We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[DOC] Minor discrepancy in comparing means example
### Is there an existing issue for this?

- [X] I have searched the existing issues

### Describe your proposed suggestion in detail.

Using a movie watching dataset as an example but calling it "resting state" in several places

### List any pages that would be impacted.

The "comparing means of images with `math_img`" example: https://nilearn.github.io/stable/auto_examples/06_manipulating_images/plot_compare_mean_image.html

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/06_manipulating_images/plot_compare_mean_image.py`
Content:
```
1 """
2 Comparing the means of 2 images
3 ===============================
4 
5 The goal of this example is to illustrate the use of the function
6 :func:`nilearn.image.math_img` with a list of images as input.
7 We compare the means of 2 resting state 4D images. The mean of the images
8 could have been computed with nilearn :func:`nilearn.image.mean_img` function.
9 """
10 
11 # %%
12 # Fetching 2 subject movie watching brain development :term:`fMRI` datasets.
13 from nilearn import datasets
14 
15 dataset = datasets.fetch_development_fmri(n_subjects=2)
16 
17 
18 # %%
19 # Print basic information on the adhd subjects resting state datasets.
20 print(f"Subject 1 resting state dataset at: {dataset.func[0]}")
21 print(f"Subject 2 resting state dataset at: {dataset.func[1]}")
22 
23 
24 # %%
25 # Comparing the means of the 2 movie watching datasets.
26 from nilearn import image, plotting
27 
28 result_img = image.math_img(
29     "np.mean(img1, axis=-1) - np.mean(img2, axis=-1)",
30     img1=dataset.func[0],
31     img2=dataset.func[1],
32 )
33 
34 plotting.plot_stat_map(
35     result_img, title="Comparing means of 2 resting state 4D images."
36 )
37 plotting.show()
38
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/examples/06_manipulating_images/plot_compare_mean_image.py b/examples/06_manipulating_images/plot_compare_mean_image.py
--- a/examples/06_manipulating_images/plot_compare_mean_image.py
+++ b/examples/06_manipulating_images/plot_compare_mean_image.py
@@ -4,7 +4,7 @@
 
 The goal of this example is to illustrate the use of the function
 :func:`nilearn.image.math_img` with a list of images as input.
-We compare the means of 2 resting state 4D images. The mean of the images
+We compare the means of 2 movie watching 4D images. The mean of the images
 could have been computed with nilearn :func:`nilearn.image.mean_img` function.
 """
 
@@ -16,9 +16,9 @@
 
 
 # %%
-# Print basic information on the adhd subjects resting state datasets.
-print(f"Subject 1 resting state dataset at: {dataset.func[0]}")
-print(f"Subject 2 resting state dataset at: {dataset.func[1]}")
+# Print file locations for both subjects.
+print(f"Subject 1 dataset at: {dataset.func[0]}")
+print(f"Subject 2 dataset at: {dataset.func[1]}")
 
 
 # %%
@@ -32,6 +32,6 @@
 )
 
 plotting.plot_stat_map(
-    result_img, title="Comparing means of 2 resting state 4D images."
+    result_img, title="Comparing means of 2 movie watching 4D images."
 )
 plotting.show()

{"golden_diff": "diff --git a/examples/06_manipulating_images/plot_compare_mean_image.py b/examples/06_manipulating_images/plot_compare_mean_image.py\n--- a/examples/06_manipulating_images/plot_compare_mean_image.py\n+++ b/examples/06_manipulating_images/plot_compare_mean_image.py\n@@ -4,7 +4,7 @@\n \n The goal of this example is to illustrate the use of the function\n :func:`nilearn.image.math_img` with a list of images as input.\n-We compare the means of 2 resting state 4D images. The mean of the images\n+We compare the means of 2 movie watching 4D images. The mean of the images\n could have been computed with nilearn :func:`nilearn.image.mean_img` function.\n \"\"\"\n \n@@ -16,9 +16,9 @@\n \n \n # %%\n-# Print basic information on the adhd subjects resting state datasets.\n-print(f\"Subject 1 resting state dataset at: {dataset.func[0]}\")\n-print(f\"Subject 2 resting state dataset at: {dataset.func[1]}\")\n+# Print file locations for both subjects.\n+print(f\"Subject 1 dataset at: {dataset.func[0]}\")\n+print(f\"Subject 2 dataset at: {dataset.func[1]}\")\n \n \n # %%\n@@ -32,6 +32,6 @@\n )\n \n plotting.plot_stat_map(\n-    result_img, title=\"Comparing means of 2 resting state 4D images.\"\n+    result_img, title=\"Comparing means of 2 movie watching 4D images.\"\n )\n plotting.show()\n", "issue": "[DOC] Minor discrepancy in comparing means example\n### Is there an existing issue for this?\n\n- [X] I have searched the existing issues\n\n### Describe your proposed suggestion in detail.\n\nUsing a movie watching dataset as an example but calling it \"resting state\" in several places\n\n### List any pages that would be impacted.\n\nThe \"comparing means of images with `math_img`\" example: https://nilearn.github.io/stable/auto_examples/06_manipulating_images/plot_compare_mean_image.html\n", "before_files": [{"content": "\"\"\"\nComparing the means of 2 images\n===============================\n\nThe goal of this example is to illustrate the use of the function\n:func:`nilearn.image.math_img` with a list of images as input.\nWe compare the means of 2 resting state 4D images. The mean of the images\ncould have been computed with nilearn :func:`nilearn.image.mean_img` function.\n\"\"\"\n\n# %%\n# Fetching 2 subject movie watching brain development :term:`fMRI` datasets.\nfrom nilearn import datasets\n\ndataset = datasets.fetch_development_fmri(n_subjects=2)\n\n\n# %%\n# Print basic information on the adhd subjects resting state datasets.\nprint(f\"Subject 1 resting state dataset at: {dataset.func[0]}\")\nprint(f\"Subject 2 resting state dataset at: {dataset.func[1]}\")\n\n\n# %%\n# Comparing the means of the 2 movie watching datasets.\nfrom nilearn import image, plotting\n\nresult_img = image.math_img(\n    \"np.mean(img1, axis=-1) - np.mean(img2, axis=-1)\",\n    img1=dataset.func[0],\n    img2=dataset.func[1],\n)\n\nplotting.plot_stat_map(\n    result_img, title=\"Comparing means of 2 resting state 4D images.\"\n)\nplotting.show()\n", "path": "examples/06_manipulating_images/plot_compare_mean_image.py"}], "after_files": [{"content": "\"\"\"\nComparing the means of 2 images\n===============================\n\nThe goal of this example is to illustrate the use of the function\n:func:`nilearn.image.math_img` with a list of images as input.\nWe compare the means of 2 movie watching 4D images. The mean of the images\ncould have been computed with nilearn :func:`nilearn.image.mean_img` function.\n\"\"\"\n\n# %%\n# Fetching 2 subject movie watching brain development :term:`fMRI` datasets.\nfrom nilearn import datasets\n\ndataset = datasets.fetch_development_fmri(n_subjects=2)\n\n\n# %%\n# Print file locations for both subjects.\nprint(f\"Subject 1 dataset at: {dataset.func[0]}\")\nprint(f\"Subject 2 dataset at: {dataset.func[1]}\")\n\n\n# %%\n# Comparing the means of the 2 movie watching datasets.\nfrom nilearn import image, plotting\n\nresult_img = image.math_img(\n    \"np.mean(img1, axis=-1) - np.mean(img2, axis=-1)\",\n    img1=dataset.func[0],\n    img2=dataset.func[1],\n)\n\nplotting.plot_stat_map(\n    result_img, title=\"Comparing means of 2 movie watching 4D images.\"\n)\nplotting.show()\n", "path": "examples/06_manipulating_images/plot_compare_mean_image.py"}]}