9.8.2. Massively univariate analysis of a calculation task from the Localizer datasetΒΆ

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 plot_localizer_mass_univariate_methods.py example to see how to use these.

Note

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

with:

from nilearn.input_data import NiftiMasker
# Author: Virgile Fritsch, <virgile.fritsch@inria.fr>, May. 2014
import numpy as np
import matplotlib.pyplot as plt
from nilearn import datasets
from nilearn.maskers import NiftiMasker
from nilearn.image import get_data

Load Localizer contrast

Mask data

nifti_masker = NiftiMasker(
    smoothing_fwhm=5,
    memory='nilearn_cache', memory_level=1)  # cache options
cmap_filenames = localizer_dataset.cmaps
fmri_masked = nifti_masker.fit_transform(cmap_filenames)

Anova (parametric F-scores)

Out:

/home/nicolas/GitRepos/scikit-learn-fork/sklearn/utils/validation.py:1095: 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().

Visualization

from nilearn.plotting import plot_stat_map, show

# Various plotting parameters
z_slice = 45  # plotted slice

threshold = - np.log10(0.1)  # 10% corrected

# Plot Anova p-values
fig = plt.figure(figsize=(5, 6), facecolor='w')
display = plot_stat_map(neg_log_pvals_anova_unmasked,
                        threshold=threshold,
                        display_mode='z', cut_coords=[z_slice],
                        figure=fig)

masked_pvals = np.ma.masked_less(get_data(neg_log_pvals_anova_unmasked),
                                 threshold)

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

display.title(title, y=1.1, alpha=0.8)

show()
plot localizer simple analysis

Total running time of the script: ( 0 minutes 5.013 seconds)

Estimated memory usage: 9 MB

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