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.

try:
    import matplotlib.pyplot as plt
except ImportError:
    raise RuntimeError("This script needs the matplotlib library")

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.

/opt/hostedtoolcache/Python/3.12.2/x64/lib/python3.12/site-packages/nilearn/glm/first_level/design_matrix.py:508: 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))
ax = plot_design_matrix(design_matrix, ax=ax1)
ax.set_ylabel("maps")
ax.set_title("Second level design matrix", fontsize=12)
plt.tight_layout()
plt.show()
Second level design matrix

Total running time of the script: (0 minutes 1.718 seconds)

Estimated memory usage: 10 MB

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