Note

Go to the end to download the full example code. or to run this example in your browser via Binder

Simple example of two-runs fMRI model fitting

Here, we will go through a full step-by-step example of fitting a GLM to experimental data and visualizing the results. This is done on two runs of one subject of the FIAC dataset.

Here are the steps we will go through:

  1. Set up the GLM

  2. Compare run-specific and fixed effects contrasts

  3. Compute a range of contrasts across both runs

  4. Generate a report

Technically, this example shows how to handle two runs that contain the same experimental conditions. The model directly returns a fixed effect of the statistics across the two runs.

See also

See the dataset description for more information on the data used in this example.

Create an output results in the current working directory.

from pathlib import Path

output_dir = Path.cwd() / "results" / "plot_two_runs_model"
output_dir.mkdir(exist_ok=True, parents=True)
print(f"Output will be saved to: {output_dir}")

Output will be saved to: /home/runner/work/nilearn/nilearn/examples/04_glm_first_level/results/plot_two_runs_model

Set up the GLM

Inspecting ‘data’, we note that there are two runs. We will retain those two runs in a list of 4D img objects.

from nilearn.datasets.func import fetch_fiac_first_level

data = fetch_fiac_first_level()
fmri_imgs = [data["func1"], data["func2"]]

[get_dataset_dir] Dataset found in /home/runner/nilearn_data/fiac_nilearn.glm

Create a mean image for plotting purpose.

from nilearn.image import mean_img

mean_img_ = mean_img(fmri_imgs[0], copy_header=True)

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

import numpy as np

design_matrices = [data["design_matrix1"], data["design_matrix2"]]

Initialize and run the GLM

First, we need to specify the model before fitting it to the data. Note that a brain mask was provided in the dataset, so that is what we will use.

from nilearn.glm.first_level import FirstLevelModel

fmri_glm = FirstLevelModel(
    mask_img=data["mask"],
    smoothing_fwhm=5,
    minimize_memory=True,
)

Compare run-specific and fixed effects contrasts

We can then compare run-specific and fixed effects. Here, we compare the activation produced from each run separately and then the fixed effects version.

contrast_id = "DSt_minus_SSt"

Compute the statistics for the first run.

Here, we define the contrast of interest for the first run. This may differ across runs depending on if the design matrices vary.

from nilearn.plotting import plot_stat_map, show

contrast_val = [[-1, -1, 1, 1]]

fmri_glm_run_1 = fmri_glm.fit(fmri_imgs[0], design_matrices=design_matrices[0])
summary_statistics_run_1 = fmri_glm_run_1.compute_contrast(
    contrast_val,
    output_type="all",
)

# Let's use the same plotting range and slices for all plots.
threshold = 3
vmax = 6.0
cut_coords = [-129, -126, 49]

plot_stat_map(
    summary_statistics_run_1["z_score"],
    bg_img=mean_img_,
    threshold=threshold,
    cut_coords=cut_coords,
    title=f"{contrast_id}, first run",
    vmax=vmax,
)

show()
plot two runs model
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/glm/contrasts.py:113: UserWarning: t contrasts should be of length P=13, but it has length 4. The rest of the contrast was padded with zeros.
  reg = regression_result[label_].Tcontrast(con_val)

Compute the statistics for the second run.

fmri_glm_run_2 = fmri_glm.fit(fmri_imgs[1], design_matrices=design_matrices[1])

contrast_val = np.array([[-1, -1, 1, 1]])

summary_statistics_run_2 = fmri_glm_run_2.compute_contrast(
    contrast_val,
    output_type="all",
)
plot_stat_map(
    summary_statistics_run_2["z_score"],
    bg_img=mean_img_,
    threshold=threshold,
    cut_coords=cut_coords,
    title=f"{contrast_id}, second run",
    vmax=vmax,
)

show()
plot two runs model
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/glm/contrasts.py:113: UserWarning: t contrasts should be of length P=13, but it has length 4. The rest of the contrast was padded with zeros.
  reg = regression_result[label_].Tcontrast(con_val)

Compute the fixed effects statistics using the statistical maps of both runs.

We can use compute_fixed_effects to compute the fixed effects statistics using the outputs from the run-specific FirstLevelModel results.

from nilearn.glm.contrasts import compute_fixed_effects

contrast_imgs = [
    summary_statistics_run_1["effect_size"],
    summary_statistics_run_2["effect_size"],
]
variance_imgs = [
    summary_statistics_run_1["effect_variance"],
    summary_statistics_run_2["effect_variance"],
]

fixed_fx_contrast, fixed_fx_variance, fixed_fx_stat = compute_fixed_effects(
    contrast_imgs,
    variance_imgs,
    data["mask"],
)
plot_stat_map(
    fixed_fx_stat,
    bg_img=mean_img_,
    threshold=threshold,
    cut_coords=cut_coords,
    title=f"{contrast_id}, fixed effects",
    vmax=vmax,
)

show()
plot two runs model
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:153: DeprecationWarning: 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.
  fixed_fx_contrast, fixed_fx_variance, fixed_fx_stat = compute_fixed_effects(

Not unexpectedly, the fixed effects version displays higher peaks than the input runs. Computing fixed effects enhances the signal-to-noise ratio of the resulting brain maps.

Compute the fixed effects statistics using the preprocessed data of both runs.

A more straightforward alternative to fitting run-specific GLMs, than combining the results with compute_fixed_effects, is to simply fit the GLM to both runs at once.

Since we can assume that the design matrices of both runs have the same columns, in the same order, we can again reuse the first run’s contrast vector.

fmri_glm_multirun = fmri_glm.fit(fmri_imgs, design_matrices=design_matrices)

We can just define the contrast array for one run and assume that the design matrix is the same for the other. However, if we want to be safe, we should define each contrast separately, and provide it as a list.

contrast_val = [
    np.array([[-1, -1, 1, 1]]),  # run 1
    np.array([[-1, -1, 1, 1]]),  # run 2
]

z_map = fmri_glm_multirun.compute_contrast(
    contrast_val,
    output_type="z_score",
)
plot_stat_map(
    z_map,
    bg_img=mean_img_,
    threshold=threshold,
    cut_coords=cut_coords,
    title=f"{contrast_id}, fixed effects",
    vmax=vmax,
)

show()
plot two runs model
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/glm/contrasts.py:113: UserWarning: t contrasts should be of length P=13, but it has length 4. The rest of the contrast was padded with zeros.
  reg = regression_result[label_].Tcontrast(con_val)

You may note that the results are the same as the first fixed effects analysis, but with a lot less code.

Compute a range of contrasts across both runs

It may be useful to investigate a number of contrasts. Therefore, we will move beyond the original contrast of interest and both define and compute several.

Contrast specification

n_columns = design_matrices[0].shape[1]
contrasts = {
    "SStSSp_minus_DStDSp": np.array([[1, 0, 0, -1]]),
    "DStDSp_minus_SStSSp": np.array([[-1, 0, 0, 1]]),
    "DSt_minus_SSt": np.array([[-1, -1, 1, 1]]),
    "DSp_minus_SSp": np.array([[-1, 1, -1, 1]]),
    "DSt_minus_SSt_for_DSp": np.array([[0, -1, 0, 1]]),
    "DSp_minus_SSp_for_DSt": np.array([[0, 0, -1, 1]]),
    "Deactivation": np.array([[-1, -1, -1, -1, 4]]),
    "Effects_of_interest": np.eye(n_columns)[:5, :],  # An F-contrast
}

Next, we compute and plot the statistics for these new contrasts.

print("Computing contrasts...")
for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
    print(f"  Contrast {index + 1:02g} out of {len(contrasts)}: {contrast_id}")
    # Estimate the contasts.
    z_map = fmri_glm.compute_contrast(contrast_val, output_type="z_score")

    # Write the resulting stat images to file.
    z_image_path = output_dir / f"{contrast_id}_z_map.nii.gz"
    z_map.to_filename(z_image_path)

Computing contrasts...
  Contrast 01 out of 8: SStSSp_minus_DStDSp
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:246: UserWarning: One contrast given, assuming it for all 2 runs
  z_map = fmri_glm.compute_contrast(contrast_val, output_type="z_score")
  Contrast 02 out of 8: DStDSp_minus_SStSSp
  Contrast 03 out of 8: DSt_minus_SSt
  Contrast 04 out of 8: DSp_minus_SSp
  Contrast 05 out of 8: DSt_minus_SSt_for_DSp
  Contrast 06 out of 8: DSp_minus_SSp_for_DSt
  Contrast 07 out of 8: Deactivation
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/glm/contrasts.py:113: UserWarning: t contrasts should be of length P=13, but it has length 5. The rest of the contrast was padded with zeros.
  reg = regression_result[label_].Tcontrast(con_val)
  Contrast 08 out of 8: Effects_of_interest
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/glm/contrasts.py:164: UserWarning: Running approximate fixed effects on F statistics.
  contrast = contrast_ if contrast is None else contrast + contrast_

Generating a report

Since we have already computed the FirstLevelModel and have a number of contrasts, we can quickly create a summary report.

report = fmri_glm_multirun.generate_report(
    contrasts,
    bg_img=mean_img_,
    title="two-runs fMRI model fitting",
)

/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/_utils/glm.py:169: UserWarning: One contrast given, assuming it for all 2 runs
  contrast_name: model.compute_contrast(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/_utils/plotting.py:140: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  contrast_plot = plot_contrast_matrix(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/_utils/plotting.py:140: UserWarning: Contrasts will be padded with 8 column(s) of zeros.
  contrast_plot = plot_contrast_matrix(

We have several ways to access the report:

This report can be viewed in a notebook.

report

Statistical Report - First Level Model
two-runs fMRI model fitting Implement the General Linear Model for single run :term:`fMRI` data.

Description

Data were analyzed using Nilearn (version= 0.11.2.dev243+gf74c4c5c0; RRID:SCR_001362).

At the subject level, a mass univariate analysis was performed with a linear regression at each voxel of the brain, using generalized least squares with a global ar1 noise model to account for temporal auto-correlation and a cosine drift model (high pass filter=0.01 Hz).

Input images were smoothed with gaussian kernel (full-width at half maximum=5 mm).

Model details

Value
Parameter
drift_model cosine
high_pass (Hertz) 0.01
hrf_model glover
noise_model ar1
signal_scaling 0
slice_time_ref 0.0
smoothing_fwhm (mm) 5
standardize False

Design Matrix

run 0

Plot of design matrix for 0.

correlation matrix

Plot of correlation of design matrix for run 0.

run 1

Plot of design matrix for 1.

correlation matrix

Plot of correlation of design matrix for run 1.

Contrasts

Plot of the contrast SStSSp_minus_DStDSp (run 0).
Plot of the contrast DStDSp_minus_SStSSp (run 0).
Plot of the contrast DSt_minus_SSt (run 0).
Plot of the contrast DSp_minus_SSp (run 0).
Plot of the contrast DSt_minus_SSt_for_DSp (run 0).
Plot of the contrast DSp_minus_SSp_for_DSt (run 0).
Plot of the contrast Deactivation (run 0).
Plot of the contrast Effects_of_interest (run 0).
Plot of the contrast SStSSp_minus_DStDSp (run 1).
Plot of the contrast DStDSp_minus_SStSSp (run 1).
Plot of the contrast DSt_minus_SSt (run 1).
Plot of the contrast DSp_minus_SSp (run 1).
Plot of the contrast DSt_minus_SSt_for_DSp (run 1).
Plot of the contrast DSp_minus_SSp_for_DSt (run 1).
Plot of the contrast Deactivation (run 1).
Plot of the contrast Effects_of_interest (run 1).

Mask

Mask image

Statistical Maps

SStSSp_minus_DStDSp

Stat map plot for the contrast: SStSSp_minus_DStDSp
Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -135.0 -138.0 40.0 5.42 684
2 -111.0 -57.0 92.0 5.14 396
3 -111.0 -27.0 52.0 4.85 216
4 -126.0 -150.0 80.0 4.67 108
5 -141.0 -114.0 40.0 4.61 180
6 -132.0 -105.0 88.0 4.46 108
7 -138.0 -54.0 56.0 4.37 432
8 -135.0 -138.0 80.0 4.25 540
9 -102.0 -90.0 104.0 4.23 108
10 -39.0 -102.0 60.0 4.22 72
11 -120.0 -69.0 96.0 4.15 180
12 -132.0 -39.0 52.0 4.10 36
13 -126.0 -153.0 64.0 4.05 180
14 -120.0 -96.0 20.0 4.01 108
15 -141.0 -111.0 36.0 4.00 180
16 -99.0 -144.0 36.0 3.93 72
17 -90.0 -117.0 92.0 3.86 36
18 -81.0 -30.0 76.0 3.85 144
19 -93.0 -87.0 80.0 3.83 36
20 -75.0 -30.0 56.0 3.79 144
21 -135.0 -42.0 56.0 3.72 72
22 -135.0 -63.0 76.0 3.71 72
23 -51.0 -69.0 48.0 3.71 36
24 -111.0 -72.0 84.0 3.70 72
25 -132.0 -66.0 36.0 3.69 36
26 -102.0 -105.0 112.0 3.68 36
27 -135.0 -141.0 56.0 3.68 72
28 -141.0 -87.0 60.0 3.67 180
29 -141.0 -63.0 48.0 3.66 36
30 -144.0 -120.0 76.0 3.65 180
31 -81.0 -111.0 8.0 3.64 36
32 -132.0 -129.0 92.0 3.63 72
33 -111.0 -114.0 32.0 3.62 36
34 -57.0 -102.0 88.0 3.62 36
35 -135.0 -42.0 48.0 3.61 36
36 -126.0 -39.0 48.0 3.60 36
37 -54.0 -141.0 88.0 3.55 72
38 -81.0 -108.0 64.0 3.55 36
39 -78.0 -63.0 32.0 3.55 144
40 -144.0 -93.0 64.0 3.53 72
41 -114.0 -150.0 80.0 3.52 108
42 -144.0 -129.0 68.0 3.50 36
43 -144.0 -78.0 72.0 3.50 36
44 -135.0 -90.0 88.0 3.50 36
45 -144.0 -111.0 80.0 3.48 36
46 -72.0 -126.0 56.0 3.47 36
47 -138.0 -126.0 88.0 3.46 36
48 -93.0 -60.0 100.0 3.44 36
49 -90.0 -18.0 60.0 3.42 72
50 -81.0 -33.0 68.0 3.42 36
51 -87.0 -93.0 112.0 3.41 36
52 -135.0 -57.0 72.0 3.39 36
53 -120.0 -30.0 44.0 3.38 36
54 -129.0 -147.0 72.0 3.35 36
55 -123.0 -45.0 48.0 3.34 36
56 -129.0 -87.0 44.0 3.34 36
57 -93.0 -84.0 112.0 3.34 36
58 -66.0 -150.0 12.0 3.34 36
59 -123.0 -45.0 80.0 3.34 36
60 -114.0 -33.0 76.0 3.31 36
61 -51.0 -78.0 28.0 3.31 36
62 -123.0 -102.0 100.0 3.30 36
63 -117.0 -87.0 92.0 3.30 36
64 -51.0 -87.0 96.0 3.30 36

DStDSp_minus_SStSSp

Stat map plot for the contrast: DStDSp_minus_SStSSp
Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -72.0 -147.0 8.0 5.53 252
1a -72.0 -138.0 8.0 3.54
2 -96.0 -147.0 4.0 5.28 720
2a -99.0 -144.0 12.0 3.99
3 -60.0 -126.0 4.0 5.20 144
4 -99.0 -75.0 108.0 4.98 360
5 -132.0 -129.0 52.0 4.95 720
6 -105.0 -108.0 92.0 4.56 36
7 -129.0 -60.0 48.0 4.55 324
8 -42.0 -66.0 48.0 4.51 72
9 -54.0 -111.0 48.0 4.49 144
10 -33.0 -66.0 44.0 4.42 36
11 -39.0 -84.0 88.0 4.21 72
12 -45.0 -63.0 80.0 4.10 36
13 -51.0 -42.0 24.0 4.10 36
14 -87.0 -42.0 88.0 4.09 72
15 -33.0 -114.0 68.0 4.05 72
16 -51.0 -114.0 36.0 4.03 72
17 -75.0 -150.0 8.0 4.02 36
18 -45.0 -66.0 84.0 3.94 72
19 -72.0 -147.0 84.0 3.91 36
20 -36.0 -90.0 72.0 3.91 36
21 -138.0 -99.0 44.0 3.87 72
22 -99.0 -141.0 20.0 3.85 36
23 -138.0 -105.0 48.0 3.83 108
24 -126.0 -75.0 72.0 3.82 72
25 -51.0 -108.0 88.0 3.81 72
26 -108.0 -138.0 0.0 3.80 72
27 -75.0 -63.0 60.0 3.79 108
28 -51.0 -102.0 96.0 3.76 36
29 -135.0 -78.0 68.0 3.76 72
30 -69.0 -72.0 56.0 3.75 36
31 -39.0 -78.0 48.0 3.75 216
32 -51.0 -117.0 48.0 3.71 36
33 -45.0 -54.0 64.0 3.65 72
34 -48.0 -102.0 36.0 3.64 72
35 -57.0 -96.0 48.0 3.63 36
36 -39.0 -87.0 76.0 3.61 36
37 -39.0 -78.0 84.0 3.60 36
38 -48.0 -66.0 80.0 3.59 36
39 -138.0 -108.0 76.0 3.58 36
40 -96.0 -159.0 60.0 3.58 36
41 -69.0 -135.0 8.0 3.57 72
42 -126.0 -141.0 56.0 3.51 36
43 -48.0 -114.0 64.0 3.47 36
44 -117.0 -123.0 48.0 3.44 72
45 -36.0 -93.0 76.0 3.42 72
46 -96.0 -99.0 104.0 3.39 36
47 -48.0 -123.0 40.0 3.38 36
48 -48.0 -108.0 32.0 3.35 36
49 -39.0 -87.0 52.0 3.35 36
50 -42.0 -102.0 36.0 3.34 36
51 -69.0 -87.0 96.0 3.33 72

DSt_minus_SSt

Stat map plot for the contrast: DSt_minus_SSt
Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -129.0 -129.0 48.0 5.30 540
2 -129.0 -60.0 48.0 4.48 144
3 -99.0 -75.0 108.0 4.43 288
4 -51.0 -42.0 24.0 4.41 108
5 -42.0 -66.0 48.0 4.26 72
6 -33.0 -66.0 44.0 4.24 36
7 -135.0 -57.0 20.0 4.19 36
8 -72.0 -147.0 8.0 4.03 216
9 -84.0 -81.0 44.0 4.03 36
10 -102.0 -138.0 12.0 4.00 108
11 -138.0 -105.0 48.0 3.99 108
12 -51.0 -114.0 36.0 3.91 108
13 -96.0 -147.0 4.0 3.70 36
14 -45.0 -69.0 84.0 3.67 72
15 -69.0 -135.0 8.0 3.64 36
16 -120.0 -78.0 44.0 3.54 36
17 -42.0 -60.0 24.0 3.54 36
18 -138.0 -99.0 44.0 3.36 36
19 -45.0 -57.0 16.0 3.36 36
20 -114.0 -75.0 60.0 3.35 36
21 -117.0 -60.0 64.0 3.33 36
22 -78.0 -138.0 16.0 3.32 36
23 -33.0 -114.0 68.0 3.32 36
24 -132.0 -63.0 16.0 3.31 72

DSp_minus_SSp

Stat map plot for the contrast: DSp_minus_SSp
Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -54.0 -111.0 52.0 4.92 360
2 -60.0 -126.0 4.0 4.84 108
3 -105.0 -108.0 92.0 4.60 72
4 -48.0 -114.0 64.0 4.47 72
5 -63.0 -108.0 68.0 4.12 72
6 -48.0 -102.0 32.0 4.03 216
7 -48.0 -123.0 40.0 3.97 72
8 -87.0 -42.0 88.0 3.95 72
9 -105.0 -141.0 4.0 3.93 36
10 -72.0 -147.0 8.0 3.81 108
11 -72.0 -87.0 96.0 3.74 72
12 -96.0 -147.0 4.0 3.73 72
13 -102.0 -135.0 28.0 3.72 36
14 -45.0 -63.0 80.0 3.72 36
15 -36.0 -96.0 56.0 3.70 72
16 -135.0 -108.0 76.0 3.68 72
17 -126.0 -111.0 68.0 3.63 36
18 -45.0 -84.0 48.0 3.61 36
19 -54.0 -96.0 44.0 3.57 36
20 -120.0 -123.0 48.0 3.57 36
21 -96.0 -102.0 104.0 3.56 72
22 -39.0 -114.0 56.0 3.54 180
23 -60.0 -87.0 100.0 3.51 36
24 -114.0 -87.0 36.0 3.50 36
25 -57.0 -123.0 8.0 3.46 36
26 -42.0 -111.0 36.0 3.45 108
27 -96.0 -159.0 60.0 3.41 36
28 -45.0 -105.0 56.0 3.38 72
29 -66.0 -144.0 56.0 3.36 36
30 -114.0 -153.0 36.0 3.32 36
31 -78.0 -99.0 56.0 3.32 36
32 -69.0 -69.0 56.0 3.32 36
33 -135.0 -111.0 72.0 3.31 36
34 -39.0 -84.0 88.0 3.30 36
35 -102.0 -117.0 72.0 3.30 36

DSt_minus_SSt_for_DSp

Stat map plot for the contrast: DSt_minus_SSt_for_DSp
Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -102.0 -129.0 48.0 4.46 36
2 -105.0 -135.0 0.0 4.28 72
3 -129.0 -126.0 48.0 3.81 144
4 -84.0 -81.0 44.0 3.76 36
5 -111.0 -66.0 68.0 3.35 36
6 -66.0 -168.0 48.0 3.34 36

DSp_minus_SSp_for_DSt

Stat map plot for the contrast: DSp_minus_SSp_for_DSt
Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -78.0 -60.0 40.0 3.68 36
2 -72.0 -87.0 96.0 3.68 36
3 -108.0 -69.0 76.0 3.60 36
4 -102.0 -117.0 108.0 3.58 36
5 -66.0 -99.0 92.0 3.57 72
6 -93.0 -153.0 20.0 3.49 36
7 -75.0 -90.0 100.0 3.46 36
8 -108.0 -135.0 0.0 3.38 36
9 -132.0 -111.0 72.0 3.36 36
10 -114.0 -132.0 48.0 3.34 36
11 -99.0 -99.0 104.0 3.31 72

Deactivation

Stat map plot for the contrast: Deactivation
Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -48.0 -93.0 56.0 5.97 4140
1a -39.0 -93.0 64.0 5.64
1b -51.0 -96.0 64.0 5.24
1c -39.0 -81.0 52.0 5.01
2 -51.0 -60.0 56.0 5.70 2880
3 -39.0 -114.0 60.0 5.27 504
3a -33.0 -108.0 68.0 4.83
4 -138.0 -81.0 60.0 5.05 504
4a -129.0 -81.0 64.0 3.61
5 -120.0 -114.0 56.0 5.00 3492
5a -135.0 -123.0 56.0 4.76
5b -126.0 -108.0 60.0 4.71
5c -129.0 -129.0 52.0 4.63
6 -102.0 -75.0 104.0 4.90 288
7 -90.0 -69.0 100.0 4.61 288
7a -90.0 -72.0 92.0 4.34
8 -51.0 -117.0 60.0 4.52 288
9 -57.0 -78.0 96.0 4.41 612
9a -54.0 -75.0 88.0 4.41
10 -54.0 -63.0 76.0 4.18 252
11 -132.0 -60.0 48.0 4.17 108
12 -36.0 -111.0 72.0 4.06 108
13 -48.0 -114.0 68.0 4.03 72
14 -90.0 -81.0 108.0 3.92 72
15 -111.0 -117.0 80.0 3.85 36
16 -138.0 -96.0 44.0 3.80 72
17 -120.0 -66.0 52.0 3.71 144
18 -51.0 -114.0 52.0 3.55 72
19 -54.0 -69.0 72.0 3.53 36
20 -90.0 -75.0 104.0 3.49 36
21 -45.0 -66.0 80.0 3.46 36
22 -45.0 -63.0 44.0 3.44 36
23 -63.0 -63.0 64.0 3.37 36
24 -45.0 -60.0 76.0 3.31 36

Effects_of_interest

Stat map plot for the contrast: Effects_of_interest
Cluster Table
Height control fpr
α 0.001
Threshold (computed) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0
Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -33.0 -111.0 56.0 16.12 36504
1a -36.0 -99.0 56.0 14.50
1b -45.0 -75.0 56.0 14.22
1c -45.0 -99.0 64.0 13.56
2 -141.0 -105.0 48.0 13.82 14724
2a -138.0 -96.0 48.0 13.76
2b -141.0 -117.0 48.0 13.49
2c -138.0 -111.0 56.0 13.06
3 -123.0 -153.0 56.0 8.94 10440
3a -138.0 -126.0 80.0 8.56
3b -126.0 -153.0 64.0 7.56
3c -126.0 -150.0 80.0 7.48
4 -93.0 -147.0 4.0 8.75 5328
4a -108.0 -141.0 0.0 7.89
4b -99.0 -141.0 20.0 7.05
4c -102.0 -144.0 12.0 6.98
5 -87.0 -111.0 88.0 8.40 30564
5a -90.0 -147.0 88.0 8.08
5b -96.0 -138.0 104.0 7.71
5c -84.0 -138.0 100.0 7.53
6 -96.0 -60.0 72.0 7.80 8028
6a -96.0 -57.0 80.0 6.92
6b -102.0 -33.0 56.0 6.82
6c -96.0 -69.0 80.0 6.81
7 -123.0 -42.0 72.0 7.80 6084
7a -135.0 -60.0 72.0 7.33
7b -126.0 -60.0 84.0 7.27
7c -132.0 -69.0 88.0 6.59
8 -57.0 -78.0 16.0 7.75 1368
8a -51.0 -78.0 28.0 3.92
9 -75.0 -57.0 76.0 7.68 1512
10 -54.0 -75.0 88.0 7.39 2340
10a -63.0 -69.0 96.0 5.13
11 -138.0 -78.0 64.0 7.31 720
12 -117.0 -87.0 104.0 7.23 1476
12a -111.0 -93.0 108.0 4.96
13 -72.0 -147.0 8.0 7.00 1944
13a -72.0 -156.0 20.0 6.19
13b -66.0 -147.0 20.0 5.56
14 -129.0 -60.0 48.0 6.92 864
14a -120.0 -60.0 56.0 5.80
15 -120.0 -24.0 60.0 6.89 2232
15a -120.0 -33.0 56.0 5.91
15b -129.0 -33.0 56.0 5.85
15c -135.0 -42.0 48.0 5.32
16 -102.0 -75.0 104.0 6.69 360
17 -69.0 -108.0 112.0 6.47 828
18 -111.0 -117.0 32.0 6.22 864
18a -111.0 -126.0 36.0 5.54
19 -90.0 -114.0 112.0 6.09 540
20 -120.0 -132.0 24.0 5.98 504
21 -123.0 -84.0 32.0 5.57 72
22 -114.0 -123.0 104.0 5.43 1728
22a -123.0 -129.0 104.0 4.39
23 -135.0 -54.0 36.0 5.42 864
23a -123.0 -63.0 40.0 4.47
24 -45.0 -138.0 72.0 5.41 1692
24a -51.0 -150.0 72.0 4.82
24b -48.0 -132.0 76.0 4.16
24c -48.0 -141.0 60.0 4.02
25 -51.0 -138.0 80.0 5.39 684
25a -51.0 -141.0 88.0 5.36
25b -51.0 -147.0 80.0 4.48
26 -129.0 -54.0 60.0 5.37 936
26a -138.0 -57.0 64.0 4.92
27 -93.0 -60.0 100.0 5.36 144
28 -72.0 -78.0 96.0 5.35 864
29 -132.0 -102.0 96.0 5.31 288
30 -57.0 -111.0 12.0 5.21 360
31 -48.0 -117.0 28.0 5.18 540
32 -141.0 -102.0 40.0 5.17 180
33 -90.0 -96.0 112.0 5.14 612
33a -93.0 -81.0 112.0 4.18
34 -60.0 -111.0 36.0 5.09 1044
34a -63.0 -114.0 28.0 4.23
35 -87.0 -36.0 48.0 5.07 540
36 -51.0 -135.0 16.0 5.03 396
37 -78.0 -69.0 32.0 5.03 72
38 -123.0 -117.0 32.0 5.01 108
39 -66.0 -126.0 108.0 4.99 108
40 -51.0 -42.0 24.0 4.94 108
41 -114.0 -48.0 88.0 4.94 612
42 -123.0 -108.0 80.0 4.94 540
42a -135.0 -105.0 88.0 4.91
43 -48.0 -102.0 96.0 4.86 72
44 -69.0 -84.0 24.0 4.82 108
45 -102.0 -120.0 28.0 4.76 72
46 -129.0 -57.0 64.0 4.72 36
47 -126.0 -141.0 12.0 4.66 180
48 -63.0 -54.0 92.0 4.64 36
49 -132.0 -69.0 40.0 4.62 36
50 -66.0 -54.0 24.0 4.62 36
51 -51.0 -42.0 64.0 4.60 36
52 -78.0 -84.0 24.0 4.60 108
53 -135.0 -144.0 56.0 4.60 108
54 -75.0 -114.0 116.0 4.60 72
55 -57.0 -141.0 28.0 4.58 360
56 -105.0 -93.0 112.0 4.57 180
56a -102.0 -90.0 104.0 3.93
57 -81.0 -84.0 104.0 4.55 216
58 -123.0 -102.0 64.0 4.53 36
59 -123.0 -60.0 32.0 4.50 108
60 -141.0 -87.0 56.0 4.45 468
61 -87.0 -153.0 36.0 4.44 36
62 -42.0 -126.0 56.0 4.43 108
63 -144.0 -102.0 60.0 4.40 36
64 -60.0 -159.0 40.0 4.39 216
65 -57.0 -54.0 88.0 4.38 252
66 -78.0 -126.0 0.0 4.38 252
67 -69.0 -63.0 44.0 4.37 108
68 -57.0 -93.0 88.0 4.36 504
69 -132.0 -69.0 32.0 4.35 72
70 -60.0 -126.0 4.0 4.32 36
71 -78.0 -63.0 64.0 4.30 72
72 -135.0 -93.0 88.0 4.28 108
73 -102.0 -57.0 100.0 4.25 36
74 -114.0 -150.0 28.0 4.23 216
75 -123.0 -144.0 88.0 4.20 108
76 -138.0 -78.0 40.0 4.18 36
77 -51.0 -108.0 28.0 4.17 36
78 -93.0 -150.0 24.0 4.13 36
79 -141.0 -75.0 80.0 4.11 72
80 -141.0 -135.0 72.0 4.08 36
81 -129.0 -138.0 44.0 4.08 36
82 -120.0 -96.0 20.0 4.06 36
83 -69.0 -66.0 88.0 4.06 108
84 -63.0 -54.0 68.0 4.05 144
85 -123.0 -69.0 60.0 4.04 36
86 -69.0 -99.0 36.0 4.04 108
87 -144.0 -132.0 72.0 4.03 36
88 -57.0 -138.0 52.0 4.02 72
89 -51.0 -129.0 8.0 4.01 72
90 -90.0 -75.0 104.0 4.01 36
91 -96.0 -120.0 116.0 4.01 72
92 -72.0 -117.0 0.0 4.00 72
93 -60.0 -90.0 56.0 4.00 36
94 -84.0 -138.0 32.0 3.99 288
95 -63.0 -153.0 24.0 3.98 36
96 -90.0 -132.0 4.0 3.98 72
97 -129.0 -141.0 88.0 3.98 36
98 -144.0 -108.0 36.0 3.95 36
99 -138.0 -141.0 60.0 3.95 36
100 -111.0 -27.0 52.0 3.94 36
101 -75.0 -135.0 108.0 3.94 36
102 -96.0 -111.0 52.0 3.93 36
103 -117.0 -117.0 16.0 3.93 144
104 -63.0 -138.0 104.0 3.93 108
105 -63.0 -96.0 40.0 3.92 72
106 -105.0 -117.0 96.0 3.92 72
107 -102.0 -108.0 60.0 3.91 36
108 -114.0 -66.0 68.0 3.89 72
109 -45.0 -126.0 80.0 3.89 72
110 -126.0 -78.0 56.0 3.88 36
111 -72.0 -126.0 104.0 3.88 72
112 -117.0 -96.0 24.0 3.87 36
113 -111.0 -123.0 0.0 3.86 36
114 -147.0 -99.0 64.0 3.85 36
115 -105.0 -108.0 92.0 3.84 36
116 -75.0 -39.0 80.0 3.84 36
117 -66.0 -48.0 36.0 3.84 36
118 -105.0 -45.0 80.0 3.82 36
119 -33.0 -66.0 44.0 3.81 36
120 -36.0 -93.0 80.0 3.78 36
121 -99.0 -99.0 104.0 3.76 108
122 -144.0 -129.0 68.0 3.75 36
123 -72.0 -123.0 112.0 3.75 36
124 -93.0 -87.0 92.0 3.74 72
125 -42.0 -126.0 64.0 3.73 72
126 -51.0 -138.0 48.0 3.73 72
127 -96.0 -24.0 56.0 3.73 108
128 -57.0 -105.0 44.0 3.72 72
129 -114.0 -165.0 52.0 3.71 72
130 -39.0 -108.0 88.0 3.71 36
131 -111.0 -120.0 44.0 3.70 180
132 -93.0 -81.0 40.0 3.70 72
133 -108.0 -153.0 88.0 3.70 36
134 -36.0 -81.0 68.0 3.70 36
135 -105.0 -45.0 48.0 3.69 36
136 -93.0 -153.0 20.0 3.69 72
137 -102.0 -120.0 76.0 3.69 36
138 -48.0 -117.0 12.0 3.66 36
139 -99.0 -93.0 80.0 3.66 72
140 -42.0 -117.0 80.0 3.66 36
141 -135.0 -72.0 40.0 3.66 72
142 -72.0 -93.0 96.0 3.66 36
143 -129.0 -81.0 48.0 3.66 72
144 -117.0 -150.0 16.0 3.65 36
145 -111.0 -132.0 84.0 3.64 36
146 -75.0 -135.0 44.0 3.64 36
147 -114.0 -75.0 88.0 3.63 36
148 -75.0 -51.0 92.0 3.62 36
149 -63.0 -150.0 32.0 3.62 36
150 -90.0 -99.0 16.0 3.62 72
151 -117.0 -162.0 52.0 3.62 36
152 -42.0 -132.0 20.0 3.62 36
153 -63.0 -93.0 56.0 3.62 36
154 -108.0 -99.0 112.0 3.62 72
155 -114.0 -126.0 0.0 3.61 36
156 -114.0 -132.0 48.0 3.61 36
157 -96.0 -120.0 8.0 3.61 108
158 -105.0 -93.0 104.0 3.60 36
159 -84.0 -114.0 56.0 3.60 36
160 -75.0 -45.0 44.0 3.60 36
161 -87.0 -63.0 36.0 3.60 72
162 -48.0 -81.0 64.0 3.58 36
163 -66.0 -63.0 28.0 3.58 72
164 -66.0 -90.0 32.0 3.57 72
165 -120.0 -102.0 52.0 3.57 36
166 -75.0 -120.0 112.0 3.57 36
167 -108.0 -81.0 100.0 3.57 36
168 -117.0 -144.0 64.0 3.57 72
169 -75.0 -165.0 52.0 3.57 72
170 -75.0 -57.0 88.0 3.56 72
171 -39.0 -75.0 72.0 3.55 72
172 -63.0 -60.0 40.0 3.55 36
173 -90.0 -21.0 52.0 3.55 36
174 -96.0 -90.0 76.0 3.55 36
175 -126.0 -138.0 48.0 3.54 36
176 -138.0 -51.0 44.0 3.53 36
177 -99.0 -108.0 64.0 3.53 36
178 -120.0 -135.0 12.0 3.52 72
179 -69.0 -69.0 80.0 3.52 36
180 -111.0 -150.0 60.0 3.52 36
181 -66.0 -132.0 16.0 3.52 36
182 -84.0 -54.0 48.0 3.52 72
183 -141.0 -132.0 68.0 3.51 36
184 -60.0 -33.0 64.0 3.49 36
185 -102.0 -72.0 44.0 3.49 36
186 -75.0 -63.0 52.0 3.49 72
187 -129.0 -81.0 68.0 3.48 36
188 -93.0 -120.0 0.0 3.48 36
189 -123.0 -96.0 24.0 3.47 36
190 -99.0 -108.0 20.0 3.46 72
191 -108.0 -126.0 28.0 3.46 36
192 -39.0 -132.0 60.0 3.46 36
193 -69.0 -69.0 96.0 3.46 36
194 -60.0 -90.0 64.0 3.46 36
195 -78.0 -129.0 112.0 3.45 36
196 -108.0 -120.0 4.0 3.45 36
197 -99.0 -102.0 112.0 3.45 36
198 -102.0 -156.0 44.0 3.44 36
199 -111.0 -147.0 32.0 3.44 36
200 -129.0 -120.0 68.0 3.44 36
201 -75.0 -159.0 56.0 3.44 36
202 -60.0 -75.0 76.0 3.44 36
203 -123.0 -84.0 56.0 3.44 72
204 -99.0 -51.0 96.0 3.44 36
205 -51.0 -135.0 96.0 3.43 36
206 -90.0 -162.0 40.0 3.43 36
207 -39.0 -84.0 88.0 3.42 36
208 -45.0 -69.0 84.0 3.41 36
209 -123.0 -105.0 104.0 3.41 36
210 -111.0 -117.0 80.0 3.41 36
211 -117.0 -72.0 92.0 3.41 36
212 -141.0 -48.0 32.0 3.40 36
213 -63.0 -129.0 36.0 3.40 72
214 -57.0 -126.0 0.0 3.40 36
215 -129.0 -135.0 8.0 3.40 36
216 -87.0 -162.0 72.0 3.40 72
217 -69.0 -102.0 56.0 3.39 36
218 -111.0 -90.0 24.0 3.39 36
219 -54.0 -129.0 12.0 3.39 36
220 -84.0 -63.0 68.0 3.39 36
221 -33.0 -126.0 44.0 3.38 36
222 -102.0 -156.0 16.0 3.38 36
223 -66.0 -66.0 32.0 3.37 72
224 -66.0 -36.0 76.0 3.36 36
225 -75.0 -120.0 4.0 3.36 36
226 -129.0 -57.0 32.0 3.35 36
227 -69.0 -33.0 76.0 3.35 36
228 -60.0 -132.0 104.0 3.35 36
229 -48.0 -123.0 92.0 3.35 36
230 -111.0 -114.0 96.0 3.34 36
231 -129.0 -96.0 88.0 3.34 36
232 -63.0 -87.0 36.0 3.34 36
233 -123.0 -132.0 4.0 3.33 36
234 -120.0 -87.0 36.0 3.33 36
235 -90.0 -21.0 68.0 3.33 36
236 -45.0 -87.0 40.0 3.32 36
237 -102.0 -138.0 40.0 3.32 36
238 -42.0 -123.0 84.0 3.31 36
239 -105.0 -69.0 44.0 3.31 36
240 -63.0 -72.0 76.0 3.31 36
241 -75.0 -99.0 68.0 3.31 36
242 -96.0 -126.0 84.0 3.30 36
243 -78.0 -66.0 68.0 3.30 36
244 -117.0 -105.0 36.0 3.30 36
245 -102.0 -84.0 80.0 3.30 36
246 -72.0 -162.0 64.0 3.29 36
247 -87.0 -126.0 4.0 3.29 36

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