[DOC] link terms to glossary

doc/building_blocks/manual_pipeline.rst

@@ -83,7 +83,7 @@ For example, we will now consider only the conditions *cat* and *face* from our
 This can be done as follows:
 
 .. literalinclude:: ../../examples/00_tutorials/plot_decoding_tutorial.py
-    :start-after: # (i.e. :term:`fmri<fMRI>` signal is shorter):
+    :start-after: # (i.e. :term:`fMRI` signal is shorter):
     :end-before: # %%
 
 

doc/manipulating_images/manipulating_images.rst

@@ -171,7 +171,7 @@ can be computed from the data:
 
 
 .. literalinclude:: ../../examples/01_plotting/plot_visualization.py
-     :start-after: # Simple computation of a mask from the fMRI data
+     :start-after: # Simple computation of a mask from the :term:`fMRI` data
      :end-before: # Applying the mask to extract the corresponding time series
 
 .. figure:: ../auto_examples/01_plotting/images/sphx_glr_plot_visualization_002.png

doc/manipulating_images/masker_objects.rst

@@ -101,7 +101,7 @@ slice and create a :ref:`Niimg <niimg>` in memory:
 
 
 .. literalinclude:: ../../examples/06_manipulating_images/plot_mask_computation.py
-    :start-after: Load movie watching based brain development fmri dataset
+    :start-after: Load movie watching based brain development fMRI dataset
     :end-before: # To display the background
 
 Controlling how the mask is computed from the data
@@ -151,7 +151,7 @@ opened in a new browser tab using ``report.open_in_browser()``,
 or saved as a portable HTML file ``report.save_as_html(output_filepath)``.
 
 .. literalinclude:: ../../examples/06_manipulating_images/plot_mask_computation.py
-    :start-after: # We need to specify an 'epi' mask_strategy, as this is raw EPI data
+    :start-after: # We need to specify an 'epi' mask_strategy, as this is raw :term:`EPI` data
     :end-before: # Generate mask with strong opening
 
 .. figure:: /images/niftimasker_report.png

examples/08_experimental/plot_surface_image_and_maskers.py

@@ -66,7 +66,7 @@ def plot_surf_img(
 
 ###############################################################################
 # Connectivity with a surface atlas and `SurfaceLabelsMasker`
-# ----------------------------------------------------------
+# -----------------------------------------------------------
 from nilearn import connectome, plotting
 
 img = surface.fetch_nki()[0]
@@ -136,7 +136,7 @@ def check_embedded_nifti_masker(estimator, *args, **kwargs):
 
 ###############################################################################
 # Decoding with a scikit-learn `Pipeline`
-# --------------------------------------
+# ---------------------------------------
 import numpy as np
 from sklearn import feature_selection, linear_model, pipeline, preprocessing