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plot_localizer_simple_analysis.py
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plot_localizer_simple_analysis.py
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"""
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.
"""
# Author: Virgile Fritsch, <virgile.fritsch@inria.fr>, May. 2014
import numpy as np
import matplotlib.pyplot as plt
from nilearn import datasets
from nilearn.input_data import NiftiMasker
############################################################################
# Load Localizer contrast
n_samples = 20
localizer_dataset = datasets.fetch_localizer_calculation_task(
n_subjects=n_samples)
tested_var = np.ones((n_samples, 1))
############################################################################
# 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)
from sklearn.feature_selection import f_regression
_, pvals_anova = f_regression(fmri_masked, tested_var,
center=False) # do not remove intercept
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)
############################################################################
# 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(neg_log_pvals_anova_unmasked.get_data(),
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()