diff --git a/docs/outputs.rst b/docs/outputs.rst
index fa6b0bb69..7554f3253 100644
--- a/docs/outputs.rst
+++ b/docs/outputs.rst
@@ -165,21 +165,15 @@ Denoised or residual BOLD data
    xcp_d/
       sub-<label>/[ses-<label>/]
          func/
-            # Nifti
+            # NIfTI
             <source_entities>_space-<label>_desc-denoised_bold.nii.gz
-            <source_entities>_space-<label>_desc-denoised_bold.json
             <source_entities>_space-<label>_desc-denoisedSmoothed_bold.nii.gz
-            <source_entities>_space-<label>_desc-denoisedSmoothed_bold.json
             <source_entities>_space-<label>_desc-interpolated_bold.nii.gz
-            <source_entities>_space-<label>_desc-interpolated_bold.json
 
-            # Cifti
+            # CIFTI
             <source_entities>_space-fsLR_den-91k_desc-denoised_bold.dtseries.nii
-            <source_entities>_space-fsLR_den-91k_desc-denoised_bold.json
             <source_entities>_space-fsLR_den-91k_desc-denoisedSmoothed_bold.dtseries.nii
-            <source_entities>_space-fsLR_den-91k_desc-denoisedSmoothed_bold.json
             <source_entities>_space-fsLR_den-91k_desc-interpolated_bold.dtseries.nii
-            <source_entities>_space-fsLR_den-91k_desc-interpolated_bold.json
 
 .. important::
 
@@ -212,21 +206,21 @@ This includes the atlases used to extract the timeseries.
 .. code-block::
 
    xcp_d/
-      # Nifti
+      # NIfTI
       space-<label>_atlas-<label>_dseg.nii.gz
 
-      # Cifti
+      # CIFTI
       space-<label>_atlas-<label>_dseg.dlabel.nii
 
       sub-<label>/[ses-<label>/]
          func/
-            # Nifti
+            # NIfTI
             <source_entities>_space-<label>_atlas-<label>_coverage.tsv
             <source_entities>_space-<label>_atlas-<label>_timeseries.tsv
             <source_entities>_space-<label>_atlas-<label>_measure-pearsoncorrelation_conmat.tsv
             <source_entities>_space-<label>_atlas-<label>_measure-pearsoncorrelation_desc-<INT>volumes_conmat.tsv
 
-            # Cifti
+            # CIFTI
             <source_entities>_space-fsLR_atlas-<label>_den-91k_coverage.tsv
             <source_entities>_space-fsLR_atlas-<label>_den-91k_coverage.pscalar.nii
             <source_entities>_space-fsLR_atlas-<label>_den-91k_timeseries.tsv
@@ -259,14 +253,14 @@ data.
    xcp_d/
       sub-<label>/[ses-<label>/]
          func/
-            # Nifti
+            # NIfTI
             <source_entities>_space-<label>_reho.nii.gz
             <source_entities>_space-<label>_alff.nii.gz
             <source_entities>_space-<label>_desc-smooth_alff.nii.gz
             <source_entities>_space-<label>_atlas-<atlas>_alff.tsv
             <source_entities>_space-<label>_atlas-<atlas>_reho.tsv
 
-            # Cifti
+            # CIFTI
             <source_entities>_space-fsLR_den-91k_reho.dscalar.nii
             <source_entities>_space-fsLR_den-91k_alff.dscalar.nii
             <source_entities>_space-fsLR_den-91k_desc-smooth_alff.dscalar.nii
@@ -284,21 +278,15 @@ Other outputs include quality control, framewise displacement, and confounds fil
 
       sub-<label>/[ses-<label>/]
          func/
-            # Nifti
-            <source_entities>_space-<label>_desc-linc_qc.csv
             <source_entities>[_desc-filtered]_motion.tsv
-            <source_entities>[_desc-filtered]_motion.json
             <source_entities>_outliers.tsv
-            <source_entities>_outliers.json
             <source_entities>_design.tsv
 
-            # Cifti
+            # NIfTI
+            <source_entities>_space-<label>_desc-linc_qc.csv
+
+            # CIFTI
             <source_entities>_space-fsLR_desc-linc_qc.csv
-            <source_entities>[_desc-filtered]_motion.tsv
-            <source_entities>[_desc-filtered]_motion.json
-            <source_entities>_outliers.tsv
-            <source_entities>_outliers.json
-            <source_entities>_design.tsv
 
 ``[desc-filtered]_motion.tsv`` is a tab-delimited file with seven columns:
 one for each of the six filtered motion parameters, as well as "framewise_displacement".
@@ -327,10 +315,6 @@ to 1mm FD in 0.01 steps.
    xcp_d/
       sub-<label>/[ses-<label>/]
          func/
-            # Nifti
-            <source_entities>_desc-dcan_qc.hdf5
-
-            # Cifti
             <source_entities>_desc-dcan_qc.hdf5
 
 These files have the following keys:
diff --git a/xcp_d/tests/data/test_ds001419_cifti_outputs.txt b/xcp_d/tests/data/test_ds001419_cifti_outputs.txt
index f65d6c054..8f20f2e88 100644
--- a/xcp_d/tests/data/test_ds001419_cifti_outputs.txt
+++ b/xcp_d/tests/data/test_ds001419_cifti_outputs.txt
@@ -22,10 +22,10 @@ xcp_d/atlas-Glasser_dseg.json
 xcp_d/atlas-Glasser_dseg.tsv
 xcp_d/atlas-Gordon_dseg.json
 xcp_d/atlas-Gordon_dseg.tsv
-xcp_d/atlas-Tian_dseg.json
-xcp_d/atlas-Tian_dseg.tsv
 xcp_d/atlas-HCP_dseg.json
 xcp_d/atlas-HCP_dseg.tsv
+xcp_d/atlas-Tian_dseg.json
+xcp_d/atlas-Tian_dseg.tsv
 xcp_d/dataset_description.json
 xcp_d/desc-linc_qc.json
 xcp_d/logs
@@ -42,8 +42,8 @@ xcp_d/space-fsLR_atlas-4S852Parcels_den-91k_dseg.dlabel.nii
 xcp_d/space-fsLR_atlas-4S952Parcels_den-91k_dseg.dlabel.nii
 xcp_d/space-fsLR_atlas-Glasser_den-91k_dseg.dlabel.nii
 xcp_d/space-fsLR_atlas-Gordon_den-91k_dseg.dlabel.nii
-xcp_d/space-fsLR_atlas-Tian_den-91k_dseg.dlabel.nii
 xcp_d/space-fsLR_atlas-HCP_den-91k_dseg.dlabel.nii
+xcp_d/space-fsLR_atlas-Tian_den-91k_dseg.dlabel.nii
 xcp_d/sub-01
 xcp_d/sub-01.html
 xcp_d/sub-01/anat
@@ -51,113 +51,213 @@ xcp_d/sub-01/anat/sub-01_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz
 xcp_d/sub-01/anat/sub-01_space-MNI152NLin2009cAsym_dseg.nii.gz
 xcp_d/sub-01/func
 xcp_d/sub-01/func/sub-01_task-imagery_desc-dcan_qc.hdf5
+xcp_d/sub-01/func/sub-01_task-imagery_desc-filtered_motion.json
 xcp_d/sub-01/func/sub-01_task-imagery_desc-filtered_motion.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_outliers.json
 xcp_d/sub-01/func/sub-01_task-imagery_outliers.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_desc-dcan_qc.hdf5
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_desc-filtered_motion.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_desc-filtered_motion.tsv
-xcp_d/sub-01/func/sub-01_task-imagery_run-01_design.tsv
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_design.json
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_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-fsLR_atlas-4S1052Parcels_coverage.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_coverage.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_den-91k_coverage.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_den-91k_coverage.pscalar.nii
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_den-91k_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_den-91k_measure-pearsoncorrelation_conmat.pconn.nii
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_den-91k_timeseries.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_den-91k_timeseries.ptseries.nii
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_reho.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_timeseries.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S1052Parcels_timeseries.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_coverage.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_coverage.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_den-91k_coverage.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_den-91k_coverage.pscalar.nii
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_den-91k_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_den-91k_measure-pearsoncorrelation_conmat.pconn.nii
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_den-91k_timeseries.ptseries.nii
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_reho.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_reho.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_timeseries.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S152Parcels_timeseries.tsv
+xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S252Parcels_coverage.json
 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S252Parcels_coverage.tsv
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S252Parcels_den-91k_coverage.pscalar.nii
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S252Parcels_measure-pearsoncorrelation_conmat.tsv
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S252Parcels_reho.tsv
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S252Parcels_timeseries.tsv
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S352Parcels_coverage.tsv
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S352Parcels_den-91k_measure-pearsoncorrelation_conmat.pconn.nii
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S352Parcels_measure-pearsoncorrelation_conmat.tsv
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 xcp_d/sub-01/func/sub-01_task-imagery_run-01_space-fsLR_atlas-4S452Parcels_measure-pearsoncorrelation_conmat.tsv
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diff --git a/xcp_d/tests/data/test_ds001419_nifti_outputs.txt b/xcp_d/tests/data/test_ds001419_nifti_outputs.txt
index 2f78e96bc..7bab41a10 100644
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+++ b/xcp_d/tests/data/test_ds001419_nifti_outputs.txt
@@ -22,10 +22,10 @@ xcp_d/atlas-Glasser_dseg.json
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diff --git a/xcp_d/tests/data/test_fmriprep_without_freesurfer_outputs.txt b/xcp_d/tests/data/test_fmriprep_without_freesurfer_outputs.txt
index a608fa821..7b25d0512 100644
--- a/xcp_d/tests/data/test_fmriprep_without_freesurfer_outputs.txt
+++ b/xcp_d/tests/data/test_fmriprep_without_freesurfer_outputs.txt
@@ -22,10 +22,10 @@ xcp_d/atlas-Glasser_dseg.json
 xcp_d/atlas-Glasser_dseg.tsv
 xcp_d/atlas-Gordon_dseg.json
 xcp_d/atlas-Gordon_dseg.tsv
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 xcp_d/dataset_description.json
 xcp_d/desc-linc_qc.json
 xcp_d/logs
@@ -45,8 +45,8 @@ xcp_d/space-MNI152NLin2009cAsym_atlas-4S852Parcels_dseg.nii.gz
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diff --git a/xcp_d/tests/data/test_nibabies_outputs.txt b/xcp_d/tests/data/test_nibabies_outputs.txt
index 5e1ecb352..5400dfd34 100644
--- a/xcp_d/tests/data/test_nibabies_outputs.txt
+++ b/xcp_d/tests/data/test_nibabies_outputs.txt
@@ -22,10 +22,10 @@ xcp_d/atlas-Glasser_dseg.json
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 xcp_d/sub-01/ses-1mo/func/sub-01_ses-1mo_task-rest_acq-PA_run-001_space-MNIInfant_cohort-1_desc-linc_qc.csv
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diff --git a/xcp_d/tests/data/test_pnc_cifti_outputs.txt b/xcp_d/tests/data/test_pnc_cifti_outputs.txt
index 162f64f0e..646e48a5d 100644
--- a/xcp_d/tests/data/test_pnc_cifti_outputs.txt
+++ b/xcp_d/tests/data/test_pnc_cifti_outputs.txt
@@ -22,10 +22,10 @@ xcp_d/atlas-Glasser_dseg.json
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@@ -213,4 +311,5 @@ xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleb
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 xcp_d/sub-1648798153_ses-PNC1_executive_summary.html
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 83095ec24..8fbe61cd0 100644
--- a/xcp_d/tests/data/test_pnc_cifti_t2wonly_outputs.txt
+++ b/xcp_d/tests/data/test_pnc_cifti_t2wonly_outputs.txt
@@ -22,10 +22,10 @@ xcp_d/atlas-Glasser_dseg.json
 xcp_d/atlas-Glasser_dseg.tsv
 xcp_d/atlas-Gordon_dseg.json
 xcp_d/atlas-Gordon_dseg.tsv
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 xcp_d/atlas-HCP_dseg.json
 xcp_d/atlas-HCP_dseg.tsv
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+xcp_d/atlas-Tian_dseg.tsv
 xcp_d/dataset_description.json
 xcp_d/desc-linc_qc.json
 xcp_d/logs
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 xcp_d/sub-1648798153/ses-PNC1
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@@ -211,4 +309,5 @@ 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_executive_summary.html
diff --git a/xcp_d/tests/data/test_pnc_nifti_outputs.txt b/xcp_d/tests/data/test_pnc_nifti_outputs.txt
index 1deb436ec..9180e7524 100644
--- a/xcp_d/tests/data/test_pnc_nifti_outputs.txt
+++ b/xcp_d/tests/data/test_pnc_nifti_outputs.txt
@@ -22,10 +22,10 @@ xcp_d/atlas-Glasser_dseg.json
 xcp_d/atlas-Glasser_dseg.tsv
 xcp_d/atlas-Gordon_dseg.json
 xcp_d/atlas-Gordon_dseg.tsv
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 xcp_d/atlas-HCP_dseg.tsv
+xcp_d/atlas-Tian_dseg.json
+xcp_d/atlas-Tian_dseg.tsv
 xcp_d/dataset_description.json
 xcp_d/desc-linc_qc.json
 xcp_d/logs
@@ -42,8 +42,8 @@ xcp_d/space-MNI152NLin6Asym_atlas-4S852Parcels_res-2_dseg.nii.gz
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 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_coverage.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_measure-pearsoncorrelation_conmat.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_reho.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_reho.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_timeseries.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-4S952Parcels_timeseries.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_coverage.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_coverage.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_measure-pearsoncorrelation_conmat.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_reho.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_reho.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_timeseries.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Glasser_timeseries.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_coverage.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_coverage.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_measure-pearsoncorrelation_conmat.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_reho.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_reho.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_timeseries.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Gordon_timeseries.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_coverage.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_measure-pearsoncorrelation_conmat.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_reho.tsv
-xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_timeseries.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_coverage.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_coverage.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_measure-pearsoncorrelation_conmat.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_measure-pearsoncorrelation_conmat.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_reho.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_reho.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_timeseries.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-HCP_timeseries.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_coverage.json
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_coverage.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_measure-pearsoncorrelation_conmat.json
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_measure-pearsoncorrelation_conmat.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_measure-pearsoncorrelation_desc-26volumes_conmat.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_measure-pearsoncorrelation_desc-33volumes_conmat.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_reho.json
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_reho.tsv
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_timeseries.json
+xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_atlas-Tian_timeseries.tsv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_desc-linc_qc.csv
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_res-2_desc-denoisedSmoothed_bold.json
 xcp_d/sub-1648798153/ses-PNC1/func/sub-1648798153_ses-PNC1_task-rest_acq-singleband_space-MNI152NLin6Asym_res-2_desc-denoisedSmoothed_bold.nii.gz
diff --git a/xcp_d/tests/test_utils_bids.py b/xcp_d/tests/test_utils_bids.py
index 7eddb24fc..25bc2ecf2 100644
--- a/xcp_d/tests/test_utils_bids.py
+++ b/xcp_d/tests/test_utils_bids.py
@@ -147,17 +147,22 @@ def test_write_dataset_description(datasets, tmp_path_factory, caplog):
 
     # It will work when we give it a real fmri_dir.
     fmri_dir = datasets["ds001419"]
-    xbids.write_dataset_description(fmri_dir, tmpdir)
+    xbids.write_dataset_description(fmri_dir, tmpdir, custom_confounds_folder="/fake/path4")
     assert os.path.isfile(dset_description)
 
     # Now overwrite the description.
-    xbids.write_dataset_description(fmri_dir, tmpdir)
-    assert os.path.isfile(dset_description)
-
-    # Now change the version and re-run the function.
     with open(dset_description, "r") as fo:
         desc = json.load(fo)
 
+    assert "'preprocessed' is already a dataset link" not in caplog.text
+    assert "'xcp_d' is already a dataset link" not in caplog.text
+    assert "'custom_confounds' is already a dataset link" not in caplog.text
+    xbids.write_dataset_description(tmpdir, tmpdir, custom_confounds_folder="/fake/path4")
+    assert "'preprocessed' is already a dataset link" in caplog.text
+    assert "'xcp_d' is already a dataset link" in caplog.text
+    assert "'custom_confounds' is already a dataset link" in caplog.text
+
+    # Now change the version and re-run the function.
     desc["GeneratedBy"][0]["Version"] = "0.0.1"
     with open(dset_description, "w") as fo:
         json.dump(desc, fo, indent=4)
@@ -294,3 +299,77 @@ def test_group_across_runs():
         "/path/sub-01_task-rest_dir-LR_run-2_bold.nii.gz",
         "/path/sub-01_task-rest_dir-RL_run-2_bold.nii.gz",
     ]
+
+
+def test_make_uri():
+    """Test _make_uri."""
+    in_file = "/path/to/dset/sub-01/func/sub-01_task-rest_bold.nii.gz"
+    dataset_name = "test"
+    dataset_path = "/path/to/dset"
+    uri = xbids._make_uri(in_file, dataset_name=dataset_name, dataset_path=dataset_path)
+    assert uri == "bids:test:sub-01/func/sub-01_task-rest_bold.nii.gz"
+
+    dataset_path = "/another/path/haha"
+    with pytest.raises(ValueError, match="does not start with"):
+        xbids._make_uri(in_file, dataset_name=dataset_name, dataset_path=dataset_path)
+
+
+def test_make_xcpd_uri():
+    """Test _make_xcpd_uri."""
+    out_file = "/path/to/dset/xcp_d/sub-01/func/sub-01_task-rest_bold.nii.gz"
+    uri = xbids._make_xcpd_uri(out_file, output_dir="/path/to/dset")
+    assert uri == ["bids:xcp_d:sub-01/func/sub-01_task-rest_bold.nii.gz"]
+
+    xbids._make_xcpd_uri([out_file], output_dir="/path/to/dset")
+    assert uri == ["bids:xcp_d:sub-01/func/sub-01_task-rest_bold.nii.gz"]
+
+
+def test_make_xcpd_uri_lol():
+    """Test _make_xcpd_uri_lol."""
+    in_list = [
+        [
+            "/path/to/dset/xcp_d/sub-01/func/sub-01_task-rest_run-1_bold.nii.gz",
+            "/path/to/dset/xcp_d/sub-02/func/sub-01_task-rest_run-1_bold.nii.gz",
+            "/path/to/dset/xcp_d/sub-03/func/sub-01_task-rest_run-1_bold.nii.gz",
+        ],
+        [
+            "/path/to/dset/xcp_d/sub-01/func/sub-01_task-rest_run-2_bold.nii.gz",
+            "/path/to/dset/xcp_d/sub-02/func/sub-01_task-rest_run-2_bold.nii.gz",
+            "/path/to/dset/xcp_d/sub-03/func/sub-01_task-rest_run-2_bold.nii.gz",
+        ],
+    ]
+    uris = xbids._make_xcpd_uri_lol(in_list, output_dir="/path/to/dset/")
+    assert uris == [
+        [
+            "bids:xcp_d:sub-01/func/sub-01_task-rest_run-1_bold.nii.gz",
+            "bids:xcp_d:sub-01/func/sub-01_task-rest_run-2_bold.nii.gz",
+        ],
+        [
+            "bids:xcp_d:sub-02/func/sub-01_task-rest_run-1_bold.nii.gz",
+            "bids:xcp_d:sub-02/func/sub-01_task-rest_run-2_bold.nii.gz",
+        ],
+        [
+            "bids:xcp_d:sub-03/func/sub-01_task-rest_run-1_bold.nii.gz",
+            "bids:xcp_d:sub-03/func/sub-01_task-rest_run-2_bold.nii.gz",
+        ],
+    ]
+
+
+def test_make_preproc_uri():
+    """Test _make_preproc_uri."""
+    out_file = "/path/to/dset/sub-01/func/sub-01_task-rest_bold.nii.gz"
+    uri = xbids._make_preproc_uri(out_file, fmri_dir="/path/to/dset")
+    assert uri == ["bids:preprocessed:sub-01/func/sub-01_task-rest_bold.nii.gz"]
+
+    xbids._make_preproc_uri([out_file], fmri_dir="/path/to/dset")
+    assert uri == ["bids:preprocessed:sub-01/func/sub-01_task-rest_bold.nii.gz"]
+
+
+def test_make_custom_uri():
+    """Test _make_custom_uri."""
+    out_file = "/path/to/dset/sub-01_task-rest_bold.nii.gz"
+    uri = xbids._make_custom_uri(out_file)
+    assert uri == ["bids:custom_confounds:sub-01_task-rest_bold.nii.gz"]
+
+    xbids._make_custom_uri([out_file])
+    assert uri == ["bids:custom_confounds:sub-01_task-rest_bold.nii.gz"]
diff --git a/xcp_d/tests/test_utils_utils.py b/xcp_d/tests/test_utils_utils.py
index 9754f9f01..ed717f3f8 100644
--- a/xcp_d/tests/test_utils_utils.py
+++ b/xcp_d/tests/test_utils_utils.py
@@ -378,3 +378,88 @@ def test_select_first():
 
     lst = "abc"
     assert utils._select_first(lst) == "a"
+
+
+def test_listify():
+    """Test _listify."""
+    inputs = [
+        1,
+        (1,),
+        "a",
+        ["a"],
+        ["a", ["b", "c"]],
+        ("a", "b"),
+    ]
+    outputs = [
+        [1],
+        (1,),
+        ["a"],
+        ["a"],
+        ["a", ["b", "c"]],
+        ("a", "b"),
+    ]
+    for i, input_ in enumerate(inputs):
+        expected_output = outputs[i]
+        output = utils._listify(input_)
+        assert output == expected_output
+
+
+def test_make_dictionary():
+    """Test _make_dictionary."""
+    metadata = {"Sources": ["a"]}
+    out_metadata = utils._make_dictionary(metadata, Sources=["b"])
+    # Ensure the original dictionary isn't modified.
+    assert metadata["Sources"] == ["a"]
+    assert out_metadata["Sources"] == ["a", "b"]
+
+    metadata = {"Test": "a"}
+    out_metadata = utils._make_dictionary(metadata, Sources=["b"])
+    assert out_metadata["Sources"] == ["b"]
+
+    metadata = {"Test": ["a"]}
+    out_metadata = utils._make_dictionary(metadata, Sources="b")
+    assert out_metadata["Sources"] == "b"
+
+    metadata = {"Sources": "a"}
+    out_metadata = utils._make_dictionary(metadata, Sources=["b"])
+    # Ensure the original dictionary isn't modified.
+    assert metadata["Sources"] == "a"
+    assert out_metadata["Sources"] == ["a", "b"]
+
+    metadata = {"Sources": ["a"]}
+    out_metadata = utils._make_dictionary(metadata, Sources="b")
+    # Ensure the original dictionary isn't modified.
+    assert metadata["Sources"] == ["a"]
+    assert out_metadata["Sources"] == ["a", "b"]
+
+    out_metadata = utils._make_dictionary(metadata=None, Sources=["b"])
+    assert out_metadata["Sources"] == ["b"]
+
+
+def test_transpose_lol():
+    """Test _transpose_lol."""
+    inputs = [
+        [
+            ["a", "b", "c"],
+            [1, 2, 3],
+        ],
+        [
+            ["a", "b", "c", "d"],
+            [1, 2, 3],
+        ],
+    ]
+    outputs = [
+        [
+            ["a", 1],
+            ["b", 2],
+            ["c", 3],
+        ],
+        [
+            ["a", 1],
+            ["b", 2],
+            ["c", 3],
+        ],
+    ]
+    for i, input_ in enumerate(inputs):
+        expected_output = outputs[i]
+        assert utils._transpose_lol(input_) == expected_output
diff --git a/xcp_d/tests/utils.py b/xcp_d/tests/utils.py
index 9e186cbd8..8fd3ed788 100644
--- a/xcp_d/tests/utils.py
+++ b/xcp_d/tests/utils.py
@@ -219,7 +219,7 @@ def reorder_expected_outputs():
         with open(expected_output_file, "r") as fo:
             file_contents = fo.readlines()
 
-        file_contents = sorted(file_contents)
+        file_contents = sorted(list(set(file_contents)))
 
         with open(expected_output_file, "w") as fo:
             fo.writelines(file_contents)
diff --git a/xcp_d/utils/bids.py b/xcp_d/utils/bids.py
index efa7c9aaf..7b2a8a498 100644
--- a/xcp_d/utils/bids.py
+++ b/xcp_d/utils/bids.py
@@ -7,6 +7,7 @@
 """
 import os
 import warnings
+from pathlib import Path
 
 import nibabel as nb
 import yaml
@@ -659,7 +660,7 @@ def collect_run_data(layout, bold_file, cifti, primary_anat, target_space):
     return run_data
 
 
-def write_dataset_description(fmri_dir, xcpd_dir):
+def write_dataset_description(fmri_dir, xcpd_dir, custom_confounds_folder=None):
     """Write dataset_description.json file for derivatives.
 
     Parameters
@@ -697,6 +698,26 @@ def write_dataset_description(fmri_dir, xcpd_dir):
     dset_desc["GeneratedBy"] = generated_by
     dset_desc["HowToAcknowledge"] = "Include the generated boilerplate in the methods section."
 
+    # Add DatasetLinks
+    if "DatasetLinks" not in dset_desc.keys():
+        dset_desc["DatasetLinks"] = {}
+
+    if "preprocessed" in dset_desc["DatasetLinks"].keys():
+        LOGGER.warning("'preprocessed' is already a dataset link. Overwriting.")
+
+    dset_desc["DatasetLinks"]["preprocessed"] = str(fmri_dir)
+
+    if "xcp_d" in dset_desc["DatasetLinks"].keys():
+        LOGGER.warning("'xcp_d' is already a dataset link. Overwriting.")
+
+    dset_desc["DatasetLinks"]["xcp_d"] = str(xcpd_dir)
+
+    if custom_confounds_folder:
+        if "custom_confounds" in dset_desc["DatasetLinks"].keys():
+            LOGGER.warning("'custom_confounds' is already a dataset link. Overwriting.")
+
+        dset_desc["DatasetLinks"]["custom_confounds"] = str(custom_confounds_folder)
+
     xcpd_dset_description = os.path.join(xcpd_dir, "dataset_description.json")
     if os.path.isfile(xcpd_dset_description):
         with open(xcpd_dset_description, "r") as fo:
@@ -955,3 +976,65 @@ def group_across_runs(in_files):
         out_files[group_idx].append(in_file)
 
     return out_files
+
+
+def _make_uri(in_file, dataset_name, dataset_path):
+    """Convert a filename to a BIDS URI.
+
+    Raises
+    ------
+    ValueError
+        If ``in_file`` is not relative to ``dataset_path``.
+    """
+    bids_uri = f"bids:{dataset_name}:{str(Path(in_file).relative_to(dataset_path))}"
+    return bids_uri
+
+
+def _make_xcpd_uri(out_file, output_dir):
+    """Convert postprocessing derivative's path to BIDS URI."""
+    import os
+
+    from xcp_d.utils.bids import _make_uri
+
+    dataset_path = os.path.join(output_dir, "xcp_d")
+
+    if isinstance(out_file, list):
+        return [_make_uri(of, "xcp_d", dataset_path) for of in out_file]
+    else:
+        return [_make_uri(out_file, "xcp_d", dataset_path)]
+
+
+def _make_xcpd_uri_lol(in_list, output_dir):
+    """Call _make_xcpd_uri on a list of lists and then transpose the result."""
+    from xcp_d.utils.bids import _make_xcpd_uri
+    from xcp_d.utils.utils import _transpose_lol
+
+    out = []
+    for sublist in in_list:
+        sublist_out = _make_xcpd_uri(sublist, output_dir)
+        out.append(sublist_out)
+
+    out_lol = _transpose_lol(out)
+    return out_lol
+
+
+def _make_preproc_uri(out_file, fmri_dir):
+    """Convert preprocessing derivative's path to BIDS URI."""
+    from xcp_d.utils.bids import _make_uri
+
+    if isinstance(out_file, list):
+        return [_make_uri(of, "preprocessed", fmri_dir) for of in out_file]
+    else:
+        return [_make_uri(out_file, "preprocessed", fmri_dir)]
+
+
+def _make_custom_uri(out_file):
+    """Convert custom confounds' path to BIDS URI."""
+    import os
+
+    from xcp_d.utils.bids import _make_uri
+
+    if isinstance(out_file, list):
+        return [_make_uri(of, "custom_confounds", os.path.dirname(of)) for of in out_file]
+    else:
+        return [_make_uri(out_file, "custom_confounds", os.path.dirname(out_file))]
diff --git a/xcp_d/utils/utils.py b/xcp_d/utils/utils.py
index 9360faeca..f06786d26 100644
--- a/xcp_d/utils/utils.py
+++ b/xcp_d/utils/utils.py
@@ -503,3 +503,43 @@ def list_to_str(lst):
         return " and ".join(lst_str)
     else:
         return f"{', '.join(lst_str[:-1])}, and {lst_str[-1]}"
+
+
+def _listify(obj):
+    """Wrap all non-list or tuple objects in a list.
+
+    This provides a simple way to accept flexible arguments.
+    """
+    return obj if isinstance(obj, (list, tuple, type(None), np.ndarray)) else [obj]
+
+
+def _make_dictionary(metadata=None, **kwargs):
+    """Create or modify a dictionary.
+
+    This will add kwargs to a metadata dictionary if the dictionary is provided,
+    or create a dictionary from scratch if not.
+    """
+    from copy import deepcopy
+
+    from xcp_d.utils.utils import _listify
+
+    if metadata:
+        out_metadata = deepcopy(metadata)
+        for key, value in kwargs.items():
+            if key not in metadata.keys():
+                out_metadata[key] = value
+            elif isinstance(value, list) or isinstance(out_metadata[key], list):
+                # Append the values if they're a list
+                out_metadata[key] = _listify(out_metadata[key]) + _listify(value)
+            else:
+                # Overwrite the old value
+                out_metadata[key] = value
+
+        return out_metadata
+    else:
+        return dict(kwargs)
+
+
+def _transpose_lol(lol):
+    """Transpose list of lists."""
+    return list(map(list, zip(*lol)))
diff --git a/xcp_d/workflows/base.py b/xcp_d/workflows/base.py
index f2f8a6c7d..3b1d4c1c6 100644
--- a/xcp_d/workflows/base.py
+++ b/xcp_d/workflows/base.py
@@ -715,6 +715,7 @@ def init_subject_wf(
 
             postprocess_bold_wf = init_postprocess_bold_wf(
                 bold_file=bold_file,
+                fmri_dir=fmri_dir,
                 bandpass_filter=bandpass_filter,
                 high_pass=high_pass,
                 low_pass=low_pass,
@@ -798,6 +799,7 @@ def init_subject_wf(
                 smoothing=smoothing,
                 cifti=cifti,
                 dcan_qc=dcan_qc,
+                fd_thresh=fd_thresh,
                 mem_gb=1,
                 omp_nthreads=omp_nthreads,
                 name=f"concatenate_entity_set_{ent_set}_wf",
diff --git a/xcp_d/workflows/bold.py b/xcp_d/workflows/bold.py
index 0ce607bfc..3659d612b 100644
--- a/xcp_d/workflows/bold.py
+++ b/xcp_d/workflows/bold.py
@@ -30,6 +30,7 @@
 @fill_doc
 def init_postprocess_nifti_wf(
     bold_file,
+    fmri_dir,
     bandpass_filter,
     high_pass,
     low_pass,
@@ -96,6 +97,7 @@ def init_postprocess_nifti_wf(
 
             wf = init_postprocess_nifti_wf(
                 bold_file=bold_file,
+                fmri_dir=fmri_dir,
                 bandpass_filter=True,
                 high_pass=0.01,
                 low_pass=0.08,
@@ -340,8 +342,6 @@ def init_postprocess_nifti_wf(
         ]),
         (downcast_data, prepare_confounds_wf, [("bold_file", "inputnode.preprocessed_bold")]),
         (prepare_confounds_wf, outputnode, [
-            ("outputnode.filtered_motion", "filtered_motion"),
-            ("outputnode.temporal_mask", "temporal_mask"),
             ("outputnode.fmriprep_confounds_file", "fmriprep_confounds_file"),
             ("outputnode.preprocessed_bold", "preprocessed_bold"),
         ]),
@@ -370,9 +370,6 @@ def init_postprocess_nifti_wf(
         ]),
         (denoise_bold_wf, outputnode, [
             ("outputnode.uncensored_denoised_bold", "uncensored_denoised_bold"),
-            ("outputnode.interpolated_filtered_bold", "interpolated_filtered_bold"),
-            ("outputnode.censored_denoised_bold", "censored_denoised_bold"),
-            ("outputnode.smoothed_denoised_bold", "smoothed_denoised_bold"),
         ]),
     ])
     # fmt:on
@@ -429,7 +426,6 @@ def init_postprocess_nifti_wf(
         (denoise_bold_wf, connectivity_wf, [
             ("outputnode.censored_denoised_bold", "inputnode.denoised_bold"),
         ]),
-        (connectivity_wf, outputnode, [("outputnode.timeseries", "timeseries")]),
     ])
     # fmt:on
 
@@ -513,25 +509,32 @@ def init_postprocess_nifti_wf(
     # fmt:on
 
     postproc_derivatives_wf = init_postproc_derivatives_wf(
-        smoothing=smoothing,
         name_source=bold_file,
+        source_metadata=run_data["bold_metadata"],
+        fmri_dir=fmri_dir,
         bandpass_filter=bandpass_filter,
+        low_pass=low_pass,
+        high_pass=high_pass,
+        bpf_order=bpf_order,
+        fd_thresh=fd_thresh,
+        motion_filter_type=motion_filter_type,
+        smoothing=smoothing,
         params=params,
         exact_scans=exact_scans,
         cifti=False,
         dcan_qc=dcan_qc,
         output_dir=output_dir,
-        low_pass=low_pass,
-        high_pass=high_pass,
-        fd_thresh=fd_thresh,
-        motion_filter_type=motion_filter_type,
-        TR=TR,
+        custom_confounds_file=custom_confounds_file,
         name="postproc_derivatives_wf",
     )
 
     # fmt:off
     workflow.connect([
-        (inputnode, postproc_derivatives_wf, [("atlas_names", "inputnode.atlas_names")]),
+        (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, [
             ("outputnode.confounds_file", "inputnode.confounds_file"),
             ("outputnode.confounds_metadata", "inputnode.confounds_metadata"),
@@ -554,6 +557,14 @@ def init_postprocess_nifti_wf(
             ("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"),
+            ("outputnode.interpolated_filtered_bold", "interpolated_filtered_bold"),
+            ("outputnode.censored_denoised_bold", "censored_denoised_bold"),
+            ("outputnode.smoothed_denoised_bold", "smoothed_denoised_bold"),
+            ("outputnode.timeseries", "timeseries"),
+        ]),
     ])
     # fmt:on
 
diff --git a/xcp_d/workflows/cifti.py b/xcp_d/workflows/cifti.py
index 41d08ffbf..cec7e5b8d 100644
--- a/xcp_d/workflows/cifti.py
+++ b/xcp_d/workflows/cifti.py
@@ -30,6 +30,7 @@
 @fill_doc
 def init_postprocess_cifti_wf(
     bold_file,
+    fmri_dir,
     bandpass_filter,
     high_pass,
     low_pass,
@@ -94,6 +95,7 @@ def init_postprocess_cifti_wf(
 
             wf = init_postprocess_cifti_wf(
                 bold_file=bold_file,
+                fmri_dir=fmri_dir,
                 bandpass_filter=True,
                 high_pass=0.01,
                 low_pass=0.08,
@@ -318,8 +320,6 @@ def init_postprocess_cifti_wf(
             ("bold_file", "inputnode.preprocessed_bold"),
         ]),
         (prepare_confounds_wf, outputnode, [
-            ("outputnode.filtered_motion", "filtered_motion"),
-            ("outputnode.temporal_mask", "temporal_mask"),
             ("outputnode.fmriprep_confounds_file", "fmriprep_confounds_file"),
             ("outputnode.preprocessed_bold", "preprocessed_bold"),
         ]),
@@ -347,9 +347,6 @@ def init_postprocess_cifti_wf(
         ]),
         (denoise_bold_wf, outputnode, [
             ("outputnode.uncensored_denoised_bold", "uncensored_denoised_bold"),
-            ("outputnode.interpolated_filtered_bold", "interpolated_filtered_bold"),
-            ("outputnode.censored_denoised_bold", "censored_denoised_bold"),
-            ("outputnode.smoothed_denoised_bold", "smoothed_denoised_bold"),
         ]),
     ])
     # fmt:on
@@ -407,10 +404,6 @@ def init_postprocess_cifti_wf(
         (denoise_bold_wf, connectivity_wf, [
             ("outputnode.censored_denoised_bold", "inputnode.denoised_bold"),
         ]),
-        (connectivity_wf, outputnode, [
-            ("outputnode.timeseries", "timeseries"),
-            ("outputnode.timeseries_ciftis", "timeseries_ciftis"),
-        ]),
     ])
     # fmt:on
 
@@ -485,25 +478,32 @@ def init_postprocess_cifti_wf(
     # fmt:on
 
     postproc_derivatives_wf = init_postproc_derivatives_wf(
-        smoothing=smoothing,
         name_source=bold_file,
+        source_metadata=run_data["bold_metadata"],
+        fmri_dir=fmri_dir,
         bandpass_filter=bandpass_filter,
+        low_pass=low_pass,
+        high_pass=high_pass,
+        bpf_order=bpf_order,
+        fd_thresh=fd_thresh,
+        motion_filter_type=motion_filter_type,
+        smoothing=smoothing,
         params=params,
         exact_scans=exact_scans,
         cifti=True,
         dcan_qc=dcan_qc,
         output_dir=output_dir,
-        low_pass=low_pass,
-        high_pass=high_pass,
-        fd_thresh=fd_thresh,
-        motion_filter_type=motion_filter_type,
-        TR=TR,
+        custom_confounds_file=custom_confounds_file,
         name="postproc_derivatives_wf",
     )
 
     # fmt:off
     workflow.connect([
-        (inputnode, postproc_derivatives_wf, [("atlas_names", "inputnode.atlas_names")]),
+        (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, [
             ("outputnode.interpolated_filtered_bold", "inputnode.interpolated_filtered_bold"),
             ("outputnode.censored_denoised_bold", "inputnode.censored_denoised_bold"),
@@ -530,6 +530,15 @@ def init_postprocess_cifti_wf(
             ("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"),
+            ("outputnode.interpolated_filtered_bold", "interpolated_filtered_bold"),
+            ("outputnode.censored_denoised_bold", "censored_denoised_bold"),
+            ("outputnode.smoothed_denoised_bold", "smoothed_denoised_bold"),
+            ("outputnode.timeseries", "timeseries"),
+            ("outputnode.timeseries_ciftis", "timeseries_ciftis"),
+        ]),
     ])
 
     if bandpass_filter:
diff --git a/xcp_d/workflows/concatenation.py b/xcp_d/workflows/concatenation.py
index 1e4a95b2a..39fe375e0 100644
--- a/xcp_d/workflows/concatenation.py
+++ b/xcp_d/workflows/concatenation.py
@@ -9,8 +9,9 @@
     ConcatenateInputs,
     FilterOutFailedRuns,
 )
+from xcp_d.utils.bids import _make_xcpd_uri, _make_xcpd_uri_lol
 from xcp_d.utils.doc import fill_doc
-from xcp_d.utils.utils import _select_first
+from xcp_d.utils.utils import _make_dictionary, _select_first
 from xcp_d.workflows.plotting import init_qc_report_wf
 
 
@@ -18,14 +19,15 @@
 def init_concatenate_data_wf(
     output_dir,
     motion_filter_type,
-    mem_gb,
-    omp_nthreads,
     TR,
     head_radius,
     params,
     smoothing,
     cifti,
     dcan_qc,
+    fd_thresh,
+    mem_gb,
+    omp_nthreads,
     name="concatenate_data_wf",
 ):
     """Concatenate postprocessed data across runs and directions.
@@ -40,14 +42,15 @@ def init_concatenate_data_wf(
             wf = init_concatenate_data_wf(
                 output_dir=".",
                 motion_filter_type=None,
-                mem_gb=0.1,
-                omp_nthreads=1,
                 TR=2,
                 head_radius=50,
                 params="none",
                 smoothing=None,
                 cifti=False,
                 dcan_qc=True,
+                fd_thresh=0.3,
+                mem_gb=0.1,
+                omp_nthreads=1,
                 name="concatenate_data_wf",
             )
 
@@ -55,14 +58,15 @@ def init_concatenate_data_wf(
     ----------
     %(output_dir)s
     %(motion_filter_type)s
-    %(mem_gb)s
-    %(omp_nthreads)s
     %(TR)s
     %(head_radius)s
     %(params)s
     %(smoothing)s
     %(cifti)s
     %(dcan_qc)s
+    fd_thresh
+    %(mem_gb)s
+    %(omp_nthreads)s
     %(name)s
         Default is "concatenate_data_wf".
 
@@ -131,19 +135,16 @@ def init_concatenate_data_wf(
         CleanNameSource(),
         name="clean_name_source",
     )
-
-    # fmt:off
     workflow.connect([(inputnode, clean_name_source, [("name_source", "name_source")])])
-    # fmt:on
 
-    filter_out_failed_runs = pe.Node(
+    filter_runs = pe.Node(
         FilterOutFailedRuns(),
-        name="filter_out_failed_runs",
+        name="filter_runs",
     )
 
     # fmt:off
     workflow.connect([
-        (inputnode, filter_out_failed_runs, [
+        (inputnode, filter_runs, [
             ("preprocessed_bold", "preprocessed_bold"),
             ("fmriprep_confounds_file", "fmriprep_confounds_file"),
             ("filtered_motion", "filtered_motion"),
@@ -168,7 +169,7 @@ def init_concatenate_data_wf(
 
     # fmt:off
     workflow.connect([
-        (filter_out_failed_runs, concatenate_inputs, [
+        (filter_runs, concatenate_inputs, [
             ("preprocessed_bold", "preprocessed_bold"),
             ("fmriprep_confounds_file", "fmriprep_confounds_file"),
             ("filtered_motion", "filtered_motion"),
@@ -204,7 +205,7 @@ def init_concatenate_data_wf(
             ("anat_brainmask", "inputnode.anat_brainmask"),
         ]),
         (clean_name_source, qc_report_wf, [("name_source", "inputnode.name_source")]),
-        (filter_out_failed_runs, qc_report_wf, [
+        (filter_runs, qc_report_wf, [
             # nifti-only inputs
             (("bold_mask", _select_first), "inputnode.bold_mask"),
             (("boldref", _select_first), "inputnode.boldref"),
@@ -240,6 +241,9 @@ def init_concatenate_data_wf(
     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
 
@@ -259,6 +263,28 @@ def init_concatenate_data_wf(
     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
 
@@ -268,11 +294,13 @@ def init_concatenate_data_wf(
             dismiss_entities=["desc"],
             suffix="timeseries",
             extension=".tsv",
+            # Metadata
+            SamplingFrequency="TR",
         ),
         name="ds_timeseries",
         run_without_submitting=True,
         mem_gb=1,
-        iterfield=["atlas", "in_file"],
+        iterfield=["atlas", "in_file", "meta_dict"],
     )
 
     # fmt:off
@@ -280,6 +308,7 @@ def init_concatenate_data_wf(
         (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
 
@@ -297,6 +326,25 @@ 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,
@@ -309,7 +357,7 @@ def init_concatenate_data_wf(
             name="ds_timeseries_cifti_files",
             run_without_submitting=True,
             mem_gb=1,
-            iterfield=["atlas", "in_file"],
+            iterfield=["atlas", "in_file", "meta_dict"],
         )
 
         # fmt:off
@@ -317,6 +365,7 @@ def init_concatenate_data_wf(
             (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
 
@@ -334,7 +383,7 @@ def init_concatenate_data_wf(
                 mem_gb=2,
             )
 
-        if dcan_qc:
+        if dcan_qc and (fd_thresh > 0):
             ds_interpolated_filtered_bold = pe.Node(
                 DerivativesDataSink(
                     base_directory=output_dir,
@@ -360,6 +409,7 @@ def init_concatenate_data_wf(
             run_without_submitting=True,
             mem_gb=2,
         )
+
         if smoothing:
             ds_smoothed_denoised_bold = pe.Node(
                 DerivativesDataSink(
@@ -373,7 +423,7 @@ def init_concatenate_data_wf(
                 mem_gb=2,
             )
 
-        if dcan_qc:
+        if dcan_qc and (fd_thresh > 0):
             ds_interpolated_filtered_bold = pe.Node(
                 DerivativesDataSink(
                     base_directory=output_dir,
@@ -390,6 +440,9 @@ def init_concatenate_data_wf(
     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
 
@@ -400,6 +453,9 @@ def init_concatenate_data_wf(
             (concatenate_inputs, ds_smoothed_denoised_bold, [
                 ("smoothed_denoised_bold", "in_file"),
             ]),
+            (filter_runs, ds_smoothed_denoised_bold, [
+                (("smoothed_denoised_bold", _make_xcpd_uri, output_dir), "Sources"),
+            ]),
         ])
         # fmt:on
 
@@ -410,6 +466,9 @@ def init_concatenate_data_wf(
             (concatenate_inputs, ds_interpolated_filtered_bold, [
                 ("interpolated_filtered_bold", "in_file"),
             ]),
+            (filter_runs, ds_interpolated_filtered_bold, [
+                (("interpolated_filtered_bold", _make_xcpd_uri, output_dir), "Sources"),
+            ]),
         ])
         # fmt:on
 
diff --git a/xcp_d/workflows/connectivity.py b/xcp_d/workflows/connectivity.py
index 8d2f602a8..27b2b3ed9 100644
--- a/xcp_d/workflows/connectivity.py
+++ b/xcp_d/workflows/connectivity.py
@@ -104,13 +104,7 @@ def init_load_atlases_wf(
         name="atlas_file_grabber",
         iterfield=["atlas_name"],
     )
-
-    # fmt:off
-    workflow.connect([
-        (atlas_name_grabber, atlas_file_grabber, [("atlas_names", "atlas_name")]),
-        (atlas_file_grabber, outputnode, [("atlas_labels_file", "atlas_labels_files")]),
-    ])
-    # fmt:on
+    workflow.connect([(atlas_name_grabber, atlas_file_grabber, [("atlas_names", "atlas_name")])])
 
     atlas_buffer = pe.Node(niu.IdentityInterface(fields=["atlas_file"]), name="atlas_buffer")
 
@@ -158,7 +152,6 @@ def init_load_atlases_wf(
             (get_transforms_to_bold_space, warp_atlases_to_bold_space, [
                 ("transformfile", "transforms"),
             ]),
-            (warp_atlases_to_bold_space, outputnode, [("output_image", "atlas_files")]),
             (warp_atlases_to_bold_space, atlas_buffer, [("output_image", "atlas_file")]),
         ])
         # fmt:on
@@ -175,7 +168,6 @@ def init_load_atlases_wf(
         workflow.connect([
             (inputnode, resample_atlas_to_data, [("bold_file", "template_cifti")]),
             (atlas_file_grabber, resample_atlas_to_data, [("atlas_file", "label")]),
-            (resample_atlas_to_data, outputnode, [("cifti_out", "atlas_files")]),
         ])
         # fmt:on
 
@@ -235,6 +227,7 @@ def init_load_atlases_wf(
         (inputnode, ds_atlas, [("name_source", "source_file")]),
         (atlas_name_grabber, ds_atlas, [("atlas_names", "atlas")]),
         (atlas_buffer, ds_atlas, [("atlas_file", "in_file")]),
+        (ds_atlas, outputnode, [("out_file", "atlas_files")]),
     ])
     # fmt:on
 
@@ -268,6 +261,7 @@ def init_load_atlases_wf(
         (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")]),
     ])
     # fmt:on
 
diff --git a/xcp_d/workflows/outputs.py b/xcp_d/workflows/outputs.py
index 553897f63..056921f67 100644
--- a/xcp_d/workflows/outputs.py
+++ b/xcp_d/workflows/outputs.py
@@ -7,8 +7,14 @@
 
 from xcp_d.interfaces.bids import DerivativesDataSink
 from xcp_d.interfaces.utils import FilterUndefined
-from xcp_d.utils.bids import get_entity
+from xcp_d.utils.bids import (
+    _make_custom_uri,
+    _make_preproc_uri,
+    _make_xcpd_uri,
+    get_entity,
+)
 from xcp_d.utils.doc import fill_doc
+from xcp_d.utils.utils import _make_dictionary
 
 
 @fill_doc
@@ -71,7 +77,6 @@ def init_copy_inputs_to_outputs_wf(output_dir, name="copy_inputs_to_outputs_wf")
         niu.Merge(10),
         name="collect_files",
     )
-
     # fmt:off
     workflow.connect([
         (inputnode, collect_files, [
@@ -95,7 +100,6 @@ def init_copy_inputs_to_outputs_wf(output_dir, name="copy_inputs_to_outputs_wf")
         FilterUndefined(),
         name="filter_out_undefined",
     )
-
     workflow.connect([(collect_files, filter_out_undefined, [("out", "inlist")])])
 
     ds_copied_outputs = pe.MapNode(
@@ -108,7 +112,6 @@ def init_copy_inputs_to_outputs_wf(output_dir, name="copy_inputs_to_outputs_wf")
         mem_gb=1,
         iterfield=["in_file", "source_file"],
     )
-
     # fmt:off
     workflow.connect([
         (filter_out_undefined, ds_copied_outputs, [
@@ -124,9 +127,12 @@ def init_copy_inputs_to_outputs_wf(output_dir, name="copy_inputs_to_outputs_wf")
 @fill_doc
 def init_postproc_derivatives_wf(
     name_source,
+    source_metadata,
+    fmri_dir,
     bandpass_filter,
     low_pass,
     high_pass,
+    bpf_order,
     fd_thresh,
     motion_filter_type,
     smoothing,
@@ -135,7 +141,7 @@ def init_postproc_derivatives_wf(
     cifti,
     dcan_qc,
     output_dir,
-    TR,
+    custom_confounds_file,
     name="postproc_derivatives_wf",
 ):
     """Write out the xcp_d derivatives in BIDS format.
@@ -149,9 +155,12 @@ def init_postproc_derivatives_wf(
 
             wf = init_postproc_derivatives_wf(
                 name_source="/path/to/file.nii.gz",
+                source_metadata={},
+                fmri_dir="/path/to",
                 bandpass_filter=True,
                 low_pass=0.1,
                 high_pass=0.008,
+                bpf_order=2,
                 fd_thresh=0.3,
                 motion_filter_type=None,
                 smoothing=6,
@@ -160,7 +169,7 @@ def init_postproc_derivatives_wf(
                 cifti=False,
                 dcan_qc=True,
                 output_dir=".",
-                TR=2.,
+                custom_confounds_file=None,
                 name="postproc_derivatives_wf",
             )
 
@@ -168,10 +177,14 @@ def init_postproc_derivatives_wf(
     ----------
     name_source : :obj:`str`
         bold or cifti files
+    source_metadata : :obj:`dict`
+    fmri_dir : :obj:`str`
+        Path to the preprocessing derivatives.
     low_pass : float
         low pass filter
     high_pass : float
         high pass filter
+    bpf_order
     %(fd_thresh)s
     %(motion_filter_type)s
     %(smoothing)s
@@ -181,7 +194,8 @@ def init_postproc_derivatives_wf(
     %(dcan_qc)s
     output_dir : :obj:`str`
         output directory
-    %(TR)s
+    custom_confounds_file
+        Only used for Sources metadata.
     %(name)s
         Default is "connectivity_wf".
 
@@ -189,6 +203,7 @@ def init_postproc_derivatives_wf(
     ------
     %(atlas_names)s
         Used for indexing ``timeseries`` and ``correlations``.
+    atlas_files
     %(timeseries)s
     %(correlations)s
     %(coverage)s
@@ -220,7 +235,11 @@ def init_postproc_derivatives_wf(
     inputnode = pe.Node(
         niu.IdentityInterface(
             fields=[
+                # preprocessing files to use as sources
+                "fmriprep_confounds_file",
+                # postprocessed outputs
                 "atlas_names",
+                "atlas_files",  # for Sources
                 "confounds_file",
                 "confounds_metadata",
                 "coverage",
@@ -251,47 +270,67 @@ def init_postproc_derivatives_wf(
         name="inputnode",
     )
 
+    # Outputs that may be used by the concatenation workflow, in which case we want the actual
+    # output filenames for the Sources metadata field.
+    outputnode = pe.Node(
+        niu.IdentityInterface(
+            fields=[
+                "filtered_motion",
+                "temporal_mask",
+                "interpolated_filtered_bold",
+                "censored_denoised_bold",
+                "smoothed_denoised_bold",
+                "timeseries",
+                "timeseries_ciftis",
+            ],
+        ),
+        name="outputnode",
+    )
+
     # Create dictionary of basic information
     cleaned_data_dictionary = {
-        "RepetitionTime": TR,
         "nuisance parameters": params,
+        **source_metadata,
     }
+    software_filters = None
     if bandpass_filter:
+        software_filters = {}
         if low_pass > 0 and high_pass > 0:
-            key = "Freq Band"
-            val = [high_pass, low_pass]
+            software_filters["Bandpass filter"] = {
+                "Low-pass cutoff (Hz)": low_pass,
+                "High-pass cutoff (Hz)": high_pass,
+                "Filter order": bpf_order,
+            }
         elif high_pass > 0:
-            key = "High-pass Cutoff"
-            val = high_pass
+            software_filters["High-pass filter"] = {
+                "cutoff (Hz)": high_pass,
+                "Filter order": bpf_order,
+            }
         elif low_pass > 0:
-            key = "Low-pass Cutoff"
-            val = low_pass
-        cleaned_data_dictionary[key] = val
-
-    smoothed_data_dictionary = {"FWHM": smoothing}  # Separate dictionary for smoothing
+            software_filters["Low-pass filter"] = {
+                "cutoff (Hz)": low_pass,
+                "Filter order": bpf_order,
+            }
 
     # Determine cohort (if there is one) in the original data
     cohort = get_entity(name_source, "cohort")
 
-    ds_temporal_mask = pe.Node(
-        DerivativesDataSink(
-            base_directory=output_dir,
-            dismiss_entities=["atlas", "den", "res", "space", "cohort", "desc"],
-            suffix="outliers",
-            extension=".tsv",
-            source_file=name_source,
+    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"],
         ),
-        name="ds_temporal_mask",
         run_without_submitting=True,
         mem_gb=1,
+        name="make_atlas_dict",
+        iterfield=["Sources"],
     )
-
     # fmt:off
     workflow.connect([
-        (inputnode, ds_temporal_mask, [
-            ("temporal_mask_metadata", "meta_dict"),
-            ("temporal_mask", "in_file"),
-        ]),
+        (inputnode, make_atlas_dict, [(("atlas_files", _make_xcpd_uri, output_dir), "Sources")]),
     ])
     # fmt:on
 
@@ -308,17 +347,81 @@ def init_postproc_derivatives_wf(
         run_without_submitting=True,
         mem_gb=1,
     )
-
     # fmt:off
     workflow.connect([
         (inputnode, ds_filtered_motion, [
             ("motion_metadata", "meta_dict"),
             ("filtered_motion", "in_file"),
+            (("fmriprep_confounds_file", _make_preproc_uri, fmri_dir), "Sources"),
         ]),
+        (ds_filtered_motion, outputnode, [("out_file", "filtered_motion")]),
     ])
     # fmt:on
 
+    merge_dense_src = pe.Node(
+        niu.Merge(numinputs=(1 + (1 if fd_thresh > 0 else 0) + (1 if params != "none" else 0))),
+        name="merge_dense_src",
+        run_without_submitting=True,
+        mem_gb=1,
+    )
+    merge_dense_src.inputs.in1 = preproc_bold_src
+
+    if fd_thresh > 0:
+        ds_temporal_mask = pe.Node(
+            DerivativesDataSink(
+                base_directory=output_dir,
+                dismiss_entities=["atlas", "den", "res", "space", "cohort", "desc"],
+                suffix="outliers",
+                extension=".tsv",
+                source_file=name_source,
+                # Metadata
+                Threshold=fd_thresh,
+            ),
+            name="ds_temporal_mask",
+            run_without_submitting=True,
+            mem_gb=1,
+        )
+        # fmt:off
+        workflow.connect([
+            (inputnode, ds_temporal_mask, [
+                ("temporal_mask_metadata", "meta_dict"),
+                ("temporal_mask", "in_file"),
+            ]),
+            (ds_filtered_motion, ds_temporal_mask, [
+                (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+            ]),
+            (ds_temporal_mask, outputnode, [("out_file", "temporal_mask")]),
+            (ds_temporal_mask, merge_dense_src, [
+                (("out_file", _make_xcpd_uri, output_dir), "in2"),
+            ]),
+        ])
+        # fmt:on
+
     if params != "none":
+        confounds_src = pe.Node(
+            niu.Merge(
+                numinputs=(1 + (1 if fd_thresh > 0 else 0) + (1 if custom_confounds_file else 0))
+            ),
+            name="confounds_src",
+            run_without_submitting=True,
+            mem_gb=1,
+        )
+        workflow.connect([(inputnode, confounds_src, [("fmriprep_confounds_file", "in1")])])
+        if fd_thresh > 0:
+            # fmt:off
+            workflow.connect([
+                (ds_temporal_mask, confounds_src, [
+                    (("out_file", _make_xcpd_uri, output_dir), "in2"),
+                ]),
+            ])
+            # fmt:on
+
+            if custom_confounds_file:
+                confounds_src.inputs.in3 = _make_custom_uri(custom_confounds_file)
+
+        elif custom_confounds_file:
+            confounds_src.inputs.in2 = _make_custom_uri(custom_confounds_file)
+
         ds_confounds = pe.Node(
             DerivativesDataSink(
                 base_directory=output_dir,
@@ -337,472 +440,578 @@ def init_postproc_derivatives_wf(
                 ("confounds_file", "in_file"),
                 ("confounds_metadata", "meta_dict"),
             ]),
+            (confounds_src, ds_confounds, [("out", "Sources")]),
+            (ds_confounds, merge_dense_src, [
+                (("out_file", _make_xcpd_uri, output_dir), f"in{3 if fd_thresh > 0 else 2}"),
+            ]),
         ])
         # fmt:on
 
-    ds_coverage_files = pe.MapNode(
+    # Write out derivatives via DerivativesDataSink
+    ds_denoised_bold = pe.Node(
         DerivativesDataSink(
             base_directory=output_dir,
             source_file=name_source,
-            dismiss_entities=["desc"],
+            dismiss_entities=["den"],
             cohort=cohort,
-            suffix="coverage",
-            extension=".tsv",
+            desc="denoised",
+            den="91k" if cifti else None,
+            extension=".dtseries.nii" if cifti else ".nii.gz",
+            # Metadata
+            meta_dict=cleaned_data_dictionary,
+            SoftwareFilters=software_filters,
         ),
-        name="ds_coverage_files",
+        name="ds_denoised_bold",
         run_without_submitting=True,
-        mem_gb=1,
-        iterfield=["atlas", "in_file"],
+        mem_gb=2,
     )
-    ds_timeseries = pe.MapNode(
+    # fmt:off
+    workflow.connect([
+        (inputnode, ds_denoised_bold, [("censored_denoised_bold", "in_file")]),
+        (merge_dense_src, ds_denoised_bold, [("out", "Sources")]),
+        (ds_denoised_bold, outputnode, [("out_file", "censored_denoised_bold")]),
+    ])
+    # fmt:on
+
+    if dcan_qc and (fd_thresh > 0):
+        ds_interpolated_denoised_bold = pe.Node(
+            DerivativesDataSink(
+                base_directory=output_dir,
+                source_file=name_source,
+                dismiss_entities=["den"],
+                desc="interpolated",
+                den="91k" if cifti else None,
+                extension=".dtseries.nii" if cifti else ".nii.gz",
+                # Metadata
+                meta_dict=cleaned_data_dictionary,
+            ),
+            name="ds_interpolated_denoised_bold",
+            run_without_submitting=True,
+            mem_gb=2,
+        )
+        # fmt:off
+        workflow.connect([
+            (inputnode, ds_interpolated_denoised_bold, [
+                ("interpolated_filtered_bold", "in_file"),
+            ]),
+            (ds_denoised_bold, ds_interpolated_denoised_bold, [
+                (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+            ]),
+            (ds_interpolated_denoised_bold, outputnode, [
+                ("out_file", "interpolated_filtered_bold"),
+            ]),
+        ])
+        # fmt:on
+
+    else:
+        # fmt:off
+        workflow.connect([
+            (inputnode, outputnode, [
+                ("interpolated_filtered_bold", "interpolated_filtered_bold"),
+            ]),
+        ])
+        # fmt:on
+
+    ds_qc_file = pe.Node(
         DerivativesDataSink(
             base_directory=output_dir,
             source_file=name_source,
-            dismiss_entities=["desc"],
+            dismiss_entities=["desc", "den"],
             cohort=cohort,
-            suffix="timeseries",
-            extension=".tsv",
+            den="91k" if cifti else None,
+            desc="linc",
+            suffix="qc",
+            extension=".csv",
         ),
-        name="ds_timeseries",
+        name="ds_qc_file",
         run_without_submitting=True,
         mem_gb=1,
-        iterfield=["atlas", "in_file"],
     )
-    ds_correlations = pe.MapNode(
-        DerivativesDataSink(
-            base_directory=output_dir,
-            source_file=name_source,
-            dismiss_entities=["desc"],
-            cohort=cohort,
-            measure="pearsoncorrelation",
-            suffix="conmat",
-            extension=".tsv",
+    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"],
         ),
-        name="ds_correlations",
         run_without_submitting=True,
         mem_gb=1,
-        iterfield=["atlas", "in_file"],
+        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
 
-    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(
+    if smoothing:
+        # Write out derivatives via DerivativesDataSink
+        ds_smoothed_bold = pe.Node(
             DerivativesDataSink(
                 base_directory=output_dir,
                 source_file=name_source,
-                dismiss_entities=["desc"],
+                dismiss_entities=["den"],
                 cohort=cohort,
-                measure="pearsoncorrelation",
-                desc=f"{exact_scan}volumes",
-                suffix="conmat",
-                extension=".tsv",
+                den="91k" if cifti else None,
+                desc="denoisedSmoothed",
+                extension=".dtseries.nii" if cifti else ".nii.gz",
+                check_hdr=False,
+                # Metadata
+                SoftwareFilters=software_filters,
+                FWHM=smoothing,
             ),
-            name=f"ds_correlations_exact_{i_exact_scan}",
+            name="ds_smoothed_bold",
             run_without_submitting=True,
-            mem_gb=1,
-            iterfield=["atlas", "in_file"],
+            mem_gb=2,
         )
         # fmt:off
         workflow.connect([
-            (inputnode, ds_correlations_exact, [("atlas_names", "atlas")]),
-            (select_exact_scan_files, ds_correlations_exact, [("out", "in_file")]),
+            (inputnode, ds_smoothed_bold, [("smoothed_denoised_bold", "in_file")]),
+            (ds_denoised_bold, ds_smoothed_bold, [
+                (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+            ]),
+            (ds_smoothed_bold, outputnode, [("out_file", "smoothed_denoised_bold")]),
         ])
         # fmt:on
 
-    ds_parcellated_reho = pe.MapNode(
+    # 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="reho",
+            suffix="coverage",
             extension=".tsv",
         ),
-        name="ds_parcellated_reho",
+        name="ds_coverage",
         run_without_submitting=True,
         mem_gb=1,
-        iterfield=["atlas", "in_file"],
+        iterfield=["atlas", "in_file", "meta_dict"],
     )
-
     # fmt:off
     workflow.connect([
-        (inputnode, ds_coverage_files, [
+        (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"),
         ]),
-        (inputnode, ds_parcellated_reho, [
-            ("parcellated_reho", "in_file"),
-            ("atlas_names", "atlas"),
-        ]),
+        (make_corrs_meta_dict, ds_correlations, [("metadata", "meta_dict")]),
     ])
     # fmt:on
 
-    if bandpass_filter and (fd_thresh <= 0):
-        ds_parcellated_alff = pe.MapNode(
+    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="alff",
-                extension=".tsv",
+                suffix="coverage",
+                extension=".pscalar.nii",
             ),
-            name="ds_parcellated_alff",
+            name="ds_coverage_ciftis",
             run_without_submitting=True,
             mem_gb=1,
-            iterfield=["atlas", "in_file"],
+            iterfield=["atlas", "in_file", "meta_dict"],
         )
-
         # fmt:off
         workflow.connect([
-            (inputnode, ds_parcellated_alff, [
-                ("parcellated_alff", "in_file"),
+            (inputnode, ds_coverage_ciftis, [
+                ("coverage_ciftis", "in_file"),
                 ("atlas_names", "atlas"),
             ]),
+            (add_denoised_to_src, ds_coverage_ciftis, [("metadata", "meta_dict")]),
         ])
         # fmt:on
 
-    # Write out detivatives via DerivativesDataSink
-    if not cifti:  # if Nifti
-        ds_denoised_bold = pe.Node(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                meta_dict=cleaned_data_dictionary,
-                source_file=name_source,
-                cohort=cohort,
-                desc="denoised",
-                extension=".nii.gz",
-                compression=True,
-            ),
-            name="ds_denoised_bold",
-            run_without_submitting=True,
-            mem_gb=2,
-        )
-
-        if dcan_qc:
-            ds_interpolated_denoised_bold = pe.Node(
-                DerivativesDataSink(
-                    base_directory=output_dir,
-                    meta_dict=cleaned_data_dictionary,
-                    source_file=name_source,
-                    desc="interpolated",
-                    extension=".nii.gz",
-                    compression=True,
-                ),
-                name="ds_interpolated_denoised_bold",
-                run_without_submitting=True,
-                mem_gb=2,
-            )
-
-        ds_qc_file = pe.Node(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                source_file=name_source,
-                dismiss_entities=["desc"],
-                cohort=cohort,
-                desc="linc",
-                suffix="qc",
-                extension=".csv",
-            ),
-            name="ds_qc_file",
-            run_without_submitting=True,
-            mem_gb=1,
-        )
-
-        ds_reho = pe.Node(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                source_file=name_source,
-                dismiss_entities=["desc"],
-                cohort=cohort,
-                suffix="reho",
-                extension=".nii.gz",
-                compression=True,
+        add_ccoverage_to_src = pe.MapNode(
+            niu.Function(
+                function=_make_dictionary,
+                input_names=["metadata", "Sources"],
+                output_names=["metadata"],
             ),
-            name="ds_reho",
             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
 
-        if bandpass_filter and (fd_thresh <= 0):
-            ds_alff = pe.Node(
-                DerivativesDataSink(
-                    base_directory=output_dir,
-                    source_file=name_source,
-                    dismiss_entities=["desc"],
-                    cohort=cohort,
-                    suffix="alff",
-                    extension=".nii.gz",
-                    compression=True,
-                ),
-                name="ds_alff",
-                run_without_submitting=True,
-                mem_gb=1,
-            )
-
-        if smoothing:  # if smoothed
-            # Write out detivatives via DerivativesDataSink
-            ds_smoothed_bold = pe.Node(
-                DerivativesDataSink(
-                    base_directory=output_dir,
-                    meta_dict=smoothed_data_dictionary,
-                    source_file=name_source,
-                    cohort=cohort,
-                    desc="denoisedSmoothed",
-                    extension=".nii.gz",
-                    compression=True,
-                ),
-                name="ds_smoothed_bold",
-                run_without_submitting=True,
-                mem_gb=2,
-            )
-
-            if bandpass_filter and (fd_thresh <= 0):
-                ds_smoothed_alff = pe.Node(
-                    DerivativesDataSink(
-                        base_directory=output_dir,
-                        meta_dict=smoothed_data_dictionary,
-                        source_file=name_source,
-                        cohort=cohort,
-                        desc="smooth",
-                        suffix="alff",
-                        extension=".nii.gz",
-                        compression=True,
-                    ),
-                    name="ds_smoothed_alff",
-                    run_without_submitting=True,
-                    mem_gb=1,
-                )
-
-    else:  # For cifti files
-        # Write out derivatives via DerivativesDataSink
-        ds_denoised_bold = pe.Node(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                meta_dict=cleaned_data_dictionary,
-                source_file=name_source,
-                dismiss_entities=["den"],
-                cohort=cohort,
-                desc="denoised",
-                den="91k",
-                extension=".dtseries.nii",
-            ),
-            name="ds_denoised_bold",
-            run_without_submitting=True,
-            mem_gb=2,
-        )
-
-        if dcan_qc:
-            ds_interpolated_denoised_bold = pe.Node(
-                DerivativesDataSink(
-                    base_directory=output_dir,
-                    meta_dict=cleaned_data_dictionary,
-                    source_file=name_source,
-                    dismiss_entities=["den"],
-                    desc="interpolated",
-                    den="91k",
-                    extension=".dtseries.nii",
-                ),
-                name="ds_interpolated_denoised_bold",
-                run_without_submitting=True,
-                mem_gb=2,
-            )
-
-        ds_qc_file = pe.Node(
+        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",
-                desc="linc",
-                suffix="qc",
-                extension=".csv",
+                den="91k" if cifti else None,
+                suffix="timeseries",
+                extension=".ptseries.nii",
             ),
-            name="ds_qc_file",
+            name="ds_timeseries_ciftis",
             run_without_submitting=True,
             mem_gb=1,
+            iterfield=["atlas", "in_file", "meta_dict"],
         )
+        # 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")]),
+        ])
+        # fmt:on
 
-        ds_coverage_cifti_files = pe.MapNode(
-            DerivativesDataSink(
-                base_directory=output_dir,
-                source_file=name_source,
-                check_hdr=False,
-                dismiss_entities=["desc"],
-                cohort=cohort,
-                suffix="coverage",
-                extension=".pscalar.nii",
+        make_ccorrs_meta_dict = pe.MapNode(
+            niu.Function(
+                function=_make_dictionary,
+                input_names=["Sources"],
+                output_names=["metadata"],
             ),
-            name="ds_coverage_cifti_files",
             run_without_submitting=True,
             mem_gb=1,
-            iterfield=["atlas", "in_file"],
+            name="make_ccorrs_meta_dict",
+            iterfield=["Sources"],
         )
-        ds_timeseries_cifti_files = pe.MapNode(
+        # 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",
-                suffix="timeseries",
-                extension=".ptseries.nii",
+                den="91k" if cifti else None,
+                measure="pearsoncorrelation",
+                suffix="conmat",
+                extension=".pconn.nii",
             ),
-            name="ds_timeseries_cifti_files",
+            name="ds_correlation_ciftis",
             run_without_submitting=True,
             mem_gb=1,
-            iterfield=["atlas", "in_file"],
+            iterfield=["atlas", "in_file", "meta_dict"],
+        )
+        # 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")]),
+        ])
+        # 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"],
         )
-        ds_correlation_cifti_files = pe.MapNode(
+        # 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,
-                check_hdr=False,
-                dismiss_entities=["desc", "den"],
+                dismiss_entities=["desc"],
                 cohort=cohort,
-                den="91k",
                 measure="pearsoncorrelation",
+                desc=f"{exact_scan}volumes",
                 suffix="conmat",
-                extension=".pconn.nii",
+                extension=".tsv",
             ),
-            name="ds_correlation_cifti_files",
+            name=f"ds_correlations_exact_{i_exact_scan}",
             run_without_submitting=True,
             mem_gb=1,
             iterfield=["atlas", "in_file"],
         )
-
         # fmt:off
         workflow.connect([
-            (inputnode, ds_coverage_cifti_files, [
-                ("coverage_ciftis", "in_file"),
-                ("atlas_names", "atlas"),
-            ]),
-            (inputnode, ds_timeseries_cifti_files, [
-                ("timeseries_ciftis", "in_file"),
-                ("atlas_names", "atlas"),
-            ]),
-            (inputnode, ds_correlation_cifti_files, [
-                ("correlation_ciftis", "in_file"),
-                ("atlas_names", "atlas"),
-            ]),
+            (inputnode, ds_correlations_exact, [("atlas_names", "atlas")]),
+            (select_exact_scan_files, ds_correlations_exact, [("out", "in_file")]),
         ])
         # fmt:on
 
-        ds_reho = pe.Node(
+    # Resting state metric outputs
+    ds_reho = pe.Node(
+        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="reho",
+            extension=".dscalar.nii" if cifti else ".nii.gz",
+            # Metadata
+            SoftwareFilters=software_filters,
+            Neighborhood="vertices",
+        ),
+        name="ds_reho",
+        run_without_submitting=True,
+        mem_gb=1,
+    )
+    # fmt:off
+    workflow.connect([
+        (inputnode, ds_reho, [("reho", "in_file")]),
+        (ds_denoised_bold, ds_reho, [(("out_file", _make_xcpd_uri, output_dir), "Sources")]),
+    ])
+    # 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
+
+    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
+
+    if bandpass_filter and (fd_thresh <= 0):
+        ds_alff = pe.Node(
             DerivativesDataSink(
                 base_directory=output_dir,
                 source_file=name_source,
                 check_hdr=False,
                 dismiss_entities=["desc", "den"],
                 cohort=cohort,
-                den="91k",
-                suffix="reho",
-                extension=".dscalar.nii",
+                den="91k" if cifti else None,
+                suffix="alff",
+                extension=".dscalar.nii" if cifti else ".nii.gz",
+                # Metadata
+                SoftwareFilters=software_filters,
             ),
-            name="ds_reho",
+            name="ds_alff",
             run_without_submitting=True,
             mem_gb=1,
         )
+        # fmt:off
+        workflow.connect([
+            (inputnode, ds_alff, [("alff", "in_file")]),
+            (ds_denoised_bold, ds_alff, [
+                (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+            ]),
+        ])
+        # fmt:on
 
-        if bandpass_filter and (fd_thresh <= 0):
-            ds_alff = pe.Node(
-                DerivativesDataSink(
-                    base_directory=output_dir,
-                    source_file=name_source,
-                    check_hdr=False,
-                    dismiss_entities=["desc", "den"],
-                    cohort=cohort,
-                    den="91k",
-                    suffix="alff",
-                    extension=".dscalar.nii",
-                ),
-                name="ds_alff",
-                run_without_submitting=True,
-                mem_gb=1,
-            )
-
-        if smoothing:  # If smoothed
-            # Write out detivatives via DerivativesDataSink
-            ds_smoothed_bold = pe.Node(
+        if smoothing:
+            ds_smoothed_alff = pe.Node(
                 DerivativesDataSink(
                     base_directory=output_dir,
-                    meta_dict=smoothed_data_dictionary,
                     source_file=name_source,
                     dismiss_entities=["den"],
                     cohort=cohort,
-                    den="91k",
-                    desc="denoisedSmoothed",
-                    extension=".dtseries.nii",
+                    desc="smooth",
+                    den="91k" if cifti else None,
+                    suffix="alff",
+                    extension=".dscalar.nii" if cifti else ".nii.gz",
                     check_hdr=False,
+                    # Metadata
+                    SoftwareFilters=software_filters,
+                    FWHM=smoothing,
                 ),
-                name="ds_smoothed_bold",
+                name="ds_smoothed_alff",
                 run_without_submitting=True,
-                mem_gb=2,
+                mem_gb=1,
             )
-
-            if bandpass_filter and (fd_thresh <= 0):
-                ds_smoothed_alff = pe.Node(
-                    DerivativesDataSink(
-                        base_directory=output_dir,
-                        meta_dict=smoothed_data_dictionary,
-                        source_file=name_source,
-                        dismiss_entities=["den"],
-                        cohort=cohort,
-                        desc="smooth",
-                        den="91k",
-                        suffix="alff",
-                        extension=".dscalar.nii",
-                        check_hdr=False,
-                    ),
-                    name="ds_smoothed_alff",
-                    run_without_submitting=True,
-                    mem_gb=1,
-                )
-
-    # fmt:off
-    workflow.connect([
-        (inputnode, ds_denoised_bold, [("censored_denoised_bold", "in_file")]),
-        (inputnode, ds_qc_file, [("qc_file", "in_file")]),
-        (inputnode, ds_reho, [("reho", "in_file")]),
-    ])
-    # fmt:on
-
-    if dcan_qc:
+            # fmt:off
+            workflow.connect([
+                (inputnode, ds_smoothed_alff, [("smoothed_alff", "in_file")]),
+                (ds_alff, ds_smoothed_alff, [
+                    (("out_file", _make_xcpd_uri, output_dir), "Sources"),
+                ]),
+            ])
+            # 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([
-            (inputnode, ds_interpolated_denoised_bold, [
-                ("interpolated_filtered_bold", "in_file"),
+            (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 bandpass_filter and (fd_thresh <= 0):
-        workflow.connect([(inputnode, ds_alff, [("alff", "in_file")])])
-
-    if smoothing:
-        workflow.connect([(inputnode, ds_smoothed_bold, [("smoothed_denoised_bold", "in_file")])])
-
-        if bandpass_filter and (fd_thresh <= 0):
-            workflow.connect([(inputnode, ds_smoothed_alff, [("smoothed_alff", "in_file")])])
+        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
 
     return workflow