Note

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

Decoding of a dataset after GLM fit for signal extraction

Full step-by-step example of fitting a GLM to perform a decoding experiment. In this decoding analysis, we will be doing a one-vs-all classification. We use the data from one subject of the Haxby dataset.

More specifically:

  1. Download the Haxby dataset.

  2. Extract the information to generate a glm representing the blocks of stimuli.

  3. Analyze the decoding performance using a classifier.

Fetch example Haxby dataset

We download the Haxby dataset This is a study of visual object category representation

# By default 2nd subject will be fetched
import numpy as np
import pandas as pd

from nilearn.datasets import fetch_haxby

haxby_dataset = fetch_haxby()

# repetition has to be known
t_r = 2.5

[fetch_haxby] Dataset found in /home/runner/nilearn_data/haxby2001

Load the behavioral data

# Load target information as string and give a numerical identifier to each
behavioral = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
conditions = behavioral["labels"].to_numpy()

# Record these as an array of runs
runs = behavioral["chunks"].to_numpy()
unique_runs = behavioral["chunks"].unique()

# fMRI data: a unique file for each run
func_filename = haxby_dataset.func[0]

Build a proper event structure for each run

events = {}
# events will take the form of a dictionary of Dataframes, one per run
for run in unique_runs:
    # get the condition label per run
    conditions_run = conditions[runs == run]
    # get the number of scans per run, then the corresponding
    # vector of frame times
    n_scans = len(conditions_run)
    frame_times = t_r * np.arange(n_scans)
    # each event last the full TR
    duration = t_r * np.ones(n_scans)
    # Define the events object
    events_ = pd.DataFrame(
        {
            "onset": frame_times,
            "trial_type": conditions_run,
            "duration": duration,
        }
    )
    # remove the rest condition and insert into the dictionary
    events[run] = events_[events_.trial_type != "rest"]

Instantiate and run FirstLevelModel

We generate a list of z-maps together with their run and condition index

z_maps = []
conditions_label = []
run_label = []

# Instantiate the glm
from nilearn.glm.first_level import FirstLevelModel

glm = FirstLevelModel(
    t_r=t_r,
    mask_img=haxby_dataset.mask,
    high_pass=0.008,
    smoothing_fwhm=4,
    memory="nilearn_cache",
    memory_level=1,
    verbose=1,
)

Run the GLM on data from each run

events[run].trial_type.unique()
from nilearn.image import index_img

for run in unique_runs:
    # grab the fmri data for that particular run
    fmri_run = index_img(func_filename, runs == run)

    # fit the GLM
    glm.fit(fmri_run, events=events[run])

    # set up contrasts: one per condition
    conditions = events[run].trial_type.unique()
    for condition_ in conditions:
        z_maps.append(glm.compute_contrast(condition_))
        conditions_label.append(condition_)
        run_label.append(run)

[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee5365b0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91ee5365b0>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee5e2b20>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91ee5e2b20>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca4f6070>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91ca4f6070>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1a85df0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91f1a85df0>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1ebffd0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91f1ebffd0>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca5f3040>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91ca5f3040>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca5dee50>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91ca5dee50>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ee506d30>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91ee506d30>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f18d9c70>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91f18d9c70>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1ab4760>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91f1ab4760>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91ca4437f0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91ca4437f0>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91c972d400>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[FirstLevelModel.fit] Resamping 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
0x7f91c972d400>
[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 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 2 seconds.
[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
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals
[FirstLevelModel.compute_contrast] Computing image from signals

Generating a report

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

from nilearn.image import mean_img

mean_img_ = mean_img(func_filename, copy_header=True)
report = glm.generate_report(
    contrasts=conditions,
    bg_img=mean_img_,
)

[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
[FirstLevelModel.generate_report] Computing image from signals
[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...

This report can be viewed in a notebook.

report

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

Description

Data were analyzed using Nilearn (version= 0.12.1.dev24+g347557ec2; 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=0.008 Hz).

Regressors were entered into run-specific design matrices and onsets were convolved with a glover canonical hemodynamic response function for the following conditions:

  • face
  • chair
  • scrambledpix
  • house
  • bottle
  • shoe
  • scissors
  • cat

Input images were smoothed with gaussian kernel (full-width at half maximum=4 mm).

The following contrasts were computed using a fixed-effect approach across runs :

  • bottle
  • house
  • chair
  • scrambledpix
  • face
  • shoe
  • cat
  • scissors

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) 4
standardize False
t_r (seconds) 2.5

Mask

Mask image

The mask includes 39912 voxels (24.4 %) of the image.

Statistical Maps

bottle

Stat map plot for the contrast: bottle
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 -54.25 13.12 5.62 6.49 98
2 29.75 -20.62 -24.38 6.49 836
2a 12.25 -20.62 -31.88 4.58
3 26.25 -20.62 -13.12 6.13 295
4 -36.75 -46.88 -46.88 6.11 147
5 43.75 -50.62 -5.62 6.03 1476
5a 47.25 -43.12 -1.88 4.74
5b 40.25 -28.12 -1.88 3.99
6 -1.75 -39.38 13.12 5.95 147
7 33.25 -43.12 -43.12 5.94 1181
7a 26.25 -35.62 -43.12 5.67
8 22.75 -54.38 -39.38 5.91 885
8a 8.75 -58.12 -39.38 4.15
9 -43.75 28.12 -9.38 5.90 196
10 -12.25 -24.38 -76.88 5.86 196
11 -19.25 -24.38 -43.12 5.80 590
11a -19.25 -31.88 -39.38 5.31
12 26.25 -46.88 -46.88 5.75 147
13 -57.75 -39.38 9.38 5.74 246
14 33.25 5.62 -13.12 5.69 442
14a 26.25 9.38 -16.88 5.46
15 22.75 -46.88 -61.88 5.68 442
16 19.25 -76.88 -13.12 5.60 590
17 22.75 -43.12 9.38 5.57 196
18 -29.75 9.38 -28.12 5.56 246
19 26.25 -69.38 -16.88 5.49 689
19a 36.75 -65.62 -13.12 4.91
19b 33.25 -58.12 -16.88 4.13
20 47.25 -24.38 -13.12 5.47 3248
20a 40.25 -35.62 -24.38 5.23
20b 33.25 -50.62 -20.62 4.80
20c 43.75 -16.88 -13.12 4.67
21 1.75 50.62 -16.88 5.46 295
22 -26.25 -16.88 -24.38 5.38 147
23 12.25 -84.38 -28.12 5.31 98
24 -29.75 -43.12 -50.62 5.30 442
25 1.75 -43.12 -1.88 5.29 98
26 -15.75 -28.12 -43.12 5.24 49
27 12.25 -13.12 -28.12 5.23 98
28 -47.25 -13.12 13.12 5.21 295
29 -19.25 -50.62 -39.38 5.19 639
29a -8.75 -35.62 -39.38 4.36
29b -12.25 -46.88 -39.38 3.96
30 -40.25 -35.62 -35.62 5.17 344
31 12.25 -28.12 -73.12 5.16 147
32 8.75 -50.62 -35.62 5.13 590
33 29.75 1.88 -9.38 5.11 98
34 19.25 -31.88 16.88 5.10 49
35 -1.75 -50.62 -39.38 5.08 98
36 -50.75 35.62 -16.88 5.08 196
37 -26.25 -54.38 46.88 5.06 98
38 -54.25 -31.88 -13.12 5.06 49
39 -8.75 -46.88 -65.62 5.05 295
40 1.75 -58.12 -24.38 5.05 98
41 -33.25 13.12 28.12 5.05 196
42 -12.25 5.62 -16.88 5.03 49
43 12.25 -58.12 -28.12 5.03 246
44 19.25 -43.12 -20.62 5.02 49
45 -26.25 -20.62 -13.12 5.02 98
46 -47.25 -39.38 -20.62 5.00 1082
46a -50.75 -39.38 -9.38 4.39
47 -40.25 -61.88 -16.88 4.99 295
48 5.25 -61.88 -20.62 4.97 246
49 -61.25 -5.62 20.62 4.96 98
50 40.25 16.88 -20.62 4.96 49
51 -26.25 -54.38 -46.88 4.95 49
52 -22.75 -58.12 -46.88 4.95 49
53 22.75 31.88 9.38 4.93 49
54 8.75 -16.88 46.88 4.93 49
55 -43.75 50.62 16.88 4.90 49
56 22.75 -20.62 -35.62 4.90 98
57 -29.75 -84.38 1.88 4.89 442
57a -36.75 -76.88 1.88 4.46
58 -40.25 -20.62 -31.88 4.83 49
59 -36.75 58.12 35.62 4.83 49
60 19.25 35.62 28.12 4.82 49
61 -1.75 -46.88 -24.38 4.81 147
62 33.25 -9.38 -58.12 4.80 49
63 -29.75 -58.12 -9.38 4.80 49
64 -36.75 -39.38 31.88 4.80 344
65 -15.75 -31.88 -61.88 4.80 49
66 -40.25 24.38 -9.38 4.78 49
67 33.25 31.88 -9.38 4.77 98
68 -1.75 -46.88 1.88 4.76 98
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522 22.75 69.38 5.62 3.53 98
523 -12.25 -76.88 -9.38 3.53 49
524 -1.75 -50.62 -16.88 3.52 49
525 -5.25 43.12 -16.88 3.52 49
526 -5.25 1.88 1.88 3.52 49
527 -22.75 1.88 28.12 3.52 49
528 -1.75 9.38 9.38 3.52 49
529 22.75 -54.38 61.88 3.52 49
530 19.25 -84.38 -20.62 3.52 49
531 40.25 -28.12 -69.38 3.52 98
532 40.25 39.38 28.12 3.52 49
533 15.75 20.62 43.12 3.51 49
534 22.75 -24.38 1.88 3.51 49
535 22.75 -1.88 5.62 3.51 49
536 54.25 -16.88 35.62 3.51 98
537 -47.25 -1.88 -20.62 3.51 49
538 43.75 9.38 -39.38 3.51 49
539 19.25 -58.12 -13.12 3.51 49
540 1.75 -58.12 9.38 3.50 49
541 -29.75 -65.62 -20.62 3.50 49
542 -50.75 -9.38 -13.12 3.50 49
543 15.75 -73.12 -20.62 3.50 49
544 40.25 65.62 31.88 3.50 49
545 26.25 -5.62 -1.88 3.50 49
546 40.25 -39.38 -13.12 3.50 49
547 -8.75 -13.12 5.62 3.50 49
548 5.25 -16.88 69.38 3.49 49
549 19.25 46.88 39.38 3.49 49
550 26.25 -20.62 65.62 3.49 49
551 -29.75 -80.62 20.62 3.48 49
552 61.25 -35.62 35.62 3.48 49
553 36.75 50.62 -16.88 3.48 49
554 40.25 -35.62 50.62 3.48 49
555 -1.75 -5.62 -73.12 3.48 49
556 19.25 -31.88 -54.38 3.47 49
557 -33.25 1.88 31.88 3.47 98
558 47.25 5.62 31.88 3.47 49
559 15.75 1.88 58.12 3.47 98
560 -36.75 -46.88 9.38 3.47 98
561 15.75 -24.38 -73.12 3.47 49
562 -36.75 61.88 9.38 3.47 49
563 29.75 43.12 16.88 3.46 49
564 -8.75 39.38 31.88 3.46 49
565 -40.25 -69.38 31.88 3.46 49
566 5.25 -50.62 -5.62 3.46 49
567 -47.25 1.88 -28.12 3.45 49
568 12.25 69.38 -13.12 3.45 49
569 33.25 39.38 28.12 3.45 49
570 -43.75 35.62 31.88 3.45 98
571 -1.75 20.62 -16.88 3.45 147
572 47.25 16.88 39.38 3.45 49
573 36.75 -46.88 9.38 3.45 49
574 -19.25 -50.62 -46.88 3.45 49
575 36.75 -54.38 -28.12 3.44 49
576 1.75 28.12 -5.62 3.44 49
577 26.25 1.88 -13.12 3.44 49
578 -1.75 -28.12 58.12 3.44 49
579 19.25 -61.88 -46.88 3.44 98
580 12.25 5.62 13.12 3.44 49
581 -64.75 20.62 5.62 3.44 49
582 40.25 -9.38 -31.88 3.44 49
583 -1.75 -9.38 -28.12 3.43 49
584 5.25 -16.88 -20.62 3.43 49
585 19.25 -13.12 28.12 3.43 49
586 -64.75 -35.62 -1.88 3.43 49
587 1.75 -1.88 -20.62 3.43 49
588 33.25 -24.38 -58.12 3.43 49
589 -5.25 -73.12 28.12 3.43 49
590 33.25 -43.12 -35.62 3.43 49
591 -19.25 -16.88 9.38 3.42 49
592 26.25 80.62 24.38 3.42 49
593 8.75 13.12 16.88 3.42 49
594 54.25 -28.12 24.38 3.42 49
595 -22.75 -28.12 31.88 3.42 49
596 54.25 -28.12 31.88 3.42 49
597 1.75 84.38 1.88 3.42 49
598 47.25 31.88 -24.38 3.42 49
599 -12.25 -50.62 -24.38 3.42 49
600 -29.75 24.38 5.62 3.41 49
601 -36.75 -5.62 -20.62 3.41 49
602 -15.75 -73.12 39.38 3.41 49
603 -15.75 35.62 5.62 3.41 49
604 47.25 -31.88 -39.38 3.41 98
605 26.25 -16.88 -58.12 3.41 49
606 12.25 -9.38 -24.38 3.41 49
607 -19.25 -43.12 31.88 3.41 49
608 -19.25 -31.88 -69.38 3.41 49
609 -43.75 50.62 28.12 3.40 49
610 40.25 13.12 -46.88 3.40 49
611 19.25 -46.88 31.88 3.40 49
612 12.25 -9.38 43.12 3.40 49
613 40.25 -9.38 28.12 3.40 49
614 33.25 5.62 50.62 3.40 49
615 15.75 -16.88 5.62 3.40 49
616 22.75 54.38 16.88 3.40 49
617 -22.75 -54.38 39.38 3.40 49
618 50.75 -20.62 -43.12 3.39 49
619 12.25 -1.88 24.38 3.39 49
620 -29.75 -20.62 -69.38 3.39 49
621 12.25 -35.62 -16.88 3.39 49
622 12.25 -84.38 -5.62 3.39 49
623 -29.75 -1.88 -1.88 3.38 49
624 -36.75 -50.62 -16.88 3.38 49
625 -5.25 -31.88 -13.12 3.38 49
626 26.25 -1.88 20.62 3.38 49
627 -1.75 5.62 -39.38 3.38 49
628 -50.75 1.88 9.38 3.38 49
629 -22.75 24.38 20.62 3.38 49
630 22.75 39.38 20.62 3.38 49
631 19.25 -61.88 -20.62 3.38 49
632 -12.25 16.88 -50.62 3.38 49
633 -8.75 -9.38 -20.62 3.38 49
634 8.75 24.38 39.38 3.38 49
635 40.25 -35.62 -58.12 3.38 98
636 -33.25 -1.88 13.12 3.38 49
637 -50.75 -1.88 16.88 3.37 98
638 -36.75 -16.88 -46.88 3.37 49
639 47.25 -20.62 20.62 3.37 49
640 22.75 -9.38 43.12 3.37 49
641 1.75 1.88 -16.88 3.36 49
642 19.25 -28.12 -24.38 3.36 49
643 -22.75 -28.12 -65.62 3.36 49
644 -22.75 -28.12 -13.12 3.36 98
645 -19.25 -43.12 46.88 3.36 49
646 -5.25 76.88 5.62 3.36 49
647 68.25 -28.12 16.88 3.36 49
648 -8.75 -35.62 65.62 3.36 49
649 -33.25 -24.38 -65.62 3.35 49
650 -22.75 54.38 -20.62 3.35 49
651 8.75 -5.62 -24.38 3.35 49
652 5.25 46.88 13.12 3.35 49
653 -26.25 -43.12 54.38 3.35 49
654 15.75 35.62 5.62 3.35 49
655 -33.25 -58.12 -1.88 3.35 49
656 -8.75 -54.38 -31.88 3.35 49
657 26.25 28.12 -43.12 3.34 98
658 -29.75 39.38 35.62 3.34 49
659 8.75 -76.88 9.38 3.34 49
660 -29.75 -1.88 -46.88 3.34 49
661 12.25 -35.62 65.62 3.34 49
662 54.25 1.88 9.38 3.34 49
663 -22.75 20.62 -1.88 3.34 49
664 33.25 -76.88 -28.12 3.34 49
665 -1.75 -1.88 -5.62 3.34 49
666 -26.25 16.88 9.38 3.33 49
667 47.25 -35.62 16.88 3.33 49
668 -15.75 -46.88 -24.38 3.33 49
669 -47.25 -61.88 20.62 3.33 49
670 -29.75 -46.88 -28.12 3.33 49
671 -22.75 -54.38 -35.62 3.33 49
672 -5.25 1.88 -58.12 3.33 49
673 5.25 -61.88 -46.88 3.33 49
674 33.25 -46.88 54.38 3.33 49
675 26.25 -80.62 1.88 3.33 49
676 -43.75 -35.62 -1.88 3.33 49
677 36.75 -24.38 -31.88 3.33 49
678 26.25 -5.62 -13.12 3.32 49
679 -36.75 -73.12 -9.38 3.32 49
680 -47.25 -1.88 -13.12 3.32 49
681 33.25 -35.62 -46.88 3.32 49
682 -50.75 13.12 35.62 3.32 49
683 -12.25 -13.12 -73.12 3.32 49
684 -50.75 -58.12 1.88 3.32 49
685 8.75 20.62 -35.62 3.32 49
686 22.75 20.62 16.88 3.32 49
687 -36.75 -46.88 -24.38 3.32 98
688 -5.25 -54.38 -54.38 3.32 49
689 -29.75 9.38 61.88 3.31 49
690 15.75 -46.88 20.62 3.31 49
691 19.25 -9.38 -5.62 3.31 49
692 15.75 -39.38 -35.62 3.31 49
693 36.75 -24.38 9.38 3.31 49
694 -1.75 -16.88 -58.12 3.31 49
695 -19.25 -50.62 39.38 3.31 49
696 -19.25 -54.38 13.12 3.31 49
697 54.25 -16.88 -1.88 3.30 49
698 -12.25 20.62 13.12 3.30 49
699 -36.75 9.38 54.38 3.30 49
700 29.75 -50.62 54.38 3.30 49
701 29.75 -46.88 -31.88 3.30 49
702 -12.25 -35.62 -58.12 3.30 49
703 15.75 -16.88 54.38 3.30 49
704 -26.25 50.62 28.12 3.30 49
705 61.25 1.88 24.38 3.30 49
706 15.75 -16.88 -58.12 3.30 49
707 1.75 -69.38 46.88 3.29 49
708 -8.75 54.38 43.12 3.29 49
709 -26.25 -31.88 -58.12 3.29 49
710 5.25 -73.12 -16.88 3.29 49
711 15.75 28.12 13.12 3.29 49
712 -12.25 13.12 65.62 3.29 49

house

Stat map plot for the contrast: house
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 -22.75 -39.38 -1.88 5.50 393
2 22.75 20.62 9.38 5.43 442
2a 22.75 24.38 16.88 4.16
3 -22.75 24.38 13.12 5.29 98
4 22.75 13.12 20.62 5.25 98
5 47.25 -54.38 -16.88 5.20 98
6 33.25 -43.12 -35.62 4.65 295
7 -29.75 28.12 5.62 4.60 49
8 22.75 -5.62 20.62 4.57 49
9 -33.25 -58.12 35.62 4.53 49
10 36.75 -54.38 -20.62 4.52 49
11 12.25 24.38 20.62 4.51 147
12 -15.75 24.38 9.38 4.48 492
12a -15.75 28.12 16.88 4.03
13 -50.75 9.38 -28.12 4.44 49
14 -40.25 -58.12 28.12 4.41 98
15 22.75 -31.88 1.88 4.39 49
16 22.75 -43.12 -31.88 4.38 49
17 33.25 5.62 -39.38 4.37 49
18 26.25 5.62 9.38 4.35 49
19 1.75 13.12 -16.88 4.33 49
20 8.75 -16.88 31.88 4.33 49
21 -22.75 16.88 5.62 4.27 98
22 33.25 28.12 28.12 4.25 49
23 26.25 9.38 -31.88 4.23 49
24 -22.75 5.62 24.38 4.23 295
25 1.75 -46.88 -5.62 4.21 49
26 -26.25 39.38 46.88 4.21 98
27 19.25 5.62 43.12 4.21 98
28 43.75 -1.88 20.62 4.19 98
29 8.75 -24.38 24.38 4.16 49
30 19.25 -13.12 24.38 4.16 98
31 15.75 -39.38 13.12 4.13 49
32 -12.25 -16.88 -20.62 4.12 49
33 -29.75 16.88 9.38 4.11 49
34 -22.75 20.62 20.62 4.10 147
35 -12.25 -28.12 -76.88 4.08 49
36 26.25 -61.88 31.88 4.07 49
37 15.75 -54.38 5.62 4.05 49
38 -54.25 -20.62 -9.38 4.03 49
39 -15.75 -46.88 20.62 4.02 98
40 26.25 -1.88 13.12 4.00 49
41 -15.75 -35.62 -1.88 3.99 147
42 -12.25 -43.12 -16.88 3.97 49
43 -29.75 -31.88 35.62 3.96 49
44 -8.75 35.62 43.12 3.90 49
45 26.25 13.12 13.12 3.88 147
46 8.75 -9.38 31.88 3.88 98
47 5.25 -43.12 -1.88 3.87 98
48 -15.75 -50.62 24.38 3.87 49
49 -22.75 -9.38 -65.62 3.86 49
50 -15.75 -24.38 -9.38 3.85 98
51 -26.25 -50.62 -46.88 3.84 49
52 -8.75 -43.12 -9.38 3.84 49
53 22.75 -43.12 9.38 3.83 49
54 -8.75 -28.12 5.62 3.82 49
55 -12.25 -43.12 -50.62 3.82 49
56 -15.75 -31.88 -50.62 3.82 49
57 22.75 -39.38 46.88 3.81 49
58 22.75 -76.88 20.62 3.81 49
59 -40.25 -69.38 20.62 3.80 49
60 -29.75 -9.38 -31.88 3.78 49
61 -15.75 -28.12 -5.62 3.78 49
62 -12.25 -39.38 46.88 3.77 49
63 5.25 31.88 50.62 3.77 49
64 -22.75 -58.12 24.38 3.75 49
65 -15.75 16.88 13.12 3.75 49
66 -1.75 -61.88 -39.38 3.75 49
67 50.75 -31.88 -50.62 3.74 49
68 15.75 28.12 13.12 3.73 49
69 50.75 50.62 -1.88 3.73 49
70 33.25 -50.62 -24.38 3.72 147
71 29.75 1.88 13.12 3.71 49
72 1.75 31.88 -13.12 3.69 49
73 26.25 9.38 -24.38 3.69 49
74 15.75 -28.12 -5.62 3.68 49
75 -43.75 58.12 9.38 3.67 49
76 -8.75 5.62 28.12 3.67 98
77 26.25 -13.12 54.38 3.66 49
78 33.25 -76.88 -24.38 3.64 49
79 -33.25 -24.38 35.62 3.64 49
80 19.25 -76.88 -16.88 3.63 98
81 -40.25 50.62 1.88 3.63 49
82 40.25 31.88 -24.38 3.62 49
83 33.25 43.12 50.62 3.62 49
84 26.25 -24.38 -69.38 3.62 49
85 33.25 9.38 1.88 3.62 49
86 -26.25 20.62 -1.88 3.62 49
87 22.75 -65.62 20.62 3.60 49
88 -15.75 20.62 1.88 3.60 49
89 -15.75 20.62 20.62 3.59 49
90 -33.25 -73.12 -31.88 3.58 49
91 22.75 -73.12 -13.12 3.58 49
92 57.75 -54.38 1.88 3.57 49
93 29.75 -43.12 -20.62 3.56 49
94 -22.75 -24.38 46.88 3.56 49
95 40.25 -13.12 -43.12 3.56 49
96 -29.75 -13.12 -20.62 3.55 49
97 -40.25 -28.12 -24.38 3.55 49
98 -43.75 -9.38 39.38 3.55 49
99 -26.25 -50.62 46.88 3.54 49
100 -26.25 13.12 16.88 3.54 49
101 -29.75 50.62 20.62 3.54 49
102 -22.75 -13.12 46.88 3.54 49
103 -36.75 24.38 13.12 3.53 49
104 22.75 -9.38 13.12 3.53 49
105 -12.25 1.88 -61.88 3.52 98
106 5.25 9.38 39.38 3.52 49
107 29.75 -54.38 -24.38 3.51 49
108 22.75 -43.12 31.88 3.51 49
109 5.25 -65.62 54.38 3.50 49
110 -50.75 31.88 9.38 3.49 49
111 -12.25 9.38 5.62 3.49 49
112 22.75 1.88 20.62 3.48 49
113 12.25 -5.62 -5.62 3.48 49
114 50.75 -43.12 -1.88 3.47 49
115 15.75 -1.88 69.38 3.46 49
116 26.25 -73.12 31.88 3.46 49
117 36.75 -61.88 -13.12 3.46 49
118 -1.75 39.38 -20.62 3.45 98
119 -26.25 -54.38 43.12 3.45 49
120 29.75 -46.88 -24.38 3.45 49
121 -57.75 20.62 13.12 3.45 49
122 -15.75 -50.62 -9.38 3.44 49
123 -26.25 -20.62 16.88 3.44 49
124 19.25 24.38 5.62 3.44 49
125 -26.25 1.88 13.12 3.44 49
126 -36.75 -61.88 35.62 3.44 49
127 61.25 -1.88 13.12 3.43 49
128 57.75 -16.88 16.88 3.43 49
129 15.75 1.88 58.12 3.43 49
130 -36.75 -65.62 -16.88 3.43 49
131 22.75 -46.88 -20.62 3.42 49
132 12.25 -31.88 -73.12 3.42 49
133 22.75 -16.88 16.88 3.42 49
134 33.25 54.38 28.12 3.41 49
135 -33.25 9.38 5.62 3.41 49
136 43.75 35.62 46.88 3.41 49
137 15.75 -28.12 24.38 3.41 49
138 -26.25 -76.88 -9.38 3.39 49
139 22.75 -13.12 20.62 3.39 49
140 -40.25 -46.88 39.38 3.39 49
141 -33.25 61.88 39.38 3.38 49
142 -36.75 -69.38 31.88 3.38 98
143 8.75 -61.88 -50.62 3.38 49
144 22.75 5.62 24.38 3.38 49
145 5.25 -20.62 -13.12 3.38 49
146 19.25 -35.62 -28.12 3.38 49
147 -15.75 -61.88 43.12 3.37 49
148 -33.25 13.12 -1.88 3.37 49
149 22.75 -46.88 -5.62 3.37 49
150 -15.75 -20.62 -28.12 3.37 49
151 12.25 -13.12 54.38 3.37 49
152 29.75 5.62 39.38 3.36 49
153 26.25 -20.62 24.38 3.36 49
154 15.75 -73.12 -28.12 3.36 49
155 -8.75 61.88 -16.88 3.36 49
156 -36.75 -50.62 -16.88 3.36 49
157 -64.75 -24.38 -20.62 3.35 49
158 -57.75 16.88 5.62 3.34 49
159 -15.75 58.12 5.62 3.34 49
160 -26.25 16.88 50.62 3.34 49
161 -15.75 -65.62 46.88 3.34 49
162 40.25 -39.38 13.12 3.34 49
163 36.75 -13.12 -31.88 3.34 49
164 26.25 -16.88 -9.38 3.34 49
165 15.75 16.88 50.62 3.34 49
166 -54.25 -13.12 -1.88 3.34 49
167 40.25 -50.62 -58.12 3.34 49
168 -8.75 -24.38 1.88 3.33 49
169 -22.75 -16.88 24.38 3.33 49
170 -1.75 28.12 61.88 3.33 49
171 26.25 -58.12 -16.88 3.33 98
172 8.75 -39.38 1.88 3.33 49
173 54.25 9.38 -31.88 3.33 49
174 -22.75 35.62 -24.38 3.32 49
175 8.75 -80.62 -5.62 3.32 49
176 40.25 -16.88 -46.88 3.32 49
177 -15.75 -5.62 -13.12 3.31 49
178 -47.25 -76.88 9.38 3.31 49
179 -40.25 -1.88 24.38 3.30 49
180 33.25 -28.12 -13.12 3.30 49
181 8.75 -58.12 -24.38 3.29 49
182 19.25 -50.62 -13.12 3.29 49
183 12.25 -73.12 -13.12 3.29 49

chair

Stat map plot for the contrast: chair
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 40.25 -46.88 13.12 5.02 147
2 12.25 35.62 -5.62 4.88 196
3 -50.75 39.38 16.88 4.65 49
4 -15.75 -1.88 -16.88 4.21 49
5 33.25 -58.12 -1.88 4.16 49
6 -36.75 -76.88 1.88 4.15 49
7 -29.75 20.62 46.88 4.12 49
8 -54.25 -43.12 46.88 4.09 49
9 12.25 28.12 -16.88 4.08 49
10 12.25 5.62 -24.38 4.06 98
11 33.25 43.12 -16.88 3.99 49
12 33.25 -58.12 -16.88 3.97 49
13 -22.75 -43.12 -31.88 3.97 98
14 8.75 -35.62 -35.62 3.94 49
15 26.25 -46.88 -35.62 3.93 49
16 -5.25 43.12 50.62 3.89 49
17 12.25 16.88 -20.62 3.88 49
18 -36.75 -61.88 43.12 3.85 49
19 -50.75 -39.38 -1.88 3.83 147
20 -15.75 20.62 13.12 3.81 49
21 -22.75 1.88 5.62 3.78 49
22 -57.75 -9.38 -1.88 3.73 49
23 -1.75 5.62 31.88 3.69 49
24 -57.75 13.12 1.88 3.67 98
25 -36.75 -46.88 50.62 3.65 49
26 33.25 13.12 -1.88 3.64 49
27 36.75 -24.38 20.62 3.62 49
28 54.25 20.62 5.62 3.61 49
29 22.75 24.38 16.88 3.61 49
30 33.25 -20.62 -58.12 3.55 49
31 -22.75 -9.38 20.62 3.53 49
32 -22.75 24.38 13.12 3.53 49
33 29.75 -43.12 -35.62 3.52 49
34 12.25 16.88 -50.62 3.51 49
35 12.25 -1.88 69.38 3.51 49
36 -15.75 -39.38 -54.38 3.50 49
37 26.25 -69.38 31.88 3.50 98
38 -22.75 -31.88 -69.38 3.49 49
39 36.75 -1.88 28.12 3.48 49
40 -22.75 50.62 9.38 3.47 49
41 -22.75 -39.38 -28.12 3.47 49
42 26.25 16.88 16.88 3.46 49
43 -22.75 9.38 24.38 3.46 49
44 33.25 13.12 61.88 3.44 49
45 19.25 20.62 20.62 3.43 98
46 12.25 9.38 -1.88 3.43 49
47 36.75 20.62 -43.12 3.42 49
48 19.25 76.88 31.88 3.41 49
49 1.75 24.38 -39.38 3.40 49
50 29.75 9.38 -16.88 3.36 49
51 -26.25 -1.88 -50.62 3.36 49
52 33.25 9.38 -13.12 3.35 49
53 -68.25 1.88 5.62 3.33 49
54 -15.75 -39.38 -24.38 3.33 49
55 5.25 -1.88 31.88 3.33 49
56 22.75 -46.88 -24.38 3.32 49
57 22.75 -24.38 -61.88 3.31 49
58 43.75 -28.12 -13.12 3.30 49

scrambledpix

Stat map plot for the contrast: scrambledpix
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 1.75 24.38 -13.12 5.06 49
2 -29.75 -61.88 -31.88 4.96 49
3 -22.75 -46.88 43.12 4.91 98
4 -15.75 61.88 -9.38 4.69 147
5 5.25 43.12 -28.12 4.49 49
6 -22.75 -9.38 20.62 4.29 49
7 -1.75 -35.62 5.62 4.17 98
8 50.75 16.88 -28.12 4.12 147
9 -1.75 5.62 31.88 4.04 49
10 40.25 -16.88 -39.38 3.93 49
11 -15.75 -43.12 -16.88 3.85 49
12 -64.75 -31.88 39.38 3.77 49
13 -1.75 -46.88 -9.38 3.75 49
14 50.75 5.62 -35.62 3.68 49
15 -22.75 -54.38 -9.38 3.64 147
16 22.75 -24.38 46.88 3.59 49
17 54.25 -9.38 16.88 3.55 49
18 19.25 43.12 13.12 3.51 49
19 22.75 46.88 -5.62 3.50 49
20 -29.75 -65.62 20.62 3.46 49
21 -22.75 -28.12 39.38 3.42 49
22 33.25 -20.62 43.12 3.40 49
23 -40.25 -16.88 -54.38 3.39 49
24 -26.25 -69.38 39.38 3.39 49
25 15.75 -39.38 -31.88 3.38 49
26 57.75 13.12 -9.38 3.38 49
27 -5.25 -50.62 61.88 3.33 49
28 26.25 -20.62 43.12 3.33 49
29 -22.75 -9.38 -65.62 3.31 49
30 -26.25 -65.62 16.88 3.31 49

face

Stat map plot for the contrast: face
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 -50.75 -50.62 13.12 5.43 196
1a -47.25 -58.12 13.12 4.43
2 -47.25 -43.12 -35.62 5.08 49
3 -33.25 50.62 16.88 4.92 442
3a -40.25 46.88 13.12 4.22
4 -47.25 -39.38 16.88 4.89 98
5 -12.25 -58.12 -16.88 4.84 98
6 68.25 -9.38 -9.38 4.82 147
7 -36.75 31.88 16.88 4.66 147
8 -1.75 -39.38 5.62 4.64 98
9 -15.75 13.12 73.12 4.60 196
10 33.25 -61.88 -13.12 4.57 196
11 -43.75 -31.88 35.62 4.46 98
12 1.75 -43.12 -1.88 4.45 49
13 -47.25 24.38 20.62 4.45 49
14 40.25 -39.38 -24.38 4.30 49
15 -22.75 39.38 13.12 4.27 49
16 22.75 -50.62 -24.38 4.27 49
17 -47.25 -50.62 20.62 4.22 49
18 -47.25 -54.38 -9.38 4.19 49
19 -15.75 -13.12 24.38 4.18 98
20 -29.75 24.38 -13.12 4.15 147
21 -40.25 -43.12 -16.88 4.14 49
22 40.25 -43.12 -9.38 4.13 49
23 -1.75 28.12 61.88 4.07 98
24 -1.75 -58.12 -16.88 4.07 49
25 -57.75 -16.88 1.88 4.06 98
26 29.75 -46.88 35.62 4.02 49
27 5.25 -58.12 -24.38 4.00 393
28 -36.75 -31.88 -46.88 4.00 49
29 -8.75 13.12 65.62 3.99 49
30 33.25 -46.88 -39.38 3.99 98
31 1.75 -43.12 -16.88 3.98 196
32 -36.75 31.88 35.62 3.95 442
33 22.75 -1.88 58.12 3.94 98
34 -19.25 -73.12 39.38 3.93 98
35 1.75 24.38 -13.12 3.92 49
36 -43.75 -43.12 -9.38 3.90 98
37 -8.75 -39.38 -5.62 3.87 49
38 -47.25 -46.88 9.38 3.86 98
39 5.25 20.62 65.62 3.86 49
40 40.25 43.12 16.88 3.85 49
41 -26.25 -43.12 -43.12 3.83 49
42 19.25 -46.88 -20.62 3.81 196
43 -36.75 -43.12 39.38 3.80 147
44 -8.75 -28.12 61.88 3.79 49
45 -8.75 -46.88 -31.88 3.79 49
46 43.75 35.62 39.38 3.76 147
47 -22.75 -46.88 -50.62 3.75 49
48 -47.25 -61.88 -5.62 3.74 49
49 -47.25 -28.12 28.12 3.74 49
50 -29.75 28.12 13.12 3.73 49
51 29.75 -46.88 -31.88 3.73 49
52 19.25 -16.88 39.38 3.72 49
53 -47.25 -9.38 13.12 3.71 49
54 -47.25 -46.88 -31.88 3.68 49
55 33.25 50.62 20.62 3.67 49
56 -29.75 -13.12 5.62 3.67 49
57 36.75 -46.88 9.38 3.66 49
58 -50.75 20.62 20.62 3.65 49
59 -47.25 -16.88 -35.62 3.61 49
60 36.75 -35.62 -20.62 3.61 49
61 33.25 -50.62 -20.62 3.61 147
62 33.25 -54.38 43.12 3.60 49
63 5.25 -24.38 -20.62 3.59 49
64 -47.25 -46.88 16.88 3.59 49
65 -57.75 -9.38 -1.88 3.58 49
66 -12.25 -76.88 28.12 3.58 49
67 22.75 31.88 28.12 3.57 49
68 -36.75 5.62 43.12 3.57 49
69 -33.25 39.38 54.38 3.56 49
70 -1.75 -46.88 -9.38 3.56 49
71 26.25 -28.12 24.38 3.56 49
72 29.75 -50.62 -39.38 3.56 49
73 40.25 28.12 39.38 3.56 98
74 -12.25 -39.38 58.12 3.55 49
75 29.75 -54.38 46.88 3.55 49
76 -50.75 -43.12 1.88 3.53 147
77 -36.75 20.62 50.62 3.53 49
78 19.25 -1.88 50.62 3.52 49
79 -57.75 31.88 31.88 3.52 49
80 -43.75 -43.12 46.88 3.51 49
81 -29.75 5.62 58.12 3.51 49
82 26.25 1.88 13.12 3.51 49
83 5.25 -65.62 13.12 3.49 49
84 29.75 -46.88 -43.12 3.47 49
85 33.25 -43.12 -24.38 3.47 49
86 -47.25 -16.88 -43.12 3.46 49
87 -36.75 -46.88 50.62 3.45 49
88 -54.25 -9.38 -31.88 3.44 49
89 -5.25 -58.12 -13.12 3.44 49
90 -33.25 31.88 46.88 3.43 49
91 -26.25 -84.38 -9.38 3.43 98
92 12.25 28.12 61.88 3.42 49
93 -15.75 16.88 16.88 3.41 49
94 -33.25 16.88 50.62 3.41 49
95 -5.25 61.88 -9.38 3.41 49
96 -47.25 -61.88 -28.12 3.41 49
97 -33.25 -9.38 -28.12 3.41 49
98 -40.25 50.62 9.38 3.41 49
99 -19.25 -61.88 -9.38 3.40 49
100 22.75 -24.38 -58.12 3.39 49
101 -29.75 35.62 -1.88 3.39 49
102 -54.25 -20.62 -39.38 3.39 49
103 40.25 -46.88 -20.62 3.39 49
104 8.75 -58.12 -28.12 3.38 49
105 -47.25 -46.88 -1.88 3.38 49
106 36.75 54.38 24.38 3.38 49
107 -29.75 31.88 -9.38 3.37 49
108 40.25 -35.62 -43.12 3.37 49
109 -50.75 -58.12 -13.12 3.36 49
110 36.75 61.88 28.12 3.35 49
111 -47.25 58.12 -1.88 3.35 49
112 -47.25 24.38 28.12 3.34 49
113 -47.25 -9.38 5.62 3.34 49
114 -33.25 -43.12 43.12 3.34 49
115 -50.75 -35.62 43.12 3.34 49
116 -47.25 -46.88 -58.12 3.34 49
117 68.25 -28.12 16.88 3.33 49
118 47.25 35.62 13.12 3.33 49
119 19.25 5.62 13.12 3.32 49
120 29.75 -46.88 -20.62 3.32 49
121 -8.75 -13.12 20.62 3.32 49
122 5.25 31.88 -35.62 3.32 49
123 15.75 -54.38 -20.62 3.32 49
124 43.75 9.38 50.62 3.32 49
125 15.75 5.62 9.38 3.31 49
126 8.75 -1.88 9.38 3.31 49
127 33.25 20.62 43.12 3.31 49
128 43.75 -54.38 9.38 3.30 49
129 -40.25 39.38 43.12 3.30 49
130 -47.25 -58.12 -31.88 3.30 49
131 12.25 -88.12 5.62 3.30 49
132 40.25 -69.38 -16.88 3.30 49
133 -43.75 -31.88 -9.38 3.29 49
134 50.75 1.88 43.12 3.29 49
135 43.75 16.88 31.88 3.29 49
136 -47.25 -50.62 31.88 3.29 49
137 15.75 13.12 43.12 3.29 49

shoe

Stat map plot for the contrast: shoe
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 19.25 73.12 20.62 4.65 49
2 22.75 -9.38 35.62 4.48 49
3 19.25 -20.62 -28.12 4.08 49
4 12.25 5.62 31.88 3.97 49
5 26.25 35.62 28.12 3.97 49
6 61.25 28.12 31.88 3.87 98
7 15.75 16.88 58.12 3.83 49
8 -5.25 -76.88 -28.12 3.74 49
9 36.75 46.88 -16.88 3.71 49
10 -19.25 -28.12 35.62 3.64 49
11 26.25 -46.88 -24.38 3.61 49
12 -12.25 39.38 65.62 3.61 49
13 -29.75 9.38 35.62 3.60 49
14 -8.75 84.38 20.62 3.60 49
15 -54.25 -65.62 16.88 3.59 49
16 -26.25 -13.12 -43.12 3.58 49
17 19.25 -28.12 -54.38 3.57 49
18 -12.25 1.88 -69.38 3.56 49
19 47.25 -28.12 -5.62 3.55 49
20 22.75 -69.38 -28.12 3.53 49
21 33.25 9.38 61.88 3.50 49
22 -8.75 20.62 73.12 3.49 49
23 22.75 61.88 31.88 3.49 49
24 40.25 -54.38 39.38 3.47 49
25 1.75 -24.38 -39.38 3.46 49
26 5.25 -13.12 24.38 3.46 49
27 -68.25 -20.62 24.38 3.45 49
28 -12.25 39.38 24.38 3.42 49
29 50.75 -35.62 -35.62 3.41 49
30 -1.75 -28.12 -73.12 3.39 49
31 29.75 24.38 -28.12 3.38 49
32 22.75 -54.38 -43.12 3.35 49

cat

Stat map plot for the contrast: cat
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 12.25 -61.88 -31.88 5.00 295
2 -40.25 -65.62 13.12 4.41 147
3 5.25 -61.88 -24.38 4.34 98
4 -29.75 39.38 54.38 4.32 49
5 -12.25 -58.12 -16.88 4.29 49
6 -12.25 -76.88 31.88 4.27 98
7 43.75 -5.62 16.88 4.11 196
8 22.75 -24.38 -58.12 4.05 49
9 19.25 -31.88 -69.38 4.01 98
10 43.75 1.88 9.38 3.94 196
11 5.25 -80.62 9.38 3.90 98
12 61.25 -31.88 20.62 3.86 49
13 -12.25 -20.62 -58.12 3.86 98
14 1.75 35.62 -13.12 3.86 49
15 -29.75 35.62 50.62 3.83 49
16 -36.75 -50.62 -39.38 3.82 98
17 33.25 -1.88 46.88 3.80 49
18 50.75 1.88 -24.38 3.79 49
19 47.25 1.88 -46.88 3.79 49
20 50.75 28.12 24.38 3.78 49
21 -5.25 -50.62 -13.12 3.76 49
22 22.75 -61.88 -54.38 3.76 49
23 -12.25 -73.12 -31.88 3.73 49
24 36.75 28.12 43.12 3.72 98
25 36.75 69.38 5.62 3.71 49
26 -1.75 50.62 -9.38 3.70 49
27 5.25 -31.88 54.38 3.66 49
28 36.75 28.12 -20.62 3.62 49
29 -26.25 -80.62 9.38 3.61 98
30 8.75 -65.62 -24.38 3.60 49
31 -8.75 -46.88 -28.12 3.60 49
32 50.75 9.38 -35.62 3.60 49
33 43.75 5.62 1.88 3.59 49
34 -36.75 46.88 39.38 3.58 49
35 15.75 -80.62 -31.88 3.57 49
36 -54.25 -50.62 5.62 3.56 49
37 36.75 9.38 -35.62 3.55 49
38 29.75 -35.62 -50.62 3.53 49
39 -43.75 -65.62 5.62 3.53 49
40 15.75 -50.62 -39.38 3.50 49
41 61.25 -31.88 43.12 3.50 49
42 12.25 -54.38 -43.12 3.49 49
43 5.25 -61.88 -39.38 3.49 49
44 33.25 -54.38 43.12 3.49 49
45 -64.75 -9.38 -24.38 3.48 49
46 -54.25 -50.62 39.38 3.48 49
47 -40.25 -46.88 50.62 3.48 98
48 8.75 16.88 -16.88 3.48 98
49 -29.75 -46.88 5.62 3.47 49
50 12.25 -39.38 -31.88 3.44 49
51 -40.25 -5.62 50.62 3.44 49
52 64.75 20.62 9.38 3.43 49
53 5.25 -24.38 -73.12 3.42 49
54 12.25 -43.12 1.88 3.42 49
55 -19.25 16.88 69.38 3.42 49
56 -29.75 -54.38 -35.62 3.42 49
57 36.75 28.12 35.62 3.42 49
58 -64.75 -31.88 16.88 3.38 49
59 5.25 43.12 50.62 3.37 49
60 15.75 39.38 -20.62 3.37 49
61 29.75 9.38 -31.88 3.36 49
62 -36.75 -50.62 -20.62 3.35 49
63 -47.25 -43.12 43.12 3.35 49
64 -40.25 -46.88 16.88 3.35 49
65 -43.75 -1.88 -28.12 3.34 49
66 -40.25 -1.88 -31.88 3.34 49
67 12.25 5.62 5.62 3.34 49
68 -64.75 -16.88 24.38 3.33 49
69 -36.75 -76.88 5.62 3.33 98
70 43.75 31.88 39.38 3.33 49
71 -47.25 -50.62 46.88 3.33 49
72 -40.25 -69.38 35.62 3.31 49
73 -29.75 35.62 58.12 3.30 49
74 -22.75 76.88 5.62 3.29 49
75 47.25 13.12 43.12 3.29 49

scissors

Stat map plot for the contrast: scissors
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 -50.75 20.62 -24.38 6.02 935
2 -12.25 20.62 -46.88 5.79 49
3 -15.75 16.88 -46.88 5.74 295
4 -19.25 84.38 20.62 5.60 295
5 -12.25 16.88 -50.62 5.51 49
6 -26.25 24.38 28.12 5.28 147
7 29.75 5.62 -5.62 5.26 147
8 36.75 20.62 -31.88 5.18 246
9 47.25 24.38 -1.88 5.12 147
10 12.25 13.12 -43.12 5.09 393
11 -22.75 -58.12 -50.62 5.08 393
11a -22.75 -46.88 -50.62 4.60
12 -19.25 61.88 50.62 5.05 49
13 -50.75 -9.38 -20.62 5.01 246
14 50.75 31.88 -31.88 4.97 49
15 -19.25 31.88 -35.62 4.91 196
16 1.75 -1.88 -61.88 4.86 295
17 33.25 5.62 -46.88 4.82 49
18 -12.25 1.88 -28.12 4.76 49
19 12.25 -61.88 -46.88 4.76 49
20 1.75 -28.12 -61.88 4.68 49
21 12.25 -35.62 -69.38 4.66 147
22 22.75 -58.12 -50.62 4.61 49
23 -54.25 -39.38 -31.88 4.60 49
24 1.75 13.12 -16.88 4.54 49
25 47.25 24.38 9.38 4.54 49
26 26.25 -46.88 1.88 4.54 49
27 40.25 5.62 31.88 4.53 98
28 -15.75 -43.12 -46.88 4.53 98
29 40.25 -58.12 -5.62 4.50 147
30 19.25 16.88 -46.88 4.50 49
31 5.25 24.38 -46.88 4.47 442
31a -1.75 28.12 -39.38 3.59
32 -5.25 -16.88 -39.38 4.45 98
33 50.75 24.38 -35.62 4.41 147
34 12.25 80.62 35.62 4.41 98
35 33.25 -35.62 -35.62 4.37 98
36 1.75 1.88 31.88 4.37 196
37 -12.25 76.88 20.62 4.36 98
38 47.25 -5.62 20.62 4.36 295
39 19.25 -46.88 -35.62 4.34 49
40 29.75 -1.88 24.38 4.33 49
41 -12.25 9.38 -58.12 4.29 98
42 54.25 5.62 50.62 4.29 49
43 15.75 -35.62 -73.12 4.28 49
44 19.25 80.62 1.88 4.28 98
45 54.25 1.88 43.12 4.27 98
46 15.75 76.88 28.12 4.26 49
47 -29.75 76.88 20.62 4.26 49
48 -54.25 9.38 -16.88 4.26 49
49 -1.75 -31.88 9.38 4.26 49
50 -22.75 -5.62 -20.62 4.25 49
51 -40.25 61.88 5.62 4.24 98
52 -47.25 24.38 20.62 4.22 49
53 26.25 13.12 -39.38 4.22 49
54 -1.75 -39.38 5.62 4.21 98
55 -8.75 28.12 54.38 4.17 196
56 40.25 -1.88 61.88 4.17 246
56a 40.25 -9.38 58.12 3.83
57 36.75 31.88 -39.38 4.17 246
58 -36.75 -1.88 -20.62 4.17 98
59 -5.25 -9.38 31.88 4.14 49
60 22.75 -54.38 -43.12 4.14 49
61 -29.75 20.62 -46.88 4.13 49
62 22.75 -1.88 9.38 4.11 49
63 -26.25 80.62 16.88 4.11 246
64 29.75 -46.88 -61.88 4.10 49
65 15.75 -28.12 -54.38 4.10 49
66 33.25 24.38 -43.12 4.08 98
67 -40.25 -16.88 -65.62 4.07 49
68 -47.25 31.88 -28.12 4.05 49
69 -26.25 76.88 24.38 4.05 147
70 19.25 -5.62 -35.62 4.04 49
71 54.25 1.88 35.62 4.03 196
72 12.25 24.38 -35.62 4.03 98
73 -26.25 -13.12 1.88 4.02 49
74 -5.25 80.62 31.88 4.02 147
75 -19.25 50.62 13.12 4.02 49
76 40.25 -43.12 -5.62 4.01 49
77 43.75 -31.88 -13.12 4.01 49
78 15.75 5.62 9.38 4.01 49
79 -33.25 -65.62 -16.88 4.00 49
80 26.25 -50.62 -54.38 4.00 98
81 33.25 -5.62 -1.88 3.99 49
82 40.25 -39.38 58.12 3.98 49
83 22.75 -43.12 -61.88 3.98 98
84 -19.25 20.62 24.38 3.98 49
85 57.75 -20.62 28.12 3.97 49
86 -5.25 84.38 1.88 3.96 49
87 40.25 -24.38 -35.62 3.96 49
88 -19.25 9.38 -35.62 3.95 49
89 12.25 80.62 -5.62 3.94 49
90 -5.25 -28.12 -76.88 3.94 147
91 50.75 50.62 20.62 3.93 49
92 19.25 -16.88 -54.38 3.93 49
93 33.25 -39.38 24.38 3.93 49
94 40.25 28.12 -43.12 3.92 98
95 -26.25 16.88 28.12 3.92 49
96 1.75 39.38 -16.88 3.92 49
97 -26.25 -24.38 20.62 3.91 49
98 -15.75 -9.38 28.12 3.91 49
99 -8.75 24.38 -35.62 3.90 98
100 50.75 -16.88 46.88 3.89 49
101 -15.75 76.88 31.88 3.89 49
102 1.75 -28.12 -76.88 3.89 49
103 47.25 16.88 -20.62 3.89 49
104 19.25 -28.12 73.12 3.87 49
105 57.75 -13.12 39.38 3.87 49
106 -8.75 84.38 20.62 3.87 98
107 29.75 76.88 13.12 3.87 49
108 40.25 13.12 -39.38 3.87 49
109 33.25 -69.38 -13.12 3.87 49
110 19.25 54.38 50.62 3.85 49
111 -12.25 -50.62 -50.62 3.85 49
112 -19.25 -1.88 -35.62 3.85 49
113 29.75 -50.62 -50.62 3.84 49
114 12.25 -61.88 9.38 3.84 49
115 -29.75 13.12 61.88 3.83 196
116 5.25 -1.88 5.62 3.83 98
117 15.75 -16.88 46.88 3.83 49
118 -1.75 -1.88 -76.88 3.82 98
119 -36.75 65.62 20.62 3.82 98
120 33.25 -39.38 -20.62 3.81 49
121 29.75 65.62 43.12 3.79 49
122 -50.75 -1.88 20.62 3.79 49
123 50.75 -39.38 -16.88 3.79 49
124 -12.25 -43.12 -13.12 3.78 49
125 -22.75 58.12 -20.62 3.77 246
126 5.25 39.38 -20.62 3.77 49
127 -26.25 31.88 -39.38 3.76 49
128 -1.75 -13.12 -54.38 3.76 98
129 -33.25 -58.12 -31.88 3.75 49
130 15.75 88.12 9.38 3.75 98
131 43.75 13.12 -28.12 3.75 98
132 26.25 -16.88 -16.88 3.75 49
133 -19.25 31.88 -28.12 3.74 49
134 5.25 9.38 -58.12 3.73 49
135 -8.75 9.38 65.62 3.73 49
136 -29.75 -69.38 9.38 3.73 98
137 -64.75 -5.62 5.62 3.71 98
138 12.25 -31.88 -46.88 3.71 49
139 15.75 50.62 -9.38 3.71 49
140 -40.25 -9.38 -5.62 3.70 49
141 33.25 -20.62 43.12 3.70 49
142 -12.25 -50.62 -13.12 3.69 49
143 26.25 -13.12 13.12 3.69 49
144 -5.25 80.62 13.12 3.68 49
145 -26.25 16.88 -16.88 3.67 49
146 19.25 -1.88 -1.88 3.66 49
147 19.25 16.88 -35.62 3.66 49
148 -1.75 73.12 35.62 3.66 49
149 29.75 -54.38 -61.88 3.65 49
150 1.75 -9.38 1.88 3.65 49
151 15.75 73.12 20.62 3.65 49
152 33.25 -50.62 -20.62 3.65 49
153 12.25 -35.62 -24.38 3.65 49
154 15.75 24.38 69.38 3.64 49
155 -47.25 -5.62 -28.12 3.64 49
156 -1.75 1.88 13.12 3.64 49
157 -40.25 69.38 13.12 3.63 49
158 12.25 -28.12 -31.88 3.62 49
159 8.75 1.88 24.38 3.62 49
160 29.75 -9.38 9.38 3.62 49
161 -15.75 -65.62 5.62 3.61 49
162 -26.25 -80.62 -5.62 3.61 49
163 33.25 5.62 16.88 3.60 49
164 33.25 -5.62 24.38 3.60 98
165 57.75 -1.88 5.62 3.60 49
166 15.75 -50.62 -35.62 3.60 49
167 33.25 -39.38 -43.12 3.60 49
168 33.25 24.38 61.88 3.60 49
169 -1.75 -35.62 -65.62 3.58 49
170 22.75 -20.62 20.62 3.58 49
171 -8.75 24.38 43.12 3.58 49
172 40.25 -13.12 46.88 3.58 49
173 -5.25 -20.62 54.38 3.58 98
174 -1.75 -13.12 31.88 3.57 49
175 -47.25 20.62 43.12 3.57 98
176 -33.25 69.38 20.62 3.56 98
177 57.75 -24.38 5.62 3.56 49
178 5.25 -65.62 20.62 3.56 49
179 26.25 -16.88 -35.62 3.55 49
180 -1.75 -46.88 -1.88 3.55 98
181 -50.75 13.12 20.62 3.55 49
182 40.25 69.38 16.88 3.55 49
183 1.75 16.88 -20.62 3.55 49
184 -29.75 20.62 20.62 3.54 49
185 -29.75 31.88 -9.38 3.54 49
186 40.25 -16.88 9.38 3.54 49
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About

  • Date preprocessed:


In a jupyter notebook, the report will be automatically inserted, as above.

# We can access the report via a browser:
# report.open_in_browser()

# Or we can save as an html file.
from pathlib import Path

output_dir = Path.cwd() / "results" / "plot_haxby_glm_decoding"
output_dir.mkdir(exist_ok=True, parents=True)
report.save_as_html(output_dir / "report.html")

Build the decoding pipeline

To define the decoding pipeline we use Decoder object, we choose :

  • a prediction model, here a Support Vector Classifier, with a linear kernel

  • the mask to use, here a ventral temporal ROI in the visual cortex

  • although it usually helps to decode better, z-maps time series don’t need to be rescaled to a 0 mean, variance of 1 so we use standardize=False.

  • we use univariate feature selection to reduce the dimension of the problem keeping only 5% of voxels which are most informative.

  • a cross-validation scheme, here we use LeaveOneGroupOut cross-validation on the runs which corresponds to a leave-one-run-out

We fit directly this pipeline on the Niimgs outputs of the GLM, with corresponding conditions labels and run labels (for the cross validation).

from sklearn.model_selection import LeaveOneGroupOut

from nilearn.decoding import Decoder

decoder = Decoder(
    estimator="svc",
    mask=haxby_dataset.mask,
    standardize=False,
    screening_percentile=5,
    cv=LeaveOneGroupOut(),
    verbose=1,
)
decoder.fit(z_maps, conditions_label, groups=run_label)

# Return the corresponding mean prediction accuracy compared to chance
# for classifying one-vs-all items.

classification_accuracy = np.mean(list(decoder.cv_scores_.values()))
chance_level = 1.0 / len(np.unique(conditions))
print(
    f"Classification accuracy: {classification_accuracy:.4f} / "
    f"Chance level: {chance_level}"
)

[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Resamping mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91c39a6eb0>
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.82724e+06mm^3
[Decoder.fit] Original screening-percentile: 5
[Decoder.fit] Corrected screening-percentile: 4.65085
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done  96 out of  96 | elapsed:    7.4s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Classification accuracy: 0.7589 / Chance level: 0.125

Total running time of the script: (2 minutes 16.507 seconds)

Estimated memory usage: 1015 MB

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