diff --git a/applications/nilearn/nilearn.ipynb b/applications/nilearn/nilearn.ipynb
index 08f1cc8e3d9b0d8ef211ff69debf012253a2e31c..7d966e2b8ee7845a82f8f143464162b38a9cc90b 100644
--- a/applications/nilearn/nilearn.ipynb
+++ b/applications/nilearn/nilearn.ipynb
@@ -40,6 +40,7 @@
     "from nilearn import plotting, datasets, surface\n",
     "import matplotlib as mpl\n",
     "import matplotlib.pyplot as plt\n",
+    "import nibabel as nb\n",
     "%matplotlib inline\n",
     "\n",
     "# get path to FSL installation for the FSLDIR environment variable\n",
@@ -181,6 +182,49 @@
     "display.savefig('myplot.png')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`nilearn` plotting is built upon `matplotlib`, so we can use constructs from `matplotlib` to help us create more complex figures.  \n",
+    "\n",
+    "In this example we:\n",
+    "1. create a 1x2 grid of subplots using `subplots` from `matplotlib`\n",
+    "2. plot a single slice of the MNI152 T1w in the first subplot using `plot_anat` from `nilearn`\n",
+    "3. plot a histogram of the intensities of the MNI152 T12 in the second subplot using `hist` from `matplotlib`\n",
+    "4. style the histogram by setting the x/y labels\n",
+    "\n",
+    "> **NOTE:** Here we use `load` and `get_fdata` from the [`nibabel`](https://nipy.org/nibabel/) package to load the data from the MNI152 T1w nifti for the histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create matplotlib figure with 1x2 subplots\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "\n",
+    "# plot MNI T1w slice in first subplot\n",
+    "mni_t1 = f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'\n",
+    "\n",
+    "display = plotting.plot_anat(\n",
+    "    mni_t1, \n",
+    "    dim=-0.5, \n",
+    "    axes=ax[0],\n",
+    "    display_mode='z', \n",
+    "    cut_coords=[15]\n",
+    ")\n",
+    "\n",
+    "# plot histogram of MNI T1w intensity in second subplot\n",
+    "mni_t1_data = nb.load(mni_t1).get_fdata().ravel()\n",
+    "\n",
+    "ax[1].hist(mni_t1_data, bins=25)\n",
+    "ax[1].set_ylabel('count')\n",
+    "ax[1].set_xlabel('intensity')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -434,7 +478,7 @@
     ">\n",
     "> **Exercise:**\n",
     "> \n",
-    "> Visualise thee **Harvard-Oxford** atlas on a surface in the weeb Browser"
+    "> Visualise thee **Harvard-Oxford** atlas on a surface in the web browser"
    ]
   },
   {
@@ -470,7 +514,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,