docs/using/index.rst

@@ -16,3 +16,17 @@ the results. Finally, we will show how to use the ``queue`` command to interact
    codeless
    running
    queueing
+
+
+.. _using_components:
+
+Using junifer common components
+-------------------------------
+
+The following sections explains common components of junifer that can be used across many steps of the pipeline.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   masks

docs/using/masks.rst

@@ -0,0 +1,68 @@
+.. include:: ../links.inc
+
+.. _using_masks:
+
+Masks
+=====
+
+Masks are essentially boolean arrays that are used to constrain the extraction of features to voxels that are
+meaningful. For example, in an fMRI imaging study, a mask can be used to constrain the extraction of features to
+voxels that contain a certain ratio of gray matter to white matter / cerebrospinal fluid, ensuring that the features
+are not extracted from voxels that contain mostly white matter or cerebrospinal fluid, which could add noise to the
+BOLD signal.
+
+Junifer provides a number of built-in masks, which can be listed using the :func:`junifer.data.masks.list_masks`. Some
+masks are images, while other masks can be computed using :ref:`nilearn` functions. 
+
+For markers and steps that accept ``masks`` as an argument, the mask can be specified as a string, which will be the
+name of a built-in mask, or as a dictionary in which the **only** key is the built-in mask name and the value is a 
+dictionary of keyword arguments to pass to the mask function. 
+
+For example, the following is a valid mask specification that specified the ``GM_prob0.2`` mask.
+
+.. code-block:: yaml
+
+    masks: GM_prob0.2
+
+The following is a valid mask specification that specifies the ``compute_brain_mask``  mask (function from nilearn),
+with a threshold of 0.5.
+
+.. code-block:: yaml
+
+    masks: 
+        compute_brain_mask:
+            threshold: 0.5
+
+Furthermore, junifer allows you to combine several masks using :func:`nilearn.masking.intersect_masks`. This is done by
+specifying a list of masks, where each mask is a string or dictionary as described above. For example, the following
+is a valid mask specification that specifies the intersection of the ``GM_prob0.2`` and ``compute_brain_mask`` masks.
+
+.. code-block:: yaml
+
+    masks: 
+        - GM_prob0.2
+        - compute_brain_mask:
+            threshold: 0.5
+
+We can also specify the arguments of :func:`nilearn.masking.intersect_masks` (``threshold`` and ``connected``). The
+following example combines the same masks as the previous one, but computing the full intersection.
+
+
+.. code-block:: yaml
+
+    masks: 
+        - GM_prob0.2
+        - compute_brain_mask:
+            threshold: 0.5
+        - threshold: 1  # intersection
+
+Alternatively, we can also compute the union, even if the voxels do not form a connected component:
+
+.. code-block:: yaml
+
+    masks: 
+        - GM_prob0.2
+        - compute_brain_mask:
+            threshold: 0.5
+        - threshold: 0  # union
+        - connected: False  # keep disconnected components

junifer/data/masks.py

@@ -23,6 +23,7 @@ from nilearn.masking import (
     compute_background_mask,
     compute_brain_mask,
     compute_epi_mask,
+    intersect_masks,
 )
 
 from ..utils.logging import logger, raise_error
@@ -36,7 +37,10 @@ if TYPE_CHECKING:
 _masks_path = Path(__file__).parent / "masks"
 
 
-def _fetch_icbm152_brain_gm_mask(target_img: "Nifti1Image", **kwargs):
+def _fetch_icbm152_brain_gm_mask(
+    target_img: "Nifti1Image",
+    **kwargs,
+):
     """Fetch ICBM152 brain mask and resample.
 
     Parameters
@@ -65,6 +69,9 @@ data.
 
 The built-in masks are files that are shipped with the package in the
 data/masks directory. The user can also register their own masks.
+
+Callable masks should be functions that take at least one parameter:
+* `target_img`: the image to which the mask will be applied.
 """
 _available_masks: Dict[str, Dict[str, Any]] = {
     "GM_prob0.2": {"family": "Vickery-Patil"},
@@ -146,19 +153,23 @@ def list_masks() -> List[str]:
 
 
 def get_mask(
-    mask: Union[str, Dict],
+    masks: Union[str, Dict, List[Union[Dict, str]]],
     target_data: Dict[str, Any],
+    extra_input: Optional[Dict[str, Any]] = None,
 ) -> "Nifti1Image":
     """Get mask, tailored for the target image.
 
     Parameters
     ----------
-    masks : str or dict
+    masks : str, dict or list of dict or str
         The name of the mask, or the name of a callable mask and the parameters
-        of the mask.
+        of the mask as a dictionary. Several masks can be passed as a list.
     target_data : dict
         The corresponding item of the data object to which the mask will be
         applied.
+    extra_input : dict, optional
+        The other fields in the data object. Useful for accessing other data
+        kinds that needs to be used in the computation of masks (default None).
 
     Returns
     -------
@@ -167,20 +178,68 @@ def get_mask(
     """
     # Get the min of the voxels sizes and use it as the resolution
     target_img = target_data["data"]
+    inherited_mask_item = target_data.get("mask_item", None)
     resolution = np.min(target_img.header.get_zooms()[:3])
 
-    if isinstance(mask, dict):
+    if not isinstance(masks, list):
-        if len(mask) != 1:
+        masks = [masks]
+
+    # Check that dicts have only one key
+    invalid_elements = [
+        x for x in masks if isinstance(x, dict) and len(x) != 1
+    ]
+    if len(invalid_elements) > 0:
         raise_error(
-                "The mask dictionary must have only one key, "
+            "Each of the masks dictionary must have only one key, "
-                "the name of the mask."
+            "the name of the mask. The following dictionaries are invalid: "
+            f"{invalid_elements}"
         )
-        mask_name = list(mask.keys())[0]
+
-        mask_params = mask[mask_name]
+    # Check params for the intersection function
+    intersect_params = {}
+    true_masks = []
+    for t_mask in masks:
+        if isinstance(t_mask, dict):
+            if "threshold" in t_mask:
+                intersect_params["threshold"] = t_mask["threshold"]
+                continue
+            elif "connected" in t_mask:
+                intersect_params["connected"] = t_mask["connected"]
+                continue
+        # All the other elements are masks
+        true_masks.append(t_mask)
+
+    if len(true_masks) == 0:
+        raise_error("No mask was passed. At least one mask is required.")
+    # Get all the masks
+    all_masks = []
+    for t_mask in true_masks:
+        if isinstance(t_mask, dict):
+            mask_name = list(t_mask.keys())[0]
+            mask_params = t_mask[mask_name]
         else:
-        mask_name = mask
+            mask_name = t_mask
             mask_params = None
 
+        if mask_name == "inherit":
+            if extra_input is None:
+                raise_error(
+                    "Cannot inherit mask from another data item "
+                    "because no extra data was passed."
+                )
+            if inherited_mask_item is None:
+                raise_error(
+                    "Cannot inherit mask from another data item "
+                    "because no mask item was specified "
+                    "(missing `mask_item` key in the data object)."
+                )
+            if inherited_mask_item not in extra_input:
+                raise_error(
+                    "Cannot inherit mask from another data item "
+                    f"because the item ({inherited_mask_item}) does not exist."
+                )
+            mask_img = extra_input[inherited_mask_item]["data"]
+        else:
             mask_object, _ = load_mask(
                 mask_name, path_only=False, resolution=resolution
             )
@@ -190,13 +249,26 @@ def get_mask(
                 mask_img = mask_object(target_img, **mask_params)
             else:  # Mask is a Nifti1Image
                 if mask_params is not None:
-            raise_error("Cannot pass callable params to a non-callable mask.")
+                    raise_error(
+                        "Cannot pass callable params to a non-callable mask."
+                    )
                 mask_img = resample_to_img(
                     mask_object,
                     target_img,
                     interpolation="nearest",
                     copy=True,
                 )
+        all_masks.append(mask_img)
+    if len(all_masks) > 1:
+        mask_img = intersect_masks(all_masks, **intersect_params)
+    else:
+        if len(intersect_params) > 0:
+            # Yes, I'm this strict!
+            raise_error(
+                "Cannot pass parameters to the intersection function "
+                "when there is only one mask."
+            )
+        mask_img = all_masks[0]
 
     return mask_img
 

junifer/data/tests/test_masks.py

@@ -6,8 +6,9 @@
 # License: AGPL
 
 from pathlib import Path
-from typing import Callable, Dict, Union
+from typing import Callable, Dict, List, Union
 
+import numpy as np
 import pytest
 from nilearn.datasets import fetch_icbm152_brain_gm_mask
 from nilearn.image import resample_to_img
@@ -15,6 +16,7 @@ from nilearn.masking import (
     compute_background_mask,
     compute_brain_mask,
     compute_epi_mask,
+    intersect_masks,
 )
 from numpy.testing import assert_array_almost_equal, assert_array_equal
 
@@ -56,7 +58,9 @@ def test_register_mask_already_registered() -> None:
         name="testmask",
         mask_path="testmask.nii.gz",
     )
-    assert load_mask("testmask", path_only=True)[1].name == "testmask.nii.gz"
+    out = load_mask("testmask", path_only=True)
+    assert out[1] is not None
+    assert out[1].name == "testmask.nii.gz"
 
     # Try registering again
     with pytest.raises(ValueError, match=r"already registered."):
@@ -70,7 +74,9 @@ def test_register_mask_already_registered() -> None:
         overwrite=True,
     )
 
-    assert load_mask("testmask", path_only=True)[1].name == "testmask2.nii.gz"
+    out = load_mask("testmask", path_only=True)
+    assert out[1] is not None
+    assert out[1].name == "testmask2.nii.gz"
 
 
 @pytest.mark.parametrize(
@@ -110,6 +116,7 @@ def test_register_mask(
     # Load registered mask
     _, fname = load_mask(name=name, path_only=True)
     # Check values for registered mask
+    assert fname is not None
     assert fname.name == f"{name}.nii.gz"
 
 
@@ -146,6 +153,7 @@ def test_vickery_patil() -> None:
         mask.header["pixdim"][1:4], [1.5, 1.5, 1.5]  # type: ignore
     )
 
+    assert fname is not None
     assert fname.name == "CAT12_IXI555_MNI152_TMP_GS_GMprob0.2_clean.nii.gz"
 
     mask, fname = load_mask("GM_prob0.2", resolution=3)
@@ -153,6 +161,7 @@ def test_vickery_patil() -> None:
         mask.header["pixdim"][1:4], [3.0, 3.0, 3.0]  # type: ignore
     )
 
+    assert fname is not None
     assert (
         fname.name == "CAT12_IXI555_MNI152_TMP_GS_GMprob0.2_clean_3mm.nii.gz"
     )
@@ -162,6 +171,7 @@ def test_vickery_patil() -> None:
         mask.header["pixdim"][1:4], [3.0, 3.0, 3.0]  # type: ignore
     )
 
+    assert fname is not None
     assert fname.name == "GMprob0.2_cortex_3mm_NA_rm.nii.gz"
 
     with pytest.raises(ValueError, match=r"find a Vickery-Patil mask "):
@@ -176,7 +186,7 @@ def test_get_mask() -> None:
         input = reader.fit_transform(input)
         vbm_gm = input["VBM_GM"]
         vbm_gm_img = vbm_gm["data"]
-        mask = get_mask(mask="GM_prob0.2", target_data=vbm_gm)
+        mask = get_mask(masks="GM_prob0.2", target_data=vbm_gm)
 
         assert mask.shape == vbm_gm_img.shape
         assert_array_equal(mask.affine, vbm_gm_img.affine)
@@ -204,7 +214,7 @@ def test_mask_callable() -> None:
         input = reader.fit_transform(input)
         vbm_gm = input["VBM_GM"]
         vbm_gm_img = vbm_gm["data"]
-        mask = get_mask(mask="identity", target_data=vbm_gm)
+        mask = get_mask(masks="identity", target_data=vbm_gm)
 
         assert_array_equal(mask.get_fdata(), vbm_gm_img.get_fdata())
 
@@ -218,11 +228,48 @@ def test_get_mask_errors() -> None:
         input = dg["sub-01"]
         input = reader.fit_transform(input)
         vbm_gm = input["VBM_GM"]
+        # Test wrong masks definitions (more than one key per dict)
         with pytest.raises(ValueError, match=r"only one key"):
-            get_mask(mask={"GM_prob0.2": {}, "Other": {}}, target_data=vbm_gm)
+            get_mask(masks={"GM_prob0.2": {}, "Other": {}}, target_data=vbm_gm)
 
+        # Test wrong masks definitions (pass paramaeters to non-callable mask)
         with pytest.raises(ValueError, match=r"callable params"):
-            get_mask(mask={"GM_prob0.2": {"param": 1}}, target_data=vbm_gm)
+            get_mask(masks={"GM_prob0.2": {"param": 1}}, target_data=vbm_gm)
+
+        # Pass only parametesr to the intersection function
+        with pytest.raises(
+            ValueError, match=r" At least one mask is required."
+        ):
+            get_mask(masks={"threshold": 1}, target_data=vbm_gm)
+
+        # Pass parameters to the intersection function when only one mask
+        with pytest.raises(
+            ValueError, match=r"parameters to the intersection"
+        ):
+            get_mask(
+                masks=["GM_prob0.2", {"threshold": 1}], target_data=vbm_gm
+            )
+
+        # Test "inherited" masks errors
+
+        # 1) No extra_data parameter
+        with pytest.raises(ValueError, match=r"no extra data was passed"):
+            get_mask(masks="inherit", target_data=vbm_gm)
+
+        extra_input = {"VBM_MASK": {}}
+
+        # 2) No mask_item key in target_data
+        with pytest.raises(ValueError, match=r"no mask item was specified"):
+            get_mask(
+                masks="inherit", target_data=vbm_gm, extra_input=extra_input
+            )
+
+        # 3) mask_item not in extra data
+        with pytest.raises(ValueError, match=r"does not exist"):
+            vbm_gm["mask_item"] = "wrong"
+            get_mask(
+                masks="inherit", target_data=vbm_gm, extra_input=extra_input
+            )
 
 
 @pytest.mark.parametrize(
@@ -271,7 +318,7 @@ def test_nilearn_compute_masks(
         else:
             mask_spec = {mask_name: params}
 
-        mask = get_mask(mask=mask_spec, target_data=bold)
+        mask = get_mask(masks=mask_spec, target_data=bold)
 
         assert_array_equal(mask.affine, bold_img.affine)
 
@@ -287,3 +334,114 @@ def test_nilearn_compute_masks(
                 copy=True,
             )
         assert_array_equal(mask.get_fdata(), ni_mask.get_fdata())
+
+
+def test_get_mask_inherit() -> None:
+    """Test using the inherit mask functionality."""
+    reader = DefaultDataReader()
+    with SPMAuditoryTestingDatagrabber() as dg:
+        input = dg["sub001"]
+        input = reader.fit_transform(input)
+        # Compute brain mask using nilearn
+        gm_mask = compute_brain_mask(input["BOLD"]["data"], threshold=0.2)
+
+        # Get mask using the compute_brain_mask function
+        mask1 = get_mask(
+            masks={"compute_brain_mask": {"threshold": 0.2}},
+            target_data=input["BOLD"],
+        )
+
+        # Now get the mask using the inherit functionality, passing the
+        # computed mask as extra data
+        extra_input = {"BOLD_MASK": {"data": gm_mask}}
+        input["BOLD"]["mask_item"] = "BOLD_MASK"
+        mask2 = get_mask(
+            masks="inherit", target_data=input["BOLD"], extra_input=extra_input
+        )
+
+        # Both masks should be equal
+        assert_array_equal(mask1.get_fdata(), mask2.get_fdata())
+
+
+@pytest.mark.parametrize(
+    "masks,params",
+    [
+        (["GM_prob0.2", "compute_brain_mask"], {}),
+        (
+            ["GM_prob0.2", "compute_brain_mask"],
+            {"threshold": 0.2},
+        ),
+        (
+            [
+                "GM_prob0.2",
+                "compute_brain_mask",
+                "fetch_icbm152_brain_gm_mask",
+            ],
+            {"threshold": 1, "connected": True},
+        ),
+    ],
+)
+def test_get_mask_multiple(
+    masks: Union[str, Dict, List[Union[Dict, str]]], params: Dict
+) -> None:
+    """Test getting multiple masks.
+
+    Parameters
+    ----------
+    masks : str, dict, list of str or dict
+        Masks to get, junifer style.
+    params : dict
+        Parameters to pass to the intersect_masks function.
+    """
+    reader = DefaultDataReader()
+    with SPMAuditoryTestingDatagrabber() as dg:
+        input = dg["sub001"]
+        input = reader.fit_transform(input)
+        if not isinstance(masks, list):
+            junifer_masks = [masks]
+        else:
+            junifer_masks = masks.copy()
+        if len(params) > 0:
+            # Convert params to junifer style (one dict per param)
+            junifer_params = [{k: params[k]} for k in params.keys()]
+            junifer_masks.extend(junifer_params)
+        target_img = input["BOLD"]["data"]
+        resolution = np.min(target_img.header.get_zooms()[:3])
+
+        computed = get_mask(masks=junifer_masks, target_data=input["BOLD"])
+
+        masks_names = [
+            list(x.keys())[0] if isinstance(x, dict) else x for x in masks
+        ]
+
+        mask_funcs = [
+            x
+            for x in masks_names
+            if _available_masks[x]["family"] == "Callable"
+        ]
+        mask_files = [
+            x
+            for x in masks_names
+            if _available_masks[x]["family"] != "Callable"
+        ]
+
+        mask_imgs = [
+            load_mask(t_mask, path_only=False, resolution=resolution)[0]
+            for t_mask in mask_files
+        ]
+
+        for t_func in mask_funcs:
+            mask_imgs.append(_available_masks[t_func]["func"](target_img))
+
+        mask_imgs = [
+            resample_to_img(
+                t_mask,
+                target_img,
+                interpolation="nearest",
+                copy=True,
+            )
+            for t_mask in mask_imgs
+        ]
+
+        expected = intersect_masks(mask_imgs, **params)
+        assert_array_equal(computed.get_fdata(), expected.get_fdata())

junifer/markers/ets_rss.py

@@ -32,10 +32,10 @@ class RSSETSMarker(BaseMarker):
     agg_method_params : dict, optional
         Parameters to pass to the aggregation function. Check valid options in
         :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. If None, will use the class name (default
         None).
@@ -49,13 +49,13 @@ class RSSETSMarker(BaseMarker):
         parcellation: Union[str, List[str]],
         agg_method: str = "mean",
         agg_method_params: Optional[Dict] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.parcellation = parcellation
         self.agg_method = agg_method
         self.agg_method_params = agg_method_params
-        self.mask = mask
+        self.masks = masks
         super().__init__(name=name)
 
     def get_valid_inputs(self) -> List[str]:
@@ -126,11 +126,11 @@ class RSSETSMarker(BaseMarker):
             parcellation=self.parcellation,
             method=self.agg_method,
             method_params=self.agg_method_params,
-            mask=self.mask,
+            masks=self.masks,
         )
         # Compute the parcel aggregation
         out = parcel_aggregation.compute(input=input, extra_input=extra_input)
-        edge_ts = _ets(out["data"])
+        edge_ts, _ = _ets(out["data"])
         # Compute the RSS
         out["data"] = np.sum(edge_ts**2, 1) ** 0.5
         # Set correct column label

junifer/markers/falff/falff_parcels.py

@@ -35,10 +35,10 @@ class AmplitudeLowFrequencyFluctuationParcels(
     use_afni : bool, optional
         Whether to use AFNI for computing. If None, will use AFNI only
         if available (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     method : str, optional
         The method to perform aggregation using. Check valid options in
         :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
@@ -70,13 +70,13 @@ class AmplitudeLowFrequencyFluctuationParcels(
         lowpass: float = 0.1,
         tr: Optional[float] = None,
         use_afni: Optional[bool] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         method: str = "mean",
         method_params: Optional[Dict] = None,
         name: Optional[str] = None,
     ) -> None:
         self.parcellation = parcellation
-        self.mask = mask
+        self.masks = masks
         self.method = method
         self.method_params = method_params
         super().__init__(
@@ -116,7 +116,7 @@ class AmplitudeLowFrequencyFluctuationParcels(
             parcellation=self.parcellation,
             method=self.method,
             method_params=self.method_params,
-            mask=self.mask,
+            masks=self.masks,
             on="fALFF",
         )
 

junifer/markers/falff/falff_spheres.py

@@ -5,7 +5,7 @@
 #          Kaustubh R. Patil <k.patil@fz-juelich.de>
 # License: AGPL
 
-from typing import Dict, Optional, Union
+from typing import Dict, List, Optional, Union
 
 from ...api.decorators import register_marker
 from .. import SphereAggregation
@@ -39,10 +39,10 @@ class AmplitudeLowFrequencyFluctuationSpheres(
     use_afni : bool, optional
         Whether to use AFNI for computing. If None, will use AFNI only
         if available (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     method : str, optional
         The method to perform aggregation using. Check valid options in
         :func:`junifer.stats.get_aggfunc_by_name` (default "mean").
@@ -75,14 +75,14 @@ class AmplitudeLowFrequencyFluctuationSpheres(
         lowpass: float = 0.1,
         tr: Optional[float] = None,
         use_afni: Optional[bool] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         method: str = "mean",
         method_params: Optional[Dict] = None,
         name: Optional[str] = None,
     ) -> None:
         self.coords = coords
         self.radius = radius
-        self.mask = mask
+        self.masks = masks
         self.method = method
         self.method_params = method_params
         super().__init__(
@@ -123,7 +123,7 @@ class AmplitudeLowFrequencyFluctuationSpheres(
             radius=self.radius,
             method=self.method,
             method_params=self.method_params,
-            mask=self.mask,
+            masks=self.masks,
             on="fALFF",
         )
 

junifer/markers/functional_connectivity/crossparcellation_functional_connectivity.py

@@ -30,10 +30,10 @@ class CrossParcellationFC(BaseMarker):
     correlation_method : str, optional
         Any method that can be passed to
         :any:`pandas.DataFrame.corr` (default "pearson").
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. If None, will use the class name
         (default None).
@@ -47,7 +47,7 @@ class CrossParcellationFC(BaseMarker):
         parcellation_two: str,
         aggregation_method: str = "mean",
         correlation_method: str = "pearson",
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         if parcellation_one == parcellation_two:
@@ -58,7 +58,7 @@ class CrossParcellationFC(BaseMarker):
         self.parcellation_two = parcellation_two
         self.aggregation_method = aggregation_method
         self.correlation_method = correlation_method
-        self.mask = mask
+        self.masks = masks
         super().__init__(on=["BOLD"], name=name)
 
     def get_valid_inputs(self) -> List[str]:
@@ -129,12 +129,12 @@ class CrossParcellationFC(BaseMarker):
         parcellation_one_dict = ParcelAggregation(
             parcellation=self.parcellation_one,
             method=self.aggregation_method,
-            mask=self.mask,
+            masks=self.masks,
         ).compute(input)
         parcellation_two_dict = ParcelAggregation(
             parcellation=self.parcellation_two,
             method=self.aggregation_method,
-            mask=self.mask,
+            masks=self.masks,
         ).compute(input)
 
         parcellated_ts_one = parcellation_one_dict["data"]

junifer/markers/functional_connectivity/edge_functional_connectivity_parcels.py

@@ -35,10 +35,10 @@ class EdgeCentricFCParcels(FunctionalConnectivityBase):
     cor_method_params : dict, optional
         Parameters to pass to the correlation function. Check valid options in
         :class:`nilearn.connectome.ConnectivityMeasure` (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. If None, will use the class name (default
         None).
@@ -59,7 +59,7 @@ class EdgeCentricFCParcels(FunctionalConnectivityBase):
         agg_method_params: Optional[Dict] = None,
         cor_method: str = "covariance",
         cor_method_params: Optional[Dict] = None,
-        mask: Optional[str] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.parcellation = parcellation
@@ -68,7 +68,7 @@ class EdgeCentricFCParcels(FunctionalConnectivityBase):
             agg_method_params=agg_method_params,
             cor_method=cor_method,
             cor_method_params=cor_method_params,
-            mask=mask,
+            masks=masks,
             name=name,
         )
 
@@ -78,7 +78,7 @@ class EdgeCentricFCParcels(FunctionalConnectivityBase):
             parcellation=self.parcellation,
             method=self.agg_method,
             method_params=self.agg_method_params,
-            mask=self.mask,
+            masks=self.masks,
             on="BOLD",
         )
 

junifer/markers/functional_connectivity/edge_functional_connectivity_spheres.py

@@ -4,7 +4,7 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional, Union
 
 from ...api.decorators import register_marker
 from ..sphere_aggregation import SphereAggregation
@@ -37,10 +37,10 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
     cor_method_params : dict, optional
         Parameters to pass to the correlation function. Check valid options in
         :class:`nilearn.connectome.ConnectivityMeasure` (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. By default, it will use
         KIND_EdgeCentricFCSpheres where KIND is the kind of data it
@@ -63,7 +63,7 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
         agg_method_params: Optional[Dict] = None,
         cor_method: str = "covariance",
         cor_method_params: Optional[Dict] = None,
-        mask: Optional[str] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.coords = coords
@@ -75,7 +75,7 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
             agg_method_params=agg_method_params,
             cor_method=cor_method,
             cor_method_params=cor_method_params,
-            mask=mask,
+            masks=masks,
             name=name,
         )
 
@@ -86,7 +86,7 @@ class EdgeCentricFCSpheres(FunctionalConnectivityBase):
             radius=self.radius,
             method=self.agg_method,
             method_params=self.agg_method_params,
-            mask=self.mask,
+            masks=self.masks,
             on="BOLD",
         )
         bold_aggregated = sphere_aggregation.compute(input)

junifer/markers/functional_connectivity/functional_connectivity_base.py

@@ -32,10 +32,10 @@ class FunctionalConnectivityBase(BaseMarker):
     cor_method_params : dict, optional
         Parameters to pass to the correlation function. Check valid options in
         :class:`nilearn.connectome.ConnectivityMeasure` (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. If None, will use the class name (default
         None).
@@ -50,7 +50,7 @@ class FunctionalConnectivityBase(BaseMarker):
         agg_method_params: Optional[Dict] = None,
         cor_method: str = "covariance",
         cor_method_params: Optional[Dict] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.agg_method = agg_method
@@ -62,7 +62,7 @@ class FunctionalConnectivityBase(BaseMarker):
         self.cor_method_params["empirical"] = self.cor_method_params.get(
             "empirical", False
         )
-        self.mask = mask
+        self.masks = masks
         super().__init__(on="BOLD", name=name)
 
     @abstractmethod
@@ -134,7 +134,7 @@ class FunctionalConnectivityBase(BaseMarker):
         # Compute correlation
         if self.cor_method_params["empirical"]:
             connectivity = ConnectivityMeasure(
-                cov_estimator=EmpiricalCovariance(),
+                cov_estimator=EmpiricalCovariance(),  # type: ignore
                 kind=self.cor_method,
             )
         else:

junifer/markers/functional_connectivity/functional_connectivity_parcels.py

@@ -34,10 +34,10 @@ class FunctionalConnectivityParcels(FunctionalConnectivityBase):
     cor_method_params : dict, optional
         Parameters to pass to the correlation function. Check valid options in
         :class:`nilearn.connectome.ConnectivityMeasure` (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. If None, will use the class name (default
         None).
@@ -51,7 +51,7 @@ class FunctionalConnectivityParcels(FunctionalConnectivityBase):
         agg_method_params: Optional[Dict] = None,
         cor_method: str = "covariance",
         cor_method_params: Optional[Dict] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.parcellation = parcellation
@@ -60,7 +60,7 @@ class FunctionalConnectivityParcels(FunctionalConnectivityBase):
             agg_method_params=agg_method_params,
             cor_method=cor_method,
             cor_method_params=cor_method_params,
-            mask=mask,
+            masks=masks,
             name=name,
         )
 
@@ -70,7 +70,7 @@ class FunctionalConnectivityParcels(FunctionalConnectivityBase):
             parcellation=self.parcellation,
             method=self.agg_method,
             method_params=self.agg_method_params,
-            mask=self.mask,
+            masks=self.masks,
             on="BOLD",
         )
         # Return the 2D timeseries after parcel aggregation

junifer/markers/functional_connectivity/functional_connectivity_spheres.py

@@ -5,7 +5,7 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 from ...api.decorators import register_marker
 from ..sphere_aggregation import SphereAggregation
@@ -38,10 +38,10 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
     cor_method_params : dict, optional
         Parameters to pass to the correlation function. Check valid options in
         :class:`nilearn.connectome.ConnectivityMeasure` (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. By default, it will use
         KIND_FunctionalConnectivitySpheres where KIND is the kind of data it
@@ -57,7 +57,7 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
         agg_method_params: Optional[Dict] = None,
         cor_method: str = "covariance",
         cor_method_params: Optional[Dict] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.coords = coords
@@ -69,7 +69,7 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
             agg_method_params=agg_method_params,
             cor_method=cor_method,
             cor_method_params=cor_method_params,
-            mask=mask,
+            masks=masks,
             name=name,
         )
 
@@ -80,7 +80,7 @@ class FunctionalConnectivitySpheres(FunctionalConnectivityBase):
             radius=self.radius,
             method=self.agg_method,
             method_params=self.agg_method_params,
-            mask=self.mask,
+            masks=self.masks,
             on="BOLD",
         )
         # Return the 2D timeseries after sphere aggregation

junifer/markers/functional_connectivity/tests/test_functional_connectivity_base.py

@@ -6,10 +6,12 @@
 import pytest
 
 # done to keep line length 79
-import junifer.markers.functional_connectivity as fc
+from junifer.markers.functional_connectivity import (
+    functional_connectivity_base as fcb,
+)
 
 
 def test_base_functional_connectivity_marker_abstractness() -> None:
     """Test FunctionalConnectivityBase is an abstract base class."""
     with pytest.raises(TypeError, match="abstract"):
-        fc.functional_connectivity_base.FunctionalConnectivityBase()
+        fcb.FunctionalConnectivityBase()  # type: ignore

junifer/markers/parcel_aggregation.py

@@ -32,10 +32,10 @@ class ParcelAggregation(BaseMarker):
     method_params : dict, optional
         Parameters to pass to the aggregation function. Check valid options in
         :func:`junifer.stats.get_aggfunc_by_name`.
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     on : {"T1w", "BOLD", "VBM_GM", "VBM_WM", "fALFF", "GCOR", "LCOR"} \
          or list of the options, optional
         The data types to apply the marker to. If None, will work on all
@@ -52,7 +52,7 @@ class ParcelAggregation(BaseMarker):
         parcellation: Union[str, List[str]],
         method: str,
         method_params: Optional[Dict[str, Any]] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         on: Union[List[str], str, None] = None,
         name: Optional[str] = None,
     ) -> None:
@@ -61,7 +61,7 @@ class ParcelAggregation(BaseMarker):
         self.parcellation = parcellation
         self.method = method
         self.method_params = method_params or {}
-        self.mask = mask
+        self.masks = masks
         super().__init__(on=on, name=name)
 
     def get_valid_inputs(self) -> List[str]:
@@ -184,9 +184,11 @@ class ParcelAggregation(BaseMarker):
             img=parcellation_img_res,
         )
 
-        if self.mask is not None:
+        if self.masks is not None:
-            logger.debug(f"Masking with {self.mask}")
+            logger.debug(f"Masking with {self.masks}")
-            mask_img = get_mask(mask=self.mask, target_data=input)
+            mask_img = get_mask(
+                masks=self.masks, target_data=input, extra_input=extra_input
+            )
 
             parcellation_bin = math_img(
                 "np.logical_and(img, mask)",

junifer/markers/reho/reho_parcels.py

@@ -4,7 +4,7 @@
 # License: AGPL
 
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 
@@ -74,10 +74,10 @@ class ReHoParcels(ReHoBase):
     agg_method_params : dict, optional
         Parameters to pass to the aggregation function. Check valid options in
         :func:`junifer.stats.get_aggfunc_by_name` (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. If None, it will use the class name
         (default None).
@@ -91,14 +91,14 @@ class ReHoParcels(ReHoBase):
         reho_params: Optional[Dict] = None,
         agg_method: str = "mean",
         agg_method_params: Optional[Dict] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.parcellation = parcellation
         self.reho_params = reho_params
         self.agg_method = agg_method
         self.agg_method_params = agg_method_params
-        self.mask = mask
+        self.masks = masks
         super().__init__(use_afni=use_afni, name=name)
 
     def compute(
@@ -136,7 +136,7 @@ class ReHoParcels(ReHoBase):
             parcellation=self.parcellation,
             method=self.agg_method,
             method_params=self.agg_method_params,
-            mask=self.mask,
+            masks=self.masks,
             on="BOLD",
         )
         # Perform aggregation on reho map

junifer/markers/reho/reho_spheres.py

@@ -4,7 +4,7 @@
 # License: AGPL
 
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 
@@ -79,10 +79,10 @@ class ReHoSpheres(ReHoBase):
         (default None).
     agg_method_params : dict, optional
         The parameters to pass to the aggregation method (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     name : str, optional
         The name of the marker. If None, it will use the class name
         (default None).
@@ -97,7 +97,7 @@ class ReHoSpheres(ReHoBase):
         reho_params: Optional[Dict] = None,
         agg_method: str = "mean",
         agg_method_params: Optional[Dict] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         name: Optional[str] = None,
     ) -> None:
         self.coords = coords
@@ -105,7 +105,7 @@ class ReHoSpheres(ReHoBase):
         self.reho_params = reho_params
         self.agg_method = agg_method
         self.agg_method_params = agg_method_params
-        self.mask = mask
+        self.masks = masks
         super().__init__(use_afni=use_afni, name=name)
 
     def compute(
@@ -144,7 +144,7 @@ class ReHoSpheres(ReHoBase):
             radius=self.radius,
             method=self.agg_method,
             method_params=self.agg_method_params,
-            mask=self.mask,
+            masks=self.masks,
             on="BOLD",
         )
         # Perform aggregation on reho map

junifer/markers/sphere_aggregation.py

@@ -34,10 +34,10 @@ class SphereAggregation(BaseMarker):
         (default "mean").
     method_params : dict, optional
         The parameters to pass to the aggregation method (default None).
-    mask : str, optional
+    masks : str, dict or list of dict or str, optional
-        The name of the mask to apply to regions before extracting signals.
+        The specification of the masks to apply to regions before extracting
-        Check valid options by calling :func:`junifer.data.masks.list_masks`
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        (default None).
+        If None, will not apply any mask (default None).
     on : {"T1w", "BOLD", "VBM_GM", "VBM_WM", "fALFF", "GCOR", "LCOR"} or \
          list of the options, optional
         The data types to apply the marker to. If None, will work on all
@@ -56,7 +56,7 @@ class SphereAggregation(BaseMarker):
         radius: Optional[float] = None,
         method: str = "mean",
         method_params: Optional[Dict[str, Any]] = None,
-        mask: Union[str, Dict, None] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
         on: Union[List[str], str, None] = None,
         name: Optional[str] = None,
     ) -> None:
@@ -64,7 +64,7 @@ class SphereAggregation(BaseMarker):
         self.radius = radius
         self.method = method
         self.method_params = method_params or {}
-        self.mask = mask
+        self.masks = masks
         super().__init__(on=on, name=name)
 
     def get_valid_inputs(self) -> List[str]:
@@ -137,9 +137,11 @@ class SphereAggregation(BaseMarker):
         )
         # Load mask
         mask_img = None
-        if self.mask is not None:
+        if self.masks is not None:
-            logger.debug(f"Masking with {self.mask}")
+            logger.debug(f"Masking with {self.masks}")
-            mask_img = get_mask(mask=self.mask, target_data=input)
+            mask_img = get_mask(
+                masks=self.masks, target_data=input, extra_input=extra_input
+            )
         # Get seeds and labels
         coords, out_labels = load_coordinates(name=self.coords)
         masker = JuniferNiftiSpheresMasker(

junifer/markers/tests/test_collection.py

@@ -39,7 +39,7 @@ def test_marker_collection_incorrect_markers() -> None:
         ),
     ]
     with pytest.raises(ValueError, match=r"must have different names"):
-        MarkerCollection(wrong_markers)
+        MarkerCollection(wrong_markers)  # type: ignore
 
 
 def test_marker_collection() -> None:
@@ -62,7 +62,7 @@ def test_marker_collection() -> None:
             name="gmd_schaefer100x7_trim_mean90",
         ),
     ]
-    mc = MarkerCollection(markers=markers)
+    mc = MarkerCollection(markers=markers)  # type: ignore
     assert mc._markers == markers
     assert mc._preprocessing is None
     assert mc._storage is None
@@ -96,7 +96,7 @@ def test_marker_collection() -> None:
             return input
 
     mc2 = MarkerCollection(
-        markers=markers,
+        markers=markers,  # type: ignore
         preprocessing=BypassPreprocessing(),
         datareader=DefaultDataReader(),
     )
@@ -127,7 +127,7 @@ def test_marker_collection_with_preprocessing() -> None:
         ),
     ]
     mc = MarkerCollection(
-        markers=markers,
+        markers=markers,  # type: ignore
         preprocessing=fMRIPrepConfoundRemover(),
     )
     assert mc._markers == markers
@@ -173,7 +173,7 @@ def test_marker_collection_storage(tmp_path: Path) -> None:
     uri = tmp_path / "test_marker_collection_storage.sqlite"
     storage = SQLiteFeatureStorage(uri=uri)
     mc = MarkerCollection(
-        markers=markers,
+        markers=markers,  # type: ignore
         storage=storage,
         datareader=DefaultDataReader(),
     )
@@ -185,7 +185,9 @@ def test_marker_collection_storage(tmp_path: Path) -> None:
         out = mc.fit(input)
         assert out is None
 
-    mc2 = MarkerCollection(markers=markers, datareader=DefaultDataReader())
+    mc2 = MarkerCollection(
+        markers=markers, datareader=DefaultDataReader()  # type: ignore
+    )
     mc2.validate(dg)
     assert mc2._storage is None
 

junifer/markers/tests/test_marker_utils.py

@@ -27,13 +27,14 @@ def test_ets() -> None:
     n_edges = int(n_rois * (n_rois - 1) / 2)
 
     # test without labels
-    edge_ts = _ets(bold_ts)
+    edge_ts, _ = _ets(bold_ts)
     assert edge_ts.shape == (n_time, n_edges)
 
     # test with labels
     roi_labels = [f"Label_{x}" for x in range(n_rois)]
     edge_ts, edge_labels = _ets(bold_ts, roi_labels)
     assert edge_ts.shape == (n_time, n_edges)
+    assert edge_labels is not None
     assert len(edge_labels) == n_edges
 
 

junifer/markers/tests/test_parcel_aggregation.py

@@ -229,7 +229,7 @@ def test_ParcelAggregation_3D_mask() -> None:
     marker = ParcelAggregation(
         parcellation="Schaefer100x7",
         method="mean",
-        mask="GM_prob0.2",
+        masks="GM_prob0.2",
         name="gmd_schaefer100x7_mean",
         on="VBM_GM",
     )  # Test passing "on" as a keyword argument
@@ -274,7 +274,7 @@ def test_ParcelAggregation_3D_mask_computed() -> None:
     marker = ParcelAggregation(
         parcellation="Schaefer100x7",
         method="mean",
-        mask={"compute_brain_mask": {"threshold": 0.2}},
+        masks={"compute_brain_mask": {"threshold": 0.2}},
         name="gmd_schaefer100x7_mean",
         on="VBM_GM",
     )  # Test passing "on" as a keyword argument

junifer/markers/tests/test_sphere_aggregation.py

@@ -159,7 +159,7 @@ def test_SphereAggregation_3D_mask() -> None:
         method="mean",
         radius=RADIUS,
         on="VBM_GM",
-        mask="GM_prob0.2",
+        masks="GM_prob0.2",
     )
     input = {"VBM_GM": {"data": img, "meta": {}}}
     jun_values4d = marker.fit_transform(input)["VBM_GM"]["data"]

junifer/markers/utils.py

@@ -7,7 +7,7 @@
 #          Federico Raimondo <f.raimondo@fz-juelich.de>
 # License: AGPL
 
-from typing import Any, Callable, Dict, List, Type, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
 
 import numpy as np
 import pandas as pd
@@ -56,8 +56,9 @@ def singleton(cls: Type) -> Type:
 
 
 def _ets(
-    bold_ts: np.ndarray, roi_names: Union[None, List[str]] = None
+    bold_ts: np.ndarray,
-) -> np.ndarray:
+    roi_names: Union[None, List[str]] = None,
+) -> Tuple[np.ndarray, Optional[List[str]]]:
     """Compute the edge-wise time series based on BOLD time series.
 
     Take a timeseries of brain areas, and calculate timeseries for each
@@ -80,9 +81,8 @@ def _ets(
         edge-wise time series, i.e. estimate of functional connectivity at each
         time point.
     edge_names : List[str]
-        List of edge names corresponding to columns in the
+        List of edge names corresponding to columns in the edge-wise time
-        edge-wise time series. This is only returned if the roi_names
+        series. If roi_names are not specified, this is None.
-        are specified.
 
     References
     ----------
@@ -102,18 +102,18 @@ def _ets(
     ets = timeseries[:, u] * timeseries[:, v]
     # Obtain the corresponding edge labels if specified else return
     if roi_names is None:
-        return ets
+        return ets, None
     else:
         if len(roi_names) != n_roi:
             raise_error(
                 "List of roi names does not correspond "
                 "to the number of ROIs in the timeseries!"
             )
-        roi_names = np.array(roi_names)
+        _roi_names = np.array(roi_names)
         edge_names = [
-            "~".join([x, y]) for x, y in zip(roi_names[u], roi_names[v])
+            "~".join([x, y]) for x, y in zip(_roi_names[u], _roi_names[v])
         ]
-        return ets, list(edge_names)
+        return ets, edge_names
 
 
 def _correlate_dataframes(

junifer/preprocess/base.py

@@ -115,12 +115,22 @@ class BasePreprocessor(ABC, PipelineStepMixin, UpdateMetaMixin):
             if type_ in input.keys():
                 logger.info(f"Computing {type_}")
                 t_input = input[type_]
-                extra_input = input.copy()
+
+                # Pass the other data types as extra input, removing
+                # the current type
+                extra_input = input
                 extra_input.pop(type_)
                 key, t_out = self.preprocess(
                     input=t_input, extra_input=extra_input
                 )
+
+                # Add the output to the Junifer Data object
                 out[key] = t_out
+
+                # In case we are creating a new type, re-add the original input
+                if key != type_:
+                    out[type_] = t_input
+
                 self.update_meta(out[key], "preprocess")
         return out
 

junifer/preprocess/confounds/fmriprep_confound_remover.py

@@ -11,9 +11,9 @@ import numpy as np
 import pandas as pd
 from nilearn._utils.niimg_conversions import check_niimg_4d
 from nilearn.image import clean_img
-from nilearn.masking import compute_brain_mask
 
 from ...api.decorators import register_preprocessor
+from ...data import get_mask
 from ...utils import logger, raise_error
 from ..base import BasePreprocessor
 
@@ -134,11 +134,10 @@ class fMRIPrepConfoundRemover(BasePreprocessor):
     t_r : float, optional
         Repetition time, in second (sampling period).
         If None, it will use t_r from nifti header (default None).
-    mask_img: Niimg-like object, optional
+    masks : str, dict or list of dict or str, optional
-        If provided, signal is only cleaned from voxels inside the mask.
+        The specification of the masks to apply to regions before extracting
-        If mask is provided, it should have same shape and affine as imgs.
+        signals. Check :ref:`Using Masks <using_masks>` for more details.
-        If not provided, a mask is computed using
+        If None, will not apply any mask (default None).
-        :func:`nilearn.masking.compute_brain_mask` (default None).
 
     """
 
@@ -153,7 +152,7 @@ class fMRIPrepConfoundRemover(BasePreprocessor):
         low_pass: Optional[float] = None,
         high_pass: Optional[float] = None,
         t_r: Optional[float] = None,
-        mask_img: Optional["Nifti1Image"] = None,
+        masks: Union[str, Dict, List[Union[Dict, str]], None] = None,
     ) -> None:
         """Initialise the class."""
         if strategy is None:
@@ -169,7 +168,7 @@ class fMRIPrepConfoundRemover(BasePreprocessor):
         self.low_pass = low_pass
         self.high_pass = high_pass
         self.t_r = t_r
-        self.mask_img = mask_img
+        self.masks = masks
 
         self._valid_components = ["motion", "wm_csf", "global_signal"]
         self._valid_confounds = ["basic", "power2", "derivatives", "full"]
@@ -521,19 +520,20 @@ class fMRIPrepConfoundRemover(BasePreprocessor):
             raise ValueError(f"Invalid confounds format {t_format}")
 
     def _remove_confounds(
-        self, bold_img: "Nifti1Image", confounds_df: pd.DataFrame
+        self,
+        input: Dict[str, Any],
+        extra_input: Optional[Dict[str, Any]] = None,
     ) -> Union["Nifti1Image", "Nifti2Image", "MGHImage", List]:
         """Remove confounds from the BOLD image.
 
         Parameters
         ----------
-        bold_img : Niimg-like object
+        input : dict
-            4D image. The signals in the last dimension are filtered
+            Dictionary containing the ``BOLD`` value from the
-            (see http://nilearn.github.io/manipulating_images/input_output.html
+            Junifer Data object.
-            for a detailed description of the valid input types).
+        extra_input : dict, optional
-        confounds_df : pd.DataFrame
+            Dictionary containing the rest of the Junifer Data object. Must
-            Dataframe containing confounds to remove. Number of rows should
+            include the ``BOLD_confounds`` key.
-            correspond to number of volumes in the BOLD image.
 
         Returns
         --------
@@ -541,8 +541,11 @@ class fMRIPrepConfoundRemover(BasePreprocessor):
             Input image with confounds removed.
 
         """
+        assert extra_input is not None  # Not the case, data is validated
+        confounds_df = self._pick_confounds(extra_input["BOLD_confounds"])
         confounds_array = confounds_df.values
 
+        bold_img = input["data"]
         t_r = self.t_r
         if t_r is None:
             logger.info("No `t_r` specified, using t_r from nifti header")
@@ -552,10 +555,17 @@ class fMRIPrepConfoundRemover(BasePreprocessor):
                 f"Read t_r from nifti header: {t_r}",
             )
 
-        mask_img = self.mask_img
+        mask_img = None
-        if mask_img is None:
+        if self.masks is not None:
-            logger.info("Computing brain mask from image")
+            logger.debug(f"Masking with {self.masks}")
-            mask_img = compute_brain_mask(bold_img)
+            mask_img = get_mask(
+                masks=self.masks, target_data=input, extra_input=extra_input
+            )
+            # Save the mask in the extra input and link it to the bold data
+            # this allows to use "inherit" down the pipeline
+            if extra_input is not None:
+                extra_input["BOLD_mask"] = {"data": mask_img}
+                input["mask_item"] = "BOLD_mask"
 
         logger.info("Cleaning image")
         logger.debug(f"\tdetrend: {self.detrend}")
@@ -600,8 +610,5 @@ class fMRIPrepConfoundRemover(BasePreprocessor):
 
         """
         self._validate_data(input, extra_input)
-        assert extra_input is not None
+        input["data"] = self._remove_confounds(input, extra_input=extra_input)
-        bold_img = input["data"]
-        confounds_df = self._pick_confounds(extra_input["BOLD_confounds"])
-        input["data"] = self._remove_confounds(bold_img, confounds_df)
         return "BOLD", input

junifer/preprocess/confounds/tests/test_fmriprep_confound_remover.py

@@ -452,10 +452,10 @@ def test_fMRIPrepConfoundRemover__remove_confounds() -> None:
     with PartlyCloudyTestingDataGrabber(reduce_confounds=False) as dg:
         input = dg["sub-01"]
         input = reader.fit_transform(input)
-        confounds = confound_remover._pick_confounds(input["BOLD_confounds"])
         raw_bold = input["BOLD"]["data"]
+        extra_input = {k: v for k, v in input.items() if k != "BOLD"}
         clean_bold = confound_remover._remove_confounds(
-            bold_img=raw_bold, confounds_df=confounds
+            input=input["BOLD"], extra_input=extra_input
         )
         clean_bold = typing.cast(nib.Nifti1Image, clean_bold)
         # TODO: Find a better way to test functionality here
@@ -533,7 +533,63 @@ def test_fMRIPrepConfoundRemover_fit_transform() -> None:
         assert t_meta["low_pass"] is None
         assert t_meta["high_pass"] is None
         assert t_meta["t_r"] is None
-        assert t_meta["mask_img"] is None
+        assert t_meta["masks"] is None
+
+        assert "dependencies" in output["BOLD"]["meta"]
+        dependencies = output["BOLD"]["meta"]["dependencies"]
+        assert dependencies == {"numpy", "nilearn"}
+
+
+def test_fMRIPrepConfoundRemover_fit_transform_masks() -> None:
+    """Test fMRIPrepConfoundRemover with all confounds present."""
+
+    # need reader for the data
+    reader = DefaultDataReader()
+    # All strategies full, no spike
+    confound_remover = fMRIPrepConfoundRemover(
+        masks={"compute_brain_mask": {"threshold": 0.2}}
+    )
+
+    with PartlyCloudyTestingDataGrabber(reduce_confounds=False) as dg:
+        input = dg["sub-01"]
+        input = reader.fit_transform(input)
+        orig_bold = input["BOLD"]["data"].get_fdata().copy()
+        output = confound_remover.fit_transform(input)
+        trans_bold = output["BOLD"]["data"].get_fdata()
+        # Transformation is in place
+        assert_array_equal(trans_bold, input["BOLD"]["data"].get_fdata())
+
+        # Data should have the same shape
+        assert orig_bold.shape == trans_bold.shape
+
+        # but be different
+        assert_raises(
+            AssertionError, assert_array_equal, orig_bold, trans_bold
+        )
+
+        assert "meta" in output["BOLD"]
+        assert "preprocess" in output["BOLD"]["meta"]
+        t_meta = output["BOLD"]["meta"]["preprocess"]
+        assert t_meta["class"] == "fMRIPrepConfoundRemover"
+        # It should have all the default parameters
+        assert t_meta["strategy"] == confound_remover.strategy
+        assert t_meta["spike"] is None
+        assert t_meta["detrend"] is True
+        assert t_meta["standardize"] is True
+        assert t_meta["low_pass"] is None
+        assert t_meta["high_pass"] is None
+        assert t_meta["t_r"] is None
+        assert isinstance(t_meta["masks"], dict)
+        assert t_meta["masks"] is not None
+        assert len(t_meta["masks"]) == 1
+        assert "compute_brain_mask" in t_meta["masks"]
+        assert len(t_meta["masks"]["compute_brain_mask"]) == 1
+        assert "threshold" in t_meta["masks"]["compute_brain_mask"]
+        assert t_meta["masks"]["compute_brain_mask"]["threshold"] == 0.2
+
+        assert "BOLD_mask" in output
+        assert "mask_item" in output["BOLD"]
+        assert output["BOLD"]["mask_item"] == "BOLD_mask"
 
         assert "dependencies" in output["BOLD"]["meta"]
         dependencies = output["BOLD"]["meta"]["dependencies"]