Default Mode Network extraction of ADHD dataset

This example shows a full step-by-step workflow of fitting a GLM to data extracted from a seed on the Posterior Cingulate Cortex and saving the results.

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)

Estimate contrasts

Specify the contrasts.

seed_masker = NiftiSpheresMasker(
    [pcc_coords],
    radius=10,
    detrend=True,
    standardize=True,
    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)
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}

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:349: FutureWarning:

The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.

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
)

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/glm/first_level/first_level.py:61: 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.save_as_html('report.html')
# report.open_in_browser()

Estimated memory usage: 588 MB