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

This example shows how to use the Localizer dataset in a basic analysis. A standard Anova is performed (massively univariate F-test) and the resulting Bonferroni-corrected p-values are plotted. We use a calculation task and 20 subjects out of the 94 available.

The Localizer dataset contains many contrasts and subject-related variates. The user can refer to the example to see how to use these.


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
except ImportError:
    raise RuntimeError("This script needs the matplotlib library")
import numpy as np

from nilearn import datasets
from nilearn.image import get_data
from nilearn.maskers import NiftiMasker

Load Localizer contrast

Mask data

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

Anova (parametric F-scores)

/opt/hostedtoolcache/Python/3.12.2/x64/lib/python3.12/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().


from nilearn.plotting import plot_stat_map, show

# Various plotting parameters
plotted_slice = 45
threshold = -np.log10(0.1)  # 10% corrected

# Plot Anova p-values
fig = plt.figure(figsize=(5, 6), facecolor="w")
display = plot_stat_map(

masked_pvals =
    get_data(neg_log_pvals_anova_unmasked), threshold

title = (
    "Negative $\\log_{10}$ p-values"
    "\n(Parametric + Bonferroni correction)"
    f"\n{(~masked_pvals.mask).sum()} detections"

display.title(title, y=1, alpha=0.8)

plot localizer simple analysis

Total running time of the script: (0 minutes 3.403 seconds)

Estimated memory usage: 9 MB

Gallery generated by Sphinx-Gallery