Note
Go to the end to download the full example code. or to run this example in your browser via Binder
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 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
from nilearn._utils.helpers import check_matplotlib
check_matplotlib()
import matplotlib.pyplot as plt
import numpy as np
from nilearn import datasets
from nilearn.image import get_data
from nilearn.maskers import NiftiMasker
Load Localizer contrast
n_samples = 20
localizer_dataset = datasets.fetch_localizer_calculation_task(
n_subjects=n_samples, legacy_format=False
)
tested_var = np.ones((n_samples, 1))
[get_dataset_dir] Dataset found in
/home/runner/nilearn_data/brainomics_localizer
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)
from sklearn.feature_selection import f_regression
# Center=False is used to not remove intercept
_, pvals_anova = f_regression(fmri_masked, tested_var, center=False)
pvals_anova *= fmri_masked.shape[1]
pvals_anova[np.isnan(pvals_anova)] = 1
pvals_anova[pvals_anova > 1] = 1
neg_log_pvals_anova = -np.log10(pvals_anova)
neg_log_pvals_anova_unmasked = nifti_masker.inverse_transform(
neg_log_pvals_anova
)
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/sklearn/utils/validation.py:1229: 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
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(
neg_log_pvals_anova_unmasked,
threshold=threshold,
display_mode="z",
cut_coords=[plotted_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)"
f"\n{(~masked_pvals.mask).sum()} detections"
)
display.title(title, y=1, alpha=0.8)
show()
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/plotting/displays/_slicers.py:1510: MatplotlibDeprecationWarning:
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
Total running time of the script: (0 minutes 3.444 seconds)
Estimated memory usage: 147 MB