We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[DOC] improve instructions in "Default Mode Network extraction of ADHD dataset" example
### Is there an existing issue for this?

- [X] I have searched the existing issues

### Describe your proposed suggestion in detail.

It seems the instructions in [this example](https://nilearn.github.io/dev/auto_examples/04_glm_first_level/plot_adhd_dmn.html#default-mode-network-extraction-of-adhd-dataset) need some improvement. There was a confusion mentioned on [NeuroStar](https://neurostars.org/t/why-is-there-glm-for-resting-state-data/25841). After discussing with @Remi-Gau, we concluded that maybe we can add one or two lines saying that in this example we extract the activity of a seed region and then use the extracted signal as regressor in a GLM and this will yield the correlation of each region with the seed region.

### List any pages that would be impacted.

https://nilearn.github.io/dev/auto_examples/04_glm_first_level/plot_adhd_dmn.html#default-mode-network-extraction-of-adhd-dataset

--- 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/04_glm_first_level/plot_adhd_dmn.py`
Content:
```
1 """
2 Default Mode Network extraction of ADHD dataset
3 ===============================================
4 
5 This example shows a full step-by-step workflow of fitting a GLM to data
6 extracted from a seed on the Posterior Cingulate Cortex and saving the results.
7 
8 More specifically:
9 
10 1. A sequence of fMRI volumes are loaded.
11 2. A design matrix with the Posterior Cingulate Cortex seed is defined.
12 3. A GLM is applied to the dataset (effect/covariance,
13    then contrast estimation).
14 4. The Default Mode Network is displayed.
15 
16 .. include:: ../../../examples/masker_note.rst
17 
18 """
19 import numpy as np
20 from nilearn import datasets, plotting
21 from nilearn.glm.first_level import (
22     FirstLevelModel,
23     make_first_level_design_matrix,
24 )
25 from nilearn.maskers import NiftiSpheresMasker
26 
27 #########################################################################
28 # Prepare data and analysis parameters
29 # ------------------------------------
30 # Prepare the data.
31 adhd_dataset = datasets.fetch_adhd(n_subjects=1)
32 
33 # Prepare timing
34 t_r = 2.0
35 slice_time_ref = 0.0
36 n_scans = 176
37 
38 # Prepare seed
39 pcc_coords = (0, -53, 26)
40 
41 #########################################################################
42 # Estimate contrasts
43 # ------------------
44 # Specify the contrasts.
45 seed_masker = NiftiSpheresMasker(
46     [pcc_coords],
47     radius=10,
48     detrend=True,
49     standardize="zscore_sample",
50     low_pass=0.1,
51     high_pass=0.01,
52     t_r=2.0,
53     memory="nilearn_cache",
54     memory_level=1,
55     verbose=0,
56 )
57 seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])
58 frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)
59 design_matrix = make_first_level_design_matrix(
60     frametimes,
61     hrf_model="spm",
62     add_regs=seed_time_series,
63     add_reg_names=["pcc_seed"],
64 )
65 dmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))
66 contrasts = {"seed_based_glm": dmn_contrast}
67 
68 #########################################################################
69 # Perform first level analysis
70 # ----------------------------
71 # Setup and fit GLM.
72 first_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)
73 first_level_model = first_level_model.fit(
74     run_imgs=adhd_dataset.func[0], design_matrices=design_matrix
75 )
76 
77 #########################################################################
78 # Estimate the contrast.
79 print("Contrast seed_based_glm computed.")
80 z_map = first_level_model.compute_contrast(
81     contrasts["seed_based_glm"], output_type="z_score"
82 )
83 
84 # Saving snapshots of the contrasts
85 filename = "dmn_z_map.png"
86 display = plotting.plot_stat_map(
87     z_map, threshold=3.0, title="Seed based GLM", cut_coords=pcc_coords
88 )
89 display.add_markers(
90     marker_coords=[pcc_coords], marker_color="g", marker_size=300
91 )
92 display.savefig(filename)
93 print(f"Save z-map in '{filename}'.")
94 
95 ###########################################################################
96 # Generating a report
97 # -------------------
98 # It can be useful to quickly generate a
99 # portable, ready-to-view report with most of the pertinent information.
100 # This is easy to do if you have a fitted model and the list of contrasts,
101 # which we do here.
102 from nilearn.reporting import make_glm_report
103 
104 report = make_glm_report(
105     first_level_model,
106     contrasts=contrasts,
107     title="ADHD DMN Report",
108     cluster_threshold=15,
109     min_distance=8.0,
110     plot_type="glass",
111 )
112 
113 #########################################################################
114 # We have several ways to access the report:
115 
116 # report  # This report can be viewed in a notebook
117 # report.save_as_html('report.html')
118 # report.open_in_browser()
119
```
--- 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/04_glm_first_level/plot_adhd_dmn.py b/examples/04_glm_first_level/plot_adhd_dmn.py
--- a/examples/04_glm_first_level/plot_adhd_dmn.py
+++ b/examples/04_glm_first_level/plot_adhd_dmn.py
@@ -2,8 +2,11 @@
 Default Mode Network extraction of ADHD dataset
 ===============================================
 
-This example shows a full step-by-step workflow of fitting a GLM to data
+This example shows a full step-by-step workflow of fitting a GLM to signal
 extracted from a seed on the Posterior Cingulate Cortex and saving the results.
+More precisely, this example shows how to use a signal extracted from a
+seed region as the regressor in a GLM to determine the correlation
+of each region in the dataset with the seed region.
 
 More specifically:
 
@@ -39,9 +42,9 @@
 pcc_coords = (0, -53, 26)
 
 #########################################################################
-# Estimate contrasts
-# ------------------
-# Specify the contrasts.
+# Extract the seed region's time course
+# -------------------------------------
+# Extract the time course of the seed region.
 seed_masker = NiftiSpheresMasker(
     [pcc_coords],
     radius=10,
@@ -56,6 +59,22 @@
 )
 seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])
 frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)
+
+#########################################################################
+# Plot the time course of the seed region.
+import matplotlib.pyplot as plt
+
+fig = plt.figure(figsize=(9, 3))
+ax = fig.add_subplot(111)
+ax.plot(frametimes, seed_time_series, linewidth=2, label="seed region")
+ax.legend(loc=2)
+ax.set_title("Time course of the seed region")
+plt.show()
+
+#########################################################################
+# Estimate contrasts
+# ------------------
+# Specify the contrasts.
 design_matrix = make_first_level_design_matrix(
     frametimes,
     hrf_model="spm",

{"golden_diff": "diff --git a/examples/04_glm_first_level/plot_adhd_dmn.py b/examples/04_glm_first_level/plot_adhd_dmn.py\n--- a/examples/04_glm_first_level/plot_adhd_dmn.py\n+++ b/examples/04_glm_first_level/plot_adhd_dmn.py\n@@ -2,8 +2,11 @@\n Default Mode Network extraction of ADHD dataset\n ===============================================\n \n-This example shows a full step-by-step workflow of fitting a GLM to data\n+This example shows a full step-by-step workflow of fitting a GLM to signal\n extracted from a seed on the Posterior Cingulate Cortex and saving the results.\n+More precisely, this example shows how to use a signal extracted from a\n+seed region as the regressor in a GLM to determine the correlation\n+of each region in the dataset with the seed region.\n \n More specifically:\n \n@@ -39,9 +42,9 @@\n pcc_coords = (0, -53, 26)\n \n #########################################################################\n-# Estimate contrasts\n-# ------------------\n-# Specify the contrasts.\n+# Extract the seed region's time course\n+# -------------------------------------\n+# Extract the time course of the seed region.\n seed_masker = NiftiSpheresMasker(\n     [pcc_coords],\n     radius=10,\n@@ -56,6 +59,22 @@\n )\n seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])\n frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)\n+\n+#########################################################################\n+# Plot the time course of the seed region.\n+import matplotlib.pyplot as plt\n+\n+fig = plt.figure(figsize=(9, 3))\n+ax = fig.add_subplot(111)\n+ax.plot(frametimes, seed_time_series, linewidth=2, label=\"seed region\")\n+ax.legend(loc=2)\n+ax.set_title(\"Time course of the seed region\")\n+plt.show()\n+\n+#########################################################################\n+# Estimate contrasts\n+# ------------------\n+# Specify the contrasts.\n design_matrix = make_first_level_design_matrix(\n     frametimes,\n     hrf_model=\"spm\",\n", "issue": "[DOC] improve instructions in \"Default Mode Network extraction of ADHD dataset\" example\n### Is there an existing issue for this?\r\n\r\n- [X] I have searched the existing issues\r\n\r\n### Describe your proposed suggestion in detail.\r\n\r\nIt seems the instructions in [this example](https://nilearn.github.io/dev/auto_examples/04_glm_first_level/plot_adhd_dmn.html#default-mode-network-extraction-of-adhd-dataset) need some improvement. There was a confusion mentioned on [NeuroStar](https://neurostars.org/t/why-is-there-glm-for-resting-state-data/25841). After discussing with @Remi-Gau, we concluded that maybe we can add one or two lines saying that in this example we extract the activity of a seed region and then use the extracted signal as regressor in a GLM and this will yield the correlation of each region with the seed region.\r\n\r\n### List any pages that would be impacted.\r\n\r\nhttps://nilearn.github.io/dev/auto_examples/04_glm_first_level/plot_adhd_dmn.html#default-mode-network-extraction-of-adhd-dataset\n", "before_files": [{"content": "\"\"\"\nDefault Mode Network extraction of ADHD dataset\n===============================================\n\nThis example shows a full step-by-step workflow of fitting a GLM to data\nextracted from a seed on the Posterior Cingulate Cortex and saving the results.\n\nMore specifically:\n\n1. A sequence of fMRI volumes are loaded.\n2. A design matrix with the Posterior Cingulate Cortex seed is defined.\n3. A GLM is applied to the dataset (effect/covariance,\n   then contrast estimation).\n4. The Default Mode Network is displayed.\n\n.. include:: ../../../examples/masker_note.rst\n\n\"\"\"\nimport numpy as np\nfrom nilearn import datasets, plotting\nfrom nilearn.glm.first_level import (\n    FirstLevelModel,\n    make_first_level_design_matrix,\n)\nfrom nilearn.maskers import NiftiSpheresMasker\n\n#########################################################################\n# Prepare data and analysis parameters\n# ------------------------------------\n# Prepare the data.\nadhd_dataset = datasets.fetch_adhd(n_subjects=1)\n\n# Prepare timing\nt_r = 2.0\nslice_time_ref = 0.0\nn_scans = 176\n\n# Prepare seed\npcc_coords = (0, -53, 26)\n\n#########################################################################\n# Estimate contrasts\n# ------------------\n# Specify the contrasts.\nseed_masker = NiftiSpheresMasker(\n    [pcc_coords],\n    radius=10,\n    detrend=True,\n    standardize=\"zscore_sample\",\n    low_pass=0.1,\n    high_pass=0.01,\n    t_r=2.0,\n    memory=\"nilearn_cache\",\n    memory_level=1,\n    verbose=0,\n)\nseed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])\nframetimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)\ndesign_matrix = make_first_level_design_matrix(\n    frametimes,\n    hrf_model=\"spm\",\n    add_regs=seed_time_series,\n    add_reg_names=[\"pcc_seed\"],\n)\ndmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))\ncontrasts = {\"seed_based_glm\": dmn_contrast}\n\n#########################################################################\n# Perform first level analysis\n# ----------------------------\n# Setup and fit GLM.\nfirst_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)\nfirst_level_model = first_level_model.fit(\n    run_imgs=adhd_dataset.func[0], design_matrices=design_matrix\n)\n\n#########################################################################\n# Estimate the contrast.\nprint(\"Contrast seed_based_glm computed.\")\nz_map = first_level_model.compute_contrast(\n    contrasts[\"seed_based_glm\"], output_type=\"z_score\"\n)\n\n# Saving snapshots of the contrasts\nfilename = \"dmn_z_map.png\"\ndisplay = plotting.plot_stat_map(\n    z_map, threshold=3.0, title=\"Seed based GLM\", cut_coords=pcc_coords\n)\ndisplay.add_markers(\n    marker_coords=[pcc_coords], marker_color=\"g\", marker_size=300\n)\ndisplay.savefig(filename)\nprint(f\"Save z-map in '{filename}'.\")\n\n###########################################################################\n# Generating a report\n# -------------------\n# It can be useful to quickly generate a\n# portable, ready-to-view report with most of the pertinent information.\n# This is easy to do if you have a fitted model and the list of contrasts,\n# which we do here.\nfrom nilearn.reporting import make_glm_report\n\nreport = make_glm_report(\n    first_level_model,\n    contrasts=contrasts,\n    title=\"ADHD DMN Report\",\n    cluster_threshold=15,\n    min_distance=8.0,\n    plot_type=\"glass\",\n)\n\n#########################################################################\n# We have several ways to access the report:\n\n# report  # This report can be viewed in a notebook\n# report.save_as_html('report.html')\n# report.open_in_browser()\n", "path": "examples/04_glm_first_level/plot_adhd_dmn.py"}], "after_files": [{"content": "\"\"\"\nDefault Mode Network extraction of ADHD dataset\n===============================================\n\nThis example shows a full step-by-step workflow of fitting a GLM to signal\nextracted from a seed on the Posterior Cingulate Cortex and saving the results.\nMore precisely, this example shows how to use a signal extracted from a\nseed region as the regressor in a GLM to determine the correlation\nof each region in the dataset with the seed region.\n\nMore specifically:\n\n1. A sequence of fMRI volumes are loaded.\n2. A design matrix with the Posterior Cingulate Cortex seed is defined.\n3. A GLM is applied to the dataset (effect/covariance,\n   then contrast estimation).\n4. The Default Mode Network is displayed.\n\n.. include:: ../../../examples/masker_note.rst\n\n\"\"\"\nimport numpy as np\nfrom nilearn import datasets, plotting\nfrom nilearn.glm.first_level import (\n    FirstLevelModel,\n    make_first_level_design_matrix,\n)\nfrom nilearn.maskers import NiftiSpheresMasker\n\n#########################################################################\n# Prepare data and analysis parameters\n# ------------------------------------\n# Prepare the data.\nadhd_dataset = datasets.fetch_adhd(n_subjects=1)\n\n# Prepare timing\nt_r = 2.0\nslice_time_ref = 0.0\nn_scans = 176\n\n# Prepare seed\npcc_coords = (0, -53, 26)\n\n#########################################################################\n# Extract the seed region's time course\n# -------------------------------------\n# Extract the time course of the seed region.\nseed_masker = NiftiSpheresMasker(\n    [pcc_coords],\n    radius=10,\n    detrend=True,\n    standardize=\"zscore_sample\",\n    low_pass=0.1,\n    high_pass=0.01,\n    t_r=2.0,\n    memory=\"nilearn_cache\",\n    memory_level=1,\n    verbose=0,\n)\nseed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])\nframetimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)\n\n#########################################################################\n# Plot the time course of the seed region.\nimport matplotlib.pyplot as plt\n\nfig = plt.figure(figsize=(9, 3))\nax = fig.add_subplot(111)\nax.plot(frametimes, seed_time_series, linewidth=2, label=\"seed region\")\nax.legend(loc=2)\nax.set_title(\"Time course of the seed region\")\nplt.show()\n\n#########################################################################\n# Estimate contrasts\n# ------------------\n# Specify the contrasts.\ndesign_matrix = make_first_level_design_matrix(\n    frametimes,\n    hrf_model=\"spm\",\n    add_regs=seed_time_series,\n    add_reg_names=[\"pcc_seed\"],\n)\ndmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))\ncontrasts = {\"seed_based_glm\": dmn_contrast}\n\n#########################################################################\n# Perform first level analysis\n# ----------------------------\n# Setup and fit GLM.\nfirst_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)\nfirst_level_model = first_level_model.fit(\n    run_imgs=adhd_dataset.func[0], design_matrices=design_matrix\n)\n\n#########################################################################\n# Estimate the contrast.\nprint(\"Contrast seed_based_glm computed.\")\nz_map = first_level_model.compute_contrast(\n    contrasts[\"seed_based_glm\"], output_type=\"z_score\"\n)\n\n# Saving snapshots of the contrasts\nfilename = \"dmn_z_map.png\"\ndisplay = plotting.plot_stat_map(\n    z_map, threshold=3.0, title=\"Seed based GLM\", cut_coords=pcc_coords\n)\ndisplay.add_markers(\n    marker_coords=[pcc_coords], marker_color=\"g\", marker_size=300\n)\ndisplay.savefig(filename)\nprint(f\"Save z-map in '{filename}'.\")\n\n###########################################################################\n# Generating a report\n# -------------------\n# It can be useful to quickly generate a\n# portable, ready-to-view report with most of the pertinent information.\n# This is easy to do if you have a fitted model and the list of contrasts,\n# which we do here.\nfrom nilearn.reporting import make_glm_report\n\nreport = make_glm_report(\n    first_level_model,\n    contrasts=contrasts,\n    title=\"ADHD DMN Report\",\n    cluster_threshold=15,\n    min_distance=8.0,\n    plot_type=\"glass\",\n)\n\n#########################################################################\n# We have several ways to access the report:\n\n# report  # This report can be viewed in a notebook\n# report.save_as_html('report.html')\n# report.open_in_browser()\n", "path": "examples/04_glm_first_level/plot_adhd_dmn.py"}]}