[DOC] fix rendering hrf list (nilearn#4784)

nilearn/_utils/docs.py

@@ -370,45 +370,52 @@ def custom_function(vertices):
     implemented in Nilearn.
     Valid names are:
 
-        - `"spm"`: This is the :term:`HRF` model used in :term:`SPM`.
-          See :func:`nilearn.glm.first_level.spm_hrf`.
-        - `"spm + derivative"`: SPM model plus its time derivative.
-          This gives 2 regressors.
-          See :func:`nilearn.glm.first_level.spm_hrf`, and
-          :func:`nilearn.glm.first_level.spm_time_derivative`.
-        - `"spm + derivative + dispersion"`: Idem, plus dispersion derivative.
-          This gives 3 regressors.
-          See :func:`nilearn.glm.first_level.spm_hrf`,
-          :func:`nilearn.glm.first_level.spm_time_derivative`,
-          and :func:`nilearn.glm.first_level.spm_dispersion_derivative`.
-        - `"glover"`: This corresponds to the Glover :term:`HRF`.
-          See :func:`nilearn.glm.first_level.glover_hrf`.
-        - `"glover + derivative"`: The Glover :term:`HRF` + time derivative.
-          This gives 2 regressors.
-          See :func:`nilearn.glm.first_level.glover_hrf`, and
-          :func:`nilearn.glm.first_level.glover_time_derivative`.
-        - `"glover"+ derivative + dispersion"`:
-          Idem, plus dispersion derivative.
-          This gives 3 regressors.
-          See :func:`nilearn.glm.first_level.glover_hrf`,
-          :func:`nilearn.glm.first_level.glover_time_derivative`, and
-          :func:`nilearn.glm.first_level.glover_dispersion_derivative`.
-        - `"fir"`: Finite impulse response basis.
-          This is a set of delayed dirac models.
+    - ``"spm"``:
+        This is the :term:`HRF` model used in :term:`SPM`.
+        See :func:`~nilearn.glm.first_level.spm_hrf`.
+    - ``"spm + derivative"``:
+        SPM model plus its time derivative.
+        This gives 2 regressors.
+        See :func:`~nilearn.glm.first_level.spm_hrf`, and
+        :func:`~nilearn.glm.first_level.spm_time_derivative`.
+    - ``"spm + derivative + dispersion"``:
+        Same as above plus dispersion derivative.
+        This gives 3 regressors.
+        See :func:`~nilearn.glm.first_level.spm_hrf`,
+        :func:`nilearn.glm.first_level.spm_time_derivative`,
+        and :func:`~nilearn.glm.first_level.spm_dispersion_derivative`.
+    - ``"glover"``:
+        This corresponds to the Glover :term:`HRF`.
+        See :func:`~nilearn.glm.first_level.glover_hrf`.
+    - ``"glover + derivative"``:
+        The Glover :term:`HRF` + time derivative.
+        This gives 2 regressors.
+        See :func:`~nilearn.glm.first_level.glover_hrf`, and
+        :func:`~nilearn.glm.first_level.glover_time_derivative`.
+    - ``"glover"+ derivative + dispersion"``:
+        Same as above plus dispersion derivative.
+        This gives 3 regressors.
+        See :func:`~nilearn.glm.first_level.glover_hrf`,
+        :func:`~nilearn.glm.first_level.glover_time_derivative`, and
+        :func:`~nilearn.glm.first_level.glover_dispersion_derivative`.
+    - ``"fir"``:
+        Finite impulse response basis.
+        This is a set of delayed dirac models.
 
     It can also be a custom model.
     In this case, a function should be provided for each regressor.
     Each function should behave as the other models implemented within Nilearn.
-    That is, it should take both `t_r` and `oversampling` as inputs
+    That is, it should take both ``t_r`` and ``oversampling`` as inputs
     and return a sample numpy array of appropriate shape.
 
     .. note::
-        It is expected that `"spm"` standard and `"glover"` models
+        It is expected that ``"spm"`` standard and ``"glover"`` models
         would not yield large differences in most cases.
 
     .. note::
-        In case of `"glover"` and `"spm"` models, the derived regressors
-        are orthogonalized with respect to the main one.
+        In case of ``"glover"`` and ``"spm"`` models,
+        the derived regressors are orthogonalized
+        with respect to the main one.
 
 """