Massively univariate analysis of a motor task from the Localizer dataset#

This example shows the results obtained in a massively univariate analysis performed at the inter-subject level with various methods. We use the [left button press (auditory cue)] task from the Localizer dataset and seek association between the contrast values and a variate that measures the speed of pseudo-word reading. No confounding variate is included in the model.

  1. A standard ANOVA is performed. Data smoothed at 5 voxels FWHM are used.

  2. A permuted Ordinary Least Squares algorithm is run at each voxel. Data smoothed at 5 voxels FWHM are used.


If you are using Nilearn with a version older than 0.9.0, then you should either upgrade your version or import maskers from the input_data module instead of the maskers module.

That is, you should manually replace in the following example all occurrences of:

from nilearn.maskers import NiftiMasker


from nilearn.input_data import NiftiMasker
import matplotlib.pyplot as plt
import numpy as np

from nilearn import datasets
from nilearn.maskers import NiftiMasker
from nilearn.mass_univariate import permuted_ols

Load Localizer contrast

n_samples = 94
localizer_dataset = datasets.fetch_localizer_contrasts(
    ["left button press (auditory cue)"],

# print basic information on the dataset
    "First contrast nifti image (3D) is located "
    f"at: {localizer_dataset.cmaps[0]}"

tested_var = localizer_dataset.ext_vars["pseudo"]

# Quality check / Remove subjects with bad tested variate
mask_quality_check = np.where(np.logical_not(np.isnan(tested_var)))[0]
n_samples = mask_quality_check.size
contrast_map_filenames = [
    localizer_dataset.cmaps[i] for i in mask_quality_check
tested_var = tested_var[mask_quality_check].values.reshape((-1, 1))
print(f"Actual number of subjects after quality check: {int(n_samples)}")
First contrast nifti image (3D) is located at: /home/runner/work/nilearn/nilearn/nilearn_data/brainomics_localizer/brainomics_data/S01/cmaps_LeftAuditoryClick.nii.gz
Actual number of subjects after quality check: 89

Mask data

nifti_masker = NiftiMasker(
    smoothing_fwhm=5, memory="nilearn_cache", memory_level=1
fmri_masked = nifti_masker.fit_transform(contrast_map_filenames)

Anova (parametric F-scores)

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/sklearn/utils/ DataConversionWarning:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

Perform massively univariate analysis with permuted OLS

This method will produce both voxel-level FWE-corrected -log10 p-values and TFCE-based FWE-corrected -log10 p-values.


permuted_ols can support a wide range of analysis designs, depending on the tested_var. For example, if you wished to perform a one-sample test, you could simply provide an array of ones (e.g., np.ones(n_samples)).

ols_outputs = permuted_ols(
    tested_var,  # this is equivalent to the design matrix, in array form
    n_perm=200,  # 200 for the sake of time. Ideally, this should be 10000.
    verbose=1,  # display progress bar
    n_jobs=1,  # can be changed to use more CPUs
neg_log_pvals_permuted_ols_unmasked = nifti_masker.inverse_transform(
    ols_outputs["logp_max_t"][0, :]  # select first regressor
neg_log_pvals_tfce_unmasked = nifti_masker.inverse_transform(
    ols_outputs["logp_max_tfce"][0, :]  # select first regressor
Job #1, processed 0/200 permutations (0.00%, 7108.516693115234 seconds remaining)
Job #1, processed 10/200 permutations (5.00%, 146.674818277359 seconds remaining)
Job #1, processed 20/200 permutations (10.00%, 132.59110808372498 seconds remaining)
Job #1, processed 30/200 permutations (15.00%, 123.0526024500529 seconds remaining)
Job #1, processed 40/200 permutations (20.00%, 114.73154640197754 seconds remaining)
Job #1, processed 50/200 permutations (25.00%, 106.88271903991699 seconds remaining)
Job #1, processed 60/200 permutations (30.00%, 99.24664735794067 seconds remaining)
Job #1, processed 70/200 permutations (35.00%, 91.9281760283879 seconds remaining)
Job #1, processed 80/200 permutations (40.00%, 84.6456767320633 seconds remaining)
Job #1, processed 90/200 permutations (45.00%, 77.53092935350207 seconds remaining)
Job #1, processed 100/200 permutations (50.00%, 70.37631750106812 seconds remaining)
Job #1, processed 110/200 permutations (55.00%, 63.30306562510404 seconds remaining)
Job #1, processed 120/200 permutations (60.00%, 56.24527136484782 seconds remaining)
Job #1, processed 130/200 permutations (65.00%, 49.18626003998976 seconds remaining)
Job #1, processed 140/200 permutations (70.00%, 42.11867543629237 seconds remaining)
Job #1, processed 150/200 permutations (75.00%, 35.06750043233235 seconds remaining)
Job #1, processed 160/200 permutations (80.00%, 28.04154008626938 seconds remaining)
Job #1, processed 170/200 permutations (85.00%, 21.01176637761733 seconds remaining)
Job #1, processed 180/200 permutations (90.00%, 13.999336004257202 seconds remaining)
Job #1, processed 190/200 permutations (95.00%, 6.9958026911083016 seconds remaining)


from nilearn import plotting
from nilearn.image import get_data

# Various plotting parameters
z_slice = 12  # plotted slice
threshold = -np.log10(0.1)  # 10% corrected

vmax = max(

images_to_plot = {
    "Parametric Test\n(Bonferroni FWE)": neg_log_pvals_anova_unmasked,
    "Permutation Test\n(Max t-statistic FWE)": (
    "Permutation Test\n(Max TFCE FWE)": neg_log_pvals_tfce_unmasked,

fig, axes = plt.subplots(figsize=(12, 3), ncols=3)
for i_col, (title, img) in enumerate(images_to_plot.items()):
    ax = axes[i_col]
    n_detections = (get_data(img) > threshold).sum()
    new_title = f"{title}\n{n_detections} sig. voxels"


    "Group left button press ($-\\log_{10}$ p-values)",
Group left button press ($-\log_{10}$ p-values), Parametric Test (Bonferroni FWE) 3 sig. voxels, Permutation Test (Max t-statistic FWE) 15 sig. voxels, Permutation Test (Max TFCE FWE) 862 sig. voxels
Text(0.5, 1.3, 'Group left button press ($-\\log_{10}$ p-values)')

Total running time of the script: (2 minutes 27.160 seconds)

Estimated memory usage: 61 MB

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