We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Spelling Error
<!--Describe your proposed enhancement in detail.-->
I think the authors meant to describe ADHD but have written ADHD as AHDH. It is just a simple spelling or typographic error.
<!--List any pages that would be impacted by the enhancement.-->
### Affected pages
1. https://nilearn.github.io/dev/auto_examples/04_glm_first_level/plot_adhd_dmn.html#sphx-glr-auto-examples-04-glm-first-level-plot-adhd-dmn-py
 2. https://nilearn.github.io/dev/glm/first_level_model.html#fitting-a-first-level-model

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

{"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@@ -1,4 +1,4 @@\n-\"\"\"Default Mode Network extraction of AHDH dataset\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", "issue": "Spelling Error\n<!--Describe your proposed enhancement in detail.-->\r\nI think the authors meant to describe ADHD but have written ADHD as AHDH. It is just a simple spelling or typographic error.\r\n<!--List any pages that would be impacted by the enhancement.-->\r\n### Affected pages\r\n1. https://nilearn.github.io/dev/auto_examples/04_glm_first_level/plot_adhd_dmn.html#sphx-glr-auto-examples-04-glm-first-level-plot-adhd-dmn-py\r\n 2. https://nilearn.github.io/dev/glm/first_level_model.html#fitting-a-first-level-model\n", "before_files": [{"content": "\"\"\"Default Mode Network extraction of AHDH 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, then contrast estimation).\n4. The Default Mode Network is displayed.\n\n.. include:: ../../../examples/masker_note.rst\n\n\"\"\"\nimport numpy as np\n\nfrom nilearn import datasets, plotting\nfrom nilearn.maskers import NiftiSpheresMasker\n\nfrom nilearn.glm.first_level import FirstLevelModel\nfrom nilearn.glm.first_level import make_first_level_design_matrix\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.\nslice_time_ref = 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([pcc_coords], radius=10, detrend=True,\n                                 standardize=True, low_pass=0.1,\n                                 high_pass=0.01, t_r=2.,\n                                 memory='nilearn_cache',\n                                 memory_level=1, verbose=0)\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(frametimes, hrf_model='spm',\n                                               add_regs=seed_time_series,\n                                               add_reg_names=[\"pcc_seed\"])\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(run_imgs=adhd_dataset.func[0],\n                                    design_matrices=design_matrix)\n\n#########################################################################\n# Estimate the contrast.\nprint('Contrast seed_based_glm computed.')\nz_map = first_level_model.compute_contrast(contrasts['seed_based_glm'],\n                                           output_type='z_score')\n\n# Saving snapshots of the contrasts\nfilename = 'dmn_z_map.png'\ndisplay = plotting.plot_stat_map(z_map, threshold=3.0, title='Seed based GLM',\n                                 cut_coords=pcc_coords)\ndisplay.add_markers(marker_coords=[pcc_coords], marker_color='g',\n                    marker_size=300)\ndisplay.savefig(filename)\nprint(\"Save z-map in '{0}'.\".format(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.\n\nfrom nilearn.reporting import make_glm_report\n\nreport = make_glm_report(first_level_model,\n                         contrasts=contrasts,\n                         title='ADHD DMN Report',\n                         cluster_threshold=15,\n                         min_distance=8.,\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": "\"\"\"Default 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, then contrast estimation).\n4. The Default Mode Network is displayed.\n\n.. include:: ../../../examples/masker_note.rst\n\n\"\"\"\nimport numpy as np\n\nfrom nilearn import datasets, plotting\nfrom nilearn.maskers import NiftiSpheresMasker\n\nfrom nilearn.glm.first_level import FirstLevelModel\nfrom nilearn.glm.first_level import make_first_level_design_matrix\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.\nslice_time_ref = 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([pcc_coords], radius=10, detrend=True,\n                                 standardize=True, low_pass=0.1,\n                                 high_pass=0.01, t_r=2.,\n                                 memory='nilearn_cache',\n                                 memory_level=1, verbose=0)\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(frametimes, hrf_model='spm',\n                                               add_regs=seed_time_series,\n                                               add_reg_names=[\"pcc_seed\"])\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(run_imgs=adhd_dataset.func[0],\n                                    design_matrices=design_matrix)\n\n#########################################################################\n# Estimate the contrast.\nprint('Contrast seed_based_glm computed.')\nz_map = first_level_model.compute_contrast(contrasts['seed_based_glm'],\n                                           output_type='z_score')\n\n# Saving snapshots of the contrasts\nfilename = 'dmn_z_map.png'\ndisplay = plotting.plot_stat_map(z_map, threshold=3.0, title='Seed based GLM',\n                                 cut_coords=pcc_coords)\ndisplay.add_markers(marker_coords=[pcc_coords], marker_color='g',\n                    marker_size=300)\ndisplay.savefig(filename)\nprint(\"Save z-map in '{0}'.\".format(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.\n\nfrom nilearn.reporting import make_glm_report\n\nreport = make_glm_report(first_level_model,\n                         contrasts=contrasts,\n                         title='ADHD DMN Report',\n                         cluster_threshold=15,\n                         min_distance=8.,\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"}]}