Decoding of a dataset after GLM fit for signal extraction

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

More specifically:

1. Download the Haxby dataset.

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

3. Analyze the decoding performance using a classifier.

Fetch example Haxby dataset

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

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

from nilearn.datasets import fetch_haxby

haxby_dataset = fetch_haxby()

[fetch_haxby] Dataset found in /home/remi-gau/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 0x7587916021d0>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x7587916021d0>
[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 - 0.5s, 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 0x7587918a1de0>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x7587918a1de0>
[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 - 0.5s, 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 0x7587914572e0>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x7587914572e0>
[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 - 0.4s, 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 0x7587918b2860>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x7587918b2860>
[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 - 0.4s, 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 0x75879161fee0>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x75879161fee0>
[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 - 0.4s, 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 0x75879177d180>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x75879177d180>
[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 - 0.4s, 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 0x7587918a1ae0>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x7587918a1ae0>
[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 - 0.5s, 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 0x75879177d330>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x75879177d330>
[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 - 0.5s, 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 0x7587918a1a80>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x7587918a1a80>
[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 - 0.4s, 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 0x7587916eeb00>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x7587916eeb00>
[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 - 0.4s, 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 0x7587918b2860>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x7587918b2860>
[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 - 0.4s, 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 0x75879161c9d0>
[FirstLevelModel.fit] Loading mask from '/home/remi-gau/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 0x75879161c9d0>
[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 - 0.4s, 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= 0.13.0; 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:

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

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

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

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

Model details

	Value
Parameter
drift_model	cosine
high_pass (Hertz)	0.01
hrf_model	glover
noise_model	ar1
signal_scaling	0
slice_time_ref	0.0
smoothing_fwhm (mm)	4
standardize	False
t_r (seconds)	2.5

Design Matrix

correlation matrix

Contrasts

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: 2026-01-06T07:40:16

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 0x75879177c850>, <nibabel.nifti1.Nifti1Image object at 0x7587916020e0>,
<nibabel.nifti1.Nifti1Image object at 0x7587914876d0>, <nibabel.nifti1.Nifti1Image object at 0x758791485270>, <nibabel.nifti1.Nifti1Image object at
0x7587914851e0>, <nibabel.nifti1.Nifti1Image object at 0x758791486da0>, <nibabel.nifti1.Nifti1Image object at 0x7587914873a0>, <nibabel.nifti1.Nifti1Image
object at 0x758791486ef0>, <nibabel.nifti1.Nifti1Image object at 0x758791457e50>, <nibabel.nifti1.Nifti1Image object at 0x7587914573d0>,
<nibabel.nifti1.Nifti1Image object at 0x758791455ea0>, <nibabel.nifti1.Nifti1Image object at 0x758791457610>, <nibabel.nifti1.Nifti1Image object at
0x7587914574f0>, <nibabel.nifti1.Nifti1Image object at 0x758791486500>, <nibabel.nifti1.Nifti1Image object at 0x758791485960>, <nibabel.nifti1.Nifti1Image
object at 0x758791486950>, <nibabel.nifti1.Nifti1Image object at 0x75879177e470>, <nibabel.nifti1.Nifti1Image object at 0x7587918a1f90>,
<nibabel.nifti1.Nifti1Image object at 0x7587919db6d0>, <nibabel.nifti1.Nifti1Image object at 0x7587918b0af0>, <nibabel.nifti1.Nifti1Image object at
0x7587918b17e0>, <nibabel.nifti1.Nifti1Image object at 0x7587918a1c90>, <nibabel.nifti1.Nifti1Image object at 0x7587916ec100>, <nibabel.nifti1.Nifti1Image
object at 0x7587916ed540>, <nibabel.nifti1.Nifti1Image object at 0x75879161eaa0>, <nibabel.nifti1.Nifti1Image object at 0x75879161e1a0>,
<nibabel.nifti1.Nifti1Image object at 0x75879161c670>, <nibabel.nifti1.Nifti1Image object at 0x75879161e350>, <nibabel.nifti1.Nifti1Image object at
0x75879161c940>, <nibabel.nifti1.Nifti1Image object at 0x75879161e9b0>, <nibabel.nifti1.Nifti1Image object at 0x75879161e620>, <nibabel.nifti1.Nifti1Image
object at 0x758791487400>, <nibabel.nifti1.Nifti1Image object at 0x758791603ee0>, <nibabel.nifti1.Nifti1Image object at 0x7587916ee0b0>,
<nibabel.nifti1.Nifti1Image object at 0x7587ec1722c0>, <nibabel.nifti1.Nifti1Image object at 0x7587ec173040>, <nibabel.nifti1.Nifti1Image object at
0x7587ec171390>, <nibabel.nifti1.Nifti1Image object at 0x7587ec171300>, <nibabel.nifti1.Nifti1Image object at 0x7587ec170b50>, <nibabel.nifti1.Nifti1Image
object at 0x7587ec171150>, <nibabel.nifti1.Nifti1Image object at 0x7587918a2380>, <nibabel.nifti1.Nifti1Image object at 0x75879161e230>,
<nibabel.nifti1.Nifti1Image object at 0x7587a4fa8700>, <nibabel.nifti1.Nifti1Image object at 0x7587a4fa80d0>, <nibabel.nifti1.Nifti1Image object at
0x7587a4fa9150>, <nibabel.nifti1.Nifti1Image object at 0x7587a4fab760>, <nibabel.nifti1.Nifti1Image object at 0x7587a4fa8130>, <nibabel.nifti1.Nifti1Image
object at 0x7587a4fa94b0>, <nibabel.nifti1.Nifti1Image object at 0x75879177f280>, <nibabel.nifti1.Nifti1Image object at 0x7587918a18d0>,
<nibabel.nifti1.Nifti1Image object at 0x7587ec171f00>, <nibabel.nifti1.Nifti1Image object at 0x7587ec171510>, <nibabel.nifti1.Nifti1Image object at
0x7587ec171b40>, <nibabel.nifti1.Nifti1Image object at 0x7587ec172e90>, <nibabel.nifti1.Nifti1Image object at 0x7587ec172620>, <nibabel.nifti1.Nifti1Image
object at 0x7587ec170ac0>, <nibabel.nifti1.Nifti1Image object at 0x7587918a1ae0>, <nibabel.nifti1.Nifti1Image object at 0x7587a4fabb80>,
<nibabel.nifti1.Nifti1Image object at 0x7587a4fa88e0>, <nibabel.nifti1.Nifti1Image object at 0x7587a4faac50>, <nibabel.nifti1.Nifti1Image object at
0x7587a4faa170>, <nibabel.nifti1.Nifti1Image object at 0x7587a4fa8730>, <nibabel.nifti1.Nifti1Image object at 0x7587a4fa87c0>, <nibabel.nifti1.Nifti1Image
object at 0x7587a4fa91b0>, <nibabel.nifti1.Nifti1Image object at 0x7587916eead0>, <nibabel.nifti1.Nifti1Image object at 0x758791485930>,
<nibabel.nifti1.Nifti1Image object at 0x758791486e00>, <nibabel.nifti1.Nifti1Image object at 0x7587916eea10>, <nibabel.nifti1.Nifti1Image object at
0x7587916ee410>, <nibabel.nifti1.Nifti1Image object at 0x758791484b80>, <nibabel.nifti1.Nifti1Image object at 0x758791485150>, <nibabel.nifti1.Nifti1Image
object at 0x7587914875e0>, <nibabel.nifti1.Nifti1Image object at 0x75879177df60>, <nibabel.nifti1.Nifti1Image object at 0x75879177d1b0>,
<nibabel.nifti1.Nifti1Image object at 0x75879177d630>, <nibabel.nifti1.Nifti1Image object at 0x75879177eb30>, <nibabel.nifti1.Nifti1Image object at
0x75879177e230>, <nibabel.nifti1.Nifti1Image object at 0x7587a4fa92d0>, <nibabel.nifti1.Nifti1Image object at 0x7587a4faaec0>, <nibabel.nifti1.Nifti1Image
object at 0x7587a4fab730>, <nibabel.nifti1.Nifti1Image object at 0x7587918a2530>, <nibabel.nifti1.Nifti1Image object at 0x7587918a25c0>,
<nibabel.nifti1.Nifti1Image object at 0x7587918a0e20>, <nibabel.nifti1.Nifti1Image object at 0x7587918a1f60>, <nibabel.nifti1.Nifti1Image object at
0x7587918a0a60>, <nibabel.nifti1.Nifti1Image object at 0x7587918a1930>, <nibabel.nifti1.Nifti1Image object at 0x7587eb595ab0>, <nibabel.nifti1.Nifti1Image
object at 0x7587eb594ee0>, <nibabel.nifti1.Nifti1Image object at 0x758791cab790>, <nibabel.nifti1.Nifti1Image object at 0x7587916120b0>,
<nibabel.nifti1.Nifti1Image object at 0x758791611e40>, <nibabel.nifti1.Nifti1Image object at 0x758791611b70>, <nibabel.nifti1.Nifti1Image object at
0x758791611ff0>, <nibabel.nifti1.Nifti1Image object at 0x7587916116c0>, <nibabel.nifti1.Nifti1Image object at 0x758791612050>, <nibabel.nifti1.Nifti1Image
object at 0x758791ca8940>]
[Decoder.fit] Loading mask from '/home/remi-gau/nilearn_data/haxby2001/mask.nii.gz'
/home/remi-gau/github/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 0x7587ebc468f0>
[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:    1.4s
[Parallel(n_jobs=1)]: Done  96 out of  96 | elapsed:    3.4s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Classification accuracy: 0.7589 / Chance level: 0.125

Estimated memory usage: 1006 MB