Repository: SIMEXP/load_confounds (language: python)

Issue:
add initial volume suppression to reach steady state
this would be implemented just like scrubbing.

See this issue:
https://github.com/courtois-neuromod/cneuromod_docs/issues/23
generate `sample_mask` 
both scrubbing and removal of initial time points can lead to remove time samples entirely. 

It would be useful to generate (and document) usage of a `sample_mask_` attribute in the confound object after loading. 
This mechanism is actually necessary to implement correctly the `scrubbing` strategy of Ciric et al. 2017. 

A related issue should be reported in nilearn: the `sample_mask` option is featured at the initialization of a masker, while there is no reason for the time points removal (or even time points #) to be fixed in the life cycle of a masker object. The `sample_mask` argument should rather be passed to the `*transform*` methods, just like `confounds` is. 
add README with minimal documentation, including usage examples



Discussion / hints:
relevant neurostars thread https://neurostars.org/t/non-steady-state-outlier-xx-not-found-in-confound-files/18380/2

please have a look
LGTM

Fix patch:
diff --git a/load_confounds/confounds.py b/load_confounds/confounds.py
index 7870bf9..6b04187 100644
--- a/load_confounds/confounds.py
+++ b/load_confounds/confounds.py
@@ -39,14 +39,13 @@ def _check_params(confounds_raw, params):
 
 def _find_confounds(confounds_raw, keywords):
     """Find confounds that contain certain keywords."""
-    list_confounds = []
-    missing_keys = []
+    list_confounds, missing_keys = [], []
     for key in keywords:
         key_found = [col for col in confounds_raw.columns if key in col]
-        if not key_found:
-            missing_keys.append(key)
-        else:
+        if key_found:
             list_confounds.extend(key_found)
+        elif key != "non_steady_state":
+            missing_keys.append(key)
     if missing_keys:
         raise MissingConfound(keywords=missing_keys)
     return list_confounds
@@ -217,24 +216,59 @@ def _confounds_to_df(image_file, flag_acompcor, flag_full_aroma):
     return confounds_raw, confounds_json
 
 
+def _get_outlier_cols(confounds_columns):
+    """Get outlier regressor column names."""
+    outlier_cols = {
+        col
+        for col in confounds_columns
+        if "motion_outlier" in col or "non_steady_state" in col
+    }
+    confounds_col = set(confounds_columns) - outlier_cols
+    return outlier_cols, confounds_col
+
+
+def _extract_outlier_regressors(confounds):
+    """Separate confounds and outlier regressors."""
+    outlier_cols, confounds_col = _get_outlier_cols(confounds.columns)
+    outliers = confounds[outlier_cols] if outlier_cols else pd.DataFrame()
+    confounds = confounds[confounds_col]
+    sample_mask = _outlier_to_sample_mask(outliers)
+    return sample_mask, confounds, outliers
+
+
+def _outlier_to_sample_mask(outlier_flag):
+    """Generate sample mask from outlier regressors."""
+    if outlier_flag.size == 0:  # Do not supply sample mask
+        return None  # consistency with nilearn sample_mask
+    outlier_flag = outlier_flag.sum(axis=1).values
+    return np.where(outlier_flag == 0)[0].tolist()
+
+
 def _confounds_to_ndarray(confounds, demean):
     """Convert confounds from a pandas dataframe to a numpy array."""
-    # Convert from DataFrame to numpy ndarray
-    labels = confounds.columns
-    confounds = confounds.values
-
-    # Derivatives have NaN on the first row
-    # Replace them by estimates at second time point,
-    # otherwise nilearn will crash.
+    sample_mask, confounds, outliers = _extract_outlier_regressors(confounds)
     if confounds.size != 0:  # ica_aroma = "full" generate empty output
-        mask_nan = np.isnan(confounds[0, :])
-        confounds[0, mask_nan] = confounds[1, mask_nan]
-
-        # Optionally demean confounds
+        # Derivatives have NaN on the first row
+        # Replace them by estimates at second time point,
+        # otherwise nilearn will crash.
+        mask_nan = np.isnan(confounds.values[0, :])
+        confounds.iloc[0, mask_nan] = confounds.iloc[1, mask_nan]
         if demean:
-            confounds = scale(confounds, axis=0, with_std=False)
-
-    return confounds, labels
+            confounds = _demean_confounds(confounds, sample_mask)
+    labels = list(confounds.columns)
+    confounds = confounds.values
+    return sample_mask, confounds, labels
+
+
+def _demean_confounds(confounds, sample_mask):
+    """Demean the confounds. The mean is calculated on non-outlier values."""
+    confound_cols = confounds.columns
+    if sample_mask is None:
+        confounds= scale(confounds, axis=0, with_std=False)
+    else:  # calculate the mean without outliers.
+        confounds_mean = confounds.iloc[sample_mask, :].mean(axis=0)
+        confounds -= confounds_mean
+    return pd.DataFrame(confounds, columns=confound_cols)
 
 
 class MissingConfound(Exception):
diff --git a/load_confounds/data/nonss_desc-confounds_regressors.tsv b/load_confounds/data/nonss_desc-confounds_regressors.tsv
new file mode 100644
index 0000000..7ba8514
--- /dev/null
+++ b/load_confounds/data/nonss_desc-confounds_regressors.tsv
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diff --git a/load_confounds/data/nonss_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz b/load_confounds/data/nonss_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
new file mode 100644
index 0000000..6a1a321
Binary files /dev/null and b/load_confounds/data/nonss_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz differ
diff --git a/load_confounds/parser.py b/load_confounds/parser.py
index efd0354..b317cc1 100644
--- a/load_confounds/parser.py
+++ b/load_confounds/parser.py
@@ -16,6 +16,7 @@
     "compcor",
     "ica_aroma",
     "scrub",
+    "non_steady_state",
 ]
 
 
@@ -27,6 +28,9 @@ def _sanitize_strategy(strategy):
                 raise ValueError(f"{conf} is not a supported type of confounds.")
     else:
         raise ValueError("strategy needs to be a list of strings")
+    # add non steady state if not present
+    if "non_steady_state" not in strategy:
+        strategy.append("non_steady_state")
     return strategy
 
 
@@ -124,6 +128,7 @@ class Confounds:
         using nilearn with no or zscore standardization, but should be turned off
         with "spc" normalization.
 
+
     Attributes
     ----------
     `confounds_` : ndarray
@@ -132,6 +137,14 @@ class Confounds:
     `columns_`: list of str
         The labels of the different confounds
 
+    `sample_mask_` : list of int
+        The index of the niimgs along time/fourth dimension.
+        This list includes indices for valid volumes for subsequent analysis.
+        This attribute should be passed to parameter `sample_mask` of nilearn.NiftiMasker.
+        Volumnes are removed if flagges as following:
+            - Non-steady-state volumes (if present)
+            - Motion outliers detected by scrubbing
+
     Notes
     -----
     The predefined strategies implemented in this class are
@@ -183,7 +196,7 @@ def __init__(
 
     def load(self, img_files):
         """
-        Load fMRIprep confounds
+        Load fMRIprep confounds and sample mask
 
         Parameters
         ----------
@@ -198,27 +211,39 @@ def load(self, img_files):
         confounds :  ndarray or list of ndarray
             A reduced version of fMRIprep confounds based on selected strategy and flags.
             An intercept is automatically added to the list of confounds.
+        sample_mask : list or list of list
+            Index of time point to be preserved in the analysis
         """
+        return self._parse(img_files)
+
+    def _parse(self, img_files):
+        """Parse input image, find confound files and scrubbing etc."""
         img_files, flag_single = cf._sanitize_confounds(img_files)
+
         confounds_out = []
         columns_out = []
+        sample_mask_out = []
         self.missing_confounds_ = []
         self.missing_keys_ = []
 
         for file in img_files:
-            conf, col = self._load_single(file)
+            sample_mask, conf, col = self._load_single(file)
             confounds_out.append(conf)
             columns_out.append(col)
+            sample_mask_out.append(sample_mask)
 
         # If a single input was provided,
         # send back a single output instead of a list
         if flag_single:
             confounds_out = confounds_out[0]
             columns_out = columns_out[0]
+            sample_mask_out = sample_mask_out[0]
 
         self.confounds_ = confounds_out
         self.columns_ = columns_out
-        return confounds_out
+        self.sample_mask_ = sample_mask_out
+        return confounds_out, sample_mask_out
+
 
     def _load_single(self, confounds_raw):
         """Load a single confounds file from fmriprep."""
@@ -237,8 +262,10 @@ def _load_single(self, confounds_raw):
             confounds = pd.concat([confounds, loaded_confounds], axis=1)
 
         _check_error(self.missing_confounds_, self.missing_keys_)
-        confounds, labels = cf._confounds_to_ndarray(confounds, self.demean)
-        return confounds, labels
+        sample_mask, confounds, labels = cf._confounds_to_ndarray(
+            confounds, self.demean
+        )
+        return sample_mask, confounds, labels
 
     def _load_confound(self, confounds_raw, confound):
         """Load a single type of confound."""
@@ -324,3 +351,11 @@ def _load_scrub(self, confounds_raw):
         ]
         motion_outlier_regressors.columns = column_names
         return motion_outlier_regressors
+
+    def _load_non_steady_state(self, confounds_raw):
+        """Find non steady state regressors."""
+        nss_outliers = cf._find_confounds(confounds_raw, ["non_steady_state"])
+        if nss_outliers:
+            return confounds_raw[nss_outliers]
+        else:
+            return pd.DataFrame()
diff --git a/load_confounds/strategies.py b/load_confounds/strategies.py
index 5e2c32a..4c8a24a 100644
--- a/load_confounds/strategies.py
+++ b/load_confounds/strategies.py
@@ -58,7 +58,7 @@ def __init__(self, motion="full", wm_csf="basic", demean=True, **kwargs):
         # check if global signal is supplied as a parameter
         # if so, add to strategy
         global_signal = kwargs.get("global_signal", False)
-        strategy = ["high_pass", "motion", "wm_csf"]
+        strategy = ["high_pass", "motion", "wm_csf", "non_steady_state"]
         strategy, global_signal = _update_strategy(strategy, global_signal)
         # warn user for supplying useless parameter
         _check_invalid_parameter(kwargs, valid_keys=["global_signal"])
@@ -146,7 +146,7 @@ def __init__(
         # check if global signal is supplied as a parameter
         # if so, add to strategy
         global_signal = kwargs.get("global_signal", False)
-        strategy = ["high_pass", "motion", "wm_csf", "scrub"]
+        strategy = ["high_pass", "motion", "wm_csf", "scrub", "non_steady_state"]
         strategy, global_signal = _update_strategy(strategy, global_signal)
         # warn user for supplying useless parameter
         _check_invalid_parameter(kwargs, valid_keys=["global_signal"])
@@ -214,7 +214,7 @@ def __init__(
     ):
         """Default parameters."""
         # set attributes
-        self.strategy = ["high_pass", "motion", "compcor"]
+        self.strategy = ["high_pass", "motion", "compcor", "non_steady_state"]
         self.motion = motion
         self.n_motion = 0
         self.compcor = compcor
@@ -277,7 +277,6 @@ class ICAAROMA(Confounds):
         "derivatives" global signal and derivative (2 parameters)
         "full" global signal + derivatives + quadratic terms + power2d derivatives (4 parameters)
 
-
     Returns
     -------
     conf :  a Confounds object
@@ -297,7 +296,7 @@ class ICAAROMA(Confounds):
 
     def __init__(self, wm_csf="basic", demean=True, **kwargs):
         """Default parameters."""
-        strategy = ["wm_csf", "high_pass", "ica_aroma"]
+        strategy = ["wm_csf", "high_pass", "ica_aroma", "non_steady_state"]
         global_signal = kwargs.get("global_signal", False)
         strategy, global_signal = _update_strategy(strategy, global_signal)
         # warn user for supplying useless parameter


Test patch:
diff --git a/load_confounds/data/test_desc-confounds_regressors.tsv b/load_confounds/data/test_desc-confounds_regressors.tsv
index 7ba8514..e29975d 100644
--- a/load_confounds/data/test_desc-confounds_regressors.tsv
+++ b/load_confounds/data/test_desc-confounds_regressors.tsv
@@ -1,31 +1,31 @@
-csf	csf_derivative1	csf_power2	csf_derivative1_power2	white_matter	white_matter_derivative1	white_matter_derivative1_power2	white_matter_power2	global_signal	global_signal_derivative1	global_signal_power2	global_signal_derivative1_power2	std_dvars	dvars	framewise_displacement	t_comp_cor_00	t_comp_cor_01	t_comp_cor_02	t_comp_cor_03	t_comp_cor_04	t_comp_cor_05	a_comp_cor_00	a_comp_cor_01	a_comp_cor_02	a_comp_cor_03	a_comp_cor_04	a_comp_cor_05	a_comp_cor_06	a_comp_cor_07	a_comp_cor_08	a_comp_cor_09	a_comp_cor_10	a_comp_cor_11	a_comp_cor_12	a_comp_cor_13	a_comp_cor_14	a_comp_cor_15	a_comp_cor_16	a_comp_cor_17	a_comp_cor_18	a_comp_cor_19	a_comp_cor_20	a_comp_cor_21	a_comp_cor_22	a_comp_cor_23	a_comp_cor_24	a_comp_cor_25	a_comp_cor_26	a_comp_cor_27	a_comp_cor_28	a_comp_cor_29	a_comp_cor_30	a_comp_cor_31	a_comp_cor_32	a_comp_cor_33	a_comp_cor_34	a_comp_cor_35	a_comp_cor_36	a_comp_cor_37	a_comp_cor_38	a_comp_cor_39	a_comp_cor_40	a_comp_cor_41	a_comp_cor_42	a_comp_cor_43	a_comp_cor_44	a_comp_cor_45	a_comp_cor_46	a_comp_cor_47	a_comp_cor_48	a_comp_cor_49	a_comp_cor_50	a_comp_cor_51	a_comp_cor_52	a_comp_cor_53	a_comp_cor_54	a_comp_cor_55	a_comp_cor_56	a_comp_cor_57	a_comp_cor_58	a_comp_cor_59	a_comp_cor_60	a_comp_cor_61	a_comp_cor_62	a_comp_cor_63	a_comp_cor_64	a_comp_cor_65	a_comp_cor_66	a_comp_cor_67	a_comp_cor_68	a_comp_cor_69	a_comp_cor_70	a_comp_cor_71	a_comp_cor_72	a_comp_cor_73	a_comp_cor_74	a_comp_cor_75	a_comp_cor_76	a_comp_cor_77	a_comp_cor_78	a_comp_cor_79	a_comp_cor_80	a_comp_cor_81	a_comp_cor_82	a_comp_cor_83	a_comp_cor_84	a_comp_cor_85	a_comp_cor_86	a_comp_cor_87	a_comp_cor_88	a_comp_cor_89	a_comp_cor_90	a_comp_cor_91	a_comp_cor_92	a_comp_cor_93	a_comp_cor_94	a_comp_cor_95	a_comp_cor_96	a_comp_cor_97	a_comp_cor_98	a_comp_cor_99	a_comp_cor_100	a_comp_cor_101	a_comp_cor_102	a_comp_cor_103	a_comp_cor_104	a_comp_cor_105	a_comp_cor_106	a_comp_cor_107	a_comp_cor_108	a_comp_cor_109	a_comp_cor_110	a_comp_cor_111	a_comp_cor_112	a_comp_cor_113	a_comp_cor_114	a_comp_cor_115	a_comp_cor_116	a_comp_cor_117	a_comp_cor_118	a_comp_cor_119	a_comp_cor_120	a_comp_cor_121	a_comp_cor_122	a_comp_cor_123	a_comp_cor_124	a_comp_cor_125	cosine00	cosine01	cosine02	cosine03	trans_x	trans_x_derivative1	trans_x_power2	trans_x_derivative1_power2	trans_y	trans_y_derivative1	trans_y_derivative1_power2	trans_y_power2	trans_z	trans_z_derivative1	trans_z_power2	trans_z_derivative1_power2	rot_x	rot_x_derivative1	rot_x_derivative1_power2	rot_x_power2	rot_y	rot_y_derivative1	rot_y_derivative1_power2	rot_y_power2	rot_z	rot_z_derivative1	rot_z_power2	rot_z_derivative1_power2	aroma_motion_01	aroma_motion_02	aroma_motion_05	aroma_motion_11	aroma_motion_15	aroma_motion_16	aroma_motion_17	aroma_motion_18	aroma_motion_21	aroma_motion_23	aroma_motion_24	aroma_motion_25	aroma_motion_26
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diff --git a/load_confounds/tests/test_parser.py b/load_confounds/tests/test_parser.py
index e8c948d..bba631d 100644
--- a/load_confounds/tests/test_parser.py
+++ b/load_confounds/tests/test_parser.py
@@ -14,6 +14,9 @@
 file_confounds = os.path.join(
     path_data, "test_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz"
 )
+file_no_none_steady = os.path.join(
+    path_data, "nonss_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz"
+)
 
 
 def _simu_img(demean=True):
@@ -25,12 +28,16 @@ def _simu_img(demean=True):
     # as we will stack slices with confounds on top of slices with noise
     nz = 2
     # Load a simple 6 parameters motion models as confounds
-    X = lc.Confounds(strategy=["motion"], motion="basic", demean=demean).load(
+    X, _ = lc.Confounds(strategy=["motion"], motion="basic", demean=demean).load(
         file_confounds
     )
+    # the first row is non-steady state, replace it with the imput from the second row
+    non_steady = X[0, :]
+    X[0, :] = X[1, :]
     # repeat X in length (axis = 0) three times to increase the degree of freedom
     X = np.tile(X, (3, 1))
-
+    # put non-steady state volume back at the first sample
+    X[0, :] = non_steady
     # the number of time points is based on the example confound file
     nt = X.shape[0]
     # initialize an empty 4D volume
@@ -62,16 +69,20 @@ def _simu_img(demean=True):
     return img, mask_conf, mask_rand, X
 
 
-def _tseries_std(img, mask_img, confounds, standardize):
+def _tseries_std(img, mask_img, confounds, sample_mask, standardize):
     """Get the std of time series in a mask."""
-    masker = NiftiMasker(mask_img=mask_img, standardize=standardize)
+    masker = NiftiMasker(
+        mask_img=mask_img, standardize=standardize, sample_mask=sample_mask
+    )
     tseries = masker.fit_transform(img, confounds=confounds)
     return tseries.std(axis=0)
 
 
-def _denoise(img, mask_img, confounds, standardize):
+def _denoise(img, mask_img, confounds, sample_mask, standardize):
     """Extract time series with and without confounds."""
-    masker = NiftiMasker(mask_img=mask_img, standardize=standardize)
+    masker = NiftiMasker(
+        mask_img=mask_img, standardize=standardize, sample_mask=sample_mask
+    )
     tseries_raw = masker.fit_transform(img)
     tseries_clean = masker.fit_transform(img, confounds=confounds)
     return tseries_raw, tseries_clean
@@ -85,14 +96,14 @@ def _corr_tseries(tseries1, tseries2):
     return corr
 
 
-def _regression(confounds):
+def _regression(confounds, sample_mask):
     """Simple regression with nilearn."""
     # Simulate data
     img, mask_conf, _, _ = _simu_img(demean=True)
     confounds = np.tile(confounds, (3, 1))  # matching L29 (_simu_img)
 
     # Do the regression
-    masker = NiftiMasker(mask_img=mask_conf, standardize=True)
+    masker = NiftiMasker(mask_img=mask_conf, standardize=True, sample_mask=sample_mask)
     tseries_clean = masker.fit_transform(img, confounds)
     assert tseries_clean.shape[0] == confounds.shape[0]
 
@@ -101,35 +112,36 @@ def _regression(confounds):
 def test_nilearn_regress():
     """Try regressing out all motion types in nilearn."""
     # Regress full motion
-    confounds = lc.Confounds(strategy=["motion"], motion="full").load(file_confounds)
-    _regression(confounds)
+    confounds, _ = lc.Confounds(strategy=["motion"], motion="full").load(file_confounds)
+    sample_mask = None  # not testing sample mask here
+    _regression(confounds, sample_mask)
 
     # Regress high_pass
-    confounds = lc.Confounds(strategy=["high_pass"]).load(file_confounds)
-    _regression(confounds)
+    confounds, _ = lc.Confounds(strategy=["high_pass"]).load(file_confounds)
+    _regression(confounds, sample_mask)
 
     # Regress wm_csf
-    confounds = lc.Confounds(strategy=["wm_csf"], wm_csf="full").load(file_confounds)
-    _regression(confounds)
+    confounds, _ = lc.Confounds(strategy=["wm_csf"], wm_csf="full").load(file_confounds)
+    _regression(confounds, sample_mask)
     # Regress global
-    confounds = lc.Confounds(strategy=["global"], global_signal="full").load(
+    confounds, _ = lc.Confounds(strategy=["global"], global_signal="full").load(
         file_confounds
     )
-    _regression(confounds)
+    _regression(confounds, sample_mask)
 
     # Regress AnatCompCor
-    confounds = lc.Confounds(strategy=["compcor"], compcor="anat").load(file_confounds)
-    _regression(confounds)
+    confounds, _ = lc.Confounds(strategy=["compcor"], compcor="anat").load(file_confounds)
+    _regression(confounds, sample_mask)
 
     # Regress TempCompCor
-    confounds = lc.Confounds(strategy=["compcor"], compcor="temp").load(file_confounds)
-    _regression(confounds)
+    confounds, _ = lc.Confounds(strategy=["compcor"], compcor="temp").load(file_confounds)
+    _regression(confounds, sample_mask)
 
     # Regress ICA-AROMA
-    confounds = lc.Confounds(strategy=["ica_aroma"], ica_aroma="basic").load(
+    confounds, _ = lc.Confounds(strategy=["ica_aroma"], ica_aroma="basic").load(
         file_confounds
     )
-    _regression(confounds)
+    _regression(confounds, sample_mask)
 
 
 @pytest.mark.filterwarnings("ignore")
@@ -140,12 +152,12 @@ def test_nilearn_standardize_false():
 
     # Check that most variance is removed
     # in voxels composed of pure confounds
-    tseries_std = _tseries_std(img, mask_conf, X, False)
+    tseries_std = _tseries_std(img, mask_conf, X, None, False)
     assert np.mean(tseries_std < 0.0001)
 
     # Check that most variance is preserved
     # in voxels composed of random noise
-    tseries_std = _tseries_std(img, mask_rand, X, False)
+    tseries_std = _tseries_std(img, mask_rand, X, None, False)
     assert np.mean(tseries_std > 0.9)
 
 
@@ -160,7 +172,7 @@ def test_nilearn_standardize_zscore():
     # in voxels composed of pure confounds
     # the correlation before and after denoising should be very low
     # as most of the variance is removed by denoising
-    tseries_raw, tseries_clean = _denoise(img, mask_conf, X, "zscore")
+    tseries_raw, tseries_clean = _denoise(img, mask_conf, X, None, "zscore")
     corr = _corr_tseries(tseries_raw, tseries_clean)
     assert corr.mean() < 0.2
 
@@ -168,7 +180,7 @@ def test_nilearn_standardize_zscore():
     # with vs without confounds in voxels where the signal is uncorrelated
     # with confounds. The correlation before and after denoising should be very
     # high as very little of the variance is removed by denoising
-    tseries_raw, tseries_clean = _denoise(img, mask_rand, X, "zscore")
+    tseries_raw, tseries_clean = _denoise(img, mask_rand, X, None, "zscore")
     corr = _corr_tseries(tseries_raw, tseries_clean)
     assert corr.mean() > 0.8
 
@@ -181,12 +193,12 @@ def test_nilearn_standardize_psc():
     img, mask_conf, mask_rand, X = _simu_img(demean=False)
 
     # Areas with confound
-    tseries_raw, tseries_clean = _denoise(img, mask_conf, X, "psc")
+    tseries_raw, tseries_clean = _denoise(img, mask_conf, X, None, "psc")
     corr = _corr_tseries(tseries_raw, tseries_clean)
     assert corr.mean() < 0.2
 
     # Areas with random noise
-    tseries_raw, tseries_clean = _denoise(img, mask_rand, X, "psc")
+    tseries_raw, tseries_clean = _denoise(img, mask_rand, X, None, "psc")
     corr = _corr_tseries(tseries_raw, tseries_clean)
     assert corr.mean() > 0.8
 
@@ -212,6 +224,9 @@ def test_sanitize_strategy():
     with pytest.raises(ValueError):
         lc.Confounds(strategy=[0])
 
+    conf = lc.Confounds(strategy=["motion"])
+    assert "non_steady_state" in conf.strategy
+
 
 def test_motion():
 
@@ -388,7 +403,8 @@ def test_ica_aroma():
     conf = lc.Confounds(strategy=["ica_aroma"], ica_aroma="basic")
     conf.load(file_confounds)
     for col_name in conf.columns_:
-        assert re.match("aroma_motion_+", col_name)
+        # only aroma and non-steady state columns will be present
+        assert re.match("(?:aroma_motion_+|non_steady_state+)", col_name)
 
     # Non-agressive strategy
     conf = lc.Confounds(strategy=["ica_aroma"], ica_aroma="full")
@@ -400,3 +416,28 @@ def test_ica_aroma():
         conf = lc.Confounds(strategy=["ica_aroma"], ica_aroma=None)
         conf.load(file_confounds)
     assert "ICA-AROMA strategy" in exc_info.value.args[0]
+
+
+def test_sample_mask():
+    """Test load method and sample mask."""
+    # create a version with srub_mask not applied;
+    # This is not recommanded
+    conf = lc.Confounds(strategy=["motion", "scrub"], scrub="full", fd_thresh=0.15)
+    reg, mask = conf.load(file_confounds)
+
+    # the current test data has 6 time points marked as motion outliers,
+    # and one nonsteady state (overlap with the first motion outlier)
+    # 2 time points removed due to the "full" srubbing strategy
+    assert reg.shape[0] - len(mask) == 8
+    # nilearn requires unmasked confound regressors
+    assert reg.shape[0] == 30
+
+    # non steady state will always be removed
+    conf = lc.Confounds(strategy=["motion"])
+    reg, mask = conf.load(file_confounds)
+    assert reg.shape[0] - len(mask) == 1
+
+    # When no non-steady state volumes are present
+    conf = lc.Confounds(strategy=["motion"])
+    reg, mask = conf.load(file_no_none_steady)
+    assert mask is None
diff --git a/load_confounds/tests/test_strategies.py b/load_confounds/tests/test_strategies.py
index 8ff19e6..671d93b 100644
--- a/load_confounds/tests/test_strategies.py
+++ b/load_confounds/tests/test_strategies.py
@@ -19,7 +19,7 @@ def test_Minimal():
     """Test the Minimal strategy."""
     # Try to load the confounds, whithout PCA reduction
     conf = lc.Minimal()
-    assert conf.strategy == ["high_pass", "motion", "wm_csf"]
+    assert conf.strategy == ["high_pass", "motion", "wm_csf", "non_steady_state"]
     assert hasattr(conf, "global_signal") == False
     conf.load(file_confounds)
 
@@ -39,7 +39,7 @@ def test_Minimal():
 
     # maker sure global signal works
     conf = lc.Minimal(global_signal="basic")
-    assert conf.strategy == ["high_pass", "motion", "wm_csf", "global"]
+    assert conf.strategy == ["high_pass", "motion", "wm_csf", "non_steady_state", "global"]
     assert conf.global_signal == "basic"
 
 
@@ -47,7 +47,7 @@ def test_Scrubbing():
     """Test the Scrubbing strategy."""
     conf = lc.Scrubbing(fd_thresh=0.15)
     # make sure global signal is not there
-    assert conf.strategy == ["high_pass", "motion", "wm_csf", "scrub"]
+    assert conf.strategy == ["high_pass", "motion", "wm_csf", "scrub", "non_steady_state"]
     assert hasattr(conf, "global_signal") == False
     conf.load(file_confounds)
 
@@ -77,26 +77,28 @@ def test_Scrubbing():
         "csf_power2",
         "csf_derivative1_power2",
         "white_matter_derivative1",
-        "motion_outlier_0",
-        "motion_outlier_1",
     ]
 
     for check in list_check:
         assert check in conf.columns_
+    # out of 30 vols, should have 6 motion outliers from scrubbing,
+    # and 2 vol removed by srubbing strategy "full"
+    assert len(conf.sample_mask_) == 22
+    # shape of confound regressors untouched
+    assert len(conf.confounds_) == 30
 
     # also load confounds with very liberal scrubbing thresholds
     # this should not produce an error
     conf = lc.Scrubbing(fd_thresh=1, std_dvars_thresh=5)
     conf.load(file_confounds)
-    assert "motion_outlier_0" not in conf.columns_
+    assert len(conf.sample_mask_) == 29  # only non-steady volumes removed
 
     # maker sure global signal works
     conf = lc.Scrubbing(global_signal="full")
-    assert conf.strategy == ["high_pass", "motion", "wm_csf", "scrub", "global"]
+    assert conf.strategy == ["high_pass", "motion", "wm_csf", "scrub", "non_steady_state", "global"]
     assert conf.global_signal == "full"
 
 
-
 def test_CompCor_anatomical():
     """Test the anatomical CompCor strategy."""
     # Try to load the confounds, whithout PCA reduction
@@ -231,7 +233,7 @@ def test_ICAAROMA():
     """Test the (non-aggressive) ICA-AROMA strategy."""
     conf = lc.ICAAROMA(global_signal="basic")
     assert conf.global_signal == "basic"
-    assert conf.strategy == ["wm_csf", "high_pass", "ica_aroma", "global"]
+    assert conf.strategy == ["wm_csf", "high_pass", "ica_aroma", "non_steady_state", "global"]
     conf.load(file_aroma)
 
     # Check that all fixed name model categories have been successfully loaded