9.6.1. 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")

9.6.1.1. Create a simple experimental paradigm

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

n_subjects = 20
subjects_label = ['sub-%02d' % i 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] * int(n_subjects / 2)})

9.6.1.2. 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)

Out:

/home/nicolas/GitRepos/nilearn-fork/nilearn/glm/first_level/design_matrix.py:475: UserWarning:

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

Let’s plot it.

from nilearn.plotting import plot_design_matrix
ax = plot_design_matrix(design_matrix)
ax.set_title('Second level design matrix', fontsize=12)
ax.set_ylabel('maps')
plt.tight_layout()
plt.show()

Estimated memory usage: 9 MB