Example of explicit fixed effects fMRI model fitting#

This example illustrates how to run a fixed effects model based on pre-computed statistics. This is helpful when the initial models have to be fit separately.

For details on the data, please see:

Dehaene-Lambertz G, Dehaene S, Anton JL, Campagne A, Ciuciu P, Dehaene

G, Denghien I, Jobert A, LeBihan D, Sigman M, Pallier C, Poline JB. Functional segregation of cortical language areas by sentence repetition. Hum Brain Mapp. 2006: 27:360–371. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2653076#R11

Please see Simple example of two-session fMRI model fitting for details. The main difference is that the fixed-effects model is run explicitly here, after GLM fitting on two sessions.

Prepare data and analysis parameters#

Inspecting ‘data’, we note that there are two sessions

from nilearn.datasets import func

data = func.fetch_fiac_first_level()
fmri_img = [data['func1'], data['func2']]
Dataset created in /home/remi/nilearn_data/fiac_nilearn.glm

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Extracting data from /home/remi/nilearn_data/fiac_nilearn.glm/nipy-data-0.2.tar.gz..... done.

Create a mean image for plotting purpose

from nilearn.image import mean_img

mean_img_ = mean_img(fmri_img[0])

The design matrices were pre-computed, we simply put them in a list of DataFrames

design_files = [data['design_matrix1'], data['design_matrix2']]
import numpy as np
import pandas as pd

design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files]

GLM estimation#

GLM specification. Note that the mask was provided in the dataset. So we use it.

from nilearn.glm.first_level import FirstLevelModel

fmri_glm = FirstLevelModel(mask_img=data['mask'], smoothing_fwhm=5,

Compute fixed effects of the two runs and compute related images For this, we first define the contrasts as we would do for a single session

n_columns = design_matrices[0].shape[1]
contrast_val = np.hstack(([-1, -1, 1, 1], np.zeros(n_columns - 4)))

Statistics for the first session

from nilearn import plotting

cut_coords = [-129, -126, 49]
contrast_id = 'DSt_minus_SSt'

fmri_glm = fmri_glm.fit(fmri_img[0], design_matrices=design_matrices[0])
summary_statistics_session1 = fmri_glm.compute_contrast(
    contrast_val, output_type='all')
    bg_img=mean_img_, threshold=3.0, cut_coords=cut_coords,
    title=f'{contrast_id}, first session')
plot fixed effects
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd1b29a3d10>

Statistics for the second session

plot fixed effects
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd1f292a510>

Fixed effects statistics

from nilearn.glm.contrasts import compute_fixed_effects

contrast_imgs = [summary_statistics_session1['effect_size'],
variance_imgs = [summary_statistics_session1['effect_variance'],

_, _, fixed_fx_stat = compute_fixed_effects(
    contrast_imgs, variance_imgs, data['mask'])
    title=f'{contrast_id}, fixed effects'
plot fixed effects
/home/remi/github/nilearn/env/lib/python3.11/site-packages/nilearn/glm/contrasts.py:504: FutureWarning:

The behavior of this function will be changed in release 0.13 to have an additionalreturn value 'fixed_fx_z_score_img'  by default. Please set return_z_score to True.

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd1b2353e10>

Not unexpectedly, the fixed effects version displays higher peaks than the input sessions. Computing fixed effects enhances the signal-to-noise ratio of the resulting brain maps Note however that, technically, the output maps of the fixed effects map is a t statistic (not a z statistic)

Total running time of the script: (0 minutes 42.700 seconds)

Estimated memory usage: 468 MB

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