diff --git a/CHANGELOG.md b/CHANGELOG.md
index ce200d012..229604909 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -14,6 +14,8 @@
   [#1586](https://github.com/NeurodataWithoutBorders/pynwb/pull/1586)
 - More informative error message for common installation error. @bendichter, @rly
   [#1591](https://github.com/NeurodataWithoutBorders/pynwb/pull/1591)
+- Update the [images tutorial](https://pynwb.readthedocs.io/en/stable/tutorials/domain/images.html) to provide example usage of an ``IndexSeries``
+  with a reference to ``Images``. @bendichter [#1602](https://github.com/NeurodataWithoutBorders/pynwb/pull/1602)
 
 ## PyNWB 2.2.0 (October 19, 2022)
 
diff --git a/docs/gallery/domain/images.py b/docs/gallery/domain/images.py
index 0705cac1d..ab0b4ac50 100644
--- a/docs/gallery/domain/images.py
+++ b/docs/gallery/domain/images.py
@@ -245,6 +245,58 @@
 nwbfile.add_acquisition(images)
 
 ####################
+# IndexSeries for repeated images
+# -------------------------------
+#
+# You may want to set up a time series of images where some images are repeated many
+# times. You could create an :py:class:`~pynwb.image.ImageSeries` that repeats the data
+# each time the image is shown, but that would be inefficient, becuase it would store
+# the same data multiple times. A better solution would be to store the unique images
+# once and reference those images. This is how :py:class:`~pynwb.image.IndexSeries`
+# works. First, create an :py:class:`~pynwb.base.Images` container with the order of
+# images defined using an :py:class:`~pynwb.base.ImageReferences`. Then create an
+# :py:class:`~pynwb.image.IndexSeries` that indexes into the
+# :py:class:`~pynwb.base.Images`.
+
+from scipy import misc
+
+from pynwb.image import Images, IndexSeries, GrayscaleImage, RGBImage
+from pynwb.base import ImageReferences
+
+
+gs_face = GrayscaleImage(
+    name="gs_face",
+    data=misc.face(gray=True),
+    description="Grayscale version of a raccoon.",
+    resolution=35.433071,
+)
+
+rgb_face = RGBImage(
+    name="rgb_face",
+    data=misc.face(),
+    resolution=70.0,
+    description="RGB version of a raccoon.",
+)
+
+images = Images(
+    name="raccoons",
+    images=[rgb_face, gs_face],
+    description="A collection of raccoons.",
+    order_of_images=ImageReferences("order_of_images", [rgb_face, gs_face]),
+)
+
+idx_series = IndexSeries(
+    name="stimuli",
+    data=[0, 1, 0, 1],
+    indexed_images=images,
+    unit="N/A",
+    timestamps=[.1, .2, .3, .4],
+)
+
+####################
+# Here `data` contains the (0-indexed) index of the displayed image as they are ordered
+# in the :py:class:`~pynwb.base.ImageReference`.
+#
 # Writing the images to an NWB File
 # ---------------------------------------
 # As demonstrated in the :ref:`basic_writing` tutorial, we will use :py:class:`~pynwb.NWBHDF5IO`
diff --git a/src/pynwb/image.py b/src/pynwb/image.py
index c399289ed..0295183b8 100644
--- a/src/pynwb/image.py
+++ b/src/pynwb/image.py
@@ -212,23 +212,46 @@ class IndexSeries(TimeSeries):
     array indicates when that image was displayed.
     '''
 
-    __nwbfields__ = ('indexed_timeseries',)
+    __nwbfields__ = ("indexed_timeseries",)
 
     # # value used when an ImageSeries is read and missing data
     # DEFAULT_UNIT = 'N/A'
 
-    @docval(*get_docval(TimeSeries.__init__, 'name'),  # required
-            {'name': 'data', 'type': ('array_data', 'data', TimeSeries), 'shape': (None, ),  # required
-            'doc': ('The data values. Must be 1D, where the first dimension must be time (frame)')},
-            *get_docval(TimeSeries.__init__, 'unit'),  # required
-            {'name': 'indexed_timeseries', 'type': TimeSeries,  # required
-             'doc': 'Link to TimeSeries containing images that are indexed.', 'default': None},
-            {'name': 'indexed_images', 'type': Images,  # required
-             'doc': ("Link to Images object containing an ordered set of images that are indexed. The Images object "
-                     "must contain a 'ordered_images' dataset specifying the order of the images in the Images type."),
-             'default': None},
-            *get_docval(TimeSeries.__init__, 'resolution', 'conversion', 'timestamps', 'starting_time', 'rate',
-                        'comments', 'description', 'control', 'control_description', 'offset'))
+    @docval(
+        *get_docval(TimeSeries.__init__, 'name'),  # required
+        {
+            'name': 'data',
+            'type': ('array_data', 'data', TimeSeries),
+            'shape': (None,),  # required
+            'doc': 'The data values. Must be 1D, where the first dimension must be time (frame)',
+        },
+        *get_docval(TimeSeries.__init__, 'unit'),  # required
+        {
+            'name': 'indexed_timeseries', 'type': TimeSeries,  # required
+            'doc': 'Link to TimeSeries containing images that are indexed.',
+            'default': None,
+        },
+        {
+            'name': 'indexed_images',
+            'type': Images,  # required
+            'doc': "Link to Images object containing an ordered set of images that are indexed. The Images object must "
+                   "contain a 'ordered_images' dataset specifying the order of the images in the Images type.",
+            'default': None
+        },
+        *get_docval(
+            TimeSeries.__init__,
+            'resolution',
+            'conversion',
+            'timestamps',
+            'starting_time',
+            'rate',
+            'comments',
+            'description',
+            'control',
+            'control_description',
+            'offset',
+        ),
+    )
     def __init__(self, **kwargs):
         indexed_timeseries, indexed_images = popargs('indexed_timeseries', 'indexed_images', kwargs)
         if kwargs['unit'] and kwargs['unit'] != 'N/A':