Default Mode Network extraction of ADHD dataset

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:

1. A sequence of fMRI volumes are loaded.

2. A design matrix with the Posterior Cingulate Cortex seed is defined.

3. A GLM is applied to the dataset (effect/covariance, then contrast estimation).

4. The Default Mode Network is displayed.

Note

If you are using Nilearn with a version older than 0.9.0, then you should either upgrade your version or import maskers from the input_data module instead of the maskers module.

That is, you should manually replace in the following example all occurrences of:

from nilearn.maskers import NiftiMasker

with:

from nilearn.input_data import NiftiMasker

import numpy as np

from nilearn import datasets, plotting
from nilearn.glm.first_level import (
    FirstLevelModel,
    make_first_level_design_matrix,
)
from nilearn.maskers import NiftiSpheresMasker

Prepare data and analysis parameters

Prepare the data.

adhd_dataset = datasets.fetch_adhd(n_subjects=1)

# Prepare timing
t_r = 2.0
slice_time_ref = 0.0
n_scans = 176

# Prepare seed
pcc_coords = (0, -53, 26)

[get_dataset_dir] Dataset found in /home/remi/nilearn_data/adhd

Extract the seed region’s time course

Extract the time course of the seed region.

seed_masker = NiftiSpheresMasker(
    [pcc_coords],
    radius=10,
    detrend=True,
    standardize="zscore_sample",
    low_pass=0.1,
    high_pass=0.01,
    t_r=2.0,
    memory="nilearn_cache",
    memory_level=1,
    verbose=0,
)
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",
    add_regs=seed_time_series,
    add_reg_names=["pcc_seed"],
)
dmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))
contrasts = {"seed_based_glm": dmn_contrast}

Perform first level analysis

Setup and fit GLM.

first_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)
first_level_model = first_level_model.fit(
    run_imgs=adhd_dataset.func[0], design_matrices=design_matrix
)

/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/glm/first_level/first_level.py:600: UserWarning:

Mean values of 0 observed. The data have probably been centered.Scaling might not work as expected

Estimate the contrast.

print("Contrast seed_based_glm computed.")
z_map = first_level_model.compute_contrast(
    contrasts["seed_based_glm"], output_type="z_score"
)

# Saving snapshots of the contrasts
filename = "dmn_z_map.png"
display = plotting.plot_stat_map(
    z_map, threshold=3.0, title="Seed based GLM", cut_coords=pcc_coords
)
display.add_markers(
    marker_coords=[pcc_coords], marker_color="g", marker_size=300
)
display.savefig(filename)
print(f"Save z-map in '{filename}'.")

Contrast seed_based_glm computed.
Save z-map in 'dmn_z_map.png'.

Generating a report

It can be useful to quickly generate a portable, ready-to-view report with most of the pertinent information. This is easy to do if you have a fitted model and the list of contrasts, which we do here.

from nilearn.reporting import make_glm_report

report = make_glm_report(
    first_level_model,
    contrasts=contrasts,
    title="ADHD DMN Report",
    cluster_threshold=15,
    min_distance=8.0,
    plot_type="glass",
)

We have several ways to access the report:

# report  # This report can be viewed in a notebook
# report.open_in_browser()

# or we can save as an html file
# from pathlib import Path
# output_dir = Path.cwd() / "results" / "plot_adhd_dmn"
# output_dir.mkdir(exist_ok=True, parents=True)
# report.save_as_html(output_dir / 'report.html')

Estimated memory usage: 1056 MB