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:

Download the Haxby dataset.
Extract the information to generate a glm representing the blocks of stimuli.
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()

[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 = haxby_dataset.t_r * np.arange(n_scans)
    # each event last the full TR
    duration = haxby_dataset.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=haxby_dataset.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
0x7fac36dda650>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac36dda650>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.114769, ..., -2.149296],
       ...,
       [ 2.367151, ...,  0.779998]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
[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
0x7fac36d1f6d0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac36d1f6d0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 12.660587, ..., -13.536042],
       ...,
       [ -3.254408, ..., -33.842804]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac5aab1930>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac5aab1930>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 5.205584, ..., 26.587189],
       ...,
       [-6.836576, ..., 10.676956]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac57779f60>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac57779f60>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-2.026206, ...,  5.974948],
       ...,
       [ 2.616334, ...,  0.104535]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac36dda410>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac36dda410>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 53.033577, ..., -55.45955 ],
       ...,
       [-51.57195 , ..., -55.994713]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac36677250>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac36677250>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-27.150482, ...,  -5.81308 ],
       ...,
       [-30.204891, ...,   7.417917]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac369d3b80>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac369d3b80>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[129.51173 , ..., -15.279282],
       ...,
       [-18.911755, ...,  21.839058]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac36676cb0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac36676cb0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-15.915996, ...,  22.07737 ],
       ...,
       [-16.981215, ...,   3.372383]], shape=(121, 39912), dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.352245, ...,  1.      ]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac5aab0430>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac5aab0430>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.292987, ..., 18.392956],
       ...,
       [-3.935719, ...,  0.602484]], shape=(121, 39912), dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.352245, ...,  1.      ]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac368a7a90>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac368a7a90>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-5.223948, ..., -5.959582],
       ...,
       [-7.677519, ..., 16.024363]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac5aab3b50>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac5aab3b50>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-19.66533 , ...,  -6.299562],
       ...,
       [-24.647343, ...,   2.331865]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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
0x7fac5aa647c0>
[FirstLevelModel.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.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
0x7fac5aa647c0>
[FirstLevelModel.fit] Smoothing images
[FirstLevelModel.fit] Extracting region signals
[FirstLevelModel.fit] Cleaning extracted signals
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-1.095605, ..., 16.449202],
       ...,
       [ 2.59974 , ..., -2.179998]], shape=(121, 39912), dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]], shape=(121, 13)), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.8s, 0.0min
[FirstLevelModel.fit] GLM took 0 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 1 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)
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...

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 Implement the General Linear Model for single run :term:`fMRI` data.

Description

Data were analyzed using Nilearn (version= 0.13.1.dev25+g67cc0a224; 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:

shoe
cat
scissors
scrambledpix
face
bottle
chair
house

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

First Level Model

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Design Matrix

correlation matrix

Plot of correlation of design matrix for run 0.

Contrasts

Plot of the contrast scrambledpix (run 3).

Mask

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

Statistical Maps

bottle

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	-54.25	13.12	5.62	6.49	98
2	29.75	-20.62	-24.38	6.49	984
2a	12.25	-20.62	-31.88	4.58
2b	29.75	-28.12	-24.38	4.06
3	26.25	-20.62	-13.12	6.13	344
4	-36.75	-46.88	-46.88	6.11	196
5	43.75	-50.62	-5.62	6.03	1821
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	2313
7a	26.25	-35.62	-43.12	5.67
7b	8.75	-50.62	-35.62	5.13
8	22.75	-54.38	-39.38	5.91	1033
8a	12.25	-54.38	-43.12	4.67
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	787
11a	-19.25	-31.88	-39.38	5.31
11b	-26.25	-39.38	-43.12	3.64
12	26.25	-46.88	-46.88	5.75	147
13	-57.75	-39.38	9.38	5.74	295
14	33.25	5.62	-13.12	5.69	689
14a	26.25	9.38	-16.88	5.46
15	22.75	-46.88	-61.88	5.68	541
16	19.25	-76.88	-13.12	5.60	639
17	22.75	-43.12	9.38	5.57	246
18	-29.75	9.38	-28.12	5.56	246
19	26.25	-69.38	-16.88	5.49	836
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	3445
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	196
23	12.25	-84.38	-28.12	5.31	98
24	-29.75	-43.12	-50.62	5.30	787
24a	-15.75	-39.38	-50.62	4.64
24b	-15.75	-43.12	-43.12	4.00
25	1.75	-43.12	-1.88	5.29	147
26	-15.75	-28.12	-43.12	5.24	49
27	12.25	-13.12	-28.12	5.23	147
28	-47.25	-13.12	13.12	5.21	442
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	19.25	-31.88	16.88	5.10	49
33	-1.75	-50.62	-39.38	5.08	98
34	-50.75	35.62	-16.88	5.08	196
35	-26.25	-54.38	46.88	5.06	98
36	-54.25	-31.88	-13.12	5.06	49
37	-8.75	-46.88	-65.62	5.05	344
38	1.75	-58.12	-24.38	5.05	246
39	-33.25	13.12	28.12	5.05	836
39a	-36.75	20.62	35.62	4.60
40	-12.25	5.62	-16.88	5.03	98
41	12.25	-58.12	-28.12	5.03	344
42	19.25	-43.12	-20.62	5.02	98
43	-26.25	-20.62	-13.12	5.02	147
44	-47.25	-39.38	-20.62	5.00	1476
44a	-47.25	-24.38	-16.88	4.41
44b	-50.75	-39.38	-9.38	4.39
45	-40.25	-61.88	-16.88	4.99	344
46	5.25	-61.88	-20.62	4.97	689
46a	19.25	-65.62	-24.38	4.25
46b	12.25	-61.88	-20.62	3.99
46c	26.25	-61.88	-16.88	3.58
47	-61.25	-5.62	20.62	4.96	98
48	40.25	16.88	-20.62	4.96	49
49	-26.25	-54.38	-46.88	4.95	147
50	22.75	31.88	9.38	4.93	49
51	8.75	-16.88	46.88	4.93	49
52	-43.75	50.62	16.88	4.90	98
53	22.75	-20.62	-35.62	4.90	98
54	-29.75	-84.38	1.88	4.89	590
54a	-36.75	-76.88	1.88	4.46
54b	-36.75	-69.38	5.62	4.13
55	-40.25	-20.62	-31.88	4.83	49
56	-36.75	58.12	35.62	4.83	98
57	19.25	35.62	28.12	4.82	49
58	-1.75	-46.88	-24.38	4.81	295
59	33.25	-9.38	-58.12	4.80	98
60	-29.75	-58.12	-9.38	4.80	49
61	-36.75	-39.38	31.88	4.80	344
62	-15.75	-31.88	-61.88	4.80	49
63	-40.25	24.38	-9.38	4.78	49
64	33.25	31.88	-9.38	4.77	98
65	-1.75	-46.88	1.88	4.76	147
66	1.75	-1.88	31.88	4.76	98
67	-40.25	-28.12	-20.62	4.75	49
68	-33.25	-13.12	-13.12	4.75	344
69	-15.75	-1.88	-69.38	4.75	49
70	-47.25	24.38	20.62	4.75	442
70a	-54.25	13.12	20.62	4.03
71	26.25	31.88	-31.88	4.75	147
72	-12.25	-58.12	-16.88	4.74	49
73	-12.25	-5.62	28.12	4.74	689
73a	-12.25	9.38	24.38	4.48
74	36.75	-61.88	-5.62	4.74	147
75	-33.25	-73.12	31.88	4.72	49
76	12.25	43.12	-16.88	4.72	49
77	-1.75	-9.38	-20.62	4.71	49
78	-47.25	-54.38	-9.38	4.69	393
79	8.75	-91.88	5.62	4.68	49
80	-50.75	9.38	9.38	4.67	49
81	8.75	-20.62	-13.12	4.67	147
82	-22.75	16.88	5.62	4.66	344
82a	-29.75	24.38	5.62	3.41
83	29.75	-50.62	-50.62	4.66	246
84	-5.25	-13.12	-69.38	4.66	147
85	19.25	-35.62	69.38	4.65	49
86	22.75	-35.62	54.38	4.64	295
86a	26.25	-43.12	54.38	4.06
87	-47.25	-9.38	5.62	4.63	295
88	-1.75	-54.38	-9.38	4.62	98
89	19.25	-39.38	-13.12	4.61	49
90	33.25	9.38	-24.38	4.61	98
91	22.75	46.88	-16.88	4.61	98
92	22.75	-16.88	-9.38	4.61	98
93	-43.75	24.38	-1.88	4.59	196
94	15.75	50.62	58.12	4.59	98
95	19.25	16.88	28.12	4.59	49
96	-40.25	-65.62	-5.62	4.59	98
97	-29.75	-80.62	-9.38	4.58	98
98	-15.75	-76.88	28.12	4.57	344
99	-57.75	-16.88	1.88	4.57	98
100	22.75	9.38	16.88	4.57	295
101	-1.75	13.12	-20.62	4.57	98
102	40.25	-35.62	-43.12	4.56	49
103	-5.25	-31.88	58.12	4.55	49
104	12.25	24.38	61.88	4.55	98
105	26.25	28.12	-20.62	4.55	196
106	-57.75	5.62	13.12	4.55	492
106a	-61.25	-1.88	9.38	4.19
106b	-57.75	-9.38	9.38	4.13
107	19.25	-1.88	28.12	4.54	147
108	5.25	-76.88	24.38	4.54	49
109	33.25	20.62	-20.62	4.54	98
110	-15.75	16.88	43.12	4.53	196
111	-47.25	-28.12	5.62	4.51	49
112	-29.75	13.12	-13.12	4.51	295
112a	-26.25	5.62	-13.12	4.20
113	12.25	-80.62	13.12	4.51	147
114	-8.75	84.38	20.62	4.50	196
115	15.75	-35.62	73.12	4.50	246
116	-15.75	-16.88	24.38	4.49	147
117	-15.75	9.38	-20.62	4.49	98
118	22.75	-28.12	69.38	4.49	147
119	12.25	-9.38	13.12	4.49	393
119a	26.25	-9.38	13.12	4.01
120	5.25	-43.12	-9.38	4.49	49
121	-1.75	-9.38	-46.88	4.49	344
122	-26.25	13.12	46.88	4.48	49
123	-12.25	-13.12	-46.88	4.48	147
124	-1.75	-50.62	-1.88	4.47	49
125	1.75	24.38	31.88	4.47	49
126	-5.25	-24.38	-20.62	4.45	196
127	8.75	16.88	-20.62	4.45	49
128	15.75	-24.38	-35.62	4.45	344
129	-22.75	31.88	-24.38	4.45	98
130	-47.25	-28.12	43.12	4.44	49
131	-33.25	13.12	13.12	4.44	295
132	-47.25	-61.88	-5.62	4.44	98
133	-50.75	39.38	9.38	4.44	49
134	-50.75	-20.62	9.38	4.43	98
135	33.25	-24.38	-13.12	4.43	147
136	-15.75	-20.62	-58.12	4.43	98
137	-54.25	-46.88	-5.62	4.42	98
138	40.25	31.88	13.12	4.42	49
139	-43.75	16.88	58.12	4.42	49
140	-12.25	-58.12	-46.88	4.40	49
141	-22.75	-9.38	13.12	4.40	49
142	22.75	16.88	5.62	4.39	196
143	50.75	1.88	43.12	4.39	147
144	-1.75	-39.38	1.88	4.39	295
145	-50.75	-16.88	24.38	4.38	49
146	36.75	50.62	1.88	4.38	147
147	22.75	13.12	35.62	4.36	344
147a	12.25	20.62	35.62	4.14
148	-36.75	16.88	50.62	4.36	196
149	36.75	43.12	-28.12	4.35	49
150	33.25	24.38	39.38	4.35	49
151	36.75	5.62	35.62	4.34	196
152	29.75	-31.88	39.38	4.34	541
152a	40.25	-35.62	39.38	3.94
153	-15.75	-50.62	-1.88	4.33	98
154	-5.25	-46.88	-16.88	4.33	738
155	-1.75	-65.62	16.88	4.33	344
155a	5.25	-73.12	16.88	4.13
156	-47.25	35.62	16.88	4.33	344
157	-36.75	43.12	24.38	4.32	98
158	-54.25	-20.62	-9.38	4.32	49
159	-15.75	20.62	-46.88	4.32	98
160	47.25	-54.38	-16.88	4.31	49
161	-50.75	-50.62	20.62	4.31	49
162	-22.75	1.88	-1.88	4.31	49
163	-26.25	1.88	43.12	4.31	98
164	-15.75	-54.38	43.12	4.31	49
165	8.75	13.12	50.62	4.30	98
166	-15.75	-73.12	-13.12	4.30	147
167	54.25	-24.38	-5.62	4.29	246
168	-15.75	-1.88	16.88	4.29	49
169	-43.75	43.12	-28.12	4.28	147
170	68.25	-1.88	-9.38	4.28	196
170a	68.25	-13.12	-9.38	4.25
171	-50.75	16.88	-1.88	4.28	49
172	19.25	-69.38	-13.12	4.28	147
173	12.25	50.62	-1.88	4.28	98
174	19.25	-9.38	20.62	4.28	49
175	-19.25	1.88	58.12	4.27	98
176	-29.75	-80.62	-24.38	4.27	49
177	19.25	-1.88	43.12	4.26	98
178	-19.25	-58.12	-9.38	4.26	49
179	-5.25	31.88	-20.62	4.25	147
180	-15.75	-46.88	-5.62	4.25	98
181	54.25	-9.38	20.62	4.25	246
182	-47.25	-58.12	13.12	4.25	98
183	50.75	-54.38	-9.38	4.23	49
184	-50.75	-5.62	-5.62	4.23	196
185	-22.75	-69.38	-13.12	4.23	196
186	19.25	16.88	13.12	4.23	147
187	15.75	9.38	43.12	4.22	246
188	1.75	13.12	9.38	4.22	49
189	12.25	-54.38	-54.38	4.22	49
190	15.75	-88.12	5.62	4.21	98
191	-40.25	5.62	-28.12	4.21	246
192	40.25	-31.88	54.38	4.21	98
193	-1.75	-1.88	1.88	4.20	246
194	-1.75	1.88	13.12	4.20	49
195	12.25	-9.38	5.62	4.20	49
196	12.25	24.38	-39.38	4.19	393
196a	12.25	20.62	-46.88	3.64
197	50.75	43.12	-5.62	4.19	49
198	-1.75	-58.12	-54.38	4.19	49
199	-47.25	-28.12	13.12	4.18	98
200	-26.25	50.62	5.62	4.18	196
201	15.75	-1.88	-16.88	4.17	196
202	54.25	1.88	35.62	4.17	98
203	-47.25	-43.12	9.38	4.17	147
204	40.25	5.62	-24.38	4.17	49
205	40.25	35.62	-1.88	4.17	49
206	22.75	46.88	13.12	4.16	49
207	50.75	-5.62	5.62	4.16	49
208	-40.25	-58.12	9.38	4.16	98
209	-26.25	-31.88	16.88	4.16	49
210	-22.75	-54.38	-61.88	4.16	147
211	-12.25	20.62	73.12	4.15	246
212	-36.75	43.12	1.88	4.15	196
213	19.25	-24.38	16.88	4.15	49
214	8.75	46.88	-5.62	4.14	49
215	19.25	-9.38	5.62	4.14	98
216	-26.25	-28.12	-31.88	4.13	49
217	54.25	-20.62	39.38	4.13	196
218	36.75	-35.62	-9.38	4.13	393
219	-19.25	61.88	5.62	4.13	49
220	26.25	-61.88	31.88	4.13	49
221	5.25	-28.12	-9.38	4.13	147
222	-54.25	1.88	13.12	4.13	49
223	-50.75	13.12	-5.62	4.12	49
224	-8.75	-46.88	-31.88	4.12	49
225	-15.75	13.12	50.62	4.12	147
226	-40.25	-46.88	1.88	4.10	147
227	-33.25	-35.62	-65.62	4.10	98
228	12.25	80.62	35.62	4.10	246
229	29.75	-31.88	-31.88	4.10	49
230	-33.25	1.88	-16.88	4.10	147
231	-29.75	-1.88	-24.38	4.09	98
232	-54.25	46.88	9.38	4.09	98
233	-43.75	-65.62	24.38	4.08	49
234	1.75	-5.62	-13.12	4.08	49
235	1.75	-5.62	-54.38	4.07	49
236	19.25	13.12	-1.88	4.07	98
237	-61.25	-20.62	13.12	4.07	49
238	22.75	-35.62	-20.62	4.06	147
239	29.75	-43.12	-58.12	4.06	49
240	26.25	24.38	13.12	4.06	147
241	-8.75	5.62	-24.38	4.06	49
242	-15.75	24.38	-9.38	4.05	98
243	-15.75	-1.88	-31.88	4.05	49
244	8.75	20.62	-24.38	4.05	49
245	-22.75	-1.88	-20.62	4.05	442
246	26.25	1.88	13.12	4.04	147
247	-33.25	28.12	31.88	4.04	246
248	29.75	-58.12	-46.88	4.04	49
249	5.25	-61.88	54.38	4.03	196
250	40.25	9.38	-20.62	4.02	98
251	-54.25	5.62	-16.88	4.01	246
252	-33.25	-35.62	-9.38	4.01	98
253	-26.25	-73.12	-35.62	4.01	98
254	47.25	43.12	9.38	4.00	49
255	1.75	-76.88	35.62	4.00	49
256	-33.25	-69.38	39.38	4.00	49
257	-47.25	-65.62	1.88	4.00	196
258	-12.25	46.88	28.12	4.00	49
259	-1.75	-24.38	9.38	3.99	49
260	-1.75	58.12	13.12	3.99	49
261	-19.25	13.12	28.12	3.99	49
262	29.75	65.62	43.12	3.98	49
263	40.25	-20.62	20.62	3.98	147
264	36.75	28.12	-35.62	3.97	49
265	-57.75	-31.88	-5.62	3.97	49
266	-19.25	24.38	31.88	3.97	49
267	-50.75	28.12	46.88	3.97	98
268	61.25	-31.88	39.38	3.97	49
269	-47.25	20.62	-13.12	3.96	246
270	47.25	16.88	-39.38	3.96	98
271	64.75	-43.12	5.62	3.95	49
272	-15.75	-31.88	-16.88	3.95	49
273	5.25	-88.12	-1.88	3.95	49
274	22.75	-69.38	1.88	3.95	147
275	12.25	9.38	28.12	3.94	147
276	36.75	-73.12	5.62	3.94	98
277	33.25	43.12	35.62	3.94	49
278	33.25	43.12	24.38	3.93	49
279	26.25	-20.62	-65.62	3.93	246
280	-22.75	-76.88	-16.88	3.93	49
281	54.25	-28.12	-9.38	3.93	98
282	12.25	-9.38	50.62	3.92	49
283	-12.25	-28.12	-50.62	3.92	196
284	15.75	-35.62	31.88	3.92	98
285	15.75	-1.88	-1.88	3.92	49
286	5.25	-28.12	73.12	3.92	49
287	-26.25	-61.88	35.62	3.92	147
288	5.25	-58.12	-54.38	3.92	49
289	-19.25	-76.88	-28.12	3.91	98
290	19.25	-76.88	-35.62	3.91	98
291	22.75	5.62	-1.88	3.91	49
292	-54.25	-20.62	-24.38	3.91	98
293	-1.75	-9.38	-5.62	3.91	49
294	15.75	-20.62	73.12	3.91	147
295	19.25	65.62	9.38	3.91	49
296	-64.75	-13.12	-24.38	3.91	49
297	-15.75	-5.62	61.88	3.91	147
298	-26.25	24.38	13.12	3.90	98
299	-19.25	-13.12	61.88	3.90	49
300	-22.75	13.12	-24.38	3.90	49
301	19.25	54.38	1.88	3.90	147
302	-15.75	54.38	54.38	3.90	98
303	1.75	-31.88	1.88	3.89	147
304	-33.25	39.38	54.38	3.89	49
305	33.25	50.62	50.62	3.89	49
306	5.25	-35.62	-58.12	3.89	98
307	-47.25	-28.12	28.12	3.89	49
308	12.25	69.38	5.62	3.89	49
309	5.25	-50.62	-20.62	3.88	98
310	15.75	88.12	9.38	3.88	98
311	-19.25	-50.62	16.88	3.88	49
312	-47.25	16.88	5.62	3.88	98
313	-47.25	-13.12	-54.38	3.88	98
314	-1.75	-16.88	-28.12	3.87	49
315	33.25	-28.12	65.62	3.87	344
315a	33.25	-13.12	65.62	3.63
315b	26.25	-20.62	65.62	3.49
316	19.25	35.62	1.88	3.87	147
317	33.25	5.62	28.12	3.86	49
318	-47.25	16.88	-24.38	3.86	49
319	-54.25	-24.38	-39.38	3.86	98
320	22.75	-16.88	43.12	3.86	49
321	-47.25	20.62	46.88	3.86	49
322	57.75	-20.62	28.12	3.85	49
323	36.75	1.88	20.62	3.85	98
324	-47.25	-39.38	16.88	3.85	49
325	50.75	-50.62	9.38	3.85	98
326	-54.25	-20.62	-50.62	3.85	49
327	36.75	-39.38	-69.38	3.85	49
328	15.75	-73.12	-31.88	3.85	49
329	-26.25	-35.62	-9.38	3.85	147
330	19.25	20.62	20.62	3.84	147
331	-19.25	-20.62	-35.62	3.84	98
332	12.25	-9.38	31.88	3.83	196
333	-33.25	-35.62	46.88	3.83	98
334	1.75	16.88	-39.38	3.83	246
334a	-1.75	24.38	-39.38	3.64
335	-29.75	-46.88	-13.12	3.82	49
336	26.25	-65.62	46.88	3.82	49
337	1.75	-20.62	-35.62	3.82	49
338	26.25	5.62	5.62	3.82	98
339	-54.25	-39.38	1.88	3.81	246
339a	-57.75	-46.88	-1.88	3.78
340	-40.25	16.88	24.38	3.81	98
341	-1.75	-43.12	-9.38	3.81	49
342	-5.25	-84.38	-20.62	3.81	49
343	33.25	-35.62	-5.62	3.80	49
344	1.75	-35.62	-61.88	3.78	98
345	-1.75	-28.12	20.62	3.78	98
346	1.75	-58.12	-13.12	3.78	49
347	-36.75	61.88	16.88	3.78	98
348	12.25	-46.88	-46.88	3.78	49
349	-12.25	-20.62	-46.88	3.78	49
350	29.75	16.88	35.62	3.78	49
351	26.25	13.12	9.38	3.77	98
352	15.75	-20.62	-20.62	3.77	49
353	-33.25	35.62	-24.38	3.77	98
354	8.75	-24.38	9.38	3.77	49
355	-22.75	-50.62	54.38	3.76	49
356	36.75	-73.12	-9.38	3.76	49
357	8.75	-73.12	35.62	3.76	49
358	-1.75	-16.88	24.38	3.76	49
359	-15.75	-16.88	-43.12	3.76	49
360	22.75	-73.12	-5.62	3.76	49
361	12.25	16.88	-50.62	3.76	49
362	-12.25	54.38	-9.38	3.75	98
363	22.75	24.38	43.12	3.75	147
364	19.25	43.12	-5.62	3.75	49
365	-12.25	-39.38	-5.62	3.75	344
366	43.75	-5.62	-13.12	3.74	49
367	47.25	13.12	-20.62	3.74	49
368	-54.25	-9.38	20.62	3.74	98
369	12.25	-35.62	9.38	3.74	49
370	-57.75	-1.88	-13.12	3.74	98
371	-33.25	-43.12	-28.12	3.74	196
372	47.25	13.12	5.62	3.74	98
373	1.75	-84.38	-16.88	3.74	49
374	40.25	28.12	-39.38	3.74	49
375	33.25	50.62	24.38	3.74	98
376	-1.75	-9.38	69.38	3.74	98
377	19.25	-16.88	16.88	3.74	98
378	-33.25	65.62	20.62	3.73	98
379	-29.75	16.88	-5.62	3.73	49
380	22.75	46.88	5.62	3.73	49
381	1.75	-16.88	-24.38	3.73	49
382	12.25	-65.62	-13.12	3.73	49
383	22.75	16.88	50.62	3.73	98
384	19.25	-50.62	-73.12	3.73	49
385	-33.25	-43.12	58.12	3.72	98
386	-47.25	-58.12	-28.12	3.72	49
387	19.25	9.38	20.62	3.72	49
388	-5.25	-43.12	69.38	3.72	147
389	-8.75	39.38	5.62	3.72	49
390	26.25	20.62	-24.38	3.71	98
391	-29.75	35.62	-1.88	3.71	49
392	33.25	43.12	-16.88	3.71	49
393	40.25	-58.12	5.62	3.71	98
394	40.25	-5.62	46.88	3.70	147
395	33.25	-9.38	-9.38	3.70	49
396	-68.25	-24.38	13.12	3.70	49
397	-15.75	-76.88	20.62	3.70	49
398	43.75	31.88	5.62	3.70	49
399	1.75	65.62	-16.88	3.70	98
400	26.25	-20.62	20.62	3.70	49
401	47.25	20.62	5.62	3.70	98
402	-29.75	20.62	-24.38	3.70	49
403	-47.25	46.88	20.62	3.70	49
404	15.75	1.88	35.62	3.69	49
405	40.25	-43.12	-28.12	3.69	49
406	-40.25	39.38	43.12	3.69	246
407	-54.25	-35.62	-5.62	3.69	49
408	-12.25	-20.62	-61.88	3.69	49
409	-50.75	13.12	-39.38	3.68	49
410	5.25	1.88	13.12	3.68	98
411	43.75	-69.38	-1.88	3.68	49
412	-22.75	-80.62	1.88	3.68	49
413	-36.75	-31.88	-46.88	3.68	49
414	26.25	-39.38	35.62	3.68	98
415	8.75	28.12	-24.38	3.67	49
416	-15.75	5.62	65.62	3.67	98
417	5.25	-65.62	-13.12	3.67	98
418	54.25	-5.62	13.12	3.67	49
419	-33.25	46.88	35.62	3.67	98
420	33.25	-20.62	-9.38	3.67	49
421	36.75	-31.88	13.12	3.66	49
422	36.75	35.62	16.88	3.66	344
423	-36.75	-35.62	39.38	3.66	49
424	1.75	9.38	-16.88	3.65	49
425	-47.25	-13.12	35.62	3.65	49
426	-50.75	5.62	-9.38	3.65	49
427	19.25	-9.38	35.62	3.65	49
428	-5.25	-20.62	1.88	3.65	49
429	57.75	-39.38	5.62	3.64	49
430	-36.75	16.88	1.88	3.64	49
431	-36.75	-13.12	9.38	3.64	49
432	-8.75	-39.38	-31.88	3.64	49
433	12.25	-1.88	-69.38	3.64	98
434	-1.75	-9.38	31.88	3.64	98
435	-15.75	-50.62	-31.88	3.63	196
436	-40.25	-16.88	-50.62	3.63	49
437	29.75	-16.88	58.12	3.62	49
438	43.75	24.38	-5.62	3.62	49
439	-1.75	-43.12	-31.88	3.62	49
440	-8.75	46.88	24.38	3.62	98
441	19.25	-61.88	35.62	3.61	49
442	-12.25	-16.88	-65.62	3.61	49
443	50.75	50.62	-1.88	3.61	49
444	33.25	-50.62	-28.12	3.61	49
445	-8.75	39.38	-28.12	3.61	49
446	22.75	-5.62	20.62	3.61	147
447	50.75	-43.12	9.38	3.61	49
448	-54.25	-28.12	-16.88	3.61	49
449	-12.25	16.88	20.62	3.61	147
450	-22.75	-5.62	-1.88	3.60	49
451	29.75	35.62	39.38	3.60	49
452	-47.25	-24.38	-5.62	3.60	49
453	12.25	61.88	5.62	3.60	49
454	-43.75	43.12	5.62	3.59	49
455	33.25	-28.12	58.12	3.59	49
456	19.25	28.12	31.88	3.59	49
457	-19.25	-61.88	20.62	3.59	49
458	57.75	35.62	28.12	3.58	98
459	33.25	-35.62	-35.62	3.58	49
460	-19.25	-9.38	-65.62	3.58	49
461	-40.25	-46.88	-5.62	3.58	98
462	5.25	-80.62	-1.88	3.58	49
463	-15.75	-5.62	1.88	3.58	98
464	29.75	-1.88	-20.62	3.58	49
465	5.25	5.62	-69.38	3.57	49
466	5.25	-9.38	-20.62	3.57	49
467	15.75	-13.12	-73.12	3.57	98
468	-57.75	-9.38	16.88	3.57	147
469	-1.75	1.88	69.38	3.56	98
470	-43.75	13.12	13.12	3.56	98
471	26.25	31.88	43.12	3.56	49
472	-33.25	73.12	20.62	3.56	49
473	36.75	20.62	31.88	3.56	49
474	-12.25	13.12	58.12	3.56	49
475	-43.75	58.12	20.62	3.56	98
476	-29.75	-24.38	-9.38	3.55	49
477	-33.25	-16.88	-5.62	3.55	49
478	-12.25	5.62	35.62	3.55	295
479	19.25	-31.88	-16.88	3.55	49
480	-61.25	-16.88	9.38	3.55	49
481	50.75	-13.12	5.62	3.54	49
482	-26.25	-35.62	54.38	3.54	49
483	26.25	-54.38	-13.12	3.54	98
484	-1.75	46.88	54.38	3.54	98
485	47.25	28.12	-39.38	3.54	49
486	-29.75	43.12	39.38	3.54	98
487	-47.25	43.12	9.38	3.54	49
488	57.75	-13.12	16.88	3.54	49
489	-40.25	13.12	-43.12	3.53	49
490	22.75	69.38	5.62	3.53	98
491	-12.25	-76.88	-9.38	3.53	49
492	-1.75	-50.62	-16.88	3.52	49
493	-5.25	43.12	-16.88	3.52	49
494	-5.25	1.88	1.88	3.52	49
495	-22.75	1.88	28.12	3.52	49
496	-1.75	9.38	9.38	3.52	49
497	22.75	-54.38	61.88	3.52	49
498	19.25	-84.38	-20.62	3.52	49
499	40.25	-28.12	-69.38	3.52	98
500	40.25	39.38	28.12	3.52	49
501	15.75	20.62	43.12	3.51	49
502	22.75	-24.38	1.88	3.51	49
503	22.75	-1.88	5.62	3.51	49
504	-47.25	-1.88	-20.62	3.51	49
505	43.75	9.38	-39.38	3.51	49
506	19.25	-58.12	-13.12	3.51	49
507	1.75	-58.12	9.38	3.50	49
508	-29.75	-65.62	-20.62	3.50	49
509	-50.75	-9.38	-13.12	3.50	49
510	15.75	-73.12	-20.62	3.50	49
511	40.25	65.62	31.88	3.50	49
512	26.25	-5.62	-1.88	3.50	49
513	-8.75	-13.12	5.62	3.50	49
514	5.25	-16.88	69.38	3.49	49
515	19.25	46.88	39.38	3.49	49
516	-29.75	-80.62	20.62	3.48	49
517	61.25	-35.62	35.62	3.48	49
518	36.75	50.62	-16.88	3.48	49
519	40.25	-35.62	50.62	3.48	49
520	-1.75	-5.62	-73.12	3.48	49
521	19.25	-31.88	-54.38	3.47	49
522	-33.25	1.88	31.88	3.47	98
523	47.25	5.62	31.88	3.47	49
524	15.75	1.88	58.12	3.47	98
525	-36.75	-46.88	9.38	3.47	98
526	15.75	-24.38	-73.12	3.47	49
527	-36.75	61.88	9.38	3.47	49
528	-8.75	39.38	31.88	3.46	49
529	-40.25	-69.38	31.88	3.46	49
530	5.25	-50.62	-5.62	3.46	49
531	-47.25	1.88	-28.12	3.45	49
532	12.25	69.38	-13.12	3.45	49
533	33.25	39.38	28.12	3.45	49
534	-43.75	35.62	31.88	3.45	98
535	-1.75	20.62	-16.88	3.45	147
536	47.25	16.88	39.38	3.45	49
537	36.75	-46.88	9.38	3.45	49
538	-19.25	-50.62	-46.88	3.45	49
539	36.75	-54.38	-28.12	3.44	49
540	1.75	28.12	-5.62	3.44	49
541	-1.75	-28.12	58.12	3.44	49
542	19.25	-61.88	-46.88	3.44	98
543	12.25	5.62	13.12	3.44	49
544	-64.75	20.62	5.62	3.44	98
545	40.25	-9.38	-31.88	3.44	49
546	-1.75	-9.38	-28.12	3.43	49
547	5.25	-16.88	-20.62	3.43	49
548	19.25	-13.12	28.12	3.43	49
549	-64.75	-35.62	-1.88	3.43	49
550	1.75	-1.88	-20.62	3.43	49
551	33.25	-24.38	-58.12	3.43	49
552	33.25	-43.12	-35.62	3.43	49
553	-19.25	-16.88	9.38	3.42	49
554	26.25	80.62	24.38	3.42	49
555	8.75	13.12	16.88	3.42	49
556	54.25	-28.12	24.38	3.42	49
557	-22.75	-28.12	31.88	3.42	49
558	54.25	-28.12	31.88	3.42	49
559	1.75	84.38	1.88	3.42	49
560	47.25	31.88	-24.38	3.42	49
561	-12.25	-50.62	-24.38	3.42	49
562	-36.75	-5.62	-20.62	3.41	49
563	-15.75	-73.12	39.38	3.41	98
564	-15.75	35.62	5.62	3.41	49
565	47.25	-31.88	-39.38	3.41	98
566	26.25	-16.88	-58.12	3.41	49
567	12.25	-9.38	-24.38	3.41	49
568	-19.25	-43.12	31.88	3.41	49
569	-19.25	-31.88	-69.38	3.41	98
570	-43.75	50.62	28.12	3.40	49
571	40.25	13.12	-46.88	3.40	49
572	19.25	-46.88	31.88	3.40	49
573	12.25	-9.38	43.12	3.40	49
574	40.25	-9.38	28.12	3.40	49
575	33.25	5.62	50.62	3.40	49
576	15.75	-16.88	5.62	3.40	49
577	22.75	54.38	16.88	3.40	49
578	-22.75	-54.38	39.38	3.40	49
579	50.75	-20.62	-43.12	3.39	49
580	12.25	-1.88	24.38	3.39	49
581	-29.75	-20.62	-69.38	3.39	49
582	12.25	-35.62	-16.88	3.39	49
583	12.25	-84.38	-5.62	3.39	49
584	-29.75	-1.88	-1.88	3.38	49
585	-36.75	-50.62	-16.88	3.38	49
586	-5.25	-31.88	-13.12	3.38	147
587	-1.75	5.62	-39.38	3.38	49
588	-50.75	1.88	9.38	3.38	49
589	-22.75	24.38	20.62	3.38	49
590	22.75	39.38	20.62	3.38	49
591	-12.25	16.88	-50.62	3.38	49
592	-8.75	-9.38	-20.62	3.38	49
593	8.75	24.38	39.38	3.38	49
594	40.25	-35.62	-58.12	3.38	98
595	-33.25	-1.88	13.12	3.38	49
596	-50.75	-1.88	16.88	3.37	98
597	-36.75	-16.88	-46.88	3.37	49
598	47.25	-20.62	20.62	3.37	49
599	22.75	-9.38	43.12	3.37	49
600	1.75	1.88	-16.88	3.36	49
601	19.25	-28.12	-24.38	3.36	49
602	-22.75	-28.12	-65.62	3.36	49
603	-22.75	-28.12	-13.12	3.36	98
604	-19.25	-43.12	46.88	3.36	49
605	-5.25	76.88	5.62	3.36	49
606	68.25	-28.12	16.88	3.36	49
607	-8.75	-35.62	65.62	3.36	49
608	-33.25	-24.38	-65.62	3.35	49
609	-22.75	54.38	-20.62	3.35	49
610	8.75	-5.62	-24.38	3.35	49
611	5.25	46.88	13.12	3.35	98
612	-26.25	-43.12	54.38	3.35	49
613	15.75	35.62	5.62	3.35	49
614	-33.25	-58.12	-1.88	3.35	49
615	-8.75	-54.38	-31.88	3.35	49
616	26.25	28.12	-43.12	3.34	98
617	-29.75	39.38	35.62	3.34	49
618	8.75	-76.88	9.38	3.34	49
619	-29.75	-1.88	-46.88	3.34	49
620	12.25	-35.62	65.62	3.34	98
621	54.25	1.88	9.38	3.34	49
622	-22.75	20.62	-1.88	3.34	49
623	33.25	-76.88	-28.12	3.34	49
624	-1.75	-1.88	-5.62	3.34	49
625	47.25	-35.62	16.88	3.33	49
626	-15.75	-46.88	-24.38	3.33	49
627	-47.25	-61.88	20.62	3.33	49
628	-22.75	-54.38	-35.62	3.33	49
629	-5.25	1.88	-58.12	3.33	49
630	5.25	-61.88	-46.88	3.33	49
631	33.25	-46.88	54.38	3.33	147
632	26.25	-80.62	1.88	3.33	49
633	-43.75	-35.62	-1.88	3.33	98
634	36.75	-24.38	-31.88	3.33	49
635	26.25	-5.62	-13.12	3.32	49
636	-36.75	-73.12	-9.38	3.32	98
637	-47.25	-1.88	-13.12	3.32	49
638	33.25	-35.62	-46.88	3.32	49
639	-50.75	13.12	35.62	3.32	98
640	-12.25	-13.12	-73.12	3.32	49
641	-50.75	-58.12	1.88	3.32	49
642	8.75	20.62	-35.62	3.32	49
643	-36.75	-46.88	-24.38	3.32	98
644	-5.25	-54.38	-54.38	3.32	49
645	-29.75	9.38	61.88	3.31	147
646	15.75	-46.88	20.62	3.31	49
647	19.25	-9.38	-5.62	3.31	49
648	36.75	-24.38	9.38	3.31	49
649	-1.75	-16.88	-58.12	3.31	49
650	-19.25	-50.62	39.38	3.31	98
651	-19.25	-54.38	13.12	3.31	49
652	54.25	-16.88	-1.88	3.30	49
653	-12.25	20.62	13.12	3.30	49
654	-36.75	9.38	54.38	3.30	49
655	29.75	-46.88	-31.88	3.30	49
656	-12.25	-35.62	-58.12	3.30	49
657	15.75	-16.88	54.38	3.30	49
658	-26.25	50.62	28.12	3.30	49
659	61.25	1.88	24.38	3.30	49
660	15.75	-16.88	-58.12	3.30	49
661	1.75	-69.38	46.88	3.29	49
662	-8.75	54.38	43.12	3.29	49
663	-26.25	-31.88	-58.12	3.29	49
664	5.25	-73.12	-16.88	3.29	49
665	15.75	28.12	13.12	3.29	49
666	-12.25	13.12	65.62	3.29	49
667	-40.25	16.88	-9.38	3.29	49
668	-12.25	-9.38	-24.38	3.29	49
669	-15.75	-39.38	-9.38	3.29	49
670	-26.25	-24.38	-5.62	3.29	49
671	33.25	-39.38	-58.12	3.29	49
672	29.75	-9.38	9.38	3.28	49
673	-5.25	39.38	50.62	3.28	49
674	-29.75	-31.88	-31.88	3.28	49
675	50.75	39.38	-9.38	3.28	49
676	-36.75	-9.38	31.88	3.28	49
677	-15.75	80.62	5.62	3.28	49
678	22.75	-76.88	-1.88	3.28	49
679	26.25	-80.62	-9.38	3.27	49
680	5.25	-20.62	-31.88	3.27	49
681	-33.25	24.38	-28.12	3.27	49
682	-47.25	9.38	-9.38	3.27	49
683	40.25	-13.12	-5.62	3.27	49
684	-12.25	-20.62	-13.12	3.27	49
685	-19.25	65.62	39.38	3.26	49
686	-40.25	39.38	35.62	3.26	49
687	33.25	28.12	13.12	3.26	49
688	19.25	9.38	50.62	3.26	49
689	22.75	-13.12	73.12	3.26	49
690	-29.75	1.88	-39.38	3.26	49
691	-12.25	46.88	46.88	3.26	49
692	-50.75	-61.88	-28.12	3.26	49
693	-29.75	35.62	-9.38	3.26	49
694	-47.25	46.88	28.12	3.25	49
695	-54.25	24.38	24.38	3.25	49
696	-5.25	20.62	13.12	3.25	49
697	43.75	-39.38	-43.12	3.25	49
698	29.75	-43.12	58.12	3.25	49
699	-22.75	-39.38	-20.62	3.24	49
700	47.25	-54.38	13.12	3.24	49
701	12.25	-50.62	39.38	3.24	49
702	-12.25	-39.38	5.62	3.24	49
703	-40.25	-50.62	28.12	3.24	49
704	-1.75	-69.38	-13.12	3.24	49
705	15.75	5.62	-5.62	3.23	49
706	40.25	-5.62	58.12	3.23	49
707	-15.75	-43.12	-65.62	3.23	49
708	22.75	-50.62	35.62	3.23	49
709	40.25	5.62	39.38	3.23	49
710	50.75	-61.88	-13.12	3.23	49
711	47.25	-13.12	28.12	3.23	49
712	-5.25	5.62	-16.88	3.23	49
713	57.75	-24.38	-28.12	3.22	49
714	-33.25	-28.12	35.62	3.22	49
715	-12.25	1.88	20.62	3.22	49
716	-26.25	43.12	31.88	3.22	49
717	-19.25	-16.88	1.88	3.22	49
718	1.75	-5.62	65.62	3.22	49
719	-29.75	13.12	54.38	3.22	49
720	5.25	54.38	1.88	3.22	49
721	5.25	-13.12	-39.38	3.22	49
722	-5.25	9.38	43.12	3.22	49
723	-36.75	61.88	28.12	3.21	49
724	-12.25	-16.88	9.38	3.21	49
725	-64.75	-9.38	-9.38	3.21	49
726	50.75	-13.12	43.12	3.21	49
727	29.75	-50.62	-13.12	3.21	49
728	40.25	-9.38	-13.12	3.21	49
729	-47.25	-20.62	-1.88	3.21	49
730	-33.25	16.88	-46.88	3.21	49
731	-47.25	9.38	28.12	3.21	49
732	36.75	-50.62	-1.88	3.21	49
733	-36.75	-46.88	46.88	3.21	49
734	15.75	28.12	-5.62	3.20	49
735	-19.25	-54.38	-43.12	3.20	49
736	-57.75	-31.88	24.38	3.20	98
737	-15.75	-16.88	-28.12	3.20	98
738	50.75	-16.88	28.12	3.20	49
739	1.75	-65.62	-43.12	3.19	49
740	-29.75	-24.38	-16.88	3.19	49
741	19.25	-1.88	50.62	3.19	49
742	-22.75	-20.62	-31.88	3.19	49
743	-68.25	-24.38	5.62	3.19	49
744	19.25	-20.62	39.38	3.19	49
745	33.25	-20.62	28.12	3.19	49
746	-40.25	-5.62	35.62	3.19	49
747	12.25	-58.12	46.88	3.19	98
748	-26.25	-76.88	35.62	3.19	49
749	-8.75	-65.62	-43.12	3.18	49
750	-22.75	20.62	-46.88	3.18	49
751	-40.25	-65.62	-31.88	3.18	49
752	-47.25	-20.62	-43.12	3.18	49
753	-50.75	50.62	13.12	3.18	49
754	40.25	-43.12	9.38	3.18	98
755	19.25	-84.38	24.38	3.18	49
756	-36.75	-69.38	13.12	3.18	49
757	29.75	-1.88	-54.38	3.18	49
758	26.25	16.88	-20.62	3.18	49
759	43.75	-9.38	54.38	3.18	98
760	-19.25	-31.88	24.38	3.18	49
761	-19.25	43.12	-1.88	3.18	49
762	12.25	-65.62	1.88	3.17	49
763	40.25	-31.88	-35.62	3.17	49
764	-33.25	-39.38	-35.62	3.17	49
765	-19.25	31.88	43.12	3.17	49
766	26.25	-54.38	31.88	3.17	49
767	40.25	-13.12	-24.38	3.17	49
768	8.75	-13.12	-58.12	3.17	49
769	-43.75	16.88	-43.12	3.17	49
770	26.25	-54.38	46.88	3.16	49
771	-50.75	-35.62	-54.38	3.16	49
772	33.25	-35.62	-16.88	3.16	98
773	-8.75	9.38	65.62	3.16	49
774	-29.75	-31.88	43.12	3.16	49
775	-19.25	-61.88	9.38	3.15	49
776	-40.25	-28.12	-9.38	3.15	49
777	-40.25	54.38	24.38	3.15	49
778	-19.25	-65.62	-20.62	3.15	49
779	-19.25	-9.38	-35.62	3.15	49
780	-47.25	31.88	-24.38	3.15	49
781	-50.75	-16.88	-13.12	3.15	49
782	-22.75	-5.62	-9.38	3.15	49
783	-43.75	-5.62	43.12	3.15	49
784	15.75	46.88	46.88	3.15	49
785	26.25	-20.62	58.12	3.15	49
786	12.25	1.88	20.62	3.15	49
787	-12.25	-5.62	-20.62	3.15	49
788	22.75	13.12	20.62	3.15	49
789	-15.75	54.38	5.62	3.14	49
790	43.75	5.62	-31.88	3.14	49
791	40.25	-28.12	35.62	3.14	49
792	-19.25	-31.88	-58.12	3.14	49
793	-54.25	24.38	-13.12	3.14	49
794	-15.75	-69.38	-43.12	3.14	49
795	15.75	16.88	-16.88	3.14	49
796	29.75	-39.38	-54.38	3.13	49
797	68.25	-28.12	9.38	3.13	49
798	40.25	-76.88	-9.38	3.13	49
799	8.75	-24.38	24.38	3.13	49
800	12.25	5.62	-54.38	3.13	49
801	57.75	35.62	-5.62	3.13	49
802	22.75	31.88	28.12	3.13	49
803	26.25	5.62	69.38	3.13	49
804	-50.75	50.62	-1.88	3.13	49
805	8.75	-20.62	-28.12	3.13	49
806	-1.75	-58.12	-16.88	3.13	49
807	47.25	-24.38	9.38	3.13	49
808	19.25	-28.12	9.38	3.13	49
809	-5.25	-58.12	-13.12	3.12	49
810	-19.25	-9.38	69.38	3.12	49
811	40.25	-69.38	9.38	3.12	49
812	-12.25	-54.38	-9.38	3.12	49
813	-54.25	-24.38	-31.88	3.12	49
814	-15.75	-43.12	1.88	3.12	49
815	43.75	1.88	46.88	3.12	49
816	33.25	-50.62	46.88	3.12	49
817	-1.75	-16.88	1.88	3.12	49
818	-12.25	-84.38	-5.62	3.12	49
819	-22.75	13.12	1.88	3.12	147
820	-26.25	9.38	-1.88	3.12	49
821	26.25	-43.12	5.62	3.12	49
822	29.75	-46.88	-43.12	3.11	49
823	5.25	84.38	16.88	3.11	49
824	8.75	54.38	5.62	3.11	49
825	-5.25	16.88	35.62	3.11	49
826	47.25	39.38	13.12	3.11	98
827	33.25	-24.38	-28.12	3.11	49
828	-15.75	16.88	-35.62	3.11	49
829	-33.25	-43.12	-61.88	3.11	49
830	12.25	-35.62	-61.88	3.11	49
831	12.25	-69.38	5.62	3.11	49
832	1.75	50.62	16.88	3.11	49
833	36.75	39.38	5.62	3.11	49
834	-36.75	-24.38	-61.88	3.11	49
835	-57.75	-24.38	5.62	3.11	49
836	22.75	73.12	31.88	3.10	49
837	26.25	9.38	24.38	3.10	49
838	-1.75	-1.88	24.38	3.10	49
839	-36.75	5.62	65.62	3.10	49
840	47.25	5.62	-1.88	3.10	49
841	12.25	5.62	5.62	3.10	49
842	1.75	54.38	13.12	3.10	49
843	-47.25	50.62	-9.38	3.10	49
844	-61.25	20.62	-24.38	3.10	49
845	8.75	39.38	20.62	3.10	49
846	19.25	-73.12	-1.88	3.09	49
847	22.75	9.38	46.88	3.09	49
848	-26.25	-39.38	-35.62	3.09	49

house

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	-22.75	-39.38	-1.88	5.50	442
2	22.75	20.62	9.38	5.43	787
2a	22.75	24.38	16.88	4.16
2b	26.25	13.12	13.12	3.88
2c	15.75	28.12	13.12	3.73
3	-22.75	24.38	13.12	5.29	885
3a	-22.75	16.88	5.62	4.27
3b	-15.75	28.12	16.88	4.03
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	393
7	-29.75	28.12	5.62	4.60	98
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	-50.75	9.38	-28.12	4.44	49
13	-40.25	-58.12	28.12	4.41	98
14	22.75	-31.88	1.88	4.39	49
15	22.75	-43.12	-31.88	4.38	49
16	33.25	5.62	-39.38	4.37	49
17	26.25	5.62	9.38	4.35	49
18	1.75	13.12	-16.88	4.33	49
19	8.75	-16.88	31.88	4.33	49
20	33.25	28.12	28.12	4.25	49
21	26.25	9.38	-31.88	4.23	49
22	-22.75	5.62	24.38	4.23	393
23	1.75	-46.88	-5.62	4.21	49
24	-26.25	39.38	46.88	4.21	196
25	19.25	5.62	43.12	4.21	98
26	43.75	-1.88	20.62	4.19	98
27	8.75	-24.38	24.38	4.16	49
28	19.25	-13.12	24.38	4.16	98
29	15.75	-39.38	13.12	4.13	49
30	-12.25	-16.88	-20.62	4.12	49
31	-29.75	16.88	9.38	4.11	49
32	-22.75	20.62	20.62	4.10	147
33	-12.25	-28.12	-76.88	4.08	49
34	26.25	-61.88	31.88	4.07	49
35	15.75	-54.38	5.62	4.05	49
36	-54.25	-20.62	-9.38	4.03	49
37	-15.75	-46.88	20.62	4.02	98
38	26.25	-1.88	13.12	4.00	49
39	-15.75	-35.62	-1.88	3.99	196
40	-12.25	-43.12	-16.88	3.97	49
41	-29.75	-31.88	35.62	3.96	49
42	-8.75	35.62	43.12	3.90	49
43	8.75	-9.38	31.88	3.88	98
44	5.25	-43.12	-1.88	3.87	196
45	-15.75	-50.62	24.38	3.87	49
46	-22.75	-9.38	-65.62	3.86	49
47	-15.75	-24.38	-9.38	3.85	196
48	-26.25	-50.62	-46.88	3.84	49
49	-8.75	-43.12	-9.38	3.84	49
50	22.75	-43.12	9.38	3.83	49
51	-8.75	-28.12	5.62	3.82	49
52	-12.25	-43.12	-50.62	3.82	49
53	-15.75	-31.88	-50.62	3.82	49
54	22.75	-39.38	46.88	3.81	49
55	22.75	-76.88	20.62	3.81	49
56	-40.25	-69.38	20.62	3.80	49
57	-29.75	-9.38	-31.88	3.78	49
58	-12.25	-39.38	46.88	3.77	49
59	5.25	31.88	50.62	3.77	49
60	-22.75	-58.12	24.38	3.75	49
61	-15.75	16.88	13.12	3.75	98
62	-1.75	-61.88	-39.38	3.75	49
63	50.75	-31.88	-50.62	3.74	49
64	50.75	50.62	-1.88	3.73	49
65	33.25	-50.62	-24.38	3.72	196
66	29.75	1.88	13.12	3.71	49
67	1.75	31.88	-13.12	3.69	49
68	26.25	9.38	-24.38	3.69	49
69	15.75	-28.12	-5.62	3.68	49
70	-43.75	58.12	9.38	3.67	49
71	-8.75	5.62	28.12	3.67	98
72	26.25	-13.12	54.38	3.66	49
73	33.25	-76.88	-24.38	3.64	49
74	-33.25	-24.38	35.62	3.64	49
75	19.25	-76.88	-16.88	3.63	98
76	-40.25	50.62	1.88	3.63	49
77	40.25	31.88	-24.38	3.62	49
78	33.25	43.12	50.62	3.62	49
79	26.25	-24.38	-69.38	3.62	49
80	33.25	9.38	1.88	3.62	49
81	-26.25	20.62	-1.88	3.62	49
82	22.75	-65.62	20.62	3.60	49
83	-15.75	20.62	1.88	3.60	49
84	-15.75	20.62	20.62	3.59	49
85	-33.25	-73.12	-31.88	3.58	49
86	22.75	-73.12	-13.12	3.58	49
87	57.75	-54.38	1.88	3.57	49
88	29.75	-43.12	-20.62	3.56	49
89	-22.75	-24.38	46.88	3.56	49
90	40.25	-13.12	-43.12	3.56	98
91	-29.75	-13.12	-20.62	3.55	49
92	-40.25	-28.12	-24.38	3.55	49
93	-43.75	-9.38	39.38	3.55	49
94	-26.25	-50.62	46.88	3.54	49
95	-26.25	13.12	16.88	3.54	98
96	-29.75	50.62	20.62	3.54	49
97	-22.75	-13.12	46.88	3.54	49
98	-36.75	24.38	13.12	3.53	49
99	22.75	-9.38	13.12	3.53	49
100	-12.25	1.88	-61.88	3.52	98
101	5.25	9.38	39.38	3.52	49
102	29.75	-54.38	-24.38	3.51	49
103	22.75	-43.12	31.88	3.51	49
104	5.25	-65.62	54.38	3.50	49
105	-50.75	31.88	9.38	3.49	49
106	-12.25	9.38	5.62	3.49	49
107	22.75	1.88	20.62	3.48	49
108	12.25	-5.62	-5.62	3.48	49
109	50.75	-43.12	-1.88	3.47	49
110	15.75	-1.88	69.38	3.46	49
111	26.25	-73.12	31.88	3.46	49
112	36.75	-61.88	-13.12	3.46	49
113	-1.75	39.38	-20.62	3.45	98
114	-26.25	-54.38	43.12	3.45	98
115	29.75	-46.88	-24.38	3.45	49
116	-57.75	20.62	13.12	3.45	49
117	-15.75	-50.62	-9.38	3.44	49
118	-26.25	-20.62	16.88	3.44	49
119	19.25	24.38	5.62	3.44	49
120	-26.25	1.88	13.12	3.44	49
121	-36.75	-61.88	35.62	3.44	49
122	61.25	-1.88	13.12	3.43	49
123	57.75	-16.88	16.88	3.43	49
124	15.75	1.88	58.12	3.43	49
125	-36.75	-65.62	-16.88	3.43	49
126	22.75	-46.88	-20.62	3.42	49
127	12.25	-31.88	-73.12	3.42	49
128	22.75	-16.88	16.88	3.42	49
129	33.25	54.38	28.12	3.41	49
130	-33.25	9.38	5.62	3.41	49
131	43.75	35.62	46.88	3.41	49
132	15.75	-28.12	24.38	3.41	49
133	-26.25	-76.88	-9.38	3.39	49
134	22.75	-13.12	20.62	3.39	49
135	-40.25	-46.88	39.38	3.39	49
136	-33.25	61.88	39.38	3.38	49
137	-36.75	-69.38	31.88	3.38	98
138	8.75	-61.88	-50.62	3.38	49
139	22.75	5.62	24.38	3.38	49
140	5.25	-20.62	-13.12	3.38	49
141	19.25	-35.62	-28.12	3.38	49
142	-15.75	-61.88	43.12	3.37	196
143	-33.25	13.12	-1.88	3.37	49
144	22.75	-46.88	-5.62	3.37	49
145	-15.75	-20.62	-28.12	3.37	49
146	12.25	-13.12	54.38	3.37	49
147	29.75	5.62	39.38	3.36	49
148	26.25	-20.62	24.38	3.36	49
149	15.75	-73.12	-28.12	3.36	49
150	-8.75	61.88	-16.88	3.36	49
151	-36.75	-50.62	-16.88	3.36	49
152	-64.75	-24.38	-20.62	3.35	49
153	-57.75	16.88	5.62	3.34	49
154	-15.75	58.12	5.62	3.34	49
155	-26.25	16.88	50.62	3.34	49
156	40.25	-39.38	13.12	3.34	49
157	36.75	-13.12	-31.88	3.34	49
158	26.25	-16.88	-9.38	3.34	49
159	15.75	16.88	50.62	3.34	49
160	-54.25	-13.12	-1.88	3.34	49
161	40.25	-50.62	-58.12	3.34	98
162	-8.75	-24.38	1.88	3.33	49
163	-22.75	-16.88	24.38	3.33	49
164	-1.75	28.12	61.88	3.33	49
165	26.25	-58.12	-16.88	3.33	98
166	54.25	9.38	-31.88	3.33	49
167	-22.75	35.62	-24.38	3.32	49
168	8.75	-80.62	-5.62	3.32	49
169	40.25	-16.88	-46.88	3.32	49
170	-15.75	-5.62	-13.12	3.31	98
171	-47.25	-76.88	9.38	3.31	49
172	-40.25	-1.88	24.38	3.30	49
173	33.25	-28.12	-13.12	3.30	49
174	8.75	-58.12	-24.38	3.29	49
175	19.25	-50.62	-13.12	3.29	49
176	12.25	-73.12	-13.12	3.29	49
177	-1.75	-31.88	13.12	3.29	49
178	47.25	-9.38	20.62	3.29	98
179	5.25	-35.62	-28.12	3.28	49
180	15.75	-50.62	9.38	3.28	49
181	29.75	1.88	-16.88	3.28	49
182	-19.25	61.88	20.62	3.28	49
183	-15.75	-20.62	1.88	3.27	49
184	5.25	-50.62	-5.62	3.27	49
185	36.75	-39.38	-24.38	3.27	49
186	-26.25	-20.62	-13.12	3.27	49
187	29.75	-16.88	46.88	3.26	49
188	15.75	-5.62	-16.88	3.26	49
189	26.25	-76.88	-16.88	3.26	49
190	26.25	-13.12	13.12	3.25	49
191	-12.25	50.62	-24.38	3.25	49
192	19.25	35.62	20.62	3.25	49
193	8.75	-5.62	-9.38	3.25	49
194	-15.75	-43.12	13.12	3.25	49
195	-36.75	-39.38	-46.88	3.24	49
196	-26.25	31.88	20.62	3.24	49
197	-12.25	13.12	1.88	3.24	98
198	12.25	-35.62	31.88	3.24	49
199	-40.25	24.38	35.62	3.24	49
200	-33.25	-61.88	39.38	3.24	49
201	-15.75	31.88	5.62	3.24	49
202	26.25	5.62	69.38	3.24	49
203	-40.25	43.12	46.88	3.24	49
204	50.75	-46.88	1.88	3.23	49
205	-8.75	-46.88	-31.88	3.23	49
206	-26.25	-80.62	-1.88	3.23	49
207	-19.25	5.62	61.88	3.23	49
208	19.25	-35.62	1.88	3.23	49
209	-40.25	-65.62	31.88	3.23	49
210	-15.75	-1.88	-16.88	3.22	49
211	33.25	13.12	-5.62	3.22	49
212	33.25	28.12	-46.88	3.21	49
213	12.25	5.62	61.88	3.21	49
214	-40.25	-24.38	-28.12	3.21	49
215	22.75	-54.38	-16.88	3.21	49
216	-43.75	31.88	31.88	3.20	49
217	15.75	-46.88	-58.12	3.20	49
218	8.75	43.12	-5.62	3.20	49
219	-26.25	16.88	-13.12	3.19	49
220	-12.25	-58.12	-46.88	3.19	49
221	1.75	-39.38	-1.88	3.19	49
222	22.75	46.88	-20.62	3.19	49
223	57.75	16.88	20.62	3.18	49
224	15.75	24.38	-13.12	3.18	49
225	-15.75	31.88	24.38	3.18	49
226	-12.25	24.38	-13.12	3.18	49
227	47.25	-50.62	-20.62	3.18	49
228	-15.75	-61.88	24.38	3.18	49
229	29.75	5.62	65.62	3.18	49
230	43.75	20.62	16.88	3.17	49
231	-15.75	28.12	28.12	3.17	49
232	29.75	-24.38	-54.38	3.17	49
233	-19.25	31.88	20.62	3.17	49
234	-22.75	35.62	20.62	3.17	49
235	36.75	-46.88	-9.38	3.17	49
236	-26.25	-31.88	-69.38	3.16	49
237	-12.25	43.12	-9.38	3.16	49
238	15.75	-50.62	43.12	3.15	49
239	-29.75	20.62	13.12	3.15	49
240	-15.75	13.12	73.12	3.15	49
241	-33.25	50.62	16.88	3.15	49
242	-19.25	1.88	28.12	3.15	49
243	-22.75	-16.88	-13.12	3.14	49
244	-40.25	20.62	9.38	3.14	49
245	-50.75	-35.62	-1.88	3.14	49
246	22.75	16.88	5.62	3.13	49
247	29.75	31.88	24.38	3.13	49
248	19.25	65.62	28.12	3.13	49
249	-12.25	43.12	46.88	3.12	49
250	-43.75	-69.38	24.38	3.12	49
251	-33.25	-20.62	-31.88	3.12	49
252	64.75	-43.12	1.88	3.12	49
253	-8.75	5.62	-5.62	3.12	49
254	12.25	35.62	-5.62	3.11	49
255	-19.25	-9.38	-28.12	3.11	49
256	50.75	-13.12	35.62	3.11	49
257	-40.25	-39.38	39.38	3.10	49
258	26.25	-9.38	5.62	3.10	49
259	26.25	-20.62	-28.12	3.09	49

chair

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	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	98
8	-54.25	-43.12	46.88	4.09	49
9	12.25	28.12	-16.88	4.08	98
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	98
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	147
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	98
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
59	5.25	-58.12	-24.38	3.27	49
60	-43.75	9.38	-31.88	3.26	98
61	-50.75	-28.12	-1.88	3.26	49
62	22.75	13.12	20.62	3.26	49
63	-36.75	-28.12	-9.38	3.25	49
64	-36.75	-31.88	-50.62	3.24	49
65	26.25	35.62	24.38	3.23	49
66	36.75	-28.12	-9.38	3.23	49
67	-5.25	28.12	61.88	3.23	49
68	-22.75	-1.88	-1.88	3.22	49
69	33.25	54.38	24.38	3.21	49
70	-1.75	9.38	-16.88	3.21	49
71	-57.75	28.12	31.88	3.19	49
72	29.75	-20.62	-5.62	3.19	49
73	-8.75	-20.62	-80.62	3.18	49
74	-43.75	-65.62	24.38	3.18	49
75	1.75	28.12	-35.62	3.18	49
76	26.25	31.88	20.62	3.18	49
77	-22.75	46.88	13.12	3.16	49
78	12.25	-16.88	-28.12	3.16	49
79	-29.75	24.38	-13.12	3.16	49
80	-43.75	-1.88	5.62	3.15	49
81	-29.75	-43.12	-58.12	3.15	49
82	-54.25	-5.62	-13.12	3.15	49
83	40.25	-50.62	-1.88	3.15	49
84	43.75	-35.62	-1.88	3.14	49
85	-57.75	13.12	-16.88	3.14	49
86	64.75	-20.62	39.38	3.14	49
87	15.75	16.88	28.12	3.14	49
88	-50.75	-24.38	54.38	3.14	49
89	33.25	-31.88	39.38	3.14	49
90	-15.75	-5.62	69.38	3.14	49
91	29.75	13.12	-43.12	3.13	49
92	29.75	43.12	13.12	3.12	49
93	-5.25	35.62	54.38	3.12	49
94	-50.75	16.88	31.88	3.12	49
95	50.75	-58.12	-13.12	3.12	49
96	19.25	-39.38	13.12	3.12	49
97	-15.75	-76.88	-28.12	3.12	49
98	29.75	-5.62	9.38	3.11	49
99	36.75	46.88	-16.88	3.10	49
100	26.25	-65.62	43.12	3.10	49
101	-33.25	-46.88	16.88	3.09	49

scrambledpix

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	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	98
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
31	-22.75	-31.88	-31.88	3.28	49
32	19.25	1.88	9.38	3.24	49
33	-26.25	13.12	-39.38	3.24	49
34	43.75	16.88	-13.12	3.24	49
35	-22.75	-24.38	-16.88	3.23	49
36	1.75	35.62	-24.38	3.23	49
37	33.25	-46.88	-24.38	3.22	49
38	-43.75	16.88	-9.38	3.22	49
39	-15.75	1.88	61.88	3.21	49
40	40.25	5.62	-39.38	3.20	49
41	1.75	-50.62	61.88	3.20	49
42	-1.75	-31.88	61.88	3.20	49
43	-33.25	13.12	28.12	3.19	49
44	-19.25	-73.12	39.38	3.19	49
45	33.25	1.88	35.62	3.19	49
46	-5.25	-35.62	-9.38	3.18	49
47	54.25	-46.88	-9.38	3.18	49
48	1.75	9.38	31.88	3.17	98
49	1.75	-43.12	-1.88	3.17	49
50	15.75	-61.88	24.38	3.17	49
51	36.75	50.62	1.88	3.16	49
52	-15.75	-58.12	20.62	3.16	49
53	-8.75	-54.38	-13.12	3.15	49
54	1.75	1.88	31.88	3.13	49
55	-26.25	39.38	35.62	3.13	49
56	-15.75	-61.88	-46.88	3.12	49
57	5.25	-28.12	20.62	3.12	49
58	-50.75	9.38	-39.38	3.12	49
59	-19.25	-58.12	-16.88	3.11	49
60	15.75	-76.88	5.62	3.11	49

face

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	-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	492
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	147
6	68.25	-9.38	-9.38	4.82	147
7	-36.75	31.88	16.88	4.66	246
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	295
11a	-47.25	-28.12	28.12	3.74
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	98
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	98
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	246
21a	-43.75	-43.12	-9.38	3.90
22	40.25	-43.12	-9.38	4.13	98
23	-1.75	28.12	61.88	4.07	98
24	-1.75	-58.12	-16.88	4.07	147
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	442
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	541
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	-8.75	-39.38	-5.62	3.87	49
37	-47.25	-46.88	9.38	3.86	98
38	5.25	20.62	65.62	3.86	49
39	40.25	43.12	16.88	3.85	49
40	-26.25	-43.12	-43.12	3.83	49
41	19.25	-46.88	-20.62	3.81	246
42	-36.75	-43.12	39.38	3.80	295
43	-8.75	-28.12	61.88	3.79	49
44	-8.75	-46.88	-31.88	3.79	49
45	43.75	35.62	39.38	3.76	344
45a	40.25	28.12	39.38	3.56
46	-22.75	-46.88	-50.62	3.75	49
47	-47.25	-61.88	-5.62	3.74	49
48	-29.75	28.12	13.12	3.73	49
49	29.75	-46.88	-31.88	3.73	49
50	19.25	-16.88	39.38	3.72	49
51	-47.25	-9.38	13.12	3.71	49
52	-47.25	-46.88	-31.88	3.68	49
53	33.25	50.62	20.62	3.67	49
54	-29.75	-13.12	5.62	3.67	49
55	36.75	-46.88	9.38	3.66	49
56	-50.75	20.62	20.62	3.65	49
57	-47.25	-16.88	-35.62	3.61	49
58	36.75	-35.62	-20.62	3.61	49
59	33.25	-50.62	-20.62	3.61	147
60	33.25	-54.38	43.12	3.60	49
61	5.25	-24.38	-20.62	3.59	49
62	-47.25	-46.88	16.88	3.59	49
63	-57.75	-9.38	-1.88	3.58	49
64	-12.25	-76.88	28.12	3.58	49
65	22.75	31.88	28.12	3.57	49
66	-36.75	5.62	43.12	3.57	49
67	-33.25	39.38	54.38	3.56	49
68	-1.75	-46.88	-9.38	3.56	98
69	26.25	-28.12	24.38	3.56	49
70	29.75	-50.62	-39.38	3.56	49
71	-12.25	-39.38	58.12	3.55	49
72	29.75	-54.38	46.88	3.55	49
73	-50.75	-43.12	1.88	3.53	147
74	-36.75	20.62	50.62	3.53	49
75	19.25	-1.88	50.62	3.52	49
76	-57.75	31.88	31.88	3.52	49
77	-43.75	-43.12	46.88	3.51	49
78	-29.75	5.62	58.12	3.51	98
79	26.25	1.88	13.12	3.51	49
80	5.25	-65.62	13.12	3.49	49
81	29.75	-46.88	-43.12	3.47	49
82	33.25	-43.12	-24.38	3.47	49
83	-47.25	-16.88	-43.12	3.46	49
84	-36.75	-46.88	50.62	3.45	49
85	-54.25	-9.38	-31.88	3.44	49
86	-33.25	31.88	46.88	3.43	49
87	-26.25	-84.38	-9.38	3.43	98
88	12.25	28.12	61.88	3.42	98
89	-15.75	16.88	16.88	3.41	49
90	-33.25	16.88	50.62	3.41	49
91	-5.25	61.88	-9.38	3.41	49
92	-47.25	-61.88	-28.12	3.41	49
93	-33.25	-9.38	-28.12	3.41	49
94	-40.25	50.62	9.38	3.41	49
95	-19.25	-61.88	-9.38	3.40	49
96	22.75	-24.38	-58.12	3.39	49
97	-29.75	35.62	-1.88	3.39	49
98	-54.25	-20.62	-39.38	3.39	49
99	40.25	-46.88	-20.62	3.39	49
100	8.75	-58.12	-28.12	3.38	98
101	-47.25	-46.88	-1.88	3.38	49
102	36.75	54.38	24.38	3.38	98
103	-29.75	31.88	-9.38	3.37	49
104	40.25	-35.62	-43.12	3.37	49
105	-50.75	-58.12	-13.12	3.36	49
106	36.75	61.88	28.12	3.35	49
107	-47.25	58.12	-1.88	3.35	49
108	-47.25	24.38	28.12	3.34	98
109	-47.25	-9.38	5.62	3.34	49
110	-50.75	-35.62	43.12	3.34	49
111	-47.25	-46.88	-58.12	3.34	49
112	68.25	-28.12	16.88	3.33	98
113	47.25	35.62	13.12	3.33	49
114	19.25	5.62	13.12	3.32	49
115	29.75	-46.88	-20.62	3.32	49
116	-8.75	-13.12	20.62	3.32	49
117	5.25	31.88	-35.62	3.32	49
118	15.75	-54.38	-20.62	3.32	49
119	43.75	9.38	50.62	3.32	49
120	15.75	5.62	9.38	3.31	49
121	8.75	-1.88	9.38	3.31	49
122	33.25	20.62	43.12	3.31	49
123	43.75	-54.38	9.38	3.30	98
124	-40.25	39.38	43.12	3.30	49
125	-47.25	-58.12	-31.88	3.30	98
126	12.25	-88.12	5.62	3.30	49
127	40.25	-69.38	-16.88	3.30	49
128	-43.75	-31.88	-9.38	3.29	49
129	50.75	1.88	43.12	3.29	49
130	43.75	16.88	31.88	3.29	49
131	-47.25	-50.62	31.88	3.29	49
132	15.75	13.12	43.12	3.29	49
133	1.75	-43.12	5.62	3.28	49
134	-50.75	-28.12	35.62	3.28	49
135	-54.25	-43.12	20.62	3.28	49
136	-26.25	13.12	61.88	3.28	98
137	-29.75	-50.62	1.88	3.27	49
138	29.75	-35.62	35.62	3.27	49
139	43.75	13.12	46.88	3.26	49
140	22.75	-43.12	54.38	3.26	49
141	-40.25	20.62	24.38	3.26	49
142	-33.25	58.12	20.62	3.26	49
143	29.75	9.38	13.12	3.25	49
144	-47.25	-31.88	-50.62	3.23	49
145	1.75	13.12	61.88	3.23	49
146	-12.25	-1.88	28.12	3.23	49
147	26.25	-43.12	35.62	3.23	49
148	-1.75	-31.88	9.38	3.22	49
149	-57.75	9.38	-16.88	3.22	49
150	-47.25	-61.88	20.62	3.22	49
151	12.25	-65.62	54.38	3.22	98
152	26.25	-13.12	31.88	3.22	49
153	26.25	-9.38	35.62	3.22	49
154	-15.75	-46.88	-1.88	3.22	49
155	-19.25	-39.38	1.88	3.22	49
156	-8.75	20.62	58.12	3.21	49
157	-22.75	46.88	13.12	3.21	49
158	15.75	13.12	28.12	3.21	49
159	57.75	-35.62	43.12	3.21	49
160	26.25	-28.12	69.38	3.20	49
161	-29.75	20.62	-46.88	3.20	49
162	1.75	-69.38	46.88	3.20	49
163	-33.25	-61.88	5.62	3.19	49
164	-8.75	-58.12	-43.12	3.19	49
165	-36.75	-39.38	46.88	3.19	49
166	-61.25	-46.88	20.62	3.18	49
167	12.25	1.88	5.62	3.18	49
168	-15.75	54.38	54.38	3.18	49
169	-12.25	5.62	65.62	3.17	49
170	40.25	-43.12	5.62	3.17	49
171	-54.25	-35.62	-50.62	3.17	49
172	5.25	-9.38	69.38	3.17	49
173	-1.75	39.38	-20.62	3.17	49
174	-15.75	84.38	20.62	3.17	49
175	1.75	-58.12	-13.12	3.16	49
176	40.25	-35.62	28.12	3.16	49
177	33.25	31.88	35.62	3.16	49
178	1.75	31.88	-24.38	3.16	49
179	12.25	-54.38	-16.88	3.15	49
180	-47.25	-28.12	5.62	3.15	49
181	-40.25	-65.62	-16.88	3.15	49
182	36.75	-20.62	-9.38	3.15	49
183	-47.25	-50.62	5.62	3.15	49
184	-33.25	-54.38	43.12	3.14	49
185	-8.75	-39.38	-24.38	3.14	49
186	-1.75	-50.62	-39.38	3.14	49
187	-15.75	-73.12	16.88	3.14	49
188	19.25	-46.88	-35.62	3.14	49
189	19.25	-5.62	28.12	3.14	49
190	-47.25	-39.38	31.88	3.13	49
191	-50.75	-39.38	-24.38	3.13	49
192	-26.25	-39.38	39.38	3.13	49
193	-22.75	9.38	54.38	3.13	49
194	-50.75	39.38	9.38	3.13	49
195	-40.25	-16.88	1.88	3.13	49
196	29.75	-1.88	46.88	3.12	49
197	-36.75	-65.62	-31.88	3.11	49
198	-26.25	16.88	-16.88	3.11	49
199	-12.25	-61.88	54.38	3.11	49
200	-5.25	16.88	35.62	3.10	49
201	-40.25	24.38	46.88	3.10	49
202	33.25	-50.62	46.88	3.10	49
203	-29.75	-16.88	16.88	3.10	49
204	47.25	5.62	50.62	3.10	49
205	-33.25	-46.88	61.88	3.09	49

shoe

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	19.25	73.12	20.62	4.65	49
2	22.75	-9.38	35.62	4.48	98
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	147
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	98
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	98
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	98
23	22.75	61.88	31.88	3.49	147
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	98
32	22.75	-54.38	-43.12	3.35	49
33	-12.25	-46.88	-43.12	3.28	49
34	-15.75	-24.38	35.62	3.27	49
35	47.25	-31.88	-58.12	3.26	49
36	-8.75	73.12	31.88	3.26	49
37	-8.75	-1.88	-58.12	3.26	49
38	-8.75	-46.88	-39.38	3.26	49
39	12.25	-28.12	28.12	3.25	49
40	-19.25	20.62	43.12	3.24	49
41	-36.75	5.62	31.88	3.24	49
42	-68.25	-9.38	9.38	3.21	49
43	22.75	-65.62	9.38	3.21	49
44	-47.25	-16.88	-24.38	3.21	49
45	33.25	-43.12	-35.62	3.19	49
46	-40.25	-9.38	-50.62	3.19	49
47	-40.25	-16.88	-35.62	3.18	49
48	-54.25	-35.62	-1.88	3.18	98
49	15.75	-24.38	-73.12	3.17	49
50	-33.25	31.88	58.12	3.16	49
51	-68.25	-16.88	28.12	3.16	49
52	43.75	46.88	-9.38	3.16	49
53	68.25	5.62	-13.12	3.15	49
54	5.25	39.38	50.62	3.14	49
55	36.75	-35.62	-46.88	3.14	98
56	-5.25	-54.38	-50.62	3.11	49
57	-19.25	16.88	-20.62	3.10	49

cat

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	12.25	-61.88	-31.88	5.00	590
1a	5.25	-61.88	-24.38	4.34
2	-40.25	-65.62	13.12	4.41	196
3	-29.75	39.38	54.38	4.32	98
4	-12.25	-58.12	-16.88	4.29	49
5	-12.25	-76.88	31.88	4.27	98
6	43.75	-5.62	16.88	4.11	196
7	22.75	-24.38	-58.12	4.05	49
8	19.25	-31.88	-69.38	4.01	98
9	43.75	1.88	9.38	3.94	295
10	5.25	-80.62	9.38	3.90	98
11	61.25	-31.88	20.62	3.86	49
12	-12.25	-20.62	-58.12	3.86	98
13	1.75	35.62	-13.12	3.86	49
14	-29.75	35.62	50.62	3.83	49
15	-36.75	-50.62	-39.38	3.82	98
16	33.25	-1.88	46.88	3.80	49
17	50.75	1.88	-24.38	3.79	49
18	47.25	1.88	-46.88	3.79	49
19	50.75	28.12	24.38	3.78	49
20	-5.25	-50.62	-13.12	3.76	49
21	22.75	-61.88	-54.38	3.76	49
22	-12.25	-73.12	-31.88	3.73	49
23	36.75	28.12	43.12	3.72	98
24	36.75	69.38	5.62	3.71	49
25	-1.75	50.62	-9.38	3.70	98
26	5.25	-31.88	54.38	3.66	49
27	36.75	28.12	-20.62	3.62	49
28	-26.25	-80.62	9.38	3.61	98
29	8.75	-65.62	-24.38	3.60	49
30	-8.75	-46.88	-28.12	3.60	49
31	50.75	9.38	-35.62	3.60	49
32	43.75	5.62	1.88	3.59	49
33	-36.75	46.88	39.38	3.58	49
34	15.75	-80.62	-31.88	3.57	49
35	-54.25	-50.62	5.62	3.56	98
36	36.75	9.38	-35.62	3.55	49
37	29.75	-35.62	-50.62	3.53	49
38	-43.75	-65.62	5.62	3.53	49
39	15.75	-50.62	-39.38	3.50	49
40	61.25	-31.88	43.12	3.50	49
41	12.25	-54.38	-43.12	3.49	49
42	5.25	-61.88	-39.38	3.49	49
43	33.25	-54.38	43.12	3.49	49
44	-64.75	-9.38	-24.38	3.48	49
45	-54.25	-50.62	39.38	3.48	49
46	-40.25	-46.88	50.62	3.48	98
47	8.75	16.88	-16.88	3.48	98
48	-29.75	-46.88	5.62	3.47	49
49	12.25	-39.38	-31.88	3.44	49
50	-40.25	-5.62	50.62	3.44	49
51	64.75	20.62	9.38	3.43	49
52	5.25	-24.38	-73.12	3.42	49
53	12.25	-43.12	1.88	3.42	49
54	-19.25	16.88	69.38	3.42	49
55	-29.75	-54.38	-35.62	3.42	49
56	36.75	28.12	35.62	3.42	49
57	-64.75	-31.88	16.88	3.38	49
58	5.25	43.12	50.62	3.37	49
59	15.75	39.38	-20.62	3.37	49
60	29.75	9.38	-31.88	3.36	49
61	-36.75	-50.62	-20.62	3.35	98
62	-47.25	-43.12	43.12	3.35	98
63	-40.25	-46.88	16.88	3.35	49
64	-43.75	-1.88	-28.12	3.34	49
65	-40.25	-1.88	-31.88	3.34	49
66	12.25	5.62	5.62	3.34	49
67	-64.75	-16.88	24.38	3.33	98
68	-36.75	-76.88	5.62	3.33	98
69	43.75	31.88	39.38	3.33	49
70	-47.25	-50.62	46.88	3.33	98
71	-40.25	-69.38	35.62	3.31	49
72	-29.75	35.62	58.12	3.30	98
73	-22.75	76.88	5.62	3.29	49
74	47.25	13.12	43.12	3.29	98
75	-64.75	-9.38	9.38	3.27	49
76	15.75	-16.88	46.88	3.27	49
77	-8.75	1.88	-28.12	3.27	49
78	-26.25	-46.88	-28.12	3.27	49
79	29.75	-43.12	50.62	3.27	49
80	-57.75	-50.62	13.12	3.26	49
81	-43.75	-43.12	50.62	3.26	49
82	40.25	-5.62	-5.62	3.25	49
83	-33.25	-50.62	58.12	3.24	49
84	12.25	-84.38	-24.38	3.24	49
85	-43.75	13.12	-9.38	3.23	49
86	-50.75	-65.62	-13.12	3.23	49
87	5.25	-54.38	-46.88	3.22	49
88	47.25	9.38	-13.12	3.22	49
89	-43.75	-31.88	35.62	3.21	49
90	-15.75	-84.38	-16.88	3.21	49
91	15.75	-31.88	-61.88	3.21	49
92	-26.25	-54.38	13.12	3.20	49
93	1.75	-50.62	-16.88	3.20	49
94	33.25	-46.88	-24.38	3.20	49
95	50.75	-1.88	-28.12	3.20	49
96	-50.75	-35.62	-46.88	3.19	49
97	1.75	46.88	-9.38	3.18	49
98	-40.25	-43.12	54.38	3.18	49
99	26.25	-65.62	-20.62	3.18	49
100	-47.25	-24.38	54.38	3.17	49
101	-1.75	1.88	-61.88	3.17	49
102	-8.75	-61.88	20.62	3.17	49
103	5.25	20.62	50.62	3.17	49
104	33.25	-54.38	-24.38	3.16	49
105	-8.75	-35.62	-24.38	3.16	49
106	-22.75	35.62	61.88	3.15	49
107	12.25	54.38	58.12	3.14	49
108	47.25	20.62	50.62	3.14	49
109	29.75	1.88	-31.88	3.13	49
110	8.75	-54.38	-39.38	3.12	49
111	40.25	-20.62	-39.38	3.12	49
112	36.75	-9.38	50.62	3.11	49
113	-40.25	43.12	43.12	3.10	49
114	50.75	24.38	39.38	3.10	49
115	36.75	-24.38	-54.38	3.10	49
116	-29.75	-76.88	9.38	3.09	49

scissors

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	-50.75	20.62	-24.38	6.02	1181
2	-12.25	20.62	-46.88	5.79	492
2a	-22.75	16.88	-43.12	4.73
3	-19.25	84.38	20.62	5.60	295
4	-26.25	24.38	28.12	5.28	196
5	29.75	5.62	-5.62	5.26	147
6	36.75	20.62	-31.88	5.18	246
7	47.25	24.38	-1.88	5.12	147
8	12.25	13.12	-43.12	5.09	442
9	-22.75	-58.12	-50.62	5.08	442
9a	-22.75	-46.88	-50.62	4.60
10	-19.25	61.88	50.62	5.05	49
11	-50.75	-9.38	-20.62	5.01	295
12	50.75	31.88	-31.88	4.97	49
13	-19.25	31.88	-35.62	4.91	196
14	1.75	-1.88	-61.88	4.86	295
15	33.25	5.62	-46.88	4.82	49
16	-12.25	1.88	-28.12	4.76	49
17	12.25	-61.88	-46.88	4.76	49
18	1.75	-28.12	-61.88	4.68	98
19	12.25	-35.62	-69.38	4.66	147
20	22.75	-58.12	-50.62	4.61	98
21	-54.25	-39.38	-31.88	4.60	49
22	1.75	13.12	-16.88	4.54	98
23	47.25	24.38	9.38	4.54	49
24	26.25	-46.88	1.88	4.54	98
25	40.25	5.62	31.88	4.53	98
26	-15.75	-43.12	-46.88	4.53	98
27	40.25	-58.12	-5.62	4.50	147
28	19.25	16.88	-46.88	4.50	49
29	5.25	24.38	-46.88	4.47	442
29a	-1.75	28.12	-39.38	3.59
30	-5.25	-16.88	-39.38	4.45	98
31	50.75	24.38	-35.62	4.41	147
32	12.25	80.62	35.62	4.41	196
33	33.25	-35.62	-35.62	4.37	393
33a	33.25	-39.38	-43.12	3.60
34	1.75	1.88	31.88	4.37	246
35	-12.25	76.88	20.62	4.36	98
36	47.25	-5.62	20.62	4.36	344
37	19.25	-46.88	-35.62	4.34	98
38	29.75	-1.88	24.38	4.33	49
39	-12.25	9.38	-58.12	4.29	98
40	54.25	5.62	50.62	4.29	344
40a	54.25	1.88	43.12	4.27
41	15.75	-35.62	-73.12	4.28	49
42	19.25	80.62	1.88	4.28	147
43	15.75	76.88	28.12	4.26	49
44	-29.75	76.88	20.62	4.26	49
45	-54.25	9.38	-16.88	4.26	49
46	-1.75	-31.88	9.38	4.26	246
46a	-1.75	-39.38	5.62	4.21
47	-22.75	-5.62	-20.62	4.25	49
48	-40.25	61.88	5.62	4.24	98
49	-47.25	24.38	20.62	4.22	49
50	26.25	13.12	-39.38	4.22	49
51	-8.75	28.12	54.38	4.17	196
52	40.25	-1.88	61.88	4.17	246
52a	40.25	-9.38	58.12	3.83
53	36.75	31.88	-39.38	4.17	295
54	-36.75	-1.88	-20.62	4.17	98
55	-5.25	-9.38	31.88	4.14	49
56	22.75	-54.38	-43.12	4.14	49
57	-29.75	20.62	-46.88	4.13	49
58	22.75	-1.88	9.38	4.11	49
59	-26.25	80.62	16.88	4.11	295
60	29.75	-46.88	-61.88	4.10	49
61	15.75	-28.12	-54.38	4.10	49
62	33.25	24.38	-43.12	4.08	98
63	-40.25	-16.88	-65.62	4.07	49
64	-26.25	76.88	24.38	4.05	196
65	19.25	-5.62	-35.62	4.04	49
66	54.25	1.88	35.62	4.03	246
67	12.25	24.38	-35.62	4.03	98
68	-26.25	-13.12	1.88	4.02	49
69	-5.25	80.62	31.88	4.02	196
70	-19.25	50.62	13.12	4.02	49
71	40.25	-43.12	-5.62	4.01	49
72	43.75	-31.88	-13.12	4.01	98
73	15.75	5.62	9.38	4.01	49
74	-33.25	-65.62	-16.88	4.00	49
75	26.25	-50.62	-54.38	4.00	98
76	33.25	-5.62	-1.88	3.99	98
77	40.25	-39.38	58.12	3.98	49
78	22.75	-43.12	-61.88	3.98	98
79	-19.25	20.62	24.38	3.98	49
80	57.75	-20.62	28.12	3.97	49
81	-5.25	84.38	1.88	3.96	49
82	40.25	-24.38	-35.62	3.96	49
83	-19.25	9.38	-35.62	3.95	49
84	12.25	80.62	-5.62	3.94	49
85	-5.25	-28.12	-76.88	3.94	147
86	50.75	50.62	20.62	3.93	98
87	19.25	-16.88	-54.38	3.93	49
88	33.25	-39.38	24.38	3.93	49
89	40.25	28.12	-43.12	3.92	196
90	-26.25	16.88	28.12	3.92	49
91	1.75	39.38	-16.88	3.92	49
92	-26.25	-24.38	20.62	3.91	49
93	-15.75	-9.38	28.12	3.91	98
94	-8.75	24.38	-35.62	3.90	98
95	50.75	-16.88	46.88	3.89	49
96	-15.75	76.88	31.88	3.89	49
97	1.75	-28.12	-76.88	3.89	49
98	47.25	16.88	-20.62	3.89	49
99	19.25	-28.12	73.12	3.87	49
100	57.75	-13.12	39.38	3.87	49
101	-8.75	84.38	20.62	3.87	98
102	29.75	76.88	13.12	3.87	49
103	40.25	13.12	-39.38	3.87	49
104	33.25	-69.38	-13.12	3.87	147
105	19.25	54.38	50.62	3.85	49
106	-12.25	-50.62	-50.62	3.85	49
107	-19.25	-1.88	-35.62	3.85	49
108	29.75	-50.62	-50.62	3.84	49
109	12.25	-61.88	9.38	3.84	49
110	-29.75	13.12	61.88	3.83	196
111	5.25	-1.88	5.62	3.83	98
112	15.75	-16.88	46.88	3.83	49
113	-1.75	-1.88	-76.88	3.82	196
114	-36.75	65.62	20.62	3.82	98
115	33.25	-39.38	-20.62	3.81	49
116	29.75	65.62	43.12	3.79	49
117	-50.75	-1.88	20.62	3.79	49
118	50.75	-39.38	-16.88	3.79	49
119	-12.25	-43.12	-13.12	3.78	49
120	-22.75	58.12	-20.62	3.77	246
121	5.25	39.38	-20.62	3.77	49
122	-26.25	31.88	-39.38	3.76	49
123	-1.75	-13.12	-54.38	3.76	196
124	-33.25	-58.12	-31.88	3.75	98
125	15.75	88.12	9.38	3.75	98
126	43.75	13.12	-28.12	3.75	98
127	26.25	-16.88	-16.88	3.75	98
128	-19.25	31.88	-28.12	3.74	98
129	5.25	9.38	-58.12	3.73	196
130	-8.75	9.38	65.62	3.73	49
131	-29.75	-69.38	9.38	3.73	98
132	-64.75	-5.62	5.62	3.71	98
133	12.25	-31.88	-46.88	3.71	49
134	15.75	50.62	-9.38	3.71	49
135	-40.25	-9.38	-5.62	3.70	49
136	33.25	-20.62	43.12	3.70	49
137	-12.25	-50.62	-13.12	3.69	49
138	26.25	-13.12	13.12	3.69	49
139	-5.25	80.62	13.12	3.68	49
140	-26.25	16.88	-16.88	3.67	49
141	19.25	-1.88	-1.88	3.66	49
142	19.25	16.88	-35.62	3.66	49
143	-1.75	73.12	35.62	3.66	49
144	29.75	-54.38	-61.88	3.65	49
145	1.75	-9.38	1.88	3.65	49
146	15.75	73.12	20.62	3.65	98
147	33.25	-50.62	-20.62	3.65	49
148	12.25	-35.62	-24.38	3.65	49
149	15.75	24.38	69.38	3.64	49
150	-47.25	-5.62	-28.12	3.64	49
151	-1.75	1.88	13.12	3.64	49
152	-40.25	69.38	13.12	3.63	49
153	12.25	-28.12	-31.88	3.62	49
154	8.75	1.88	24.38	3.62	49
155	29.75	-9.38	9.38	3.62	49
156	-15.75	-65.62	5.62	3.61	49
157	-26.25	-80.62	-5.62	3.61	49
158	33.25	5.62	16.88	3.60	147
159	33.25	-5.62	24.38	3.60	98
160	57.75	-1.88	5.62	3.60	49
161	15.75	-50.62	-35.62	3.60	49
162	33.25	24.38	61.88	3.60	49
163	-1.75	-35.62	-65.62	3.58	49
164	22.75	-20.62	20.62	3.58	49
165	-8.75	24.38	43.12	3.58	49
166	40.25	-13.12	46.88	3.58	49
167	-5.25	-20.62	54.38	3.58	98
168	-1.75	-13.12	31.88	3.57	49
169	-47.25	20.62	43.12	3.57	147
170	-33.25	69.38	20.62	3.56	98
171	57.75	-24.38	5.62	3.56	49
172	5.25	-65.62	20.62	3.56	49
173	26.25	-16.88	-35.62	3.55	98
174	-1.75	-46.88	-1.88	3.55	98
175	-50.75	13.12	20.62	3.55	49
176	40.25	69.38	16.88	3.55	98
177	1.75	16.88	-20.62	3.55	49
178	-29.75	20.62	20.62	3.54	49
179	-29.75	31.88	-9.38	3.54	49
180	40.25	-16.88	9.38	3.54	49
181	-12.25	88.12	9.38	3.54	49
182	-5.25	5.62	39.38	3.54	49
183	-1.75	-13.12	-5.62	3.54	98
184	-8.75	80.62	5.62	3.54	98
185	-50.75	-13.12	5.62	3.54	49
186	57.75	16.88	16.88	3.53	49
187	1.75	13.12	31.88	3.53	147
188	-33.25	39.38	54.38	3.53	49
189	-5.25	28.12	61.88	3.52	49
190	-43.75	-24.38	35.62	3.52	98
191	1.75	65.62	46.88	3.52	49
192	-5.25	5.62	-69.38	3.52	49
193	-22.75	-39.38	-54.38	3.51	49
194	33.25	-28.12	-39.38	3.51	98
195	-1.75	-5.62	-80.62	3.51	98
196	26.25	28.12	-43.12	3.51	49
197	33.25	31.88	-20.62	3.50	49
198	-33.25	73.12	9.38	3.50	49
199	-1.75	20.62	54.38	3.50	49
200	12.25	-1.88	43.12	3.50	49
201	57.75	-28.12	9.38	3.50	49
202	-12.25	1.88	9.38	3.49	49
203	-40.25	43.12	-31.88	3.49	49
204	43.75	-9.38	-16.88	3.49	49
205	47.25	-9.38	54.38	3.49	49
206	-57.75	-5.62	-1.88	3.48	147
207	15.75	88.12	16.88	3.47	49
208	54.25	9.38	28.12	3.47	98
209	-5.25	39.38	58.12	3.46	49
210	50.75	16.88	-5.62	3.46	49
211	15.75	46.88	-20.62	3.46	49
212	50.75	54.38	-5.62	3.46	49
213	33.25	-13.12	50.62	3.45	49
214	57.75	-24.38	-13.12	3.45	98
215	-22.75	-9.38	39.38	3.45	49
216	-19.25	-5.62	16.88	3.45	49
217	19.25	-5.62	61.88	3.45	49
218	-43.75	16.88	39.38	3.45	49
219	-1.75	-73.12	-31.88	3.44	49
220	-15.75	1.88	28.12	3.43	49
221	-54.25	20.62	-16.88	3.43	49
222	40.25	16.88	61.88	3.43	49
223	8.75	-1.88	28.12	3.43	49
224	47.25	-9.38	28.12	3.42	49
225	33.25	-13.12	-61.88	3.42	49
226	-8.75	20.62	58.12	3.42	49
227	-36.75	69.38	16.88	3.42	49
228	-22.75	-88.12	-1.88	3.42	49
229	33.25	-1.88	-24.38	3.41	49
230	26.25	-61.88	-16.88	3.41	49
231	-12.25	46.88	-16.88	3.41	49
232	-47.25	-16.88	-39.38	3.40	49
233	33.25	24.38	31.88	3.40	49
234	43.75	-61.88	5.62	3.40	98
235	-40.25	-16.88	-20.62	3.40	49
236	50.75	-16.88	58.12	3.40	49
237	22.75	-20.62	-5.62	3.40	49
238	1.75	76.88	16.88	3.40	49
239	33.25	-20.62	-58.12	3.40	49
240	22.75	-35.62	-9.38	3.39	49
241	36.75	1.88	65.62	3.39	49
242	-22.75	-31.88	-43.12	3.39	98
243	-15.75	-46.88	-43.12	3.39	98
244	36.75	39.38	16.88	3.38	49
245	47.25	-16.88	31.88	3.38	49
246	12.25	-24.38	-50.62	3.38	49
247	8.75	76.88	16.88	3.37	49
248	29.75	-58.12	-46.88	3.37	49
249	-47.25	20.62	-16.88	3.37	49
250	22.75	-43.12	54.38	3.37	49
251	-5.25	-1.88	-28.12	3.37	147
252	-8.75	-5.62	-58.12	3.36	49
253	57.75	-9.38	43.12	3.35	49
254	-22.75	39.38	31.88	3.35	49
255	-8.75	5.62	73.12	3.35	98
256	1.75	-39.38	-20.62	3.34	49
257	54.25	-16.88	-13.12	3.34	98
258	-36.75	24.38	-39.38	3.34	49
259	-43.75	24.38	-1.88	3.34	49
260	47.25	-5.62	50.62	3.34	49
261	57.75	-31.88	46.88	3.33	49
262	50.75	-54.38	9.38	3.33	49
263	-33.25	28.12	-39.38	3.33	49
264	5.25	35.62	-9.38	3.32	49
265	5.25	-50.62	-5.62	3.31	49
266	-57.75	-9.38	-13.12	3.31	49
267	40.25	-43.12	54.38	3.31	98
268	-40.25	-31.88	39.38	3.30	49
269	22.75	-13.12	-20.62	3.30	49
270	33.25	39.38	-16.88	3.30	49
271	8.75	46.88	-20.62	3.30	49
272	-50.75	20.62	20.62	3.30	49
273	-43.75	16.88	58.12	3.30	49
274	-19.25	-13.12	-46.88	3.30	49
275	43.75	-50.62	-5.62	3.29	49
276	12.25	1.88	-69.38	3.29	49
277	1.75	-35.62	-13.12	3.29	49
278	-33.25	9.38	35.62	3.29	49
279	-47.25	-24.38	-1.88	3.29	49
280	29.75	58.12	-16.88	3.29	49
281	-8.75	-46.88	-9.38	3.29	49
282	1.75	-1.88	-54.38	3.28	49
283	29.75	31.88	39.38	3.28	49
284	36.75	-50.62	-5.62	3.28	49
285	33.25	-16.88	65.62	3.28	147
286	-43.75	-31.88	54.38	3.28	49
287	22.75	43.12	-9.38	3.28	49
288	8.75	-58.12	-16.88	3.27	49
289	-12.25	-1.88	-58.12	3.27	49
290	-1.75	-58.12	-43.12	3.27	49
291	15.75	-76.88	-16.88	3.27	49
292	15.75	-20.62	-20.62	3.27	49
293	-50.75	24.38	46.88	3.27	49
294	33.25	-5.62	50.62	3.27	49
295	29.75	5.62	13.12	3.27	49
296	15.75	-39.38	24.38	3.27	49
297	8.75	-9.38	-76.88	3.26	49
298	-26.25	65.62	35.62	3.26	49
299	-8.75	16.88	-43.12	3.26	49
300	15.75	-50.62	46.88	3.26	49
301	22.75	-28.12	69.38	3.26	49
302	-5.25	84.38	9.38	3.25	49
303	-19.25	73.12	31.88	3.25	49
304	15.75	16.88	28.12	3.25	49
305	15.75	13.12	9.38	3.25	49
306	1.75	-43.12	5.62	3.25	49
307	43.75	54.38	35.62	3.24	49
308	8.75	-43.12	-73.12	3.24	49
309	-26.25	-28.12	31.88	3.23	49
310	-26.25	-1.88	-9.38	3.23	49
311	-43.75	16.88	-1.88	3.22	49
312	-33.25	-24.38	-43.12	3.22	49
313	19.25	46.88	39.38	3.22	98
314	-12.25	-5.62	28.12	3.22	98
315	29.75	-35.62	-20.62	3.22	49
316	1.75	35.62	-24.38	3.22	49
317	-12.25	65.62	-9.38	3.21	49
318	47.25	9.38	-1.88	3.21	49
319	5.25	-5.62	-1.88	3.21	49
320	26.25	-50.62	-16.88	3.21	49
321	22.75	-39.38	13.12	3.21	49
322	-1.75	-54.38	-61.88	3.21	49
323	47.25	-28.12	-20.62	3.21	98
324	40.25	-28.12	13.12	3.20	49
325	22.75	-54.38	-61.88	3.20	49
326	19.25	28.12	-43.12	3.20	49
327	33.25	20.62	-13.12	3.20	49
328	43.75	-43.12	9.38	3.20	49
329	-40.25	-73.12	-31.88	3.19	49
330	22.75	84.38	1.88	3.19	49
331	-19.25	28.12	39.38	3.19	49
332	-29.75	-16.88	-1.88	3.19	49
333	29.75	-9.38	-28.12	3.19	49
334	12.25	-9.38	43.12	3.19	49
335	19.25	-20.62	-28.12	3.19	49
336	-26.25	-39.38	-43.12	3.19	49
337	-8.75	20.62	31.88	3.19	49
338	33.25	-20.62	-20.62	3.19	49
339	-33.25	-84.38	-1.88	3.19	49
340	-26.25	43.12	-28.12	3.19	49
341	-57.75	-5.62	-16.88	3.18	49
342	-15.75	-9.38	35.62	3.18	98
343	19.25	-50.62	-13.12	3.18	49
344	-1.75	-1.88	-20.62	3.18	49
345	29.75	-1.88	13.12	3.18	49
346	33.25	31.88	-9.38	3.17	49
347	15.75	-20.62	65.62	3.17	49
348	-5.25	-9.38	69.38	3.17	49
349	-54.25	-9.38	-31.88	3.17	49
350	-26.25	61.88	-5.62	3.16	49
351	-19.25	-39.38	24.38	3.16	49
352	1.75	-5.62	-20.62	3.16	49
353	-33.25	31.88	13.12	3.16	49
354	15.75	-5.62	-1.88	3.15	49
355	-29.75	-13.12	-35.62	3.15	49
356	43.75	-46.88	-13.12	3.15	49
357	-12.25	-1.88	-20.62	3.15	49
358	-22.75	-9.38	-9.38	3.15	49
359	-26.25	9.38	20.62	3.15	49
360	47.25	24.38	-13.12	3.15	49
361	26.25	24.38	35.62	3.14	49
362	-22.75	-46.88	-31.88	3.14	49
363	-19.25	-1.88	-20.62	3.14	49
364	-19.25	-9.38	5.62	3.14	49
365	-19.25	9.38	9.38	3.14	49
366	-29.75	35.62	54.38	3.14	49
367	50.75	35.62	13.12	3.14	49
368	29.75	-43.12	50.62	3.13	49
369	26.25	1.88	24.38	3.13	49
370	-26.25	-50.62	-39.38	3.13	49
371	-19.25	-20.62	5.62	3.12	49
372	-40.25	39.38	43.12	3.12	49
373	36.75	73.12	13.12	3.12	49
374	-15.75	13.12	73.12	3.12	49
375	29.75	-24.38	69.38	3.12	49
376	-5.25	-39.38	-9.38	3.11	49
377	12.25	-20.62	20.62	3.11	49
378	8.75	-31.88	20.62	3.11	49
379	-50.75	16.88	35.62	3.11	49
380	-43.75	13.12	35.62	3.11	49
381	8.75	31.88	61.88	3.11	49
382	5.25	-24.38	69.38	3.11	49
383	47.25	9.38	-16.88	3.11	49
384	-54.25	-1.88	13.12	3.11	49
385	29.75	-1.88	1.88	3.11	49
386	-1.75	28.12	-5.62	3.11	98
387	19.25	-76.88	-9.38	3.10	49
388	15.75	-69.38	54.38	3.10	49
389	19.25	5.62	5.62	3.10	49
390	43.75	24.38	-9.38	3.10	49
391	-19.25	-31.88	46.88	3.10	49
392	-5.25	-46.88	-35.62	3.10	49
393	57.75	-13.12	20.62	3.09	49
394	54.25	1.88	-35.62	3.09	49
395	1.75	80.62	13.12	3.09	49
396	-15.75	13.12	-35.62	3.09	49
397	15.75	-46.88	31.88	3.09	49

About

Date preprocessed: 2026-01-21T10:03:19

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 data from [<nibabel.nifti1.Nifti1Image object at
0x7fac36d1f790>, <nibabel.nifti1.Nifti1Image object at 0x7fac365c2470>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c3160>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c1d80>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c1000>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c13c0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c3f70>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c29b0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac577cb6d0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab32e0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab2350>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab3c10>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab03a0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab0610>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab02b0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab3160>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779ae0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779f90>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779b70>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779c90>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779c00>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779ba0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779a50>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779e40>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab34c0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab1f90>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab0970>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab0790>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab0850>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab29b0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab0700>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab3a00>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36677550>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36675b40>,
<nibabel.nifti1.Nifti1Image object at 0x7fac366764d0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36677610>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36675420>,
<nibabel.nifti1.Nifti1Image object at 0x7fac366765f0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac366763e0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36677310>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab2260>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab2f80>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab3f70>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab1330>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab3ca0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab16c0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab1ed0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab3ac0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36676800>,
<nibabel.nifti1.Nifti1Image object at 0x7fac366763b0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c2230>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c2950>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c3640>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c1900>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c1180>,
<nibabel.nifti1.Nifti1Image object at 0x7fac365c3b20>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab0580>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab0730>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab1420>,
<nibabel.nifti1.Nifti1Image object at 0x7fac57779c30>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa663e0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa66950>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa66b00>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa66ad0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac368a7430>,
<nibabel.nifti1.Nifti1Image object at 0x7fac369d3b80>,
<nibabel.nifti1.Nifti1Image object at 0x7fac593c9030>,
<nibabel.nifti1.Nifti1Image object at 0x7fac593cabf0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac593c93f0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac593c80d0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac593ca740>,
<nibabel.nifti1.Nifti1Image object at 0x7fac593ca8f0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab3d90>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab2770>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab0220>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36d1fee0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa67b20>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa65480>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa650c0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa67df0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa660b0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa66fb0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa64130>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa66dd0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa65570>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa67be0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa66b90>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aa67f10>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab2d10>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab1ab0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac5aab3b20>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36d1f9d0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36bc6740>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36bc5450>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36bc43a0>,
<nibabel.nifti1.Nifti1Image object at 0x7fac36bc5360>]
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_glm_decoding.py:174: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
  decoder.fit(z_maps, conditions_label, groups=run_label)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7fac58d408b0>
[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 = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 5
[Decoder.fit] Corrected screening-percentile: 4.79274
[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    2.3s
[Parallel(n_jobs=1)]: Done  96 out of  96 | elapsed:    5.5s 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 6.150 seconds)

Estimated memory usage: 1008 MB

Gallery generated by Sphinx-Gallery

	t_r	2.5
	slice_time_ref	0.0
	hrf_model	'glover'
	drift_model	'cosine'
	high_pass	0.008
	drift_order	1
	fir_delays	None
	min_onset	-24
	mask_img	'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
	target_affine	None
	target_shape	None
	smoothing_fwhm	4
	memory	'nilearn_cache'
	memory_level	1
	standardize	False
	signal_scaling	0
	noise_model	'ar1'
	verbose	1
	n_jobs	1
	minimize_memory	True
	subject_label	None
	random_state	None