Examples of design matrices#

Three examples of design matrices specification and computation for first-level fMRI data analysis (event-related design, block design, FIR design).

This examples requires matplotlib.

Define parameters#

At first, we define parameters related to the images acquisition.

import numpy as np

from nilearn.plotting import plot_design_matrix

tr = 1.0  # repetition time is 1 second
n_scans = 128  # the acquisition comprises 128 scans
frame_times = np.arange(n_scans) * tr  # here are the corresponding frame times

Then we define parameters related to the experimental design.

# these are the types of the different trials
conditions = ["c0", "c0", "c0", "c1", "c1", "c1", "c3", "c3", "c3"]
duration = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
# these are the corresponding onset times
onsets = [30.0, 70.0, 100.0, 10.0, 30.0, 90.0, 30.0, 40.0, 60.0]
# Next, we simulate 6 motion parameters jointly observed with fMRI acquisitions
motion = np.cumsum(np.random.randn(n_scans, 6), 0)
# The 6 parameters correspond to three translations and three
# rotations describing rigid body motion
add_reg_names = ["tx", "ty", "tz", "rx", "ry", "rz"]

Create design matrices#

The same parameters allow us to obtain a variety of design matrices. We first create an events object.

import pandas as pd

events = pd.DataFrame(
    {"trial_type": conditions, "onset": onsets, "duration": duration}
)

We sample the events into a design matrix, also including additional regressors.

from nilearn.glm.first_level import make_first_level_design_matrix

hrf_model = "glover"
X1 = make_first_level_design_matrix(
    frame_times,
    events,
    drift_model="polynomial",
    drift_order=3,
    add_regs=motion,
    add_reg_names=add_reg_names,
    hrf_model=hrf_model,
)

Now we compute a block design matrix. We add duration to create the blocks. For this we first define an event structure that includes the duration parameter.

duration = 7.0 * np.ones(len(conditions))
events = pd.DataFrame(
    {"trial_type": conditions, "onset": onsets, "duration": duration}
)

Then we sample the design matrix.

X2 = make_first_level_design_matrix(
    frame_times,
    events,
    drift_model="polynomial",
    drift_order=3,
    hrf_model=hrf_model,
)

Finally we compute a FIR model

events = pd.DataFrame(
    {"trial_type": conditions, "onset": onsets, "duration": duration}
)
hrf_model = "FIR"
X3 = make_first_level_design_matrix(
    frame_times,
    events,
    hrf_model="fir",
    drift_model="polynomial",
    drift_order=3,
    fir_delays=np.arange(1, 6),
)

Here are the three designs side by side.

import matplotlib.pyplot as plt

fig, (ax1, ax2, ax3) = plt.subplots(figsize=(10, 6), nrows=1, ncols=3)
plot_design_matrix(X1, ax=ax1)
ax1.set_title("Event-related design matrix", fontsize=12)
plot_design_matrix(X2, ax=ax2)
ax2.set_title("Block design matrix", fontsize=12)
plot_design_matrix(X3, ax=ax3)
ax3.set_title("FIR design matrix", fontsize=12)
fig.show()
Event-related design matrix, Block design matrix, FIR design matrix

Total running time of the script: (0 minutes 2.453 seconds)

Estimated memory usage: 9 MB

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