("filtered_motion", _make_xcpd_uri, output_dir), "Sources"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     ds_temporal_mask = pe.Node(
         DerivativesDataSink(
@@ -257,109 +248,13 @@ def init_concatenate_data_wf(
         mem_gb=1,
     )
 
-    # fmt:off
     workflow.connect([
         (clean_name_source, ds_temporal_mask, [("name_source", "source_file")]),
         (concatenate_inputs, ds_temporal_mask, [("temporal_mask", "in_file")]),
         (filter_runs, ds_temporal_mask, [
             (("temporal_mask", _make_xcpd_uri, output_dir), "Sources"),
         ]),
-    ])
-    # fmt:on
-
-    make_timeseries_dict = pe.MapNode(
-        niu.Function(
-            function=_make_dictionary,
-            input_names=["Sources"],
-            output_names=["metadata"],
-        ),
-        run_without_submitting=True,
-        mem_gb=1,
-        name="make_timeseries_dict",
-        iterfield=["Sources"],
-    )
-    # fmt:off
-    workflow.connect([
-        (filter_runs, make_timeseries_dict, [
-            (("timeseries", _make_xcpd_uri_lol, output_dir), "Sources"),
-        ]),
-    ])
-    # fmt:on
-
-    ds_timeseries = pe.MapNode(
-        DerivativesDataSink(
-            base_directory=output_dir,
-            dismiss_entities=["desc"],
-            suffix="timeseries",
-            extension=".tsv",
-            # Metadata
-            SamplingFrequency="TR",
-        ),
-        name="ds_timeseries",
-        run_without_submitting=True,
-        mem_gb=1,
-        iterfield=["atlas", "in_file", "meta_dict"],
-    )
-
-    # fmt:off
-    workflow.connect([
-        (inputnode, ds_timeseries, [("atlas_names", "atlas")]),
-        (clean_name_source, ds_timeseries, [("name_source", "source_file")]),
-        (concatenate_inputs, ds_timeseries, [("timeseries", "in_file")]),
-        (make_timeseries_dict, ds_timeseries, [("metadata", "meta_dict")]),
-    ])
-    # fmt:on
-
-    correlate_timeseries = pe.MapNode(
-        TSVConnect(),
-        run_without_submitting=True,
-        mem_gb=1,
-        name="correlate_timeseries",
-        iterfield=["timeseries"],
-    )
-    workflow.connect([(concatenate_inputs, correlate_timeseries, [("timeseries", "timeseries")])])
-
-    make_correlations_dict = pe.MapNode(
-        niu.Function(
-            function=_make_dictionary,
-            input_names=["Sources"],
-            output_names=["metadata"],
-        ),
-        run_without_submitting=True,
-        mem_gb=1,
-        name="make_correlations_dict",
-        iterfield=["Sources"],
-    )
-    # fmt:off
-    workflow.connect([
-        (ds_timeseries, make_correlations_dict, [
-            (("out_file", _make_xcpd_uri, output_dir), "Sources"),
-        ]),
-    ])
-    # fmt:on
-
-    ds_correlations = pe.MapNode(
-        DerivativesDataSink(
-            base_directory=output_dir,
-            dismiss_entities=["desc"],
-            measure="pearsoncorrelation",
-            suffix="conmat",
-            extension=".tsv",
-        ),
-        name="ds_correlations",
-        run_without_submitting=True,
-        mem_gb=1,
-        iterfield=["atlas", "in_file", "meta_dict"],
-    )
-
-    # fmt:off
-    workflow.connect([
-        (inputnode, ds_correlations, [("atlas_names", "atlas")]),
-        (clean_name_source, ds_correlations, [("name_source", "source_file")]),
-        (correlate_timeseries, ds_correlations, [("correlations", "in_file")]),
-        (make_correlations_dict, ds_correlations, [("metadata", "meta_dict")]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     if cifti:
         ds_censored_filtered_bold = pe.Node(
@@ -375,49 +270,6 @@ def init_concatenate_data_wf(
             mem_gb=2,
         )
 
-        make_timeseries_ciftis_dict = pe.MapNode(
-            niu.Function(
-                function=_make_dictionary,
-                input_names=["Sources"],
-                output_names=["metadata"],
-            ),
-            run_without_submitting=True,
-            mem_gb=1,
-            name="make_timeseries_ciftis_dict",
-            iterfield=["Sources"],
-        )
-        # fmt:off
-        workflow.connect([
-            (filter_runs, make_timeseries_ciftis_dict, [
-                (("timeseries_ciftis", _make_xcpd_uri_lol, output_dir), "Sources"),
-            ]),
-        ])
-        # fmt:on
-
-        ds_timeseries_cifti_files = pe.MapNode(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                check_hdr=False,
-                dismiss_entities=["desc", "den"],
-                den="91k",
-                suffix="timeseries",
-                extension=".ptseries.nii",
-            ),
-            name="ds_timeseries_cifti_files",
-            run_without_submitting=True,
-            mem_gb=1,
-            iterfield=["atlas", "in_file", "meta_dict"],
-        )
-
-        # fmt:off
-        workflow.connect([
-            (clean_name_source, ds_timeseries_cifti_files, [("name_source", "source_file")]),
-            (inputnode, ds_timeseries_cifti_files, [("atlas_names", "atlas")]),
-            (concatenate_inputs, ds_timeseries_cifti_files, [("timeseries_ciftis", "in_file")]),
-            (make_timeseries_ciftis_dict, ds_timeseries_cifti_files, [("metadata", "meta_dict")]),
-        ])
-        # fmt:on
-
         if smoothing:
             ds_smoothed_denoised_bold = pe.Node(
                 DerivativesDataSink(
@@ -485,18 +337,15 @@ def init_concatenate_data_wf(
                 mem_gb=2,
             )
 
-    # fmt:off
     workflow.connect([
         (clean_name_source, ds_censored_filtered_bold, [("name_source", "source_file")]),
         (concatenate_inputs, ds_censored_filtered_bold, [("censored_denoised_bold", "in_file")]),
         (filter_runs, ds_censored_filtered_bold, [
             (("censored_denoised_bold", _make_xcpd_uri, output_dir), "Sources"),
         ]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
 
     if smoothing:
-        # fmt:off
         workflow.connect([
             (clean_name_source, ds_smoothed_denoised_bold, [("name_source", "source_file")]),
             (concatenate_inputs, ds_smoothed_denoised_bold, [
@@ -505,11 +354,9 @@ def init_concatenate_data_wf(
             (filter_runs, ds_smoothed_denoised_bold, [
                 (("smoothed_denoised_bold", _make_xcpd_uri, output_dir), "Sources"),
             ]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
 
     if dcan_qc:
-        # fmt:off
         workflow.connect([
             (clean_name_source, ds_interpolated_filtered_bold, [("name_source", "source_file")]),
             (concatenate_inputs, ds_interpolated_filtered_bold, [
@@ -518,7 +365,140 @@ def init_concatenate_data_wf(
             (filter_runs, ds_interpolated_filtered_bold, [
                 (("interpolated_filtered_bold", _make_xcpd_uri, output_dir), "Sources"),
             ]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
+
+    # Functional connectivity outputs
+    if atlases:
+        make_timeseries_dict = pe.MapNode(
+            niu.Function(
+                function=_make_dictionary,
+                input_names=["Sources"],
+                output_names=["metadata"],
+            ),
+            run_without_submitting=True,
+            mem_gb=1,
+            name="make_timeseries_dict",
+            iterfield=["Sources"],
+        )
+        workflow.connect([
+            (filter_runs, make_timeseries_dict, [
+                (("timeseries", _make_xcpd_uri_lol, output_dir), "Sources"),
+            ]),
+        ])  # fmt:skip
+
+        ds_timeseries = pe.MapNode(
+            DerivativesDataSink(
+                base_directory=output_dir,
+                dismiss_entities=["desc"],
+                suffix="timeseries",
+                extension=".tsv",
+                # Metadata
+                SamplingFrequency="TR",
+            ),
+            name="ds_timeseries",
+            run_without_submitting=True,
+            mem_gb=1,
+            iterfield=["atlas", "in_file", "meta_dict"],
+        )
+        ds_timeseries.inputs.atlas = atlases
+
+        workflow.connect([
+            (clean_name_source, ds_timeseries, [("name_source", "source_file")]),
+            (concatenate_inputs, ds_timeseries, [("timeseries", "in_file")]),
+            (make_timeseries_dict, ds_timeseries, [("metadata", "meta_dict")]),
+        ])  # fmt:skip
+
+        correlate_timeseries = pe.MapNode(
+            TSVConnect(),
+            run_without_submitting=True,
+            mem_gb=1,
+            name="correlate_timeseries",
+            iterfield=["timeseries"],
+        )
+        workflow.connect([
+            (concatenate_inputs, correlate_timeseries, [("timeseries", "timeseries")]),
+        ])  # fmt:skip
+
+        make_correlations_dict = pe.MapNode(
+            niu.Function(
+                function=_make_dictionary,
+                input_names=["Sources"],
+                output_names=["metadata"],
+            ),
+            run_without_submitting=True,
+            mem_gb=1,
+            name="make_correlations_dict",
+            iterfield=["Sources"],
+        )
+        workflow.connect([
+            (ds_timeseries, make_correlations_dict, [
+                (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+            ]),
+        ])  # fmt:skip
+
+        ds_correlations = pe.MapNode(
+            DerivativesDataSink(
+                base_directory=output_dir,
+                dismiss_entities=["desc"],
+                measure="pearsoncorrelation",
+                suffix="conmat",
+                extension=".tsv",
+            ),
+            name="ds_correlations",
+            run_without_submitting=True,
+            mem_gb=1,
+            iterfield=["atlas", "in_file", "meta_dict"],
+        )
+        ds_correlations.inputs.atlas = atlases
+
+        workflow.connect([
+            (clean_name_source, ds_correlations, [("name_source", "source_file")]),
+            (correlate_timeseries, ds_correlations, [("correlations", "in_file")]),
+            (make_correlations_dict, ds_correlations, [("metadata", "meta_dict")]),
+        ])  # fmt:skip
+
+        if cifti:
+            make_timeseries_ciftis_dict = pe.MapNode(
+                niu.Function(
+                    function=_make_dictionary,
+                    input_names=["Sources"],
+                    output_names=["metadata"],
+                ),
+                run_without_submitting=True,
+                mem_gb=1,
+                name="make_timeseries_ciftis_dict",
+                iterfield=["Sources"],
+            )
+            workflow.connect([
+                (filter_runs, make_timeseries_ciftis_dict, [
+                    (("timeseries_ciftis", _make_xcpd_uri_lol, output_dir), "Sources"),
+                ]),
+            ])  # fmt:skip
+
+            ds_timeseries_cifti_files = pe.MapNode(
+                DerivativesDataSink(
+                    base_directory=output_dir,
+                    check_hdr=False,
+                    dismiss_entities=["desc", "den"],
+                    den="91k",
+                    suffix="timeseries",
+                    extension=".ptseries.nii",
+                ),
+                name="ds_timeseries_cifti_files",
+                run_without_submitting=True,
+                mem_gb=1,
+                iterfield=["atlas", "in_file", "meta_dict"],
+            )
+            ds_timeseries_cifti_files.inputs.atlas = atlases
+
+            workflow.connect([
+                (clean_name_source, ds_timeseries_cifti_files, [("name_source", "source_file")]),
+                (concatenate_inputs, ds_timeseries_cifti_files, [
+                    ("timeseries_ciftis", "in_file"),
+                ]),
+                (make_timeseries_ciftis_dict, ds_timeseries_cifti_files, [
+                    ("metadata", "meta_dict"),
+                ]),
+            ])  # fmt:skip
 
     return workflow
diff --git a/xcp_d/workflows/connectivity.py b/xcp_d/workflows/connectivity.py
index cd3d16a19..1e8e5dc96 100644
--- a/xcp_d/workflows/connectivity.py
+++ b/xcp_d/workflows/connectivity.py
@@ -1,7 +1,7 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """Workflows for extracting time series and computing functional connectivity."""
-from nipype import Function
+from nipype import Function, logging
 from nipype.interfaces import utility as niu
 from nipype.pipeline import engine as pe
 from niworkflows.engine.workflows import LiterateWorkflow as Workflow
@@ -24,15 +24,18 @@
 from xcp_d.utils.atlas import (
     copy_atlas,
     get_atlas_cifti,
-    get_atlas_names,
     get_atlas_nifti,
+    select_atlases,
 )
 from xcp_d.utils.doc import fill_doc
 from xcp_d.utils.utils import get_std2bold_xfms
 
+LOGGER = logging.getLogger("nipype.workflow")
+
 
 @fill_doc
 def init_load_atlases_wf(
+    atlases,
     output_dir,
     cifti,
     mem_gb,
@@ -49,6 +52,7 @@ def init_load_atlases_wf(
             from xcp_d.workflows.connectivity import init_load_atlases_wf
 
             wf = init_load_atlases_wf(
+                atlases=["Glasser"],
                 output_dir=".",
                 cifti=True,
                 mem_gb=0.1,
@@ -58,6 +62,7 @@ def init_load_atlases_wf(
 
     Parameters
     ----------
+    %(atlases)s
     %(output_dir)s
     %(cifti)s
     %(mem_gb)s
@@ -72,7 +77,6 @@ def init_load_atlases_wf(
 
     Outputs
     -------
-    atlas_names
     atlas_files
     atlas_labels_files
     parcellated_atlas_files
@@ -91,7 +95,6 @@ def init_load_atlases_wf(
     outputnode = pe.Node(
         niu.IdentityInterface(
             fields=[
-                "atlas_names",
                 "atlas_files",
                 "atlas_labels_files",
                 "parcellated_atlas_files",  # only used for CIFTIs
@@ -100,25 +103,17 @@ def init_load_atlases_wf(
         name="outputnode",
     )
 
-    atlas_name_grabber = pe.Node(
-        Function(input_names=["subset"], output_names=["atlas_names"], function=get_atlas_names),
-        name="atlas_name_grabber",
-    )
-    atlas_name_grabber.inputs.subset = "all"
-
-    workflow.connect([(atlas_name_grabber, outputnode, [("atlas_names", "atlas_names")])])
-
     # get atlases via pkgrf
     atlas_file_grabber = pe.MapNode(
         Function(
-            input_names=["atlas_name"],
+            input_names=["atlas"],
             output_names=["atlas_file", "atlas_labels_file", "atlas_metadata_file"],
             function=get_atlas_cifti if cifti else get_atlas_nifti,
         ),
         name="atlas_file_grabber",
-        iterfield=["atlas_name"],
+        iterfield=["atlas"],
     )
-    workflow.connect([(atlas_name_grabber, atlas_file_grabber, [("atlas_names", "atlas_name")])])
+    atlas_file_grabber.inputs.atlas = atlases
 
     atlas_buffer = pe.Node(niu.IdentityInterface(fields=["atlas_file"]), name="atlas_buffer")
 
@@ -244,11 +239,11 @@ def init_load_atlases_wf(
         run_without_submitting=True,
     )
     ds_atlas.inputs.output_dir = output_dir
+    ds_atlas.inputs.atlas = atlases
 
     # fmt:off
     workflow.connect([
         (inputnode, ds_atlas, [("name_source", "name_source")]),
-        (atlas_name_grabber, ds_atlas, [("atlas_names", "atlas")]),
         (atlas_buffer, ds_atlas, [("atlas_file", "in_file")]),
         (ds_atlas, outputnode, [("out_file", "atlas_files")]),
     ])
@@ -278,11 +273,11 @@ def init_load_atlases_wf(
         iterfield=["atlas", "in_file"],
         run_without_submitting=True,
     )
+    ds_atlas_labels_file.inputs.atlas = atlases
 
     # fmt:off
     workflow.connect([
         (inputnode, ds_atlas_labels_file, [("name_source", "source_file")]),
-        (atlas_name_grabber, ds_atlas_labels_file, [("atlas_names", "atlas")]),
         (atlas_file_grabber, ds_atlas_labels_file, [("atlas_labels_file", "in_file")]),
         (ds_atlas_labels_file, outputnode, [("out_file", "atlas_labels_files")]),
     ])
@@ -312,11 +307,11 @@ def init_load_atlases_wf(
         iterfield=["atlas", "in_file"],
         run_without_submitting=True,
     )
+    ds_atlas_metadata.inputs.atlas = atlases
 
     # fmt:off
     workflow.connect([
         (inputnode, ds_atlas_metadata, [("name_source", "source_file")]),
-        (atlas_name_grabber, ds_atlas_metadata, [("atlas_names", "atlas")]),
         (atlas_file_grabber, ds_atlas_metadata, [("atlas_metadata_file", "in_file")]),
     ])
     # fmt:on
@@ -327,6 +322,7 @@ def init_load_atlases_wf(
 @fill_doc
 def init_parcellate_surfaces_wf(
     output_dir,
+    atlases,
     files_to_parcellate,
     min_coverage,
     mem_gb,
@@ -344,6 +340,7 @@ def init_parcellate_surfaces_wf(
 
             wf = init_parcellate_surfaces_wf(
                 output_dir=".",
+                atlases=["Glasser"],
                 files_to_parcellate=["sulcal_depth", "sulcal_curv", "cortical_thickness"],
                 min_coverage=0.5,
                 mem_gb=0.1,
@@ -354,6 +351,7 @@ def init_parcellate_surfaces_wf(
     Parameters
     ----------
     %(output_dir)s
+    atlases
     files_to_parcellate : :obj:`list` of :obj:`str`
         List of surface file types to parcellate
         (e.g., "sulcal_depth", "sulcal_curv", "cortical_thickness").
@@ -396,24 +394,28 @@ def init_parcellate_surfaces_wf(
         name="inputnode",
     )
 
-    atlas_name_grabber = pe.Node(
-        Function(input_names=["subset"], output_names=["atlas_names"], function=get_atlas_names),
-        name="atlas_name_grabber",
-    )
-    atlas_name_grabber.inputs.subset = "cortical"
+    selected_atlases = select_atlases(atlases=atlases, subset="cortical")
+
+    if not selected_atlases:
+        LOGGER.warning(
+            "No cortical atlases have been selected, so surface metrics will not be parcellated."
+        )
+        # If no cortical atlases are selected, inputnode could go unconnected, so add explicitly.
+        workflow.add_nodes([inputnode])
+
+        return workflow
 
     # Get CIFTI atlases via pkgrf
     atlas_file_grabber = pe.MapNode(
         Function(
-            input_names=["atlas_name"],
+            input_names=["atlas"],
             output_names=["atlas_file", "atlas_labels_file", "atlas_metadata_file"],
             function=get_atlas_cifti,
         ),
         name="atlas_file_grabber",
-        iterfield=["atlas_name"],
+        iterfield=["atlas"],
     )
-
-    workflow.connect([(atlas_name_grabber, atlas_file_grabber, [("atlas_names", "atlas_name")])])
+    atlas_file_grabber.inputs.atlas = selected_atlases
 
     for file_to_parcellate in files_to_parcellate:
         resample_atlas_to_surface = pe.MapNode(
@@ -481,11 +483,11 @@ def init_parcellate_surfaces_wf(
             mem_gb=1,
             iterfield=["atlas", "in_file"],
         )
+        ds_parcellated_surface.inputs.atlas = selected_atlases
 
         # fmt:off
         workflow.connect([
             (inputnode, ds_parcellated_surface, [(file_to_parcellate, "source_file")]),
-            (atlas_name_grabber, ds_parcellated_surface, [("atlas_names", "atlas")]),
             (parcellate_surface, ds_parcellated_surface, [("timeseries", "in_file")]),
         ])
         # fmt:on
@@ -535,7 +537,7 @@ def init_functional_connectivity_nifti_wf(
     %(temporal_mask)s
     alff
     reho
-    %(atlas_names)s
+    %(atlases)s
     atlas_files
     atlas_labels_files
 
@@ -575,7 +577,7 @@ def init_functional_connectivity_nifti_wf(
                 "temporal_mask",
                 "alff",  # may be Undefined
                 "reho",
-                "atlas_names",
+                "atlases",
                 "atlas_files",
                 "atlas_labels_files",
             ],
@@ -685,7 +687,7 @@ def init_functional_connectivity_nifti_wf(
     # fmt:off
     workflow.connect([
         (inputnode, connectivity_plot, [
-            ("atlas_names", "atlas_names"),
+            ("atlases", "atlases"),
             ("atlas_labels_files", "atlas_tsvs"),
         ]),
         (functional_connectivity, connectivity_plot, [("correlations", "correlations_tsv")]),
@@ -758,7 +760,7 @@ def init_functional_connectivity_cifti_wf(
     %(temporal_mask)s
     alff
     reho
-    %(atlas_names)s
+    %(atlases)s
     atlas_files
     atlas_labels_files
     parcellated_atlas_files
@@ -802,7 +804,7 @@ def init_functional_connectivity_cifti_wf(
                 "temporal_mask",
                 "alff",  # may be Undefined
                 "reho",
-                "atlas_names",
+                "atlases",
                 "atlas_files",
                 "atlas_labels_files",
                 "parcellated_atlas_files",
@@ -927,7 +929,7 @@ def init_functional_connectivity_cifti_wf(
     # fmt:off
     workflow.connect([
         (inputnode, connectivity_plot, [
-            ("atlas_names", "atlas_names"),
+            ("atlases", "atlases"),
             ("atlas_labels_files", "atlas_tsvs"),
         ]),
         (functional_connectivity, connectivity_plot, [("correlations", "correlations_tsv")]),
diff --git a/xcp_d/workflows/outputs.py b/xcp_d/workflows/outputs.py
index 9219bbab6..3aa62b194 100644
--- a/xcp_d/workflows/outputs.py
+++ b/xcp_d/workflows/outputs.py
@@ -138,6 +138,7 @@ def init_postproc_derivatives_wf(
     smoothing,
     params,
     exact_scans,
+    atlases,
     cifti,
     dcan_qc,
     output_dir,
@@ -166,6 +167,7 @@ def init_postproc_derivatives_wf(
                 smoothing=6,
                 params="36P",
                 exact_scans=[],
+                atlases=["Glasser"],
                 cifti=False,
                 dcan_qc=True,
                 output_dir=".",
@@ -190,6 +192,7 @@ def init_postproc_derivatives_wf(
     %(smoothing)s
     %(params)s
     %(exact_scans)s
+    %(atlases)s
     %(cifti)s
     %(dcan_qc)s
     output_dir : :obj:`str`
@@ -201,8 +204,6 @@ def init_postproc_derivatives_wf(
 
     Inputs
     ------
-    %(atlas_names)s
-        Used for indexing ``timeseries`` and ``correlations``.
     atlas_files
     %(timeseries)s
     %(correlations)s
@@ -238,7 +239,6 @@ def init_postproc_derivatives_wf(
                 # preprocessing files to use as sources
                 "fmriprep_confounds_file",
                 # postprocessed outputs
-                "atlas_names",
                 "atlas_files",  # for Sources
                 "confounds_file",
                 "confounds_metadata",
@@ -317,23 +317,6 @@ def init_postproc_derivatives_wf(
 
     preproc_bold_src = _make_preproc_uri(name_source, fmri_dir)
 
-    make_atlas_dict = pe.MapNode(
-        niu.Function(
-            function=_make_dictionary,
-            input_names=["Sources"],
-            output_names=["metadata"],
-        ),
-        run_without_submitting=True,
-        mem_gb=1,
-        name="make_atlas_dict",
-        iterfield=["Sources"],
-    )
-    # fmt:off
-    workflow.connect([
-        (inputnode, make_atlas_dict, [(("atlas_files", _make_xcpd_uri, output_dir), "Sources")]),
-    ])
-    # fmt:on
-
     ds_filtered_motion = pe.Node(
         DerivativesDataSink(
             base_directory=output_dir,
@@ -529,27 +512,6 @@ def init_postproc_derivatives_wf(
     )
     workflow.connect([(inputnode, ds_qc_file, [("qc_file", "in_file")])])
 
-    # Convert Sources to a dictionary, to play well with parcellation MapNodes.
-    add_denoised_to_src = pe.MapNode(
-        niu.Function(
-            function=_make_dictionary,
-            input_names=["metadata", "Sources"],
-            output_names=["metadata"],
-        ),
-        run_without_submitting=True,
-        mem_gb=1,
-        name="add_denoised_to_src",
-        iterfield=["metadata"],
-    )
-    # fmt:off
-    workflow.connect([
-        (make_atlas_dict, add_denoised_to_src, [("metadata", "metadata")]),
-        (ds_denoised_bold, add_denoised_to_src, [
-            (("out_file", _make_xcpd_uri, output_dir), "Sources"),
-        ]),
-    ])
-    # fmt:on
-
     if smoothing:
         # Write out derivatives via DerivativesDataSink
         ds_smoothed_bold = pe.Node(
@@ -581,143 +543,26 @@ def init_postproc_derivatives_wf(
         # fmt:on
 
     # Connectivity workflow outputs
-    # TODO: Add brain mask to Sources (for NIfTIs).
-    ds_coverage = pe.MapNode(
-        DerivativesDataSink(
-            base_directory=output_dir,
-            source_file=name_source,
-            dismiss_entities=["desc"],
-            cohort=cohort,
-            suffix="coverage",
-            extension=".tsv",
-        ),
-        name="ds_coverage",
-        run_without_submitting=True,
-        mem_gb=1,
-        iterfield=["atlas", "in_file", "meta_dict"],
-    )
-    # fmt:off
-    workflow.connect([
-        (inputnode, ds_coverage, [
-            ("coverage", "in_file"),
-            ("atlas_names", "atlas"),
-        ]),
-        (make_atlas_dict, ds_coverage, [("metadata", "meta_dict")]),
-    ])
-    # fmt:on
-
-    add_coverage_to_src = pe.MapNode(
-        niu.Function(
-            function=_make_dictionary,
-            input_names=["metadata", "Sources"],
-            output_names=["metadata"],
-        ),
-        run_without_submitting=True,
-        mem_gb=1,
-        name="add_coverage_to_src",
-        iterfield=["metadata", "Sources"],
-    )
-    # fmt:off
-    workflow.connect([
-        (add_denoised_to_src, add_coverage_to_src, [("metadata", "metadata")]),
-        (ds_coverage, add_coverage_to_src, [
-            (("out_file", _make_xcpd_uri, output_dir), "Sources"),
-        ]),
-    ])
-    # fmt:on
-
-    ds_timeseries = pe.MapNode(
-        DerivativesDataSink(
-            base_directory=output_dir,
-            source_file=name_source,
-            dismiss_entities=["desc"],
-            cohort=cohort,
-            suffix="timeseries",
-            extension=".tsv",
-            # Metadata
-            SamplingFrequency="TR",
-        ),
-        name="ds_timeseries",
-        run_without_submitting=True,
-        mem_gb=1,
-        iterfield=["atlas", "in_file", "meta_dict"],
-    )
-    # fmt:off
-    workflow.connect([
-        (inputnode, ds_timeseries, [
-            ("timeseries", "in_file"),
-            ("atlas_names", "atlas"),
-        ]),
-        (add_coverage_to_src, ds_timeseries, [("metadata", "meta_dict")]),
-        (ds_timeseries, outputnode, [("out_file", "timeseries")]),
-    ])
-    # fmt:on
-
-    make_corrs_meta_dict = pe.MapNode(
-        niu.Function(
-            function=_make_dictionary,
-            input_names=["Sources"],
-            output_names=["metadata"],
-        ),
-        run_without_submitting=True,
-        mem_gb=1,
-        name="make_corrs_meta_dict",
-        iterfield=["Sources"],
-    )
-    workflow.connect([(ds_timeseries, make_corrs_meta_dict, [("out_file", "Sources")])])
-
-    ds_correlations = pe.MapNode(
-        DerivativesDataSink(
-            base_directory=output_dir,
-            source_file=name_source,
-            dismiss_entities=["desc"],
-            cohort=cohort,
-            measure="pearsoncorrelation",
-            suffix="conmat",
-            extension=".tsv",
-        ),
-        name="ds_correlations",
-        run_without_submitting=True,
-        mem_gb=1,
-        iterfield=["atlas", "in_file", "meta_dict"],
-    )
-    # fmt:off
-    workflow.connect([
-        (inputnode, ds_correlations, [
-            ("correlations", "in_file"),
-            ("atlas_names", "atlas"),
-        ]),
-        (make_corrs_meta_dict, ds_correlations, [("metadata", "meta_dict")]),
-    ])
-    # fmt:on
-
-    if cifti:
-        ds_coverage_ciftis = pe.MapNode(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                source_file=name_source,
-                check_hdr=False,
-                dismiss_entities=["desc"],
-                cohort=cohort,
-                suffix="coverage",
-                extension=".pscalar.nii",
+    if atlases:
+        make_atlas_dict = pe.MapNode(
+            niu.Function(
+                function=_make_dictionary,
+                input_names=["Sources"],
+                output_names=["metadata"],
             ),
-            name="ds_coverage_ciftis",
             run_without_submitting=True,
             mem_gb=1,
-            iterfield=["atlas", "in_file", "meta_dict"],
+            name="make_atlas_dict",
+            iterfield=["Sources"],
         )
-        # fmt:off
         workflow.connect([
-            (inputnode, ds_coverage_ciftis, [
-                ("coverage_ciftis", "in_file"),
-                ("atlas_names", "atlas"),
+            (inputnode, make_atlas_dict, [
+                (("atlas_files", _make_xcpd_uri, output_dir), "Sources"),
             ]),
-            (add_denoised_to_src, ds_coverage_ciftis, [("metadata", "meta_dict")]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
 
-        add_ccoverage_to_src = pe.MapNode(
+        # Convert Sources to a dictionary, to play well with parcellation MapNodes.
+        add_denoised_to_src = pe.MapNode(
             niu.Function(
                 function=_make_dictionary,
                 input_names=["metadata", "Sources"],
@@ -725,124 +570,268 @@ def init_postproc_derivatives_wf(
             ),
             run_without_submitting=True,
             mem_gb=1,
-            name="add_ccoverage_to_src",
-            iterfield=["metadata", "Sources"],
+            name="add_denoised_to_src",
+            iterfield=["metadata"],
         )
         # fmt:off
         workflow.connect([
-            (add_denoised_to_src, add_ccoverage_to_src, [("metadata", "metadata")]),
-            (ds_coverage_ciftis, add_ccoverage_to_src, [
+            (make_atlas_dict, add_denoised_to_src, [("metadata", "metadata")]),
+            (ds_denoised_bold, add_denoised_to_src, [
                 (("out_file", _make_xcpd_uri, output_dir), "Sources"),
             ]),
         ])
         # fmt:on
 
-        ds_timeseries_ciftis = pe.MapNode(
+        # TODO: Add brain mask to Sources (for NIfTIs).
+        ds_coverage = pe.MapNode(
             DerivativesDataSink(
                 base_directory=output_dir,
                 source_file=name_source,
-                check_hdr=False,
-                dismiss_entities=["desc", "den"],
+                dismiss_entities=["desc"],
                 cohort=cohort,
-                den="91k" if cifti else None,
-                suffix="timeseries",
-                extension=".ptseries.nii",
+                suffix="coverage",
+                extension=".tsv",
             ),
-            name="ds_timeseries_ciftis",
+            name="ds_coverage",
             run_without_submitting=True,
             mem_gb=1,
             iterfield=["atlas", "in_file", "meta_dict"],
         )
+        ds_coverage.inputs.atlas = atlases
         # fmt:off
         workflow.connect([
-            (inputnode, ds_timeseries_ciftis, [
-                ("timeseries_ciftis", "in_file"),
-                ("atlas_names", "atlas"),
-            ]),
-            (add_ccoverage_to_src, ds_timeseries_ciftis, [("metadata", "meta_dict")]),
-            (ds_timeseries_ciftis, outputnode, [("out_file", "timeseries_ciftis")]),
+            (inputnode, ds_coverage, [("coverage", "in_file")]),
+            (make_atlas_dict, ds_coverage, [("metadata", "meta_dict")]),
         ])
         # fmt:on
 
-        make_ccorrs_meta_dict = pe.MapNode(
+        add_coverage_to_src = pe.MapNode(
             niu.Function(
                 function=_make_dictionary,
-                input_names=["Sources"],
+                input_names=["metadata", "Sources"],
                 output_names=["metadata"],
             ),
             run_without_submitting=True,
             mem_gb=1,
-            name="make_ccorrs_meta_dict",
-            iterfield=["Sources"],
+            name="add_coverage_to_src",
+            iterfield=["metadata", "Sources"],
         )
         # fmt:off
         workflow.connect([
-            (ds_timeseries_ciftis, make_ccorrs_meta_dict, [("out_file", "Sources")]),
+            (add_denoised_to_src, add_coverage_to_src, [("metadata", "metadata")]),
+            (ds_coverage, add_coverage_to_src, [
+                (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+            ]),
         ])
         # fmt:on
 
-        ds_correlation_ciftis = pe.MapNode(
+        ds_timeseries = pe.MapNode(
             DerivativesDataSink(
                 base_directory=output_dir,
                 source_file=name_source,
-                check_hdr=False,
-                dismiss_entities=["desc", "den"],
+                dismiss_entities=["desc"],
                 cohort=cohort,
-                den="91k" if cifti else None,
-                measure="pearsoncorrelation",
-                suffix="conmat",
-                extension=".pconn.nii",
+                suffix="timeseries",
+                extension=".tsv",
+                # Metadata
+                SamplingFrequency="TR",
             ),
-            name="ds_correlation_ciftis",
+            name="ds_timeseries",
             run_without_submitting=True,
             mem_gb=1,
             iterfield=["atlas", "in_file", "meta_dict"],
         )
+        ds_timeseries.inputs.atlas = atlases
         # fmt:off
         workflow.connect([
-            (inputnode, ds_correlation_ciftis, [
-                ("correlation_ciftis", "in_file"),
-                ("atlas_names", "atlas"),
-            ]),
-            (make_ccorrs_meta_dict, ds_correlation_ciftis, [("metadata", "meta_dict")]),
+            (inputnode, ds_timeseries, [("timeseries", "in_file")]),
+            (add_coverage_to_src, ds_timeseries, [("metadata", "meta_dict")]),
+            (ds_timeseries, outputnode, [("out_file", "timeseries")]),
         ])
         # fmt:on
 
-    for i_exact_scan, exact_scan in enumerate(exact_scans):
-        select_exact_scan_files = pe.MapNode(
-            niu.Select(index=i_exact_scan),
-            name=f"select_exact_scan_files_{i_exact_scan}",
-            iterfield=["inlist"],
+        make_corrs_meta_dict = pe.MapNode(
+            niu.Function(
+                function=_make_dictionary,
+                input_names=["Sources"],
+                output_names=["metadata"],
+            ),
+            run_without_submitting=True,
+            mem_gb=1,
+            name="make_corrs_meta_dict",
+            iterfield=["Sources"],
         )
-        # fmt:off
-        workflow.connect([
-            (inputnode, select_exact_scan_files, [("correlations_exact", "inlist")]),
-        ])
-        # fmt:on
+        workflow.connect([(ds_timeseries, make_corrs_meta_dict, [("out_file", "Sources")])])
 
-        ds_correlations_exact = pe.MapNode(
+        ds_correlations = pe.MapNode(
             DerivativesDataSink(
                 base_directory=output_dir,
                 source_file=name_source,
                 dismiss_entities=["desc"],
                 cohort=cohort,
                 measure="pearsoncorrelation",
-                desc=f"{exact_scan}volumes",
                 suffix="conmat",
                 extension=".tsv",
             ),
-            name=f"ds_correlations_exact_{i_exact_scan}",
+            name="ds_correlations",
             run_without_submitting=True,
             mem_gb=1,
-            iterfield=["atlas", "in_file"],
+            iterfield=["atlas", "in_file", "meta_dict"],
         )
+        ds_correlations.inputs.atlas = atlases
         # fmt:off
         workflow.connect([
-            (inputnode, ds_correlations_exact, [("atlas_names", "atlas")]),
-            (select_exact_scan_files, ds_correlations_exact, [("out", "in_file")]),
+            (inputnode, ds_correlations, [("correlations", "in_file")]),
+            (make_corrs_meta_dict, ds_correlations, [("metadata", "meta_dict")]),
         ])
         # fmt:on
 
+        if cifti:
+            ds_coverage_ciftis = pe.MapNode(
+                DerivativesDataSink(
+                    base_directory=output_dir,
+                    source_file=name_source,
+                    check_hdr=False,
+                    dismiss_entities=["desc"],
+                    cohort=cohort,
+                    suffix="coverage",
+                    extension=".pscalar.nii",
+                ),
+                name="ds_coverage_ciftis",
+                run_without_submitting=True,
+                mem_gb=1,
+                iterfield=["atlas", "in_file", "meta_dict"],
+            )
+            ds_coverage_ciftis.inputs.atlas = atlases
+            # fmt:off
+            workflow.connect([
+                (inputnode, ds_coverage_ciftis, [("coverage_ciftis", "in_file")]),
+                (add_denoised_to_src, ds_coverage_ciftis, [("metadata", "meta_dict")]),
+            ])
+            # fmt:on
+
+            add_ccoverage_to_src = pe.MapNode(
+                niu.Function(
+                    function=_make_dictionary,
+                    input_names=["metadata", "Sources"],
+                    output_names=["metadata"],
+                ),
+                run_without_submitting=True,
+                mem_gb=1,
+                name="add_ccoverage_to_src",
+                iterfield=["metadata", "Sources"],
+            )
+            # fmt:off
+            workflow.connect([
+                (add_denoised_to_src, add_ccoverage_to_src, [("metadata", "metadata")]),
+                (ds_coverage_ciftis, add_ccoverage_to_src, [
+                    (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+                ]),
+            ])
+            # fmt:on
+
+            ds_timeseries_ciftis = pe.MapNode(
+                DerivativesDataSink(
+                    base_directory=output_dir,
+                    source_file=name_source,
+                    check_hdr=False,
+                    dismiss_entities=["desc", "den"],
+                    cohort=cohort,
+                    den="91k" if cifti else None,
+                    suffix="timeseries",
+                    extension=".ptseries.nii",
+                ),
+                name="ds_timeseries_ciftis",
+                run_without_submitting=True,
+                mem_gb=1,
+                iterfield=["atlas", "in_file", "meta_dict"],
+            )
+            ds_timeseries_ciftis.inputs.atlas = atlases
+            # fmt:off
+            workflow.connect([
+                (inputnode, ds_timeseries_ciftis, [("timeseries_ciftis", "in_file")]),
+                (add_ccoverage_to_src, ds_timeseries_ciftis, [("metadata", "meta_dict")]),
+                (ds_timeseries_ciftis, outputnode, [("out_file", "timeseries_ciftis")]),
+            ])
+            # fmt:on
+
+            make_ccorrs_meta_dict = pe.MapNode(
+                niu.Function(
+                    function=_make_dictionary,
+                    input_names=["Sources"],
+                    output_names=["metadata"],
+                ),
+                run_without_submitting=True,
+                mem_gb=1,
+                name="make_ccorrs_meta_dict",
+                iterfield=["Sources"],
+            )
+            # fmt:off
+            workflow.connect([
+                (ds_timeseries_ciftis, make_ccorrs_meta_dict, [("out_file", "Sources")]),
+            ])
+            # fmt:on
+
+            ds_correlation_ciftis = pe.MapNode(
+                DerivativesDataSink(
+                    base_directory=output_dir,
+                    source_file=name_source,
+                    check_hdr=False,
+                    dismiss_entities=["desc", "den"],
+                    cohort=cohort,
+                    den="91k" if cifti else None,
+                    measure="pearsoncorrelation",
+                    suffix="conmat",
+                    extension=".pconn.nii",
+                ),
+                name="ds_correlation_ciftis",
+                run_without_submitting=True,
+                mem_gb=1,
+                iterfield=["atlas", "in_file", "meta_dict"],
+            )
+            ds_correlation_ciftis.inputs.atlas = atlases
+            # fmt:off
+            workflow.connect([
+                (inputnode, ds_correlation_ciftis, [("correlation_ciftis", "in_file")]),
+                (make_ccorrs_meta_dict, ds_correlation_ciftis, [("metadata", "meta_dict")]),
+            ])
+            # fmt:on
+
+        for i_exact_scan, exact_scan in enumerate(exact_scans):
+            select_exact_scan_files = pe.MapNode(
+                niu.Select(index=i_exact_scan),
+                name=f"select_exact_scan_files_{i_exact_scan}",
+                iterfield=["inlist"],
+            )
+            # fmt:off
+            workflow.connect([
+                (inputnode, select_exact_scan_files, [("correlations_exact", "inlist")]),
+            ])
+            # fmt:on
+
+            ds_correlations_exact = pe.MapNode(
+                DerivativesDataSink(
+                    base_directory=output_dir,
+                    source_file=name_source,
+                    dismiss_entities=["desc"],
+                    cohort=cohort,
+                    measure="pearsoncorrelation",
+                    desc=f"{exact_scan}volumes",
+                    suffix="conmat",
+                    extension=".tsv",
+                ),
+                name=f"ds_correlations_exact_{i_exact_scan}",
+                run_without_submitting=True,
+                mem_gb=1,
+                iterfield=["atlas", "in_file"],
+            )
+            ds_correlations_exact.inputs.atlas = atlases
+            # fmt:off
+            workflow.connect([
+                (select_exact_scan_files, ds_correlations_exact, [("out", "in_file")]),
+            ])
+            # fmt:on
+
     # Resting state metric outputs
     ds_reho = pe.Node(
         DerivativesDataSink(
@@ -869,50 +858,49 @@ def init_postproc_derivatives_wf(
     ])
     # fmt:on
 
-    add_reho_to_src = pe.MapNode(
-        niu.Function(
-            function=_make_dictionary,
-            input_names=["metadata", "Sources"],
-            output_names=["metadata"],
-        ),
-        run_without_submitting=True,
-        mem_gb=1,
-        name="add_reho_to_src",
-        iterfield=["metadata"],
-    )
-    # fmt:off
-    workflow.connect([
-        (make_atlas_dict, add_reho_to_src, [("metadata", "metadata")]),
-        (ds_reho, add_reho_to_src, [(("out_file", _make_xcpd_uri, output_dir), "Sources")]),
-    ])
-    # fmt:on
+    if atlases:
+        add_reho_to_src = pe.MapNode(
+            niu.Function(
+                function=_make_dictionary,
+                input_names=["metadata", "Sources"],
+                output_names=["metadata"],
+            ),
+            run_without_submitting=True,
+            mem_gb=1,
+            name="add_reho_to_src",
+            iterfield=["metadata"],
+        )
+        # fmt:off
+        workflow.connect([
+            (make_atlas_dict, add_reho_to_src, [("metadata", "metadata")]),
+            (ds_reho, add_reho_to_src, [(("out_file", _make_xcpd_uri, output_dir), "Sources")]),
+        ])
+        # fmt:on
 
-    ds_parcellated_reho = pe.MapNode(
-        DerivativesDataSink(
-            base_directory=output_dir,
-            source_file=name_source,
-            dismiss_entities=["desc"],
-            cohort=cohort,
-            suffix="reho",
-            extension=".tsv",
-            # Metadata
-            SoftwareFilters=software_filters,
-            Neighborhood="vertices",
-        ),
-        name="ds_parcellated_reho",
-        run_without_submitting=True,
-        mem_gb=1,
-        iterfield=["atlas", "in_file", "meta_dict"],
-    )
-    # fmt:off
-    workflow.connect([
-        (inputnode, ds_parcellated_reho, [
-            ("parcellated_reho", "in_file"),
-            ("atlas_names", "atlas"),
-        ]),
-        (add_reho_to_src, ds_parcellated_reho, [("metadata", "meta_dict")]),
-    ])
-    # fmt:on
+        ds_parcellated_reho = pe.MapNode(
+            DerivativesDataSink(
+                base_directory=output_dir,
+                source_file=name_source,
+                dismiss_entities=["desc"],
+                cohort=cohort,
+                suffix="reho",
+                extension=".tsv",
+                # Metadata
+                SoftwareFilters=software_filters,
+                Neighborhood="vertices",
+            ),
+            name="ds_parcellated_reho",
+            run_without_submitting=True,
+            mem_gb=1,
+            iterfield=["atlas", "in_file", "meta_dict"],
+        )
+        ds_parcellated_reho.inputs.atlas = atlases
+        # fmt:off
+        workflow.connect([
+            (inputnode, ds_parcellated_reho, [("parcellated_reho", "in_file")]),
+            (add_reho_to_src, ds_parcellated_reho, [("metadata", "meta_dict")]),
+        ])
+        # fmt:on
 
     if bandpass_filter:
         ds_alff = pe.Node(
@@ -970,48 +958,47 @@ def init_postproc_derivatives_wf(
             ])
             # fmt:on
 
-        add_alff_to_src = pe.MapNode(
-            niu.Function(
-                function=_make_dictionary,
-                input_names=["metadata", "Sources"],
-                output_names=["metadata"],
-            ),
-            run_without_submitting=True,
-            mem_gb=1,
-            name="add_alff_to_src",
-            iterfield=["metadata"],
-        )
-        # fmt:off
-        workflow.connect([
-            (make_atlas_dict, add_alff_to_src, [("metadata", "metadata")]),
-            (ds_alff, add_alff_to_src, [
-                (("out_file", _make_xcpd_uri, output_dir), "Sources"),
-            ]),
-        ])
-        # fmt:on
+        if atlases:
+            add_alff_to_src = pe.MapNode(
+                niu.Function(
+                    function=_make_dictionary,
+                    input_names=["metadata", "Sources"],
+                    output_names=["metadata"],
+                ),
+                run_without_submitting=True,
+                mem_gb=1,
+                name="add_alff_to_src",
+                iterfield=["metadata"],
+            )
+            # fmt:off
+            workflow.connect([
+                (make_atlas_dict, add_alff_to_src, [("metadata", "metadata")]),
+                (ds_alff, add_alff_to_src, [
+                    (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+                ]),
+            ])
+            # fmt:on
 
-        ds_parcellated_alff = pe.MapNode(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                source_file=name_source,
-                dismiss_entities=["desc"],
-                cohort=cohort,
-                suffix="alff",
-                extension=".tsv",
-            ),
-            name="ds_parcellated_alff",
-            run_without_submitting=True,
-            mem_gb=1,
-            iterfield=["atlas", "in_file", "meta_dict"],
-        )
-        # fmt:off
-        workflow.connect([
-            (inputnode, ds_parcellated_alff, [
-                ("parcellated_alff", "in_file"),
-                ("atlas_names", "atlas"),
-            ]),
-            (add_alff_to_src, ds_parcellated_alff, [("metadata", "meta_dict")]),
-        ])
-        # fmt:on
+            ds_parcellated_alff = pe.MapNode(
+                DerivativesDataSink(
+                    base_directory=output_dir,
+                    source_file=name_source,
+                    dismiss_entities=["desc"],
+                    cohort=cohort,
+                    suffix="alff",
+                    extension=".tsv",
+                ),
+                name="ds_parcellated_alff",
+                run_without_submitting=True,
+                mem_gb=1,
+                iterfield=["atlas", "in_file", "meta_dict"],
+            )
+            ds_parcellated_alff.inputs.atlas = atlases
+            # fmt:off
+            workflow.connect([
+                (inputnode, ds_parcellated_alff, [("parcellated_alff", "in_file")]),
+                (add_alff_to_src, ds_parcellated_alff, [("metadata", "meta_dict")]),
+            ])
+            # fmt:on
 
     return workflow