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:

Set up the GLM
Compare run-specific and fixed effects contrasts
Compute a range of contrasts across both runs
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.

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"]]

[fetch_fiac_first_level] 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])

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, verbose=1
)

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()

[FirstLevelModel.fit] Loading data from
'/home/runner/nilearn_data/fiac_nilearn.glm/nipy-data-0.2/data/fiac/fiac0/run1.n
ii.gz'
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/fiac_nilearn.glm/nipy-data-0.2/data/fiac/fiac0/mask.n
ii.gz'
[FirstLevelModel.fit] Resampling mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f30b7ccb4c0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 2 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 3 seconds.
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:91: UserWarning: t contrasts should be of length P=13, but it has length 4. The rest of the contrast was padded with zeros.
  summary_statistics_run_1 = fmri_glm_run_1.compute_contrast(
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals

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()

[FirstLevelModel.fit] Loading data from
'/home/runner/nilearn_data/fiac_nilearn.glm/nipy-data-0.2/data/fiac/fiac0/run2.n
ii.gz'
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/fiac_nilearn.glm/nipy-data-0.2/data/fiac/fiac0/mask.n
ii.gz'
[FirstLevelModel.fit] Resampling mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f30791446d0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 2 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 3 seconds.
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:118: UserWarning: t contrasts should be of length P=13, but it has length 4. The rest of the contrast was padded with zeros.
  summary_statistics_run_2 = fmri_glm_run_2.compute_contrast(
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals

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()

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 fixed effects statistics using 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)

[FirstLevelModel.fit] Loading data from
'/home/runner/nilearn_data/fiac_nilearn.glm/nipy-data-0.2/data/fiac/fiac0/run1.n
ii.gz'
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/fiac_nilearn.glm/nipy-data-0.2/data/fiac/fiac0/mask.n
ii.gz'
[FirstLevelModel.fit] Resampling mask
[FirstLevelModel.fit] Finished fit
[FirstLevelModel.fit] Computing run 1 out of 2 runs (go take a coffee, a big
one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f30a2fc7ca0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 2 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computing run 2 out of 2 runs (2 seconds remaining).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f30a2fc7460>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 1 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 2 runs done in 5 seconds.

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()

/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:195: UserWarning: t contrasts should be of length P=13, but it has length 4. The rest of the contrast was padded with zeros.
  z_map = fmri_glm_multirun.compute_contrast(
[FirstLevelModel.compute_contrast] Computing image from signals

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:242: RuntimeWarning: The same contrast will be used for all 2 runs. If the design matrices are not the same for all runs, (for example with different column names or column order across runs) you should pass contrast as an expression using the name of the conditions as they appear in the design matrices.
  z_map = fmri_glm.compute_contrast(contrast_val, output_type="z_score")
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:242: UserWarning: t contrasts should be of length P=13, but it has length 4. The rest of the contrast was padded with zeros.
  z_map = fmri_glm.compute_contrast(contrast_val, output_type="z_score")
[FirstLevelModel.compute_contrast] Computing image from signals
  Contrast 02 out of 8: DStDSp_minus_SStSSp
[FirstLevelModel.compute_contrast] Computing image from signals
  Contrast 03 out of 8: DSt_minus_SSt
[FirstLevelModel.compute_contrast] Computing image from signals
  Contrast 04 out of 8: DSp_minus_SSp
[FirstLevelModel.compute_contrast] Computing image from signals
  Contrast 05 out of 8: DSt_minus_SSt_for_DSp
[FirstLevelModel.compute_contrast] Computing image from signals
  Contrast 06 out of 8: DSp_minus_SSp_for_DSt
[FirstLevelModel.compute_contrast] Computing image from signals
  Contrast 07 out of 8: Deactivation
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:242: UserWarning: t contrasts should be of length P=13, but it has length 5. The rest of the contrast was padded with zeros.
  z_map = fmri_glm.compute_contrast(contrast_val, output_type="z_score")
[FirstLevelModel.compute_contrast] Computing image from signals
  Contrast 08 out of 8: Effects_of_interest
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:242: UserWarning: Running approximate fixed effects on F statistics.
  z_map = fmri_glm.compute_contrast(contrast_val, output_type="z_score")
[FirstLevelModel.compute_contrast] Computing image from signals

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/examples/04_glm_first_level/plot_two_runs_model.py:254: RuntimeWarning: The same contrast will be used for all 2 runs. If the design matrices are not the same for all runs, (for example with different column names or column order across runs) you should pass contrast as an expression using the name of the conditions as they appear in the design matrices.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: t contrasts should be of length P=13, but it has length 4. The rest of the contrast was padded with zeros.
  report = fmri_glm_multirun.generate_report(
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Computing image from signals
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: t contrasts should be of length P=13, but it has length 5. The rest of the contrast was padded with zeros.
  report = fmri_glm_multirun.generate_report(
[FirstLevelModel.generate_report] Computing image from signals
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Running approximate fixed effects on F statistics.
  report = fmri_glm_multirun.generate_report(
[FirstLevelModel.generate_report] Computing image from signals
[FirstLevelModel.generate_report] Generating contrast-level figures...
[FirstLevelModel.generate_report] Generating design matrices figures...
[FirstLevelModel.generate_report] Generating contrast matrices figures...
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 8 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 9 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_two_runs_model.py:254: UserWarning: Contrasts will be padded with 8 column(s) of zeros.
  report = fmri_glm_multirun.generate_report(

Note

The generated report can be:

displayed in a Notebook,
opened in a browser using the .open_in_browser() method,
or saved to a file using the .save_as_html(output_filepath) method.

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= 0.12.2.dev214+gada9321f0; 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

You can cycle through the different runs using your left and right arrow keys.

Mask

The mask includes 28008 voxels (22.8 %) of the image.

Statistical Maps

SStSSp_minus_DStDSp

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.09
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	828
2	-111.0	-57.0	92.0	5.14	432
3	-111.0	-27.0	52.0	4.85	324
4	-126.0	-150.0	80.0	4.67	504
4a	-126.0	-153.0	64.0	4.05
5	-141.0	-114.0	40.0	4.61	468
6	-132.0	-105.0	88.0	4.46	144
7	-138.0	-54.0	56.0	4.37	504
8	-135.0	-138.0	80.0	4.25	1152
8a	-144.0	-120.0	76.0	3.65
8b	-138.0	-126.0	88.0	3.46
9	-102.0	-90.0	104.0	4.23	144
10	-39.0	-102.0	60.0	4.22	108
11	-120.0	-69.0	96.0	4.15	180
12	-132.0	-39.0	52.0	4.10	36
13	-120.0	-96.0	20.0	4.01	108
14	-99.0	-144.0	36.0	3.93	72
15	-90.0	-117.0	92.0	3.86	36
16	-81.0	-30.0	76.0	3.85	324
16a	-90.0	-33.0	76.0	3.27
17	-93.0	-87.0	80.0	3.83	72
18	-75.0	-30.0	56.0	3.79	144
19	-135.0	-42.0	56.0	3.72	72
20	-135.0	-63.0	76.0	3.71	108
21	-51.0	-69.0	48.0	3.71	36
22	-111.0	-72.0	84.0	3.70	72
23	-132.0	-66.0	36.0	3.69	36
24	-102.0	-105.0	112.0	3.68	36
25	-135.0	-141.0	56.0	3.68	72
26	-141.0	-87.0	60.0	3.67	252
27	-141.0	-63.0	48.0	3.66	36
28	-81.0	-111.0	8.0	3.64	36
29	-132.0	-129.0	92.0	3.63	108
30	-111.0	-114.0	32.0	3.62	72
31	-57.0	-102.0	88.0	3.62	36
32	-135.0	-42.0	48.0	3.61	36
33	-126.0	-39.0	48.0	3.60	72
34	-54.0	-141.0	88.0	3.55	72
35	-81.0	-108.0	64.0	3.55	36
36	-78.0	-63.0	32.0	3.55	144
37	-144.0	-93.0	64.0	3.53	72
38	-114.0	-150.0	80.0	3.52	216
39	-144.0	-129.0	68.0	3.50	36
40	-144.0	-78.0	72.0	3.50	36
41	-135.0	-90.0	88.0	3.50	36
42	-144.0	-111.0	80.0	3.48	36
43	-72.0	-126.0	56.0	3.47	36
44	-93.0	-60.0	100.0	3.44	36
45	-90.0	-18.0	60.0	3.42	72
46	-87.0	-93.0	112.0	3.41	36
47	-135.0	-57.0	72.0	3.39	36
48	-120.0	-30.0	44.0	3.38	36
49	-129.0	-147.0	72.0	3.35	36
50	-123.0	-45.0	48.0	3.34	36
51	-129.0	-87.0	44.0	3.34	36
52	-93.0	-84.0	112.0	3.34	72
53	-66.0	-150.0	12.0	3.34	36
54	-123.0	-45.0	80.0	3.34	36
55	-114.0	-33.0	76.0	3.31	72
56	-51.0	-78.0	28.0	3.31	72
57	-123.0	-102.0	100.0	3.30	36
58	-117.0	-87.0	92.0	3.30	36
59	-51.0	-87.0	96.0	3.30	36
60	-144.0	-102.0	76.0	3.29	36
61	-129.0	-90.0	80.0	3.28	36
62	-138.0	-51.0	44.0	3.27	36
63	-123.0	-108.0	84.0	3.26	36
64	-132.0	-69.0	32.0	3.26	36
65	-129.0	-60.0	84.0	3.26	36
66	-51.0	-75.0	44.0	3.26	72
67	-90.0	-141.0	36.0	3.26	36
68	-90.0	-87.0	108.0	3.26	36
69	-114.0	-27.0	56.0	3.25	36
70	-138.0	-141.0	60.0	3.23	36
71	-57.0	-102.0	72.0	3.23	36
72	-84.0	-33.0	72.0	3.23	36
73	-75.0	-126.0	60.0	3.22	36
74	-78.0	-69.0	92.0	3.22	36
75	-141.0	-60.0	60.0	3.21	36
76	-141.0	-135.0	72.0	3.21	36
77	-93.0	-78.0	76.0	3.21	108
78	-84.0	-63.0	36.0	3.16	36
79	-111.0	-48.0	92.0	3.14	36
80	-117.0	-147.0	76.0	3.14	36
81	-87.0	-111.0	88.0	3.14	36
82	-120.0	-81.0	100.0	3.13	36
83	-111.0	-30.0	76.0	3.13	36
84	-117.0	-24.0	56.0	3.13	36
85	-108.0	-138.0	40.0	3.12	36
86	-108.0	-30.0	72.0	3.12	36
87	-120.0	-42.0	56.0	3.12	36
88	-87.0	-120.0	92.0	3.11	36
89	-126.0	-33.0	48.0	3.10	36
90	-87.0	-54.0	48.0	3.10	36
91	-54.0	-90.0	92.0	3.10	36
92	-129.0	-72.0	92.0	3.09	36
93	-132.0	-144.0	80.0	3.09	36

DStDSp_minus_SStSSp

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.09
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	504
1a	-69.0	-135.0	8.0	3.57
2	-96.0	-147.0	4.0	5.28	828
2a	-99.0	-144.0	12.0	3.99
3	-60.0	-126.0	4.0	5.20	216
4	-99.0	-75.0	108.0	4.98	396
5	-132.0	-129.0	52.0	4.95	828
6	-105.0	-108.0	92.0	4.56	36
7	-129.0	-60.0	48.0	4.55	396
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	72
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	108
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	144
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	108
27	-75.0	-63.0	60.0	3.79	108
28	-51.0	-102.0	96.0	3.76	72
29	-135.0	-78.0	68.0	3.76	144
30	-69.0	-72.0	56.0	3.75	72
31	-39.0	-78.0	48.0	3.75	396
32	-51.0	-117.0	48.0	3.71	72
33	-45.0	-54.0	64.0	3.65	72
34	-48.0	-102.0	36.0	3.64	216
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	-126.0	-141.0	56.0	3.51	36
42	-48.0	-114.0	64.0	3.47	36
43	-117.0	-123.0	48.0	3.44	72
44	-36.0	-93.0	76.0	3.42	108
45	-96.0	-99.0	104.0	3.39	72
46	-48.0	-123.0	40.0	3.38	36
47	-48.0	-108.0	32.0	3.35	36
48	-69.0	-87.0	96.0	3.33	72
49	-48.0	-156.0	48.0	3.22	36
50	-69.0	-129.0	80.0	3.22	36
51	-81.0	-69.0	64.0	3.22	36
52	-66.0	-135.0	20.0	3.19	36
53	-39.0	-93.0	72.0	3.19	36
54	-63.0	-108.0	68.0	3.19	36
55	-102.0	-129.0	80.0	3.18	36
56	-123.0	-141.0	44.0	3.15	36
57	-48.0	-51.0	64.0	3.14	36
58	-42.0	-72.0	56.0	3.13	36
59	-135.0	-57.0	20.0	3.10	36
60	-75.0	-141.0	12.0	3.10	36
61	-36.0	-126.0	76.0	3.10	36
62	-60.0	-66.0	68.0	3.10	36

DSt_minus_SSt

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.09
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	576
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	324
8a	-69.0	-135.0	8.0	3.64
9	-84.0	-81.0	44.0	4.03	72
10	-102.0	-138.0	12.0	4.00	144
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	72
14	-45.0	-69.0	84.0	3.67	108
15	-120.0	-78.0	44.0	3.54	36
16	-42.0	-60.0	24.0	3.54	36
17	-138.0	-99.0	44.0	3.36	36
18	-45.0	-57.0	16.0	3.36	36
19	-114.0	-75.0	60.0	3.35	36
20	-117.0	-60.0	64.0	3.33	36
21	-78.0	-138.0	16.0	3.32	36
22	-33.0	-114.0	68.0	3.32	36
23	-132.0	-63.0	16.0	3.31	72
24	-81.0	-84.0	44.0	3.29	72
25	-36.0	-93.0	80.0	3.26	36
26	-48.0	-54.0	16.0	3.25	36
27	-78.0	-60.0	60.0	3.20	36
28	-105.0	-135.0	0.0	3.19	36
29	-141.0	-48.0	32.0	3.19	36
30	-51.0	-117.0	48.0	3.17	36
31	-96.0	-144.0	12.0	3.17	72
32	-48.0	-120.0	48.0	3.17	36
33	-36.0	-90.0	44.0	3.17	36
34	-135.0	-78.0	68.0	3.13	36
35	-54.0	-108.0	40.0	3.11	36
36	-126.0	-69.0	28.0	3.11	36

DSp_minus_SSp

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.09
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	396
2	-60.0	-126.0	4.0	4.84	252
3	-105.0	-108.0	92.0	4.60	72
4	-48.0	-114.0	64.0	4.47	144
5	-63.0	-108.0	68.0	4.12	108
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	108
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	108
13	-102.0	-135.0	28.0	3.72	108
14	-45.0	-63.0	80.0	3.72	72
15	-36.0	-96.0	56.0	3.70	72
16	-135.0	-108.0	76.0	3.68	180
17	-126.0	-111.0	68.0	3.63	36
18	-45.0	-84.0	48.0	3.61	72
19	-54.0	-96.0	44.0	3.57	108
20	-120.0	-123.0	48.0	3.57	36
21	-96.0	-102.0	104.0	3.56	108
22	-39.0	-114.0	56.0	3.54	216
23	-60.0	-87.0	100.0	3.51	72
24	-114.0	-87.0	36.0	3.50	36
25	-42.0	-111.0	36.0	3.45	108
26	-96.0	-159.0	60.0	3.41	36
27	-45.0	-105.0	56.0	3.38	108
28	-66.0	-144.0	56.0	3.36	72
29	-114.0	-153.0	36.0	3.32	36
30	-78.0	-99.0	56.0	3.32	36
31	-69.0	-69.0	56.0	3.32	72
32	-39.0	-84.0	88.0	3.30	36
33	-102.0	-117.0	72.0	3.30	36
34	-129.0	-60.0	52.0	3.28	36
35	-126.0	-117.0	32.0	3.26	36
36	-132.0	-123.0	48.0	3.25	36
37	-39.0	-81.0	52.0	3.25	36
38	-102.0	-144.0	8.0	3.23	36
39	-81.0	-102.0	36.0	3.21	36
40	-78.0	-165.0	44.0	3.21	36
41	-60.0	-138.0	52.0	3.19	36
42	-96.0	-162.0	56.0	3.18	36
43	-51.0	-78.0	92.0	3.17	36
44	-45.0	-66.0	56.0	3.15	36
45	-48.0	-63.0	56.0	3.14	36
46	-126.0	-60.0	48.0	3.13	36
47	-45.0	-57.0	64.0	3.13	36
48	-66.0	-129.0	4.0	3.12	36
49	-75.0	-150.0	8.0	3.12	36

DSt_minus_SSt_for_DSp

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.09
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	108
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
7	-66.0	-69.0	96.0	3.23	36
8	-69.0	-69.0	64.0	3.16	36
9	-75.0	-108.0	12.0	3.14	36

DSp_minus_SSp_for_DSt

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.09
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	72
5	-66.0	-99.0	92.0	3.57	72
6	-93.0	-153.0	20.0	3.49	72
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
12	-51.0	-123.0	40.0	3.26	36
13	-81.0	-129.0	112.0	3.25	36
14	-60.0	-126.0	4.0	3.21	36
15	-63.0	-102.0	108.0	3.20	36
16	-63.0	-81.0	32.0	3.19	36
17	-117.0	-126.0	20.0	3.16	36
18	-120.0	-42.0	24.0	3.14	36
19	-72.0	-69.0	80.0	3.12	36
20	-114.0	-66.0	68.0	3.11	36
21	-102.0	-159.0	44.0	3.10	36

Deactivation

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.09
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	5652
1a	-39.0	-93.0	64.0	5.64
1b	-39.0	-114.0	60.0	5.27
1c	-51.0	-96.0	64.0	5.24
2	-51.0	-60.0	56.0	5.70	3204
3	-138.0	-81.0	60.0	5.05	576
3a	-129.0	-81.0	64.0	3.61
4	-120.0	-114.0	56.0	5.00	4284
4a	-135.0	-123.0	56.0	4.76
4b	-126.0	-108.0	60.0	4.71
4c	-129.0	-129.0	52.0	4.63
5	-102.0	-75.0	104.0	4.90	288
6	-90.0	-69.0	100.0	4.61	324
6a	-90.0	-72.0	92.0	4.34
7	-51.0	-117.0	60.0	4.52	576
8	-57.0	-78.0	96.0	4.41	756
8a	-54.0	-75.0	88.0	4.41
9	-54.0	-63.0	76.0	4.18	396
9a	-45.0	-60.0	76.0	3.31
10	-132.0	-60.0	48.0	4.17	180
11	-36.0	-111.0	72.0	4.06	108
12	-48.0	-114.0	68.0	4.03	108
13	-90.0	-81.0	108.0	3.92	72
14	-111.0	-117.0	80.0	3.85	36
15	-138.0	-96.0	44.0	3.80	108
16	-120.0	-66.0	52.0	3.71	180
17	-54.0	-69.0	72.0	3.53	36
18	-90.0	-75.0	104.0	3.49	36
19	-45.0	-66.0	80.0	3.46	36
20	-45.0	-63.0	44.0	3.44	36
21	-87.0	-93.0	76.0	3.25	36
22	-45.0	-66.0	40.0	3.21	36
23	-39.0	-87.0	84.0	3.21	72
24	-42.0	-108.0	56.0	3.19	36

Effects_of_interest

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.09
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	50760
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	27684
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	-93.0	-147.0	4.0	8.75	5868
3a	-108.0	-141.0	0.0	7.89
3b	-99.0	-141.0	20.0	7.05
3c	-102.0	-144.0	12.0	6.98
4	-87.0	-111.0	88.0	8.40	34092
4a	-90.0	-147.0	88.0	8.08
4b	-96.0	-138.0	104.0	7.71
4c	-84.0	-138.0	100.0	7.53
5	-96.0	-60.0	72.0	7.80	8964
5a	-96.0	-57.0	80.0	6.92
5b	-102.0	-33.0	56.0	6.82
5c	-96.0	-69.0	80.0	6.81
6	-57.0	-78.0	16.0	7.75	1512
6a	-51.0	-78.0	28.0	3.92
7	-75.0	-57.0	76.0	7.68	2052
7a	-69.0	-66.0	88.0	4.06
7b	-75.0	-51.0	92.0	3.62
8	-54.0	-75.0	88.0	7.39	2664
8a	-63.0	-69.0	96.0	5.13
9	-138.0	-78.0	64.0	7.31	1476
9a	-141.0	-87.0	56.0	4.45
10	-117.0	-87.0	104.0	7.23	1908
10a	-111.0	-93.0	108.0	4.96
10b	-102.0	-90.0	104.0	3.93
11	-72.0	-147.0	8.0	7.00	2196
11a	-72.0	-156.0	20.0	6.19
11b	-66.0	-147.0	20.0	5.56
12	-129.0	-60.0	48.0	6.92	972
12a	-120.0	-60.0	56.0	5.80
13	-102.0	-75.0	104.0	6.69	360
14	-69.0	-108.0	112.0	6.47	1152
15	-111.0	-117.0	32.0	6.22	936
15a	-111.0	-126.0	36.0	5.54
16	-90.0	-114.0	112.0	6.09	612
17	-120.0	-132.0	24.0	5.98	576
18	-123.0	-84.0	32.0	5.57	72
19	-114.0	-123.0	104.0	5.43	1836
19a	-123.0	-129.0	104.0	4.39
20	-45.0	-138.0	72.0	5.41	2916
20a	-51.0	-138.0	80.0	5.39
20b	-51.0	-141.0	88.0	5.36
20c	-51.0	-150.0	72.0	4.82
21	-129.0	-54.0	60.0	5.37	1080
21a	-138.0	-57.0	64.0	4.92
22	-93.0	-60.0	100.0	5.36	144
23	-72.0	-78.0	96.0	5.35	1404
23a	-81.0	-84.0	104.0	4.55
24	-132.0	-102.0	96.0	5.31	288
25	-57.0	-111.0	12.0	5.21	540
25a	-48.0	-117.0	12.0	3.66
26	-48.0	-117.0	28.0	5.18	576
27	-90.0	-96.0	112.0	5.14	684
27a	-93.0	-81.0	112.0	4.18
28	-60.0	-111.0	36.0	5.09	1404
28a	-63.0	-114.0	28.0	4.23
28b	-69.0	-99.0	36.0	4.04
29	-87.0	-36.0	48.0	5.07	720
30	-51.0	-135.0	16.0	5.03	504
31	-78.0	-69.0	32.0	5.03	108
32	-123.0	-117.0	32.0	5.01	144
33	-66.0	-126.0	108.0	4.99	288
34	-51.0	-42.0	24.0	4.94	144
35	-123.0	-108.0	80.0	4.94	648
35a	-135.0	-105.0	88.0	4.91
36	-48.0	-102.0	96.0	4.86	72
37	-69.0	-84.0	24.0	4.82	108
38	-102.0	-120.0	28.0	4.76	144
39	-126.0	-141.0	12.0	4.66	180
40	-63.0	-54.0	92.0	4.64	36
41	-132.0	-69.0	40.0	4.62	36
42	-66.0	-54.0	24.0	4.62	36
43	-51.0	-42.0	64.0	4.60	36
44	-78.0	-84.0	24.0	4.60	108
45	-135.0	-144.0	56.0	4.60	252
45a	-138.0	-141.0	68.0	3.18
46	-75.0	-114.0	116.0	4.60	72
47	-57.0	-141.0	28.0	4.58	432
48	-123.0	-102.0	64.0	4.53	72
49	-123.0	-60.0	32.0	4.50	144
50	-87.0	-153.0	36.0	4.44	36
51	-144.0	-102.0	60.0	4.40	36
52	-60.0	-159.0	40.0	4.39	216
53	-57.0	-54.0	88.0	4.38	288
54	-78.0	-126.0	0.0	4.38	324
55	-69.0	-63.0	44.0	4.37	180
56	-57.0	-93.0	88.0	4.36	684
57	-132.0	-69.0	32.0	4.35	72
58	-60.0	-126.0	4.0	4.32	36
59	-78.0	-63.0	64.0	4.30	180
60	-135.0	-93.0	88.0	4.28	144
61	-102.0	-57.0	100.0	4.25	36
62	-114.0	-150.0	28.0	4.23	252
63	-138.0	-78.0	40.0	4.18	72
64	-51.0	-108.0	28.0	4.17	36
65	-93.0	-150.0	24.0	4.13	36
66	-141.0	-75.0	80.0	4.11	144
67	-120.0	-96.0	20.0	4.06	180
68	-63.0	-54.0	68.0	4.05	180
69	-123.0	-69.0	60.0	4.04	36
70	-57.0	-138.0	52.0	4.02	144
71	-51.0	-129.0	8.0	4.01	72
72	-90.0	-75.0	104.0	4.01	36
73	-96.0	-120.0	116.0	4.01	72
74	-72.0	-117.0	0.0	4.00	108
75	-60.0	-90.0	56.0	4.00	36
76	-84.0	-138.0	32.0	3.99	540
76a	-75.0	-135.0	44.0	3.64
77	-63.0	-153.0	24.0	3.98	36
78	-90.0	-132.0	4.0	3.98	144
79	-111.0	-27.0	52.0	3.94	36
80	-96.0	-111.0	52.0	3.93	72
81	-117.0	-117.0	16.0	3.93	144
82	-63.0	-138.0	104.0	3.93	180
83	-105.0	-117.0	96.0	3.92	180
84	-102.0	-108.0	60.0	3.91	108
85	-114.0	-66.0	68.0	3.89	72
86	-45.0	-126.0	80.0	3.89	72
87	-126.0	-78.0	56.0	3.88	216
88	-111.0	-123.0	0.0	3.86	180
89	-147.0	-99.0	64.0	3.85	36
90	-105.0	-108.0	92.0	3.84	36
91	-75.0	-39.0	80.0	3.84	36
92	-66.0	-48.0	36.0	3.84	36
93	-105.0	-45.0	80.0	3.82	36
94	-33.0	-66.0	44.0	3.81	36
95	-36.0	-93.0	80.0	3.78	36
96	-99.0	-99.0	104.0	3.76	144
97	-72.0	-123.0	112.0	3.75	72
98	-42.0	-126.0	64.0	3.73	72
99	-96.0	-24.0	56.0	3.73	216
100	-57.0	-105.0	44.0	3.72	180
100a	-66.0	-105.0	44.0	3.23
101	-39.0	-108.0	88.0	3.71	36
102	-111.0	-120.0	44.0	3.70	252
102a	-102.0	-117.0	48.0	3.12
103	-93.0	-81.0	40.0	3.70	144
104	-108.0	-153.0	88.0	3.70	36
105	-36.0	-81.0	68.0	3.70	36
106	-105.0	-45.0	48.0	3.69	36
107	-102.0	-120.0	76.0	3.69	36
108	-42.0	-117.0	80.0	3.66	108
109	-135.0	-72.0	40.0	3.66	72
110	-72.0	-93.0	96.0	3.66	36
111	-129.0	-81.0	48.0	3.66	72
112	-117.0	-150.0	16.0	3.65	36
113	-111.0	-132.0	84.0	3.64	36
114	-114.0	-75.0	88.0	3.63	144
115	-90.0	-99.0	16.0	3.62	108
116	-42.0	-132.0	20.0	3.62	72
117	-63.0	-93.0	56.0	3.62	72
118	-108.0	-99.0	112.0	3.62	72
119	-114.0	-126.0	0.0	3.61	36
120	-114.0	-132.0	48.0	3.61	36
121	-96.0	-120.0	8.0	3.61	108
122	-84.0	-114.0	56.0	3.60	36
123	-87.0	-63.0	36.0	3.60	72
124	-48.0	-81.0	64.0	3.58	72
125	-66.0	-63.0	28.0	3.58	72
126	-66.0	-90.0	32.0	3.57	108
127	-120.0	-102.0	52.0	3.57	36
128	-75.0	-120.0	112.0	3.57	36
129	-108.0	-81.0	100.0	3.57	36
130	-117.0	-144.0	64.0	3.57	72
131	-75.0	-165.0	52.0	3.57	72
132	-39.0	-75.0	72.0	3.55	72
133	-96.0	-90.0	76.0	3.55	36
134	-126.0	-138.0	48.0	3.54	36
135	-138.0	-51.0	44.0	3.53	36
136	-120.0	-135.0	12.0	3.52	72
137	-69.0	-69.0	80.0	3.52	72
138	-111.0	-150.0	60.0	3.52	36
139	-66.0	-132.0	16.0	3.52	36
140	-84.0	-54.0	48.0	3.52	144
141	-141.0	-132.0	68.0	3.51	36
142	-60.0	-33.0	64.0	3.49	36
143	-102.0	-72.0	44.0	3.49	36
144	-75.0	-63.0	52.0	3.49	72
145	-93.0	-120.0	0.0	3.48	36
146	-99.0	-108.0	20.0	3.46	72
147	-108.0	-126.0	28.0	3.46	36
148	-60.0	-90.0	64.0	3.46	72
149	-78.0	-129.0	112.0	3.45	36
150	-99.0	-102.0	112.0	3.45	36
151	-102.0	-156.0	44.0	3.44	36
152	-111.0	-147.0	32.0	3.44	36
153	-129.0	-120.0	68.0	3.44	108
154	-75.0	-159.0	56.0	3.44	36
155	-60.0	-75.0	76.0	3.44	36
156	-99.0	-51.0	96.0	3.44	36
157	-51.0	-135.0	96.0	3.43	72
158	-90.0	-162.0	40.0	3.43	36
159	-39.0	-84.0	88.0	3.42	108
160	-123.0	-105.0	104.0	3.41	72
161	-111.0	-117.0	80.0	3.41	36
162	-117.0	-72.0	92.0	3.41	36
163	-141.0	-48.0	32.0	3.40	36
164	-63.0	-129.0	36.0	3.40	108
165	-57.0	-126.0	0.0	3.40	36
166	-129.0	-135.0	8.0	3.40	36
167	-87.0	-162.0	72.0	3.40	72
168	-69.0	-102.0	56.0	3.39	36
169	-111.0	-90.0	24.0	3.39	36
170	-54.0	-129.0	12.0	3.39	36
171	-84.0	-63.0	68.0	3.39	36
172	-33.0	-126.0	44.0	3.38	72
173	-102.0	-156.0	16.0	3.38	36
174	-66.0	-66.0	32.0	3.37	72
175	-66.0	-36.0	76.0	3.36	36
176	-75.0	-120.0	4.0	3.36	36
177	-129.0	-57.0	32.0	3.35	72
178	-69.0	-33.0	76.0	3.35	36
179	-60.0	-132.0	104.0	3.35	72
180	-48.0	-123.0	92.0	3.35	36
181	-129.0	-96.0	88.0	3.34	36
182	-63.0	-87.0	36.0	3.34	36
183	-123.0	-132.0	4.0	3.33	108
184	-120.0	-87.0	36.0	3.33	36
185	-90.0	-21.0	68.0	3.33	36
186	-102.0	-138.0	40.0	3.32	36
187	-42.0	-123.0	84.0	3.31	36
188	-105.0	-69.0	44.0	3.31	72
189	-63.0	-72.0	76.0	3.31	36
190	-75.0	-99.0	68.0	3.31	36
191	-96.0	-126.0	84.0	3.30	36
192	-117.0	-105.0	36.0	3.30	36
193	-102.0	-84.0	80.0	3.30	108
194	-72.0	-162.0	64.0	3.29	36
195	-105.0	-132.0	32.0	3.29	36
196	-54.0	-123.0	24.0	3.29	36
197	-123.0	-135.0	36.0	3.28	36
198	-132.0	-78.0	48.0	3.28	36
199	-72.0	-141.0	40.0	3.28	36
200	-102.0	-129.0	48.0	3.27	36
201	-60.0	-123.0	8.0	3.27	36
202	-105.0	-27.0	64.0	3.27	72
203	-60.0	-147.0	88.0	3.26	36
204	-84.0	-135.0	16.0	3.26	36
205	-51.0	-114.0	36.0	3.26	36
206	-87.0	-69.0	36.0	3.25	72
207	-36.0	-90.0	72.0	3.25	36
208	-48.0	-117.0	80.0	3.25	36
209	-66.0	-48.0	84.0	3.25	36
210	-72.0	-102.0	96.0	3.25	36
211	-99.0	-75.0	92.0	3.24	36
212	-69.0	-24.0	24.0	3.23	36
213	-93.0	-165.0	60.0	3.23	36
214	-123.0	-75.0	40.0	3.23	36
215	-96.0	-117.0	28.0	3.23	36
216	-69.0	-81.0	40.0	3.23	36
217	-114.0	-45.0	56.0	3.22	36
218	-48.0	-36.0	32.0	3.22	36
219	-69.0	-108.0	8.0	3.22	36
220	-126.0	-69.0	28.0	3.22	36
221	-57.0	-123.0	4.0	3.22	36
222	-132.0	-117.0	28.0	3.21	36
223	-90.0	-117.0	72.0	3.20	108
224	-105.0	-141.0	40.0	3.19	36
225	-87.0	-141.0	48.0	3.19	36
226	-96.0	-129.0	32.0	3.18	36
227	-105.0	-93.0	60.0	3.18	36
228	-78.0	-108.0	64.0	3.17	36
229	-48.0	-111.0	80.0	3.17	36
230	-45.0	-132.0	40.0	3.16	36
231	-72.0	-147.0	84.0	3.16	36
232	-78.0	-102.0	64.0	3.15	36
233	-102.0	-120.0	44.0	3.15	36
234	-105.0	-114.0	88.0	3.15	36
235	-132.0	-48.0	56.0	3.15	36
236	-72.0	-39.0	44.0	3.14	36
237	-111.0	-126.0	52.0	3.14	36
238	-57.0	-126.0	12.0	3.14	36
239	-144.0	-126.0	60.0	3.14	36
240	-72.0	-111.0	56.0	3.14	36
241	-75.0	-60.0	96.0	3.14	36
242	-105.0	-117.0	8.0	3.14	36
243	-45.0	-99.0	96.0	3.13	36
244	-102.0	-105.0	112.0	3.13	36
245	-144.0	-93.0	64.0	3.13	36
246	-108.0	-60.0	52.0	3.13	36
247	-138.0	-108.0	76.0	3.13	36
248	-63.0	-135.0	12.0	3.13	36
249	-78.0	-99.0	56.0	3.12	36
250	-114.0	-69.0	72.0	3.12	36
251	-51.0	-87.0	100.0	3.12	36
252	-54.0	-129.0	48.0	3.11	36
253	-144.0	-102.0	76.0	3.11	36
254	-87.0	-51.0	96.0	3.11	36
255	-120.0	-30.0	44.0	3.11	36
256	-111.0	-132.0	64.0	3.10	36
257	-138.0	-99.0	72.0	3.10	36

About

Date preprocessed: 2025-12-23T20:14:57

[0, 1]

Total running time of the script: (1 minutes 14.067 seconds)

Estimated memory usage: 842 MB

Gallery generated by Sphinx-Gallery

Simple example of two-runs fMRI model fitting¶

Set up the GLM¶

Initialize and run the GLM¶

Compare run-specific and fixed effects contrasts¶

Compute fixed effects statistics using preprocessed data of both runs¶

Compute a range of contrasts across both runs¶

Generating a report¶

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

Description

Model details

Run

Design Matrix

correlation matrix

Contrasts

Design Matrix

correlation matrix

Contrasts

Mask

Statistical Maps

SStSSp_minus_DStDSp

DStDSp_minus_SStSSp

DSt_minus_SSt

DSp_minus_SSp

DSt_minus_SSt_for_DSp

DSp_minus_SSp_for_DSt

Deactivation

Effects_of_interest

About

Simple example of two-runs fMRI model fitting¶

Set up the GLM¶

Initialize and run the GLM¶

Compare run-specific and fixed effects contrasts¶

Compute fixed effects statistics using preprocessed data of both runs¶

Compute a range of contrasts across both runs¶

Generating a report¶

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

Description

Model details

Run

Design Matrix

correlation matrix

Contrasts

Design Matrix

correlation matrix

Contrasts

Mask

Statistical Maps

SStSSp_minus_DStDSp

DStDSp_minus_SStSSp

DSt_minus_SSt

DSp_minus_SSp

DSt_minus_SSt_for_DSp

DSp_minus_SSp_for_DSt

Deactivation

Effects_of_interest

About

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