9.6.3. 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(


/home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/numpy/lib/npyio.py:2405: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
  output = genfromtxt(fname, **kwargs)

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.

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(

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(

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')
plot thresholding

Second, the p<.001 uncorrected-thresholded map (with only clusters > 10 voxels).

    thresholded_map1, cut_coords=display.cut_coords, threshold=threshold1,
    title='Thresholded z map, fpr <.001, clusters > 10 voxels')
plot thresholding


<nilearn.plotting.displays.OrthoSlicer object at 0x7ff01cd3f280>

Third, the fdr-thresholded map.

plotting.plot_stat_map(thresholded_map2, cut_coords=display.cut_coords,
                       title='Thresholded z map, expected fdr = .05',
plot thresholding


<nilearn.plotting.displays.OrthoSlicer object at 0x7ff03d94b970>

Fourth, the Bonferroni-thresholded map.

plotting.plot_stat_map(thresholded_map3, cut_coords=display.cut_coords,
                       title='Thresholded z map, expected fwer < .05',
plot thresholding


<nilearn.plotting.displays.OrthoSlicer object at 0x7ff01d0fe9a0>

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 16.242 seconds)

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