Note
Go to the end to download the full example code. or to run this example in your browser via Binder
Default Mode Network extraction¶
This example shows a full step-by-step workflow of fitting a GLM to signal extracted from a seed on the Posterior Cingulate Cortex and saving the results. More precisely, this example shows how to use a signal extracted from a seed region as the regressor in a GLM to determine the correlation of each region in the dataset with the seed region.
More specifically:
A sequence of fMRI volumes are loaded.
A design matrix with the Posterior Cingulate Cortex seed is defined.
A GLM is applied to the dataset (effect/covariance, then contrast estimation).
The Default Mode Network is displayed.
dataset
This example uses the ADHD dataset.
import numpy as np
from nilearn import plotting
from nilearn.datasets import fetch_adhd
from nilearn.glm.first_level import (
FirstLevelModel,
make_first_level_design_matrix,
)
from nilearn.maskers import NiftiSpheresMasker
Prepare data and analysis parameters¶
Prepare the data.
adhd_dataset = fetch_adhd(n_subjects=1)
# Prepare seed
pcc_coords = (0, -53, 26)
[fetch_adhd] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/adhd
Extract the seed region’s time course¶
Extract the time course of the seed region.
seed_masker = NiftiSpheresMasker(
[pcc_coords],
radius=10,
detrend=True,
standardize="zscore_sample",
low_pass=0.1,
high_pass=0.01,
t_r=adhd_dataset.t_r,
memory="nilearn_cache",
memory_level=1,
verbose=1,
)
seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])
n_scans = seed_time_series.shape[0]
frametimes = np.linspace(0, (n_scans - 1) * adhd_dataset.t_r, n_scans)
[NiftiSpheresMasker.wrapped] Loading data from 0010042_rest_tshif...
[NiftiSpheresMasker.wrapped] Finished fit
________________________________________________________________________________
[Memory] Calling nilearn.maskers.base_masker.filter_and_extract...
filter_and_extract('/home/runner/work/nilearn/nilearn/nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni.nii.gz',
<nilearn.maskers.nifti_spheres_masker._ExtractionFunctor object at 0x720321ce9b10>,
{ 'allow_overlap': False,
'clean_args': None,
'clean_kwargs': {},
'detrend': True,
'dtype': None,
'high_pass': 0.01,
'high_variance_confounds': False,
'low_pass': 0.1,
'mask_img': None,
'radius': 10,
'reports': True,
'seeds': [(0, -53, 26)],
'smoothing_fwhm': None,
'standardize': 'zscore_sample',
'standardize_confounds': True,
't_r': 2.0}, confounds=None, sample_mask=None, dtype=None, sklearn_output_config=None, memory=Memory(location=nilearn_cache/joblib), memory_level=1, verbose=1)
[NiftiSpheresMasker.wrapped] Loading data from 0010042_rest_tshif...
[NiftiSpheresMasker.wrapped] Extracting region signals
[NiftiSpheresMasker.wrapped] Cleaning extracted signals
_______________________________________________filter_and_extract - 4.1s, 0.1min
Plot the time course of the seed region.
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(9, 3))
ax = fig.add_subplot(111)
ax.plot(frametimes, seed_time_series, linewidth=2, label="seed region")
ax.legend(loc=2)
ax.set_title("Time course of the seed region")
plt.show()
Estimate contrasts¶
Specify the contrasts.
design_matrix = make_first_level_design_matrix(
frametimes,
hrf_model="spm",
add_regs=seed_time_series,
add_reg_names=["pcc_seed"],
)
dmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))
contrasts = {"seed_based_glm": dmn_contrast}
Perform first level analysis¶
Setup and fit GLM.
first_level_model = FirstLevelModel(verbose=1)
first_level_model = first_level_model.fit(
run_imgs=adhd_dataset.func[0], design_matrices=design_matrix
)
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Masking took 1 seconds.
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_adhd_dmn.py:95: UserWarning:
Mean values of 0 observed. The data have probably been centered. Scaling might not work as expected.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 00 HR 00 MIN 03 SEC.
Estimate the contrast.
print("Contrast seed_based_glm computed.")
z_map = first_level_model.compute_contrast(
contrasts["seed_based_glm"], output_type="z_score"
)
Contrast seed_based_glm computed.
Saving snapshots of the contrasts
from pathlib import Path
display = plotting.plot_stat_map(
z_map, threshold=3.0, title="Seed based GLM", cut_coords=pcc_coords
)
display.add_markers(
marker_coords=[pcc_coords], marker_color="g", marker_size=300
)
output_dir = Path.cwd() / "results" / "plot_adhd_dmn"
output_dir.mkdir(exist_ok=True, parents=True)
filename = "dmn_z_map.png"
display.savefig(output_dir / filename)
print(f"Save z-map in '{filename}'.")
Save z-map in 'dmn_z_map.png'.
Generating a report¶
It can be useful to quickly generate a portable, ready-to-view report with most of the pertinent information. This is easy to do if you have a fitted model and the list of contrasts, which we do here.
report = first_level_model.generate_report(
contrasts=contrasts,
title="ADHD DMN Report",
cluster_threshold=15,
min_distance=8.0,
plot_type="glass",
)
Note
The generated report can be:
displayed in a Notebook,
opened in a browser using the
.open_in_browser()method,or saved to a file using the
.save_as_html(output_filepath)method.
ADHD DMN Report Implement the General Linear Model for single run :term:`fMRI` data.
Description
Data were analyzed using Nilearn (version= 0.14.2; RRID:SCR_001362).
At the subject level, a mass univariate analysis was performed with a linear regression at each voxel of the brain, using generalized least squares with a global ar1 noise model to account for temporal auto-correlation and a cosine drift model (high pass filter=0.01 Hz).
Model details
| Value | |
|---|---|
| Parameter | |
| drift_model | cosine |
| high_pass (Hertz) | 0.01 |
| hrf_model | glover |
| noise_model | ar1 |
| signal_scaling | 0 |
| slice_time_ref | 0.0 |
Design Matrix
correlation matrix
Contrasts
Mask
The mask includes 62546 voxels (23.0 %) of the image.
Statistical Maps
seed_based_glm
Cluster Table
| Height control | fpr |
|---|---|
| α | 0.001 |
| Threshold (computed) | 3.09 |
| Cluster size threshold (voxels) | 15 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | 3.0 | -54.0 | 18.0 | 13.01 | 113616 |
| 1a | 0.0 | -57.0 | 30.0 | 12.68 | |
| 1b | 0.0 | -48.0 | 30.0 | 11.96 | |
| 1c | -3.0 | -51.0 | 18.0 | 11.91 | |
| 2 | 0.0 | 51.0 | -6.0 | 11.08 | 32103 |
| 2a | 3.0 | 69.0 | 3.0 | 10.62 | |
| 2b | 0.0 | 57.0 | 3.0 | 10.31 | |
| 2c | 0.0 | 63.0 | 15.0 | 10.12 | |
| 3 | 57.0 | -66.0 | 27.0 | 10.91 | 7209 |
| 3a | 48.0 | -57.0 | 30.0 | 8.99 | |
| 3b | 51.0 | -69.0 | 27.0 | 8.51 | |
| 3c | 45.0 | -69.0 | 39.0 | 6.39 | |
| 4 | 48.0 | -66.0 | -21.0 | 10.72 | 4563 |
| 4a | 42.0 | -54.0 | -24.0 | 7.29 | |
| 4b | 51.0 | -60.0 | -21.0 | 6.42 | |
| 4c | 33.0 | -78.0 | -36.0 | 5.37 | |
| 5 | -42.0 | 24.0 | -24.0 | 10.72 | 4077 |
| 5a | -57.0 | 3.0 | -15.0 | 7.02 | |
| 5b | -42.0 | 9.0 | -30.0 | 5.87 | |
| 5c | -45.0 | 3.0 | -45.0 | 5.67 | |
| 6 | 42.0 | 27.0 | -24.0 | 10.72 | 972 |
| 6a | 39.0 | 36.0 | -15.0 | 7.45 | |
| 6b | 39.0 | 21.0 | -18.0 | 4.72 | |
| 7 | -12.0 | 39.0 | 54.0 | 8.52 | 4590 |
| 7a | -24.0 | 30.0 | 48.0 | 8.51 | |
| 7b | -18.0 | 30.0 | 42.0 | 8.39 | |
| 7c | -15.0 | 30.0 | 51.0 | 7.87 | |
| 8 | 60.0 | -3.0 | -12.0 | 7.94 | 3186 |
| 8a | 66.0 | -18.0 | -6.0 | 6.62 | |
| 8b | 66.0 | -27.0 | -3.0 | 6.35 | |
| 8c | 57.0 | -12.0 | -9.0 | 6.15 | |
| 9 | 51.0 | 9.0 | -39.0 | 6.73 | 1053 |
| 9a | 51.0 | 9.0 | -27.0 | 3.30 | |
| 10 | -24.0 | -75.0 | 51.0 | 6.65 | 594 |
| 10a | -15.0 | -75.0 | 57.0 | 4.84 | |
| 10b | -36.0 | -72.0 | 51.0 | 4.52 | |
| 11 | 21.0 | -33.0 | 0.0 | 6.54 | 2295 |
| 11a | 21.0 | -21.0 | -12.0 | 5.72 | |
| 11b | 30.0 | -27.0 | -9.0 | 5.15 | |
| 11c | 9.0 | -30.0 | -6.0 | 4.64 | |
| 12 | -39.0 | 9.0 | 39.0 | 6.36 | 1809 |
| 12a | -48.0 | 9.0 | 42.0 | 5.19 | |
| 12b | -36.0 | 24.0 | 18.0 | 4.75 | |
| 13 | -39.0 | -36.0 | -21.0 | 6.27 | 1215 |
| 13a | -42.0 | -24.0 | -21.0 | 5.48 | |
| 13b | -39.0 | -12.0 | -36.0 | 4.78 | |
| 14 | 36.0 | 9.0 | 36.0 | 6.25 | 1026 |
| 14a | 36.0 | 18.0 | 36.0 | 5.15 | |
| 14b | 39.0 | 9.0 | 42.0 | 5.04 | |
| 14c | 48.0 | 12.0 | 45.0 | 4.66 | |
| 15 | -9.0 | -51.0 | -39.0 | 6.10 | 1404 |
| 15a | 3.0 | -60.0 | -51.0 | 4.72 | |
| 16 | -51.0 | -78.0 | 18.0 | 6.06 | 891 |
| 16a | -54.0 | -66.0 | 6.0 | 4.13 | |
| 16b | -48.0 | -63.0 | 0.0 | 4.07 | |
| 16c | -51.0 | -75.0 | 9.0 | 3.89 | |
| 17 | 51.0 | -45.0 | 36.0 | 5.98 | 675 |
| 17a | 57.0 | -45.0 | 27.0 | 4.64 | |
| 18 | -15.0 | 57.0 | 30.0 | 5.94 | 1458 |
| 18a | -21.0 | 54.0 | 36.0 | 5.72 | |
| 18b | -9.0 | 51.0 | 33.0 | 4.79 | |
| 18c | -27.0 | 54.0 | 30.0 | 4.70 | |
| 19 | 33.0 | -36.0 | -12.0 | 5.78 | 594 |
| 19a | 33.0 | -30.0 | -18.0 | 4.83 | |
| 20 | -27.0 | 54.0 | 0.0 | 5.36 | 837 |
| 20a | -24.0 | 54.0 | 9.0 | 3.98 | |
| 21 | 27.0 | -69.0 | -6.0 | 5.32 | 2241 |
| 21a | 21.0 | -75.0 | -9.0 | 5.22 | |
| 21b | 27.0 | -57.0 | -12.0 | 4.83 | |
| 21c | 33.0 | -51.0 | -9.0 | 4.52 | |
| 22 | 48.0 | -66.0 | -9.0 | 5.00 | 594 |
| 22a | 48.0 | -75.0 | -12.0 | 4.50 | |
| 22b | 48.0 | -60.0 | -12.0 | 4.36 | |
| 23 | -3.0 | -9.0 | 72.0 | 4.76 | 648 |
| 23a | 0.0 | 3.0 | 69.0 | 3.87 | |
| 23b | -3.0 | -9.0 | 63.0 | 3.59 | |
| 24 | -42.0 | -60.0 | -6.0 | 4.76 | 567 |
| 25 | -9.0 | -102.0 | -3.0 | 4.52 | 486 |
| 25a | -6.0 | -102.0 | 6.0 | 3.76 |
About
- Date preprocessed:
Total running time of the script: (0 minutes 15.926 seconds)
Estimated memory usage: 641 MB
