Example of second level design matrix

This example shows how a second-level design matrix is specified: assuming that the data refer to a group of individuals, with one image per subject, the design matrix typically holds the characteristics of each individual.

This is used in a second-level analysis to assess the impact of these characteristics on brain signals.

This example requires matplotlib.

from nilearn._utils.helpers import check_matplotlib

check_matplotlib()

import matplotlib.pyplot as plt

Create a simple experimental paradigm

We want to get the group result of a contrast for 20 subjects.

n_subjects = 20
subjects_label = [f"sub-{int(i):02}" for i in range(1, n_subjects + 1)]

Next, we specify extra information about the subjects to create confounders. Without confounders the design matrix would correspond to a one sample test.

import pandas as pd

extra_info_subjects = pd.DataFrame(
    {
        "subject_label": subjects_label,
        "age": range(15, 15 + n_subjects),
        "sex": [0, 1] * (n_subjects // 2),
    }
)

Create a second level design matrix

With that information we can create the second level design matrix.

from nilearn.glm.second_level import make_second_level_design_matrix

design_matrix = make_second_level_design_matrix(
    subjects_label, extra_info_subjects
)

/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/glm/first_level/design_matrix.py:513: UserWarning:

Attention: Design matrix is singular. Aberrant estimates are expected.

Let’s plot it.

from nilearn.plotting import plot_design_matrix

fig, ax1 = plt.subplots(1, 1, figsize=(3, 4), constrained_layout=True)

ax = plot_design_matrix(design_matrix, axes=ax1)
ax.set_ylabel("maps")
ax.set_title("Second level design matrix", fontsize=12)
plt.show()

Estimated memory usage: 147 MB