Update everything.

docs/workflows.rst

@@ -277,13 +277,16 @@ Denoising
 =========
 :class:`~xcp_d.interfaces.nilearn.DenoiseNifti`, :class:`~xcp_d.interfaces.nilearn.DenoiseCifti`
 
-Temporal censoring
-------------------
+Temporal censoring [OPTIONAL]
+-----------------------------
 
 Prior to confound regression, high-motion volumes will be removed from the BOLD data.
 These volumes will also be removed from the nuisance regressors.
 Please refer to :footcite:t:`power2012spurious` for more information.
 
+.. tip::
+   Censoring can be disabled by setting ``--fd-thresh 0``.
+
 
 Confound regression
 -------------------
@@ -339,6 +342,18 @@ Interpolation
 
 An interpolated version of the ``denoised BOLD`` is then created by filling in the high-motion
 outlier volumes with cubic spline interpolated data, as implemented in ``Nilearn``.
+
+.. warning::
+   In versions 0.4.0rc2 - 0.5.0, XCP-D used cubic spline interpolation,
+   followed by bandpass filtering.
+
+   However, cubic spline interpolation can introduce large spikes and drops in the signal
+   when the censored volumes are at the beginning or end of the run,
+   which are then propagated to the filtered data.
+
+   To address this, XCP-D now replaces interpolated volumes at the edges of the run with the
+   closest non-outlier volume's data, as of 0.5.1.
+
 The resulting ``interpolated, denoised BOLD`` is primarily used for bandpass filtering.
 
 

xcp_d/tests/test_cli.py

@@ -216,8 +216,9 @@ def test_pnc_cifti(data_dir, output_dir, working_dir):
         "sub-1648798153_ses-PNC1_task-rest_acq-singleband_desc-confounds_timeseries.tsv",
     )
     motion_df = pd.read_table(motion_file)
-    motion_df.loc[-5:, "trans_x"] = 100
+    motion_df.loc[-10:, "trans_x"] = 100
     motion_df.to_csv(motion_file, sep="\t", index=False)
+    print("Overwrote confounds file.")
 
     parameters = [
         dataset_dir,

xcp_d/utils/utils.py

@@ -474,18 +474,15 @@ def denoise_with_nilearn(
         first_outliers = consecutive_outliers_idx[0]
         last_outliers = consecutive_outliers_idx[-1]
 
-        LOGGER.warning(consecutive_outliers_idx)
-        LOGGER.warning(n_volumes)
-
         # Replace outliers at beginning of run
         if first_outliers[0] == 0:
             LOGGER.warning(
                 f"Outlier volumes at beginning of run ({first_outliers[0]}-{first_outliers[1]}) "
                 "will be replaced with first non-outlier volume's values."
             )
-            interpolated_unfiltered_bold[: first_outliers[1], :] = interpolated_unfiltered_bold[
-                first_outliers[1] + 1, :
-            ]
+            interpolated_unfiltered_bold[
+                : first_outliers[1] + 1, :
+            ] = interpolated_unfiltered_bold[first_outliers[1] + 1, :]
 
         # Replace outliers at end of run
         if last_outliers[1] == n_volumes: