"""
Single-subject data (two sessions) in native space
==================================================
The example shows the analysis of an :term:`SPM` dataset
studying face perception.
The analysis is performed in native space.
Realignment parameters are provided with the input images,
but those have not been resampled to a common space.
The experimental paradigm is simple, with two conditions; viewing a face image
or a scrambled face image, supposedly with the same low-level statistical
properties, to find face-specific responses.
For details on the data, please see:
Henson, R.N., Goshen-Gottstein, Y., Ganel, T., Otten, L.J., Quayle, A.,
Rugg, M.D. Electrophysiological and haemodynamic correlates of face
perception, recognition and priming. Cereb Cortex. 2003 Jul;13(7):793-805.
https://doi.org/10.1093/cercor/13.7.793
This example takes a lot of time because the input are lists of 3D images
sampled in different positions (encoded by different affine functions).
"""
# %%
print(__doc__)
# %%
# Fetch the :term:`SPM` multimodal_faces data.
from nilearn.datasets import fetch_spm_multimodal_fmri
subject_data = fetch_spm_multimodal_fmri()
# %%
# Specify timing and design matrix parameters.
# repetition time, in seconds
tr = 2.0
# Sample at the beginning of each acquisition.
slice_time_ref = 0.0
# We use a discrete cosine transform to model signal drifts.
drift_model = "Cosine"
# The cutoff for the drift model is 0.01 Hz.
high_pass = 0.01
# The hemodynamic response function
hrf_model = "spm + derivative"
# %%
# Resample the images.
#
# This is achieved by the concat_imgs function of Nilearn.
import warnings
from nilearn.image import concat_imgs, mean_img, resample_img
# Avoid getting too many warnings due to resampling
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fmri_img = [
concat_imgs(subject_data.func1, auto_resample=True),
concat_imgs(subject_data.func2, auto_resample=True),
]
affine, shape = fmri_img[0].affine, fmri_img[0].shape
print("Resampling the second image (this takes time)...")
fmri_img[1] = resample_img(fmri_img[1], affine, shape[:3])
# %%
# Let's create mean image for display purposes.
mean_image = mean_img(fmri_img)
# %%
# Make the design matrices.
import numpy as np
import pandas as pd
from nilearn.glm.first_level import make_first_level_design_matrix
design_matrices = []
# %%
# Loop over the two sessions.
for idx, img in enumerate(fmri_img, start=1):
# Build experimental paradigm
n_scans = img.shape[-1]
events = pd.read_table(subject_data[f"events{idx}"])
# Define the sampling times for the design matrix
frame_times = np.arange(n_scans) * tr
# Build design matrix with the reviously defined parameters
design_matrix = make_first_level_design_matrix(
frame_times,
events,
hrf_model=hrf_model,
drift_model=drift_model,
high_pass=high_pass,
)
# put the design matrices in a list
design_matrices.append(design_matrix)
# %%
# We can specify basic contrasts (to get :term:`beta`
# maps).
# We start by specifying canonical :term:`contrast`
# that isolate design matrix columns.
contrast_matrix = np.eye(design_matrix.shape[1])
basic_contrasts = {
column: contrast_matrix[i]
for i, column in enumerate(design_matrix.columns)
}
# %%
# We actually want more interesting contrasts. The simplest contrast
# just makes the difference between the two main conditions. We
# define the two opposite versions to run one-tailed t-tests. We also
# define the effects of interest contrast, a 2-dimensional contrasts
# spanning the two conditions.
contrasts = {
"faces-scrambled": basic_contrasts["faces"] - basic_contrasts["scrambled"],
"scrambled-faces": -basic_contrasts["faces"]
+ basic_contrasts["scrambled"],
"effects_of_interest": np.vstack(
(basic_contrasts["faces"], basic_contrasts["scrambled"])
),
}
# %%
# Fit the :term:`GLM` for the 2 sessions
# by specifying a FirstLevelModel and then fitting it.
from nilearn.glm.first_level import FirstLevelModel
print("Fitting a GLM")
fmri_glm = FirstLevelModel()
fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices)
# %%
# Now we can compute contrast-related statistical maps (in z-scale), and plot
# them.
from nilearn import plotting
print("Computing contrasts")
# Iterate on contrasts
for contrast_id, contrast_val in contrasts.items():
print(f"\tcontrast id: {contrast_id}")
# compute the contrasts
z_map = fmri_glm.compute_contrast(contrast_val, output_type="z_score")
# plot the contrasts as soon as they're generated
# the display is overlaid on the mean fMRI image
# a threshold of 3.0 is used, more sophisticated choices are possible
plotting.plot_stat_map(
z_map,
bg_img=mean_image,
threshold=3.0,
display_mode="z",
cut_coords=3,
black_bg=True,
title=contrast_id,
)
plotting.show()
# %%
# Based on the resulting maps we observe that the analysis results in
# wide activity for the 'effects of interest' contrast, showing the
# implications of large portions of the visual cortex in the
# conditions. By contrast, the differential effect between "faces" and
# "scrambled" involves sparser, more anterior and lateral regions. It
# also displays some responses in the frontal lobe.
# sphinx_gallery_dummy_images=3