Note
Click here to download the full example code or to run this example in your browser via Binder
Statistical testing of a second-level analysis#
Perform a one-sample t-test on a bunch of images (a.k.a. second-level analysis in fMRI) and threshold the resulting statistical map.
This example is based on the so-called localizer dataset. It shows activation related to a mental computation task, as opposed to narrative sentence reading/listening.
Prepare some images for a simple t test#
This is a simple manually performed second level analysis.
from nilearn import datasets
n_samples = 20
localizer_dataset = datasets.fetch_localizer_calculation_task(
n_subjects=n_samples, legacy_format=False
)
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Get the set of individual statstical maps (contrast estimates)
Perform the second level analysis#
First, we define a design matrix for the model. As the model is trivial (one-sample test), the design matrix is just one column with ones.
import pandas as pd
design_matrix = pd.DataFrame([1] * n_samples, columns=['intercept'])
Next, we specify and estimate the model.
from nilearn.glm.second_level import SecondLevelModel
second_level_model = SecondLevelModel().fit(
cmap_filenames, design_matrix=design_matrix)
Compute the only possible contrast: the one-sample test. Since there is only one possible contrast, we don’t need to specify it in detail.
z_map = second_level_model.compute_contrast(output_type='z_score')
Threshold the resulting map without multiple comparisons correction, abs(z) > 3.29 (equivalent to p < 0.001), cluster size > 10 voxels.
from nilearn.image import threshold_img
thresholded_map = threshold_img(
z_map,
threshold=3.29,
cluster_threshold=10,
two_sided=True,
)
This is equivalent to thresholding a z-statistic image with a false positive rate < .001, cluster size > 10 voxels.
from nilearn.glm import threshold_stats_img
thresholded_map1, threshold1 = threshold_stats_img(
z_map,
alpha=.001,
height_control='fpr',
cluster_threshold=10,
two_sided=True,
)
Now use FDR <.05 (False Discovery Rate) and no cluster-level threshold.
thresholded_map2, threshold2 = threshold_stats_img(
z_map, alpha=.05, height_control='fdr')
print('The FDR=.05 threshold is %.3g' % threshold2)
The FDR=.05 threshold is 2.37
Now use FWER <.05 (Family-Wise Error Rate) and no cluster-level threshold. As the data has not been intensively smoothed, we can use a simple Bonferroni correction.
thresholded_map3, threshold3 = threshold_stats_img(
z_map, alpha=.05, height_control='bonferroni')
print('The p<.05 Bonferroni-corrected threshold is %.3g' % threshold3)
The p<.05 Bonferroni-corrected threshold is 4.88
Visualize the results#
First, the unthresholded map.
from nilearn import plotting
display = plotting.plot_stat_map(z_map, title='Raw z map')
Second, the p<.001 uncorrected-thresholded map (with only clusters > 10 voxels).
plotting.plot_stat_map(
thresholded_map1, cut_coords=display.cut_coords, threshold=threshold1,
title='Thresholded z map, fpr <.001, clusters > 10 voxels')
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff8f198aef0>
Third, the fdr-thresholded map.
plotting.plot_stat_map(thresholded_map2, cut_coords=display.cut_coords,
title='Thresholded z map, expected fdr = .05',
threshold=threshold2)
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff8fae41c00>
Fourth, the Bonferroni-thresholded map.
plotting.plot_stat_map(thresholded_map3, cut_coords=display.cut_coords,
title='Thresholded z map, expected fwer < .05',
threshold=threshold3)
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff908127fa0>
These different thresholds correspond to different statistical guarantees: in the FWER-corrected image there is only a probability smaller than .05 of observing any false positive voxel. In the FDR-corrected image, 5% of the voxels found are likely to be false positive. In the uncorrected image, one expects a few tens of false positive voxels.
Total running time of the script: ( 0 minutes 57.602 seconds)
Estimated memory usage: 9 MB