def METHOD_NAME(ds001419_data, tmp_path_factory):
    """Test xcp_d.interfaces.nilearn.DenoiseCifti."""
    tmpdir = tmp_path_factory.mktemp("test_nilearn_denoisecifti")
    preprocessed_bold = ds001419_data["cifti_file"]
    confounds_file = ds001419_data["confounds_file"]
    # Select some confounds to use for denoising
    confounds_df = pd.read_table(confounds_file)
    reduced_confounds_df = confounds_df[["csf", "white_matter"]]
    reduced_confounds_df["linear_trend"] = np.arange(reduced_confounds_df.shape[0])
    reduced_confounds_df["intercept"] = np.ones(reduced_confounds_df.shape[0])
    reduced_confounds_file = os.path.join(tmpdir, "confounds.tsv")
    reduced_confounds_df.to_csv(reduced_confounds_file, sep="\t", index=False)
    # Create the censoring file
    censoring_df = confounds_df[["framewise_displacement"]]
    censoring_df["framewise_displacement"] = censoring_df["framewise_displacement"] > 0.3
    assert censoring_df["framewise_displacement"].sum() > 0
    temporal_mask = os.path.join(tmpdir, "censoring.tsv")
    censoring_df.to_csv(temporal_mask, sep="\t", index=False)
    preprocessed_img = nb.load(preprocessed_bold)
    interface = nilearn.DenoiseCifti(
        preprocessed_bold=preprocessed_bold,
        confounds_file=reduced_confounds_file,
        temporal_mask=temporal_mask,
        TR=2,
        bandpass_filter=True,
        high_pass=0.01,
        low_pass=0.08,
        filter_order=2,
    )
    results = interface.run(cwd=tmpdir)
    _check_denoising_outputs(preprocessed_img, results.outputs, cifti=True)