Repository: nilearn/nilearn

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


Discussion:
yes
a couple of lines explaining the general idea of the analysis and maybe an additional plot of the time course of the seed region

wanna take care of it @mtorabi59 ?
@Remi-Gau  Sure! Will do!

Resolving patch:
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",


Test patch: