Note
Go to the end to download the full example code. or to run this example in your browser via Binder
Single-subject data (two runs) in native space¶
The example shows the analysis of an 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 et al.[1].
This example takes a lot of time because the input are lists of 3D images sampled in different positions (encoded by different affine functions).
Fetch the SPM multimodal_faces data.
from nilearn.datasets import fetch_spm_multimodal_fmri
subject_data = fetch_spm_multimodal_fmri()
[get_dataset_dir] Dataset created in
/home/runner/nilearn_data/spm_multimodal_fmri
[_glob_spm_multimodal_fmri_data] Missing 390 functional scans for session 1.
[fetch_spm_multimodal_fmri] Data absent, downloading...
[fetch_single_file] Downloading data from
https://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/multimodal_fmri.zip ...
[_chunk_report_] Downloaded 5316608 of 134263085 bytes (4.0%%, 24.4s
remaining)
[_chunk_report_] Downloaded 11206656 of 134263085 bytes (8.3%%, 22.1s
remaining)
[_chunk_report_] Downloaded 17391616 of 134263085 bytes (13.0%%, 20.2s
remaining)
[_chunk_report_] Downloaded 23937024 of 134263085 bytes (17.8%%, 18.5s
remaining)
[_chunk_report_] Downloaded 29802496 of 134263085 bytes (22.2%%, 17.6s
remaining)
[_chunk_report_] Downloaded 34897920 of 134263085 bytes (26.0%%, 17.2s
remaining)
[_chunk_report_] Downloaded 40312832 of 134263085 bytes (30.0%%, 16.4s
remaining)
[_chunk_report_] Downloaded 45924352 of 134263085 bytes (34.2%%, 15.5s
remaining)
[_chunk_report_] Downloaded 51601408 of 134263085 bytes (38.4%%, 14.5s
remaining)
[_chunk_report_] Downloaded 57303040 of 134263085 bytes (42.7%%, 13.5s
remaining)
[_chunk_report_] Downloaded 62996480 of 134263085 bytes (46.9%%, 12.5s
remaining)
[_chunk_report_] Downloaded 68648960 of 134263085 bytes (51.1%%, 11.5s
remaining)
[_chunk_report_] Downloaded 74432512 of 134263085 bytes (55.4%%, 10.5s
remaining)
[_chunk_report_] Downloaded 80404480 of 134263085 bytes (59.9%%, 9.4s
remaining)
[_chunk_report_] Downloaded 86622208 of 134263085 bytes (64.5%%, 8.3s
remaining)
[_chunk_report_] Downloaded 90349568 of 134263085 bytes (67.3%%, 7.8s
remaining)
[_chunk_report_] Downloaded 93954048 of 134263085 bytes (70.0%%, 7.3s
remaining)
[_chunk_report_] Downloaded 97746944 of 134263085 bytes (72.8%%, 6.8s
remaining)
[_chunk_report_] Downloaded 101629952 of 134263085 bytes (75.7%%, 6.1s
remaining)
[_chunk_report_] Downloaded 105512960 of 134263085 bytes (78.6%%, 5.5s
remaining)
[_chunk_report_] Downloaded 109412352 of 134263085 bytes (81.5%%, 4.8s
remaining)
[_chunk_report_] Downloaded 113319936 of 134263085 bytes (84.4%%, 4.1s
remaining)
[_chunk_report_] Downloaded 117309440 of 134263085 bytes (87.4%%, 3.3s
remaining)
[_chunk_report_] Downloaded 121454592 of 134263085 bytes (90.5%%, 2.5s
remaining)
[_chunk_report_] Downloaded 125886464 of 134263085 bytes (93.8%%, 1.7s
remaining)
[_chunk_report_] Downloaded 130777088 of 134263085 bytes (97.4%%, 0.7s
remaining)
[fetch_single_file] ...done. (28 seconds, 0 min)
[uncompress_file] Extracting data from
/home/runner/nilearn_data/spm_multimodal_fmri/sub001/multimodal_fmri.zip...
[uncompress_file] .. done.
[fetch_single_file] Downloading data from
https://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/multimodal_smri.zip ...
[_chunk_report_] Downloaded 3915776 of 6852766 bytes (57.1%%, 0.8s remaining)
[fetch_single_file] ...done. (2 seconds, 0 min)
[uncompress_file] Extracting data from
/home/runner/nilearn_data/spm_multimodal_fmri/sub001/multimodal_smri.zip...
[uncompress_file] .. done.
Specify timing and design matrix parameters.
# repetition time, in seconds
t_r = 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], copy_header=True, force_resample=True
)
Resampling the second image (this takes time)...
Let’s create mean image for display purposes.
mean_image = mean_img(fmri_img, copy_header=True)
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 runs.
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) * t_r
# 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 beta maps). We start by specifying canonical 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 GLM for the 2 runs 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)
Fitting a GLM
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()
Computing contrasts
contrast id: faces-scrambled
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_spm_multimodal_faces.py:142: UserWarning:
One contrast given, assuming it for all 2 runs
contrast id: scrambled-faces
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_spm_multimodal_faces.py:142: UserWarning:
One contrast given, assuming it for all 2 runs
contrast id: effects_of_interest
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_spm_multimodal_faces.py:142: UserWarning:
One contrast given, assuming it for all 2 runs
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/glm/contrasts.py:166: UserWarning:
Running approximate fixed effects on F statistics.
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.
References¶
Total running time of the script: (2 minutes 13.256 seconds)
Estimated memory usage: 1108 MB