Second-level fMRI model: true positive proportion in clusters

This script showcases the so-called “All resolution inference” procedure (Rosenblatt et al.[1]), in which the proportion of true discoveries in arbitrary clusters is estimated. The clusters can be defined from the input image, i.e. in a circular way, as the error control accounts for arbitrary cluster selection.

Fetch dataset

We download a list of left vs right button press contrasts from a localizer dataset. Note that we fetch individual t-maps that represent the BOLD activity estimate divided by the uncertainty about this estimate.

from nilearn.datasets import fetch_localizer_contrasts

n_subjects = 16
data = fetch_localizer_contrasts(
    ["left vs right button press"],
    n_subjects,
)

[fetch_localizer_contrasts] Dataset found in
/home/runner/nilearn_data/brainomics_localizer

Estimate second level model

We define the input maps and the design matrix for the second level model and fit it.

import pandas as pd

second_level_input = data["cmaps"]
design_matrix = pd.DataFrame(
    [1] * len(second_level_input), columns=["intercept"]
)

Model specification and fit

from nilearn.glm.second_level import SecondLevelModel

second_level_model = SecondLevelModel(smoothing_fwhm=8.0, n_jobs=2, verbose=1)
second_level_model = second_level_model.fit(
    second_level_input, design_matrix=design_matrix
)

[SecondLevelModel.fit] Fitting second level model. Take a deep breath.
[SecondLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f9c1e98e740>
[SecondLevelModel.fit] Computing mask
[SecondLevelModel.fit] Resampling mask
[SecondLevelModel.fit] Finished fit
[SecondLevelModel.fit]
Computation of second level model done in 0.35 seconds.

To estimate the contrast is very simple. We can just provide the column name of the design matrix.

z_map = second_level_model.compute_contrast(output_type="z_score")

[SecondLevelModel.compute_contrast] Loading data from
<nibabel.nifti1.Nifti1Image object at 0x7f9c479c7670>
[SecondLevelModel.compute_contrast] Smoothing images
[SecondLevelModel.compute_contrast] Extracting region signals
[SecondLevelModel.compute_contrast] Cleaning extracted signals
[SecondLevelModel.compute_contrast] Computing image from signals

We threshold the second level contrast at uncorrected p < 0.001 and plot

from scipy.stats import norm

from nilearn.glm import cluster_level_inference
from nilearn.plotting import plot_stat_map, show

p_val = 0.001
p001_uncorrected = norm.isf(p_val)

proportion_true_discoveries_img = cluster_level_inference(
    z_map, threshold=[2.5, 3.5, 4.5], alpha=0.05
)

data = proportion_true_discoveries_img.get_fdata()

cut_coords = [-24, -9, -18, 33, 45, 57, 66]

plot_stat_map(
    proportion_true_discoveries_img,
    threshold=0.0,
    display_mode="z",
    vmax=1,
    cmap="inferno",
    title="group left-right button press, proportion true positives",
    cut_coords=cut_coords,
)

plot_stat_map(
    z_map,
    threshold=p001_uncorrected,
    display_mode="z",
    title="group left-right button press (uncorrected p < 0.001)",
    cut_coords=cut_coords,
)

show()

• 
• 

/home/runner/work/nilearn/nilearn/examples/05_glm_second_level/plot_proportion_activated_voxels.py:64: DeprecationWarning: The function 'check_niimg_3d' was moved to 'nilearn.image'.
Importing it from 'nilearn._utils.niimg_conversions' will remain possible till Nilearn 0.14.0.
Please change to import from 'nilearn.image'.
  proportion_true_discoveries_img = cluster_level_inference(

References

[1]

Jonathan D. Rosenblatt, Livio Finos, Wouter D. Weeda, Aldo Solari, and Jelle J. Goeman. All-resolutions inference for brain imaging. NeuroImage, 181:786–796, November 2018. doi:10.1016/j.neuroimage.2018.07.060.

Estimated memory usage: 100 MB