[DOC] update doc strings of the maskers

nilearn/maskers/multi_nifti_labels_masker.py

@@ -11,12 +11,16 @@
 
 @fill_doc
 class MultiNiftiLabelsMasker(NiftiLabelsMasker):
-    """Class for masking of Niimg-like objects.
+    """Class for extracting data from multiple Niimg-like objects \
+       using labels of non-overlapping brain regions.
 
     MultiNiftiLabelsMasker is useful when data from non-overlapping volumes
     and from different subjects should be extracted (contrary to
     :class:`nilearn.maskers.NiftiLabelsMasker`).
 
+    For more details on the definitions of labels in Nilearn,
+    see the :ref:`region` section.
+
     Parameters
     ----------
     labels_img : Niimg-like object

nilearn/maskers/multi_nifti_maps_masker.py

@@ -11,16 +11,24 @@
 
 @fill_doc
 class MultiNiftiMapsMasker(NiftiMapsMasker):
-    """Class for masking of Niimg-like objects.
+    """Class for extracting data from multiple Niimg-like objects \
+       using maps of potentially overlapping brain regions.
 
     MultiNiftiMapsMasker is useful when data from overlapping volumes
     and from different subjects should be extracted (contrary to
     :class:`nilearn.maskers.NiftiMapsMasker`).
 
+    Use case:
+    summarize brain signals from several subjects
+    from large-scale networks obtained by prior PCA or :term:`ICA`.
+
     .. note::
         Inf or NaN present in the given input images are automatically
         put to zero rather than considered as missing data.
 
+    For more details on the definitions of maps in Nilearn,
+    see the :ref:`region` section.
+
     Parameters
     ----------
     maps_img : 4D niimg-like object

nilearn/maskers/multi_nifti_masker.py

@@ -48,11 +48,14 @@ def _get_mask_strategy(strategy):
 
 @_utils.fill_doc
 class MultiNiftiMasker(NiftiMasker, _utils.CacheMixin):
-    """Class for masking of Niimg-like objects.
+    """Applying a mask to extract time-series from multiple Niimg-like objects.
 
     MultiNiftiMasker is useful when dealing with image sets from multiple
-    subjects. Use case: integrates well with decomposition by MultiPCA and
-    CanICA (multi-subject models)
+    subjects.
+
+    Use case:
+    integrates well with decomposition by MultiPCA and CanICA
+    (multi-subject models)
 
     Parameters
     ----------

nilearn/maskers/nifti_labels_masker.py

@@ -44,13 +44,19 @@ def __call__(self, imgs):
 
 @_utils.fill_doc
 class NiftiLabelsMasker(BaseMasker, _utils.CacheMixin):
-    """Class for masking of Niimg-like objects.
+    """Class for extracting data from Niimg-like objects \
+       using labels of non-overlapping brain regions.
 
     NiftiLabelsMasker is useful when data from non-overlapping volumes should
     be extracted (contrarily to :class:`nilearn.maskers.NiftiMapsMasker`).
-    Use case: Summarize brain signals from clusters that were obtained by prior
+
+    Use case:
+    summarize brain signals from clusters that were obtained by prior
     K-means or Ward clustering.
 
+    For more details on the definitions of labels in Nilearn,
+    see the :ref:`region` section.
+
     Parameters
     ----------
     labels_img : Niimg-like object

nilearn/maskers/nifti_maps_masker.py

@@ -33,15 +33,22 @@ def __call__(self, imgs):
 
 @_utils.fill_doc
 class NiftiMapsMasker(BaseMasker, _utils.CacheMixin):
-    """Class for masking of Niimg-like objects.
+    """Class for extracting data from Niimg-like objects \
+       using maps of potentially overlapping brain regions.
 
     NiftiMapsMasker is useful when data from overlapping volumes should be
     extracted (contrarily to :class:`nilearn.maskers.NiftiLabelsMasker`).
-    Use case: Summarize brain signals from large-scale networks obtained by
-    prior PCA or :term:`ICA`.
 
-    Note that, Inf or NaN present in the given input images are automatically
-    put to zero rather than considered as missing data.
+    Use case:
+    summarize brain signals from large-scale networks
+    obtained by prior PCA or :term:`ICA`.
+
+    .. note::
+        Inf or NaN present in the given input images are automatically
+        put to zero rather than considered as missing data.
+
+    For more details on the definitions of maps in Nilearn,
+    see the :ref:`region` section.
 
     Parameters
     ----------

nilearn/maskers/nifti_masker.py

@@ -147,7 +147,9 @@ class NiftiMasker(BaseMasker, _utils.CacheMixin):
 
     NiftiMasker is useful when preprocessing (detrending, standardization,
     resampling, etc.) of in-mask :term:`voxels<voxel>` is necessary.
-    Use case: working with time series of :term:`resting-state` or task maps.
+
+    Use case:
+    working with time series of :term:`resting-state` or task maps.
 
     Parameters
     ----------

nilearn/maskers/nifti_spheres_masker.py

@@ -224,8 +224,10 @@ class NiftiSpheresMasker(BaseMasker, CacheMixin):
     """Class for masking of Niimg-like objects using seeds.
 
     NiftiSpheresMasker is useful when data from given seeds should be
-    extracted. Use case: Summarize brain signals from seeds that were
-    obtained from prior knowledge.
+    extracted.
+
+    Use case:
+    summarize brain signals from seeds that were obtained from prior knowledge.
 
     Parameters
     ----------