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 import datasets

haxby_dataset = datasets.fetch_haxby()

# repetition has to be known
t_r = 2.5

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

Load the behavioral data

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

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

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

Build a proper event structure for each run

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

Instantiate and run FirstLevelModel

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

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

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

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

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)

________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5a0e8ecd0>, <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.114769, ..., -2.149296],
       ...,
       [ 2.367151, ...,  0.779998]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.575977, ...,  0.481522]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.00779 , ...,  0.924282]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.349128, ..., -1.50145 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.902378, ..., -0.291882]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.624816, ..., -1.682436]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.838935, ..., -1.528093]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.06136 , ..., -0.289828]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.983539, ..., 0.291568]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d48e50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6d30>, <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 12.660587, ..., -13.536042],
       ...,
       [ -3.254408, ..., -33.842804]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.940878, ..., -0.740886]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.56852 , ..., -1.374435]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.141601, ..., -1.208286]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.922613, ...,  2.268412]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.904959, ..., -1.104956]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.242388, ..., 1.270843]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.197666, ..., -1.944003]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.6546  , ..., -0.644223]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abca1400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5ab242370>, <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 5.205584, ..., 26.587189],
       ...,
       [-6.836576, ..., 10.676956]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.441012, ..., -0.384872]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.457669, ..., 1.351326]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.697867, ...,  0.601435]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.763713, ...,  0.209919]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.447911, ..., -1.13823 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.162822, ..., -2.114926]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.785086, ..., -0.260924]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.456513, ..., 0.23571 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5abcc6d30>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dda60>, <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-2.026206, ...,  5.974948],
       ...,
       [ 2.616334, ...,  0.104535]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.424686, ..., -1.190333]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.365571, ..., -0.28288 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.288251, ..., 0.627725]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.327741, ..., -0.321921]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.152188, ..., -0.366314]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.629164, ..., -0.735212]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.873351, ..., -0.14857 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.061121, ..., -0.426454]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6dd670>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5ab6abf10>, <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 53.033577, ..., -55.45955 ],
       ...,
       [-51.57195 , ..., -55.994713]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.09849 , ..., -0.026579]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.329567, ..., -0.355602]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.134325, ...,  0.590796]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.998496, ..., -0.158513]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.0983  , ...,  0.684567]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.003411, ..., -1.001089]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.28481 , ..., -0.796836]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.349998, ..., 1.77109 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42f40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5ac1a33d0>, <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-27.150482, ...,  -5.81308 ],
       ...,
       [-30.204891, ...,   7.417917]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.773093, ...,  1.506396]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.945765, ...,  0.964351]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.489424, ...,  1.36385 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.60046 , ...,  1.292458]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.115105, ...,  0.360045]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.341327, ..., -0.115557]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.0463  , ..., -0.011997]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.304337, ..., -0.465647]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ac0f6520>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5a0202ee0>, <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[129.51173 , ..., -15.279282],
       ...,
       [-18.911755, ...,  21.839058]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.091521, ..., -0.624162]), <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.375261, ..., -0.645241]), <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.852582, ..., 0.144768]), <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.861249, ..., -1.104223]), <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.147067, ...,  1.938447]), <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.043015, ..., 0.397187]), <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.518244, ..., -0.247356]), <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.292525e-03, ..., -1.546823e+00]), <nibabel.nifti1.Nifti1Image object at 0x7ff5a1b48850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddbe0>, <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-15.915996, ...,  22.07737 ],
       ...,
       [-16.981215, ...,   3.372383]], dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.352245, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.011367, ..., -0.044286]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.378178, ..., -0.435127]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.249686, ..., 1.006757]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.627259, ..., -0.703344]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.673   , ...,  0.634889]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.170346, ..., -0.003907]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.74249 , ..., 2.416095]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.813068, ..., 0.397476]), <nibabel.nifti1.Nifti1Image object at 0x7ff5ab6ddc40>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5abe61430>, <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.292987, ..., 18.392956],
       ...,
       [-3.935719, ...,  0.602484]], dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.352245, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.400515, ...,  2.602952]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.497881, ..., 0.140209]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.499281, ..., -0.49038 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.038187, ..., 0.57069 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.622503, ..., 1.679756]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.211798, ..., -1.574836]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.143717, ..., -0.364448]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.156681, ..., -1.209168]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d42370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5c7bf9c10>, <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-5.223948, ..., -5.959582],
       ...,
       [-7.677519, ..., 16.024363]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.00555 , ..., 1.955666]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.488905, ..., 1.063614]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.126069, ..., 0.347685]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.147577, ..., -0.722242]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.137613, ...,  1.219668]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.30493 , ...,  1.036247]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.64672 , ...,  0.256269]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.01524, ...,  0.32111]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c7b9f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5c5d3c2e0>, <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-19.66533 , ...,  -6.299562],
       ...,
       [-24.647343, ...,   2.331865]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.497712, ..., -3.241636]), <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.884836, ..., -2.579124]), <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.361796, ..., -2.404684]), <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.15611 , ...,  0.717663]), <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.124367, ..., 1.85051 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.322726, ...,  0.187135]), <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.943745, ..., -1.223389]), <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.298717, ..., -1.02394 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff5774affa0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff5ac19cfd0>, <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-1.095605, ..., 16.449202],
       ...,
       [ 2.59974 , ..., -2.179998]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.028052, ..., -1.076144]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.07071 , ...,  1.117228]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.278396, ..., 0.666023]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.606351, ..., -0.186859]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.228201, ..., -0.299271]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.760997, ..., 0.931553]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.005349, ...,  0.835504]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.093039, ..., -0.791928]), <nibabel.nifti1.Nifti1Image object at 0x7ff5c5d220d0>)
___________________________________________________________unmask - 0.1s, 0.0min

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
from nilearn.reporting import make_glm_report

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

report  # This report can be viewed in a notebook

Statistical Report for bottle, cat, chair, face, house, scissors, scrambledpix, shoe

First Level Model

Model details:

drift_model	cosine
drift_order	1
high_pass (Hz)	0.01
hrf_model	glover
noise_model	ar1
signal_scaling	0
slice_time_ref	0.0
smoothing_fwhm	4
standardize	False
subject_label	None
t_r (s)	2.5
target_affine	None
target_shape	None

Design Matrix:

Contrasts

Mask

Stat Maps with Cluster Tables

bottle

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.291
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-54.25	13.12	5.62	6.49	98
2	29.75	-20.62	-24.38	6.49	836
2a	12.25	-20.62	-31.88	4.58
3	26.25	-20.62	-13.12	6.13	295
4	-36.75	-46.88	-46.88	6.11	147
5	43.75	-50.62	-5.62	6.03	1476
5a	47.25	-43.12	-1.88	4.74
5b	40.25	-28.12	-1.88	3.99
6	-1.75	-39.38	13.12	5.95	147
7	33.25	-43.12	-43.12	5.94	1181
7a	26.25	-35.62	-43.12	5.67
8	22.75	-54.38	-39.38	5.91	885
8a	8.75	-58.12	-39.38	4.15
9	-43.75	28.12	-9.38	5.90	196
10	-12.25	-24.38	-76.88	5.86	196
11	-19.25	-24.38	-43.12	5.80	590
11a	-19.25	-31.88	-39.38	5.31
12	26.25	-46.88	-46.88	5.75	147
13	-57.75	-39.38	9.38	5.74	246
14	33.25	5.62	-13.12	5.69	442
14a	26.25	9.38	-16.88	5.46
15	22.75	-46.88	-61.88	5.68	442
16	19.25	-76.88	-13.12	5.60	590
17	22.75	-43.12	9.38	5.57	196
18	-29.75	9.38	-28.12	5.56	246
19	26.25	-69.38	-16.88	5.49	689
19a	36.75	-65.62	-13.12	4.91
19b	33.25	-58.12	-16.88	4.13
20	47.25	-24.38	-13.12	5.47	3248
20a	40.25	-35.62	-24.38	5.23
20b	33.25	-50.62	-20.62	4.80
20c	43.75	-16.88	-13.12	4.67
21	1.75	50.62	-16.88	5.46	295
22	-26.25	-16.88	-24.38	5.38	147
23	12.25	-84.38	-28.12	5.31	98
24	-29.75	-43.12	-50.62	5.30	442
25	1.75	-43.12	-1.88	5.29	98
26	-15.75	-28.12	-43.12	5.24	49
27	12.25	-13.12	-28.12	5.23	98
28	-47.25	-13.12	13.12	5.21	295
29	-19.25	-50.62	-39.38	5.19	639
29a	-8.75	-35.62	-39.38	4.36
29b	-12.25	-46.88	-39.38	3.96
30	-40.25	-35.62	-35.62	5.17	344
31	12.25	-28.12	-73.12	5.16	147
32	8.75	-50.62	-35.62	5.13	590
33	29.75	1.88	-9.38	5.11	98
34	19.25	-31.88	16.88	5.10	49
35	-1.75	-50.62	-39.38	5.08	98
36	-50.75	35.62	-16.88	5.08	196
37	-26.25	-54.38	46.88	5.06	98
38	-54.25	-31.88	-13.12	5.06	49
39	-8.75	-46.88	-65.62	5.05	295
40	1.75	-58.12	-24.38	5.05	98
41	-33.25	13.12	28.12	5.05	196
42	-12.25	5.62	-16.88	5.03	49
43	12.25	-58.12	-28.12	5.03	246
44	19.25	-43.12	-20.62	5.02	49
45	-26.25	-20.62	-13.12	5.02	98
46	-47.25	-39.38	-20.62	5.00	1082
46a	-50.75	-39.38	-9.38	4.39
47	-40.25	-61.88	-16.88	4.99	295
48	5.25	-61.88	-20.62	4.97	246
49	-61.25	-5.62	20.62	4.96	98
50	40.25	16.88	-20.62	4.96	49
51	-26.25	-54.38	-46.88	4.95	49
52	-22.75	-58.12	-46.88	4.95	49
53	22.75	31.88	9.38	4.93	49
54	8.75	-16.88	46.88	4.93	49
55	-43.75	50.62	16.88	4.90	49
56	22.75	-20.62	-35.62	4.90	98
57	-29.75	-84.38	1.88	4.89	442
57a	-36.75	-76.88	1.88	4.46
58	-40.25	-20.62	-31.88	4.83	49
59	-36.75	58.12	35.62	4.83	49
60	19.25	35.62	28.12	4.82	49
61	-1.75	-46.88	-24.38	4.81	147
62	33.25	-9.38	-58.12	4.80	49
63	-29.75	-58.12	-9.38	4.80	49
64	-36.75	-39.38	31.88	4.80	344
65	-15.75	-31.88	-61.88	4.80	49
66	-40.25	24.38	-9.38	4.78	49
67	33.25	31.88	-9.38	4.77	98
68	-1.75	-46.88	1.88	4.76	98
69	1.75	-1.88	31.88	4.76	98
70	-40.25	-28.12	-20.62	4.75	49
71	-33.25	-13.12	-13.12	4.75	344
72	-15.75	-1.88	-69.38	4.75	49
73	-47.25	24.38	20.62	4.75	196
74	26.25	31.88	-31.88	4.75	147
75	-12.25	-58.12	-16.88	4.74	49
76	-12.25	-5.62	28.12	4.74	246
77	36.75	-61.88	-5.62	4.74	98
78	-33.25	-73.12	31.88	4.72	49
79	12.25	43.12	-16.88	4.72	49
80	-1.75	-9.38	-20.62	4.71	49
81	-47.25	-54.38	-9.38	4.69	295
82	8.75	-91.88	5.62	4.68	49
83	-50.75	9.38	9.38	4.67	49
84	12.25	-54.38	-43.12	4.67	98
85	8.75	-20.62	-13.12	4.67	147
86	-22.75	16.88	5.62	4.66	98
87	29.75	-50.62	-50.62	4.66	196
88	-5.25	-13.12	-69.38	4.66	147
89	19.25	-35.62	69.38	4.65	49
90	-15.75	-39.38	-50.62	4.64	98
91	22.75	-35.62	54.38	4.64	295
91a	26.25	-43.12	54.38	4.06
92	-47.25	-9.38	5.62	4.63	246
93	-1.75	-54.38	-9.38	4.62	98
94	19.25	-39.38	-13.12	4.61	49
95	33.25	9.38	-24.38	4.61	98
96	22.75	46.88	-16.88	4.61	98
97	22.75	-16.88	-9.38	4.61	98
98	-36.75	20.62	35.62	4.60	344
99	-43.75	24.38	-1.88	4.59	196
100	15.75	50.62	58.12	4.59	98
101	19.25	16.88	28.12	4.59	49
102	-40.25	-65.62	-5.62	4.59	98
103	-29.75	-80.62	-9.38	4.58	49
104	-15.75	-76.88	28.12	4.57	196
105	-57.75	-16.88	1.88	4.57	49
106	22.75	9.38	16.88	4.57	246
107	-1.75	13.12	-20.62	4.57	98
108	40.25	-35.62	-43.12	4.56	49
109	-5.25	-31.88	58.12	4.55	49
110	12.25	24.38	61.88	4.55	98
111	26.25	28.12	-20.62	4.55	196
112	-57.75	5.62	13.12	4.55	492
112a	-61.25	-1.88	9.38	4.19
112b	-57.75	-9.38	9.38	4.13
113	19.25	-1.88	28.12	4.54	49
114	5.25	-76.88	24.38	4.54	49
115	33.25	20.62	-20.62	4.54	98
116	-15.75	16.88	43.12	4.53	147
117	-47.25	-28.12	5.62	4.51	49
118	-29.75	13.12	-13.12	4.51	295
118a	-26.25	5.62	-13.12	4.20
119	12.25	-80.62	13.12	4.51	98
120	-8.75	84.38	20.62	4.50	147
121	15.75	-35.62	73.12	4.50	196
122	-15.75	-16.88	24.38	4.49	98
123	-15.75	9.38	-20.62	4.49	98
124	22.75	-28.12	69.38	4.49	98
125	12.25	-9.38	13.12	4.49	98
126	5.25	-43.12	-9.38	4.49	49
127	-1.75	-9.38	-46.88	4.49	196
128	-12.25	9.38	24.38	4.48	295
129	-26.25	13.12	46.88	4.48	49
130	-12.25	-13.12	-46.88	4.48	98
131	-1.75	-50.62	-1.88	4.47	49
132	1.75	24.38	31.88	4.47	49
133	-5.25	-24.38	-20.62	4.45	147
134	8.75	16.88	-20.62	4.45	49
135	15.75	-24.38	-35.62	4.45	344
136	-22.75	31.88	-24.38	4.45	98
137	-47.25	-28.12	43.12	4.44	49
138	-33.25	13.12	13.12	4.44	246
139	-47.25	-61.88	-5.62	4.44	49
140	-50.75	39.38	9.38	4.44	49
141	-50.75	-20.62	9.38	4.43	98
142	33.25	-24.38	-13.12	4.43	147
143	-15.75	-20.62	-58.12	4.43	98
144	-54.25	-46.88	-5.62	4.42	98
145	40.25	31.88	13.12	4.42	49
146	-43.75	16.88	58.12	4.42	49
147	-47.25	-24.38	-16.88	4.41	196
148	-12.25	-58.12	-46.88	4.40	49
149	-22.75	-9.38	13.12	4.40	49
150	22.75	16.88	5.62	4.39	196
151	50.75	1.88	43.12	4.39	98
152	-1.75	-39.38	1.88	4.39	295
153	-50.75	-16.88	24.38	4.38	49
154	36.75	50.62	1.88	4.38	98
155	-1.75	-54.38	-28.12	4.36	98
156	22.75	13.12	35.62	4.36	344
156a	12.25	20.62	35.62	4.14
157	-36.75	16.88	50.62	4.36	147
158	36.75	43.12	-28.12	4.35	49
159	33.25	24.38	39.38	4.35	49
160	36.75	5.62	35.62	4.34	147
161	29.75	-31.88	39.38	4.34	442
161a	40.25	-35.62	39.38	3.94
162	-15.75	-50.62	-1.88	4.33	49
163	-5.25	-46.88	-16.88	4.33	689
164	-1.75	-65.62	16.88	4.33	344
164a	5.25	-73.12	16.88	4.13
165	-47.25	35.62	16.88	4.33	295
166	-36.75	43.12	24.38	4.32	98
167	-54.25	-20.62	-9.38	4.32	49
168	-15.75	20.62	-46.88	4.32	98
169	47.25	-54.38	-16.88	4.31	49
170	-50.75	-50.62	20.62	4.31	49
171	-22.75	1.88	-1.88	4.31	49
172	-26.25	1.88	43.12	4.31	98
173	-15.75	-54.38	43.12	4.31	49
174	8.75	13.12	50.62	4.30	98
175	-15.75	-73.12	-13.12	4.30	49
176	54.25	-24.38	-5.62	4.29	196
177	-15.75	-1.88	16.88	4.29	49
178	-43.75	43.12	-28.12	4.28	98
179	68.25	-1.88	-9.38	4.28	49
180	-50.75	16.88	-1.88	4.28	49
181	19.25	-69.38	-13.12	4.28	98
182	12.25	50.62	-1.88	4.28	98
183	19.25	-9.38	20.62	4.28	49
184	-19.25	1.88	58.12	4.27	49
185	-29.75	-80.62	-24.38	4.27	49
186	19.25	-1.88	43.12	4.26	98
187	-19.25	-58.12	-9.38	4.26	49
188	68.25	-13.12	-9.38	4.25	98
189	19.25	-65.62	-24.38	4.25	147
190	-5.25	31.88	-20.62	4.25	147
191	-15.75	-46.88	-5.62	4.25	98
192	54.25	-9.38	20.62	4.25	246
193	-47.25	-58.12	13.12	4.25	49
194	50.75	-54.38	-9.38	4.23	49
195	-50.75	-5.62	-5.62	4.23	49
196	-22.75	-69.38	-13.12	4.23	147
197	19.25	16.88	13.12	4.23	147
198	15.75	9.38	43.12	4.22	98
199	1.75	13.12	9.38	4.22	49
200	12.25	-54.38	-54.38	4.22	49
201	15.75	-88.12	5.62	4.21	98
202	-40.25	5.62	-28.12	4.21	196
203	40.25	-31.88	54.38	4.21	98
204	-1.75	-1.88	1.88	4.20	246
205	-1.75	1.88	13.12	4.20	49
206	12.25	-9.38	5.62	4.20	49
207	12.25	24.38	-39.38	4.19	196
208	50.75	43.12	-5.62	4.19	49
209	-1.75	-58.12	-54.38	4.19	49
210	-47.25	-28.12	13.12	4.18	98
211	-26.25	50.62	5.62	4.18	147
212	15.75	-1.88	-16.88	4.17	196
213	54.25	1.88	35.62	4.17	98
214	-47.25	-43.12	9.38	4.17	147
215	40.25	5.62	-24.38	4.17	49
216	40.25	35.62	-1.88	4.17	49
217	22.75	46.88	13.12	4.16	49
218	50.75	-5.62	5.62	4.16	49
219	-40.25	-58.12	9.38	4.16	98
220	-26.25	-31.88	16.88	4.16	49
221	-22.75	-54.38	-61.88	4.16	98
222	-12.25	20.62	73.12	4.15	246
223	-36.75	43.12	1.88	4.15	196
224	19.25	-24.38	16.88	4.15	49
225	8.75	46.88	-5.62	4.14	49
226	19.25	-9.38	5.62	4.14	98
227	-26.25	-28.12	-31.88	4.13	49
228	54.25	-20.62	39.38	4.13	49
229	36.75	-35.62	-9.38	4.13	295
230	-36.75	-69.38	5.62	4.13	49
231	-19.25	61.88	5.62	4.13	49
232	26.25	-61.88	31.88	4.13	49
233	5.25	-28.12	-9.38	4.13	147
234	-54.25	1.88	13.12	4.13	49
235	-50.75	13.12	-5.62	4.12	49
236	-8.75	-46.88	-31.88	4.12	49
237	-15.75	13.12	50.62	4.12	49
238	-40.25	-46.88	1.88	4.10	49
239	-33.25	-35.62	-65.62	4.10	98
240	12.25	80.62	35.62	4.10	147
241	29.75	-31.88	-31.88	4.10	49
242	-33.25	1.88	-16.88	4.10	147
243	-29.75	-1.88	-24.38	4.09	98
244	-54.25	46.88	9.38	4.09	98
245	-36.75	-50.62	1.88	4.08	49
246	-43.75	-65.62	24.38	4.08	49
247	1.75	-5.62	-13.12	4.08	49
248	1.75	-5.62	-54.38	4.07	49
249	19.25	13.12	-1.88	4.07	98
250	-61.25	-20.62	13.12	4.07	49
251	22.75	-35.62	-20.62	4.06	147
252	29.75	-43.12	-58.12	4.06	49
253	26.25	24.38	13.12	4.06	147
254	29.75	-28.12	-24.38	4.06	49
255	-8.75	5.62	-24.38	4.06	49
256	-15.75	24.38	-9.38	4.05	49
257	-15.75	-1.88	-31.88	4.05	49
258	8.75	20.62	-24.38	4.05	49
259	-22.75	-1.88	-20.62	4.05	393
260	26.25	1.88	13.12	4.04	147
261	-33.25	28.12	31.88	4.04	246
262	29.75	-58.12	-46.88	4.04	49
263	5.25	-61.88	54.38	4.03	196
264	-54.25	13.12	20.62	4.03	196
265	40.25	9.38	-20.62	4.02	98
266	12.25	5.62	43.12	4.02	98
267	-54.25	5.62	-16.88	4.01	196
268	-33.25	-35.62	-9.38	4.01	98
269	-26.25	-73.12	-35.62	4.01	49
270	26.25	-9.38	13.12	4.01	246
271	47.25	43.12	9.38	4.00	49
272	1.75	-76.88	35.62	4.00	49
273	-33.25	-69.38	39.38	4.00	49
274	-15.75	-43.12	-43.12	4.00	98
275	-47.25	-65.62	1.88	4.00	196
276	-12.25	46.88	28.12	4.00	49
277	-1.75	-24.38	9.38	3.99	49
278	-1.75	58.12	13.12	3.99	49
279	-8.75	-46.88	-24.38	3.99	49
280	-19.25	13.12	28.12	3.99	49
281	29.75	65.62	43.12	3.98	49
282	40.25	-20.62	20.62	3.98	49
283	36.75	28.12	-35.62	3.97	49
284	-57.75	-31.88	-5.62	3.97	49
285	-19.25	24.38	31.88	3.97	49
286	-50.75	28.12	46.88	3.97	98
287	61.25	-31.88	39.38	3.97	49
288	-47.25	20.62	-13.12	3.96	49
289	47.25	16.88	-39.38	3.96	98
290	47.25	-54.38	-1.88	3.96	147
291	64.75	-43.12	5.62	3.95	49
292	-15.75	-31.88	-16.88	3.95	49
293	8.75	76.88	35.62	3.95	49
294	5.25	-88.12	-1.88	3.95	49
295	22.75	-69.38	1.88	3.95	147
296	12.25	9.38	28.12	3.94	147
297	36.75	-73.12	5.62	3.94	98
298	33.25	43.12	35.62	3.94	49
299	33.25	43.12	24.38	3.93	49
300	26.25	-20.62	-65.62	3.93	196
301	-22.75	-76.88	-16.88	3.93	49
302	54.25	-28.12	-9.38	3.93	98
303	12.25	-9.38	50.62	3.92	49
304	-12.25	-28.12	-50.62	3.92	196
305	15.75	-35.62	31.88	3.92	98
306	15.75	-1.88	-1.88	3.92	49
307	5.25	-28.12	73.12	3.92	49
308	-26.25	-61.88	35.62	3.92	98
309	5.25	-58.12	-54.38	3.92	49
310	-19.25	-76.88	-28.12	3.91	98
311	19.25	-76.88	-35.62	3.91	98
312	22.75	5.62	-1.88	3.91	49
313	-54.25	-20.62	-24.38	3.91	49
314	-1.75	-9.38	-5.62	3.91	49
315	15.75	-20.62	73.12	3.91	98
316	19.25	65.62	9.38	3.91	49
317	-64.75	-13.12	-24.38	3.91	49
318	-15.75	-5.62	61.88	3.91	147
319	-26.25	24.38	13.12	3.90	98
320	-19.25	-13.12	61.88	3.90	49
321	-22.75	13.12	-24.38	3.90	49
322	19.25	54.38	1.88	3.90	147
323	-15.75	54.38	54.38	3.90	98
324	1.75	-31.88	1.88	3.89	49
325	-33.25	39.38	54.38	3.89	49
326	-50.75	16.88	-16.88	3.89	147
327	33.25	50.62	50.62	3.89	49
328	5.25	-35.62	-58.12	3.89	98
329	-47.25	-28.12	28.12	3.89	49
330	12.25	69.38	5.62	3.89	49
331	5.25	-50.62	-20.62	3.88	98
332	15.75	88.12	9.38	3.88	49
333	-19.25	-50.62	16.88	3.88	49
334	-47.25	16.88	5.62	3.88	49
335	-47.25	-13.12	-54.38	3.88	98
336	-1.75	-16.88	-28.12	3.87	49
337	33.25	-28.12	65.62	3.87	98
338	19.25	35.62	1.88	3.87	98
339	33.25	5.62	28.12	3.86	49
340	-47.25	16.88	-24.38	3.86	49
341	-54.25	-24.38	-39.38	3.86	98
342	22.75	-16.88	43.12	3.86	49
343	-47.25	20.62	46.88	3.86	49
344	57.75	-20.62	28.12	3.85	49
345	36.75	1.88	20.62	3.85	98
346	-47.25	-39.38	16.88	3.85	49
347	50.75	-50.62	9.38	3.85	98
348	-54.25	-20.62	-50.62	3.85	49
349	36.75	-39.38	-69.38	3.85	49
350	15.75	-73.12	-31.88	3.85	49
351	-26.25	-35.62	-9.38	3.85	49
352	19.25	20.62	20.62	3.84	49
353	-19.25	-20.62	-35.62	3.84	98
354	12.25	-9.38	31.88	3.83	147
355	-33.25	-35.62	46.88	3.83	49
356	1.75	16.88	-39.38	3.83	49
357	-29.75	-46.88	-13.12	3.82	49
358	26.25	-65.62	46.88	3.82	49
359	1.75	-20.62	-35.62	3.82	49
360	26.25	5.62	5.62	3.82	98
361	-54.25	-39.38	1.88	3.81	246
361a	-57.75	-46.88	-1.88	3.78
362	-40.25	16.88	24.38	3.81	98
363	-1.75	-43.12	-9.38	3.81	49
364	-5.25	-84.38	-20.62	3.81	49
365	33.25	-35.62	-5.62	3.80	49
366	1.75	-35.62	-61.88	3.78	49
367	-1.75	-28.12	20.62	3.78	98
368	1.75	-58.12	-13.12	3.78	49
369	-36.75	61.88	16.88	3.78	98
370	12.25	-46.88	-46.88	3.78	49
371	-12.25	-20.62	-46.88	3.78	49
372	29.75	16.88	35.62	3.78	49
373	26.25	13.12	9.38	3.77	98
374	15.75	-20.62	-20.62	3.77	49
375	-33.25	35.62	-24.38	3.77	49
376	8.75	-24.38	9.38	3.77	49
377	-22.75	-50.62	54.38	3.76	49
378	36.75	-73.12	-9.38	3.76	49
379	8.75	-73.12	35.62	3.76	49
380	-1.75	-16.88	24.38	3.76	49
381	-15.75	-16.88	-43.12	3.76	49
382	22.75	-73.12	-5.62	3.76	49
383	12.25	16.88	-50.62	3.76	49
384	-12.25	54.38	-9.38	3.75	49
385	8.75	-43.12	-35.62	3.75	49
386	22.75	24.38	43.12	3.75	98
387	19.25	43.12	-5.62	3.75	49
388	-12.25	-39.38	-5.62	3.75	147
389	5.25	-46.88	-39.38	3.75	49
390	43.75	-5.62	-13.12	3.74	49
391	47.25	13.12	-20.62	3.74	49
392	-54.25	-9.38	20.62	3.74	49
393	12.25	-35.62	9.38	3.74	49
394	-57.75	-1.88	-13.12	3.74	98
395	-33.25	-43.12	-28.12	3.74	98
396	47.25	13.12	5.62	3.74	49
397	1.75	-84.38	-16.88	3.74	49
398	40.25	28.12	-39.38	3.74	49
399	33.25	50.62	24.38	3.74	98
400	-1.75	-9.38	69.38	3.74	49
401	19.25	-16.88	16.88	3.74	49
402	-33.25	65.62	20.62	3.73	98
403	-29.75	16.88	-5.62	3.73	49
404	22.75	46.88	5.62	3.73	49
405	1.75	-16.88	-24.38	3.73	49
406	12.25	-65.62	-13.12	3.73	49
407	22.75	16.88	50.62	3.73	49
408	19.25	-50.62	-73.12	3.73	49
409	-33.25	-43.12	58.12	3.72	98
410	-47.25	-58.12	-28.12	3.72	49
411	19.25	9.38	20.62	3.72	49
412	-5.25	-43.12	69.38	3.72	98
413	-8.75	39.38	5.62	3.72	49
414	26.25	20.62	-24.38	3.71	98
415	-29.75	35.62	-1.88	3.71	49
416	33.25	43.12	-16.88	3.71	49
417	40.25	-58.12	5.62	3.71	98
418	40.25	-5.62	46.88	3.70	147
419	33.25	-9.38	-9.38	3.70	49
420	-68.25	-24.38	13.12	3.70	49
421	-15.75	-76.88	20.62	3.70	49
422	43.75	31.88	5.62	3.70	49
423	1.75	65.62	-16.88	3.70	98
424	26.25	-20.62	20.62	3.70	49
425	47.25	20.62	5.62	3.70	49
426	-29.75	20.62	-24.38	3.70	49
427	-47.25	46.88	20.62	3.70	49
428	15.75	1.88	35.62	3.69	49
429	40.25	-43.12	-28.12	3.69	49
430	-40.25	39.38	43.12	3.69	147
431	-54.25	-35.62	-5.62	3.69	49
432	-12.25	-20.62	-61.88	3.69	49
433	-50.75	13.12	-39.38	3.68	49
434	5.25	1.88	13.12	3.68	98
435	43.75	-69.38	-1.88	3.68	49
436	-22.75	-80.62	1.88	3.68	49
437	-36.75	-31.88	-46.88	3.68	49
438	26.25	-39.38	35.62	3.68	49
439	8.75	28.12	-24.38	3.67	49
440	15.75	-5.62	28.12	3.67	49
441	-15.75	5.62	65.62	3.67	98
442	5.25	-65.62	-13.12	3.67	98
443	54.25	-5.62	13.12	3.67	49
444	-33.25	46.88	35.62	3.67	49
445	33.25	-20.62	-9.38	3.67	49
446	36.75	-31.88	13.12	3.66	49
447	36.75	35.62	16.88	3.66	196
448	-36.75	-35.62	39.38	3.66	49
449	1.75	9.38	-16.88	3.65	49
450	-47.25	-13.12	35.62	3.65	49
451	-50.75	5.62	-9.38	3.65	49
452	19.25	-9.38	35.62	3.65	49
453	-5.25	-20.62	1.88	3.65	49
454	57.75	-39.38	5.62	3.64	49
455	-36.75	16.88	1.88	3.64	49
456	-36.75	-13.12	9.38	3.64	49
457	-1.75	24.38	-39.38	3.64	98
458	-8.75	-39.38	-31.88	3.64	49
459	12.25	20.62	-46.88	3.64	49
460	12.25	-1.88	-69.38	3.64	98
461	-1.75	-9.38	31.88	3.64	98
462	-26.25	-39.38	-43.12	3.64	98
463	33.25	-13.12	65.62	3.63	98
464	-15.75	-50.62	-31.88	3.63	196
465	-40.25	-16.88	-50.62	3.63	49
466	29.75	-16.88	58.12	3.62	49
467	43.75	24.38	-5.62	3.62	49
468	-1.75	-43.12	-31.88	3.62	49
469	-8.75	46.88	24.38	3.62	98
470	19.25	-61.88	35.62	3.61	49
471	-12.25	-16.88	-65.62	3.61	49
472	50.75	50.62	-1.88	3.61	49
473	33.25	-50.62	-28.12	3.61	49
474	-8.75	39.38	-28.12	3.61	49
475	-5.25	-31.88	1.88	3.61	49
476	22.75	-5.62	20.62	3.61	49
477	50.75	-43.12	9.38	3.61	49
478	-54.25	-28.12	-16.88	3.61	49
479	-12.25	16.88	20.62	3.61	98
480	-22.75	-5.62	-1.88	3.60	49
481	29.75	35.62	39.38	3.60	49
482	-47.25	-24.38	-5.62	3.60	49
483	12.25	61.88	5.62	3.60	49
484	-43.75	43.12	5.62	3.59	49
485	33.25	-28.12	58.12	3.59	49
486	19.25	28.12	31.88	3.59	49
487	-19.25	-61.88	20.62	3.59	49
488	57.75	35.62	28.12	3.58	98
489	33.25	-35.62	-35.62	3.58	49
490	-19.25	-9.38	-65.62	3.58	49
491	-40.25	-46.88	-5.62	3.58	49
492	5.25	-80.62	-1.88	3.58	49
493	-15.75	-5.62	1.88	3.58	98
494	26.25	-61.88	-16.88	3.58	98
495	29.75	-1.88	-20.62	3.58	49
496	5.25	5.62	-69.38	3.57	49
497	5.25	-9.38	-20.62	3.57	49
498	15.75	-13.12	-73.12	3.57	49
499	-57.75	-9.38	16.88	3.57	98
500	-1.75	1.88	69.38	3.56	49
501	-43.75	13.12	13.12	3.56	98
502	26.25	31.88	43.12	3.56	49
503	-33.25	73.12	20.62	3.56	49
504	36.75	20.62	31.88	3.56	49
505	-12.25	13.12	58.12	3.56	49
506	-43.75	58.12	20.62	3.56	49
507	-29.75	-24.38	-9.38	3.55	49
508	-33.25	-16.88	-5.62	3.55	49
509	-12.25	5.62	35.62	3.55	147
510	19.25	-31.88	-16.88	3.55	49
511	-61.25	-16.88	9.38	3.55	49
512	-54.25	-9.38	-5.62	3.55	98
513	50.75	-13.12	5.62	3.54	49
514	-26.25	-35.62	54.38	3.54	49
515	26.25	-54.38	-13.12	3.54	98
516	-1.75	46.88	54.38	3.54	49
517	47.25	28.12	-39.38	3.54	49
518	-29.75	43.12	39.38	3.54	49
519	-47.25	43.12	9.38	3.54	49
520	57.75	-13.12	16.88	3.54	49
521	-40.25	13.12	-43.12	3.53	49
522	22.75	69.38	5.62	3.53	98
523	-12.25	-76.88	-9.38	3.53	49
524	-1.75	-50.62	-16.88	3.52	49
525	-5.25	43.12	-16.88	3.52	49
526	-5.25	1.88	1.88	3.52	49
527	-22.75	1.88	28.12	3.52	49
528	-1.75	9.38	9.38	3.52	49
529	22.75	-54.38	61.88	3.52	49
530	19.25	-84.38	-20.62	3.52	49
531	40.25	-28.12	-69.38	3.52	98
532	40.25	39.38	28.12	3.52	49
533	15.75	20.62	43.12	3.51	49
534	22.75	-24.38	1.88	3.51	49
535	22.75	-1.88	5.62	3.51	49
536	54.25	-16.88	35.62	3.51	98
537	-47.25	-1.88	-20.62	3.51	49
538	43.75	9.38	-39.38	3.51	49
539	19.25	-58.12	-13.12	3.51	49
540	1.75	-58.12	9.38	3.50	49
541	-29.75	-65.62	-20.62	3.50	49
542	-50.75	-9.38	-13.12	3.50	49
543	15.75	-73.12	-20.62	3.50	49
544	40.25	65.62	31.88	3.50	49
545	26.25	-5.62	-1.88	3.50	49
546	40.25	-39.38	-13.12	3.50	49
547	-8.75	-13.12	5.62	3.50	49
548	5.25	-16.88	69.38	3.49	49
549	19.25	46.88	39.38	3.49	49
550	26.25	-20.62	65.62	3.49	49
551	-29.75	-80.62	20.62	3.48	49
552	61.25	-35.62	35.62	3.48	49
553	36.75	50.62	-16.88	3.48	49
554	40.25	-35.62	50.62	3.48	49
555	-1.75	-5.62	-73.12	3.48	49
556	19.25	-31.88	-54.38	3.47	49
557	-33.25	1.88	31.88	3.47	98
558	47.25	5.62	31.88	3.47	49
559	15.75	1.88	58.12	3.47	98
560	-36.75	-46.88	9.38	3.47	98
561	15.75	-24.38	-73.12	3.47	49
562	-36.75	61.88	9.38	3.47	49
563	29.75	43.12	16.88	3.46	49
564	-8.75	39.38	31.88	3.46	49
565	-40.25	-69.38	31.88	3.46	49
566	5.25	-50.62	-5.62	3.46	49
567	-47.25	1.88	-28.12	3.45	49
568	12.25	69.38	-13.12	3.45	49
569	33.25	39.38	28.12	3.45	49
570	-43.75	35.62	31.88	3.45	98
571	-1.75	20.62	-16.88	3.45	147
572	47.25	16.88	39.38	3.45	49
573	36.75	-46.88	9.38	3.45	49
574	-19.25	-50.62	-46.88	3.45	49
575	36.75	-54.38	-28.12	3.44	49
576	1.75	28.12	-5.62	3.44	49
577	26.25	1.88	-13.12	3.44	49
578	-1.75	-28.12	58.12	3.44	49
579	19.25	-61.88	-46.88	3.44	98
580	12.25	5.62	13.12	3.44	49
581	-64.75	20.62	5.62	3.44	49
582	40.25	-9.38	-31.88	3.44	49
583	-1.75	-9.38	-28.12	3.43	49
584	5.25	-16.88	-20.62	3.43	49
585	19.25	-13.12	28.12	3.43	49
586	-64.75	-35.62	-1.88	3.43	49
587	1.75	-1.88	-20.62	3.43	49
588	33.25	-24.38	-58.12	3.43	49
589	-5.25	-73.12	28.12	3.43	49
590	33.25	-43.12	-35.62	3.43	49
591	-19.25	-16.88	9.38	3.42	49
592	26.25	80.62	24.38	3.42	49
593	8.75	13.12	16.88	3.42	49
594	54.25	-28.12	24.38	3.42	49
595	-22.75	-28.12	31.88	3.42	49
596	54.25	-28.12	31.88	3.42	49
597	1.75	84.38	1.88	3.42	49
598	47.25	31.88	-24.38	3.42	49
599	-12.25	-50.62	-24.38	3.42	49
600	-29.75	24.38	5.62	3.41	49
601	-36.75	-5.62	-20.62	3.41	49
602	-15.75	-73.12	39.38	3.41	49
603	-15.75	35.62	5.62	3.41	49
604	47.25	-31.88	-39.38	3.41	98
605	26.25	-16.88	-58.12	3.41	49
606	12.25	-9.38	-24.38	3.41	49
607	-19.25	-43.12	31.88	3.41	49
608	-19.25	-31.88	-69.38	3.41	49
609	-43.75	50.62	28.12	3.40	49
610	40.25	13.12	-46.88	3.40	49
611	19.25	-46.88	31.88	3.40	49
612	12.25	-9.38	43.12	3.40	49
613	40.25	-9.38	28.12	3.40	49
614	33.25	5.62	50.62	3.40	49
615	15.75	-16.88	5.62	3.40	49
616	22.75	54.38	16.88	3.40	49
617	-22.75	-54.38	39.38	3.40	49
618	50.75	-20.62	-43.12	3.39	49
619	12.25	-1.88	24.38	3.39	49
620	-29.75	-20.62	-69.38	3.39	49
621	12.25	-35.62	-16.88	3.39	49
622	12.25	-84.38	-5.62	3.39	49
623	-29.75	-1.88	-1.88	3.38	49
624	-36.75	-50.62	-16.88	3.38	49
625	-5.25	-31.88	-13.12	3.38	49
626	26.25	-1.88	20.62	3.38	49
627	-1.75	5.62	-39.38	3.38	49
628	-50.75	1.88	9.38	3.38	49
629	-22.75	24.38	20.62	3.38	49
630	22.75	39.38	20.62	3.38	49
631	19.25	-61.88	-20.62	3.38	49
632	-12.25	16.88	-50.62	3.38	49
633	-8.75	-9.38	-20.62	3.38	49
634	8.75	24.38	39.38	3.38	49
635	40.25	-35.62	-58.12	3.38	98
636	-33.25	-1.88	13.12	3.38	49
637	-50.75	-1.88	16.88	3.37	98
638	-36.75	-16.88	-46.88	3.37	49
639	47.25	-20.62	20.62	3.37	49
640	22.75	-9.38	43.12	3.37	49
641	1.75	1.88	-16.88	3.36	49
642	19.25	-28.12	-24.38	3.36	49
643	-22.75	-28.12	-65.62	3.36	49
644	-22.75	-28.12	-13.12	3.36	98
645	-19.25	-43.12	46.88	3.36	49
646	-5.25	76.88	5.62	3.36	49
647	68.25	-28.12	16.88	3.36	49
648	-8.75	-35.62	65.62	3.36	49
649	-33.25	-24.38	-65.62	3.35	49
650	-22.75	54.38	-20.62	3.35	49
651	8.75	-5.62	-24.38	3.35	49
652	5.25	46.88	13.12	3.35	49
653	-26.25	-43.12	54.38	3.35	49
654	15.75	35.62	5.62	3.35	49
655	-33.25	-58.12	-1.88	3.35	49
656	-8.75	-54.38	-31.88	3.35	49
657	26.25	28.12	-43.12	3.34	98
658	-29.75	39.38	35.62	3.34	49
659	8.75	-76.88	9.38	3.34	49
660	-29.75	-1.88	-46.88	3.34	49
661	12.25	-35.62	65.62	3.34	49
662	54.25	1.88	9.38	3.34	49
663	-22.75	20.62	-1.88	3.34	49
664	33.25	-76.88	-28.12	3.34	49
665	-1.75	-1.88	-5.62	3.34	49
666	-26.25	16.88	9.38	3.33	49
667	47.25	-35.62	16.88	3.33	49
668	-15.75	-46.88	-24.38	3.33	49
669	-47.25	-61.88	20.62	3.33	49
670	-29.75	-46.88	-28.12	3.33	49
671	-22.75	-54.38	-35.62	3.33	49
672	-5.25	1.88	-58.12	3.33	49
673	5.25	-61.88	-46.88	3.33	49
674	33.25	-46.88	54.38	3.33	49
675	26.25	-80.62	1.88	3.33	49
676	-43.75	-35.62	-1.88	3.33	49
677	36.75	-24.38	-31.88	3.33	49
678	26.25	-5.62	-13.12	3.32	49
679	-36.75	-73.12	-9.38	3.32	49
680	-47.25	-1.88	-13.12	3.32	49
681	33.25	-35.62	-46.88	3.32	49
682	-50.75	13.12	35.62	3.32	49
683	-12.25	-13.12	-73.12	3.32	49
684	-50.75	-58.12	1.88	3.32	49
685	8.75	20.62	-35.62	3.32	49
686	22.75	20.62	16.88	3.32	49
687	-36.75	-46.88	-24.38	3.32	98
688	-5.25	-54.38	-54.38	3.32	49
689	-29.75	9.38	61.88	3.31	49
690	15.75	-46.88	20.62	3.31	49
691	19.25	-9.38	-5.62	3.31	49
692	15.75	-39.38	-35.62	3.31	49
693	36.75	-24.38	9.38	3.31	49
694	-1.75	-16.88	-58.12	3.31	49
695	-19.25	-50.62	39.38	3.31	49
696	-19.25	-54.38	13.12	3.31	49
697	54.25	-16.88	-1.88	3.30	49
698	-12.25	20.62	13.12	3.30	49
699	-36.75	9.38	54.38	3.30	49
700	29.75	-50.62	54.38	3.30	49
701	29.75	-46.88	-31.88	3.30	49
702	-12.25	-35.62	-58.12	3.30	49
703	15.75	-16.88	54.38	3.30	49
704	-26.25	50.62	28.12	3.30	49
705	61.25	1.88	24.38	3.30	49
706	15.75	-16.88	-58.12	3.30	49
707	1.75	-69.38	46.88	3.29	49
708	-8.75	54.38	43.12	3.29	49
709	-26.25	-31.88	-58.12	3.29	49
710	5.25	-73.12	-16.88	3.29	49
711	15.75	28.12	13.12	3.29	49
712	-12.25	13.12	65.62	3.29	49

house

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.291
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-22.75	-39.38	-1.88	5.50	393
2	22.75	20.62	9.38	5.43	442
2a	22.75	24.38	16.88	4.16
3	-22.75	24.38	13.12	5.29	98
4	22.75	13.12	20.62	5.25	98
5	47.25	-54.38	-16.88	5.20	98
6	33.25	-43.12	-35.62	4.65	295
7	-29.75	28.12	5.62	4.60	49
8	22.75	-5.62	20.62	4.57	49
9	-33.25	-58.12	35.62	4.53	49
10	36.75	-54.38	-20.62	4.52	49
11	12.25	24.38	20.62	4.51	147
12	-15.75	24.38	9.38	4.48	492
12a	-15.75	28.12	16.88	4.03
13	-50.75	9.38	-28.12	4.44	49
14	-40.25	-58.12	28.12	4.41	98
15	22.75	-31.88	1.88	4.39	49
16	22.75	-43.12	-31.88	4.38	49
17	33.25	5.62	-39.38	4.37	49
18	26.25	5.62	9.38	4.35	49
19	1.75	13.12	-16.88	4.33	49
20	8.75	-16.88	31.88	4.33	49
21	-22.75	16.88	5.62	4.27	98
22	33.25	28.12	28.12	4.25	49
23	26.25	9.38	-31.88	4.23	49
24	-22.75	5.62	24.38	4.23	295
25	1.75	-46.88	-5.62	4.21	49
26	-26.25	39.38	46.88	4.21	98
27	19.25	5.62	43.12	4.21	98
28	43.75	-1.88	20.62	4.19	98
29	8.75	-24.38	24.38	4.16	49
30	19.25	-13.12	24.38	4.16	98
31	15.75	-39.38	13.12	4.13	49
32	-12.25	-16.88	-20.62	4.12	49
33	-29.75	16.88	9.38	4.11	49
34	-22.75	20.62	20.62	4.10	147
35	-12.25	-28.12	-76.88	4.08	49
36	26.25	-61.88	31.88	4.07	49
37	15.75	-54.38	5.62	4.05	49
38	-54.25	-20.62	-9.38	4.03	49
39	-15.75	-46.88	20.62	4.02	98
40	26.25	-1.88	13.12	4.00	49
41	-15.75	-35.62	-1.88	3.99	147
42	-12.25	-43.12	-16.88	3.97	49
43	-29.75	-31.88	35.62	3.96	49
44	-8.75	35.62	43.12	3.90	49
45	26.25	13.12	13.12	3.88	147
46	8.75	-9.38	31.88	3.88	98
47	5.25	-43.12	-1.88	3.87	98
48	-15.75	-50.62	24.38	3.87	49
49	-22.75	-9.38	-65.62	3.86	49
50	-15.75	-24.38	-9.38	3.85	98
51	-26.25	-50.62	-46.88	3.84	49
52	-8.75	-43.12	-9.38	3.84	49
53	22.75	-43.12	9.38	3.83	49
54	-8.75	-28.12	5.62	3.82	49
55	-12.25	-43.12	-50.62	3.82	49
56	-15.75	-31.88	-50.62	3.82	49
57	22.75	-39.38	46.88	3.81	49
58	22.75	-76.88	20.62	3.81	49
59	-40.25	-69.38	20.62	3.80	49
60	-29.75	-9.38	-31.88	3.78	49
61	-15.75	-28.12	-5.62	3.78	49
62	-12.25	-39.38	46.88	3.77	49
63	5.25	31.88	50.62	3.77	49
64	-22.75	-58.12	24.38	3.75	49
65	-15.75	16.88	13.12	3.75	49
66	-1.75	-61.88	-39.38	3.75	49
67	50.75	-31.88	-50.62	3.74	49
68	15.75	28.12	13.12	3.73	49
69	50.75	50.62	-1.88	3.73	49
70	33.25	-50.62	-24.38	3.72	147
71	29.75	1.88	13.12	3.71	49
72	1.75	31.88	-13.12	3.69	49
73	26.25	9.38	-24.38	3.69	49
74	15.75	-28.12	-5.62	3.68	49
75	-43.75	58.12	9.38	3.67	49
76	-8.75	5.62	28.12	3.67	98
77	26.25	-13.12	54.38	3.66	49
78	33.25	-76.88	-24.38	3.64	49
79	-33.25	-24.38	35.62	3.64	49
80	19.25	-76.88	-16.88	3.63	98
81	-40.25	50.62	1.88	3.63	49
82	40.25	31.88	-24.38	3.62	49
83	33.25	43.12	50.62	3.62	49
84	26.25	-24.38	-69.38	3.62	49
85	33.25	9.38	1.88	3.62	49
86	-26.25	20.62	-1.88	3.62	49
87	22.75	-65.62	20.62	3.60	49
88	-15.75	20.62	1.88	3.60	49
89	-15.75	20.62	20.62	3.59	49
90	-33.25	-73.12	-31.88	3.58	49
91	22.75	-73.12	-13.12	3.58	49
92	57.75	-54.38	1.88	3.57	49
93	29.75	-43.12	-20.62	3.56	49
94	-22.75	-24.38	46.88	3.56	49
95	40.25	-13.12	-43.12	3.56	49
96	-29.75	-13.12	-20.62	3.55	49
97	-40.25	-28.12	-24.38	3.55	49
98	-43.75	-9.38	39.38	3.55	49
99	-26.25	-50.62	46.88	3.54	49
100	-26.25	13.12	16.88	3.54	49
101	-29.75	50.62	20.62	3.54	49
102	-22.75	-13.12	46.88	3.54	49
103	-36.75	24.38	13.12	3.53	49
104	22.75	-9.38	13.12	3.53	49
105	-12.25	1.88	-61.88	3.52	98
106	5.25	9.38	39.38	3.52	49
107	29.75	-54.38	-24.38	3.51	49
108	22.75	-43.12	31.88	3.51	49
109	5.25	-65.62	54.38	3.50	49
110	-50.75	31.88	9.38	3.49	49
111	-12.25	9.38	5.62	3.49	49
112	22.75	1.88	20.62	3.48	49
113	12.25	-5.62	-5.62	3.48	49
114	50.75	-43.12	-1.88	3.47	49
115	15.75	-1.88	69.38	3.46	49
116	26.25	-73.12	31.88	3.46	49
117	36.75	-61.88	-13.12	3.46	49
118	-1.75	39.38	-20.62	3.45	98
119	-26.25	-54.38	43.12	3.45	49
120	29.75	-46.88	-24.38	3.45	49
121	-57.75	20.62	13.12	3.45	49
122	-15.75	-50.62	-9.38	3.44	49
123	-26.25	-20.62	16.88	3.44	49
124	19.25	24.38	5.62	3.44	49
125	-26.25	1.88	13.12	3.44	49
126	-36.75	-61.88	35.62	3.44	49
127	61.25	-1.88	13.12	3.43	49
128	57.75	-16.88	16.88	3.43	49
129	15.75	1.88	58.12	3.43	49
130	-36.75	-65.62	-16.88	3.43	49
131	22.75	-46.88	-20.62	3.42	49
132	12.25	-31.88	-73.12	3.42	49
133	22.75	-16.88	16.88	3.42	49
134	33.25	54.38	28.12	3.41	49
135	-33.25	9.38	5.62	3.41	49
136	43.75	35.62	46.88	3.41	49
137	15.75	-28.12	24.38	3.41	49
138	-26.25	-76.88	-9.38	3.39	49
139	22.75	-13.12	20.62	3.39	49
140	-40.25	-46.88	39.38	3.39	49
141	-33.25	61.88	39.38	3.38	49
142	-36.75	-69.38	31.88	3.38	98
143	8.75	-61.88	-50.62	3.38	49
144	22.75	5.62	24.38	3.38	49
145	5.25	-20.62	-13.12	3.38	49
146	19.25	-35.62	-28.12	3.38	49
147	-15.75	-61.88	43.12	3.37	49
148	-33.25	13.12	-1.88	3.37	49
149	22.75	-46.88	-5.62	3.37	49
150	-15.75	-20.62	-28.12	3.37	49
151	12.25	-13.12	54.38	3.37	49
152	29.75	5.62	39.38	3.36	49
153	26.25	-20.62	24.38	3.36	49
154	15.75	-73.12	-28.12	3.36	49
155	-8.75	61.88	-16.88	3.36	49
156	-36.75	-50.62	-16.88	3.36	49
157	-64.75	-24.38	-20.62	3.35	49
158	-57.75	16.88	5.62	3.34	49
159	-15.75	58.12	5.62	3.34	49
160	-26.25	16.88	50.62	3.34	49
161	-15.75	-65.62	46.88	3.34	49
162	40.25	-39.38	13.12	3.34	49
163	36.75	-13.12	-31.88	3.34	49
164	26.25	-16.88	-9.38	3.34	49
165	15.75	16.88	50.62	3.34	49
166	-54.25	-13.12	-1.88	3.34	49
167	40.25	-50.62	-58.12	3.34	49
168	-8.75	-24.38	1.88	3.33	49
169	-22.75	-16.88	24.38	3.33	49
170	-1.75	28.12	61.88	3.33	49
171	26.25	-58.12	-16.88	3.33	98
172	8.75	-39.38	1.88	3.33	49
173	54.25	9.38	-31.88	3.33	49
174	-22.75	35.62	-24.38	3.32	49
175	8.75	-80.62	-5.62	3.32	49
176	40.25	-16.88	-46.88	3.32	49
177	-15.75	-5.62	-13.12	3.31	49
178	-47.25	-76.88	9.38	3.31	49
179	-40.25	-1.88	24.38	3.30	49
180	33.25	-28.12	-13.12	3.30	49
181	8.75	-58.12	-24.38	3.29	49
182	19.25	-50.62	-13.12	3.29	49
183	12.25	-73.12	-13.12	3.29	49

chair

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.291
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	40.25	-46.88	13.12	5.02	147
2	12.25	35.62	-5.62	4.88	196
3	-50.75	39.38	16.88	4.65	49
4	-15.75	-1.88	-16.88	4.21	49
5	33.25	-58.12	-1.88	4.16	49
6	-36.75	-76.88	1.88	4.15	49
7	-29.75	20.62	46.88	4.12	49
8	-54.25	-43.12	46.88	4.09	49
9	12.25	28.12	-16.88	4.08	49
10	12.25	5.62	-24.38	4.06	98
11	33.25	43.12	-16.88	3.99	49
12	33.25	-58.12	-16.88	3.97	49
13	-22.75	-43.12	-31.88	3.97	98
14	8.75	-35.62	-35.62	3.94	49
15	26.25	-46.88	-35.62	3.93	49
16	-5.25	43.12	50.62	3.89	49
17	12.25	16.88	-20.62	3.88	49
18	-36.75	-61.88	43.12	3.85	49
19	-50.75	-39.38	-1.88	3.83	147
20	-15.75	20.62	13.12	3.81	49
21	-22.75	1.88	5.62	3.78	49
22	-57.75	-9.38	-1.88	3.73	49
23	-1.75	5.62	31.88	3.69	49
24	-57.75	13.12	1.88	3.67	98
25	-36.75	-46.88	50.62	3.65	49
26	33.25	13.12	-1.88	3.64	49
27	36.75	-24.38	20.62	3.62	49
28	54.25	20.62	5.62	3.61	49
29	22.75	24.38	16.88	3.61	49
30	33.25	-20.62	-58.12	3.55	49
31	-22.75	-9.38	20.62	3.53	49
32	-22.75	24.38	13.12	3.53	49
33	29.75	-43.12	-35.62	3.52	49
34	12.25	16.88	-50.62	3.51	49
35	12.25	-1.88	69.38	3.51	49
36	-15.75	-39.38	-54.38	3.50	49
37	26.25	-69.38	31.88	3.50	98
38	-22.75	-31.88	-69.38	3.49	49
39	36.75	-1.88	28.12	3.48	49
40	-22.75	50.62	9.38	3.47	49
41	-22.75	-39.38	-28.12	3.47	49
42	26.25	16.88	16.88	3.46	49
43	-22.75	9.38	24.38	3.46	49
44	33.25	13.12	61.88	3.44	49
45	19.25	20.62	20.62	3.43	98
46	12.25	9.38	-1.88	3.43	49
47	36.75	20.62	-43.12	3.42	49
48	19.25	76.88	31.88	3.41	49
49	1.75	24.38	-39.38	3.40	49
50	29.75	9.38	-16.88	3.36	49
51	-26.25	-1.88	-50.62	3.36	49
52	33.25	9.38	-13.12	3.35	49
53	-68.25	1.88	5.62	3.33	49
54	-15.75	-39.38	-24.38	3.33	49
55	5.25	-1.88	31.88	3.33	49
56	22.75	-46.88	-24.38	3.32	49
57	22.75	-24.38	-61.88	3.31	49
58	43.75	-28.12	-13.12	3.30	49

scrambledpix

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.291
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	1.75	24.38	-13.12	5.06	49
2	-29.75	-61.88	-31.88	4.96	49
3	-22.75	-46.88	43.12	4.91	98
4	-15.75	61.88	-9.38	4.69	147
5	5.25	43.12	-28.12	4.49	49
6	-22.75	-9.38	20.62	4.29	49
7	-1.75	-35.62	5.62	4.17	98
8	50.75	16.88	-28.12	4.12	147
9	-1.75	5.62	31.88	4.04	49
10	40.25	-16.88	-39.38	3.93	49
11	-15.75	-43.12	-16.88	3.85	49
12	-64.75	-31.88	39.38	3.77	49
13	-1.75	-46.88	-9.38	3.75	49
14	50.75	5.62	-35.62	3.68	49
15	-22.75	-54.38	-9.38	3.64	147
16	22.75	-24.38	46.88	3.59	49
17	54.25	-9.38	16.88	3.55	49
18	19.25	43.12	13.12	3.51	49
19	22.75	46.88	-5.62	3.50	49
20	-29.75	-65.62	20.62	3.46	49
21	-22.75	-28.12	39.38	3.42	49
22	33.25	-20.62	43.12	3.40	49
23	-40.25	-16.88	-54.38	3.39	49
24	-26.25	-69.38	39.38	3.39	49
25	15.75	-39.38	-31.88	3.38	49
26	57.75	13.12	-9.38	3.38	49
27	-5.25	-50.62	61.88	3.33	49
28	26.25	-20.62	43.12	3.33	49
29	-22.75	-9.38	-65.62	3.31	49
30	-26.25	-65.62	16.88	3.31	49

face

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.291
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-50.75	-50.62	13.12	5.43	196
1a	-47.25	-58.12	13.12	4.43
2	-47.25	-43.12	-35.62	5.08	49
3	-33.25	50.62	16.88	4.92	442
3a	-40.25	46.88	13.12	4.22
4	-47.25	-39.38	16.88	4.89	98
5	-12.25	-58.12	-16.88	4.84	98
6	68.25	-9.38	-9.38	4.82	147
7	-36.75	31.88	16.88	4.66	147
8	-1.75	-39.38	5.62	4.64	98
9	-15.75	13.12	73.12	4.60	196
10	33.25	-61.88	-13.12	4.57	196
11	-43.75	-31.88	35.62	4.46	98
12	1.75	-43.12	-1.88	4.45	49
13	-47.25	24.38	20.62	4.45	49
14	40.25	-39.38	-24.38	4.30	49
15	-22.75	39.38	13.12	4.27	49
16	22.75	-50.62	-24.38	4.27	49
17	-47.25	-50.62	20.62	4.22	49
18	-47.25	-54.38	-9.38	4.19	49
19	-15.75	-13.12	24.38	4.18	98
20	-29.75	24.38	-13.12	4.15	147
21	-40.25	-43.12	-16.88	4.14	49
22	40.25	-43.12	-9.38	4.13	49
23	-1.75	28.12	61.88	4.07	98
24	-1.75	-58.12	-16.88	4.07	49
25	-57.75	-16.88	1.88	4.06	98
26	29.75	-46.88	35.62	4.02	49
27	5.25	-58.12	-24.38	4.00	393
28	-36.75	-31.88	-46.88	4.00	49
29	-8.75	13.12	65.62	3.99	49
30	33.25	-46.88	-39.38	3.99	98
31	1.75	-43.12	-16.88	3.98	196
32	-36.75	31.88	35.62	3.95	442
33	22.75	-1.88	58.12	3.94	98
34	-19.25	-73.12	39.38	3.93	98
35	1.75	24.38	-13.12	3.92	49
36	-43.75	-43.12	-9.38	3.90	98
37	-8.75	-39.38	-5.62	3.87	49
38	-47.25	-46.88	9.38	3.86	98
39	5.25	20.62	65.62	3.86	49
40	40.25	43.12	16.88	3.85	49
41	-26.25	-43.12	-43.12	3.83	49
42	19.25	-46.88	-20.62	3.81	196
43	-36.75	-43.12	39.38	3.80	147
44	-8.75	-28.12	61.88	3.79	49
45	-8.75	-46.88	-31.88	3.79	49
46	43.75	35.62	39.38	3.76	147
47	-22.75	-46.88	-50.62	3.75	49
48	-47.25	-61.88	-5.62	3.74	49
49	-47.25	-28.12	28.12	3.74	49
50	-29.75	28.12	13.12	3.73	49
51	29.75	-46.88	-31.88	3.73	49
52	19.25	-16.88	39.38	3.72	49
53	-47.25	-9.38	13.12	3.71	49
54	-47.25	-46.88	-31.88	3.68	49
55	33.25	50.62	20.62	3.67	49
56	-29.75	-13.12	5.62	3.67	49
57	36.75	-46.88	9.38	3.66	49
58	-50.75	20.62	20.62	3.65	49
59	-47.25	-16.88	-35.62	3.61	49
60	36.75	-35.62	-20.62	3.61	49
61	33.25	-50.62	-20.62	3.61	147
62	33.25	-54.38	43.12	3.60	49
63	5.25	-24.38	-20.62	3.59	49
64	-47.25	-46.88	16.88	3.59	49
65	-57.75	-9.38	-1.88	3.58	49
66	-12.25	-76.88	28.12	3.58	49
67	22.75	31.88	28.12	3.57	49
68	-36.75	5.62	43.12	3.57	49
69	-33.25	39.38	54.38	3.56	49
70	-1.75	-46.88	-9.38	3.56	49
71	26.25	-28.12	24.38	3.56	49
72	29.75	-50.62	-39.38	3.56	49
73	40.25	28.12	39.38	3.56	98
74	-12.25	-39.38	58.12	3.55	49
75	29.75	-54.38	46.88	3.55	49
76	-50.75	-43.12	1.88	3.53	147
77	-36.75	20.62	50.62	3.53	49
78	19.25	-1.88	50.62	3.52	49
79	-57.75	31.88	31.88	3.52	49
80	-43.75	-43.12	46.88	3.51	49
81	-29.75	5.62	58.12	3.51	49
82	26.25	1.88	13.12	3.51	49
83	5.25	-65.62	13.12	3.49	49
84	29.75	-46.88	-43.12	3.47	49
85	33.25	-43.12	-24.38	3.47	49
86	-47.25	-16.88	-43.12	3.46	49
87	-36.75	-46.88	50.62	3.45	49
88	-54.25	-9.38	-31.88	3.44	49
89	-5.25	-58.12	-13.12	3.44	49
90	-33.25	31.88	46.88	3.43	49
91	-26.25	-84.38	-9.38	3.43	98
92	12.25	28.12	61.88	3.42	49
93	-15.75	16.88	16.88	3.41	49
94	-33.25	16.88	50.62	3.41	49
95	-5.25	61.88	-9.38	3.41	49
96	-47.25	-61.88	-28.12	3.41	49
97	-33.25	-9.38	-28.12	3.41	49
98	-40.25	50.62	9.38	3.41	49
99	-19.25	-61.88	-9.38	3.40	49
100	22.75	-24.38	-58.12	3.39	49
101	-29.75	35.62	-1.88	3.39	49
102	-54.25	-20.62	-39.38	3.39	49
103	40.25	-46.88	-20.62	3.39	49
104	8.75	-58.12	-28.12	3.38	49
105	-47.25	-46.88	-1.88	3.38	49
106	36.75	54.38	24.38	3.38	49
107	-29.75	31.88	-9.38	3.37	49
108	40.25	-35.62	-43.12	3.37	49
109	-50.75	-58.12	-13.12	3.36	49
110	36.75	61.88	28.12	3.35	49
111	-47.25	58.12	-1.88	3.35	49
112	-47.25	24.38	28.12	3.34	49
113	-47.25	-9.38	5.62	3.34	49
114	-33.25	-43.12	43.12	3.34	49
115	-50.75	-35.62	43.12	3.34	49
116	-47.25	-46.88	-58.12	3.34	49
117	68.25	-28.12	16.88	3.33	49
118	47.25	35.62	13.12	3.33	49
119	19.25	5.62	13.12	3.32	49
120	29.75	-46.88	-20.62	3.32	49
121	-8.75	-13.12	20.62	3.32	49
122	5.25	31.88	-35.62	3.32	49
123	15.75	-54.38	-20.62	3.32	49
124	43.75	9.38	50.62	3.32	49
125	15.75	5.62	9.38	3.31	49
126	8.75	-1.88	9.38	3.31	49
127	33.25	20.62	43.12	3.31	49
128	43.75	-54.38	9.38	3.30	49
129	-40.25	39.38	43.12	3.30	49
130	-47.25	-58.12	-31.88	3.30	49
131	12.25	-88.12	5.62	3.30	49
132	40.25	-69.38	-16.88	3.30	49
133	-43.75	-31.88	-9.38	3.29	49
134	50.75	1.88	43.12	3.29	49
135	43.75	16.88	31.88	3.29	49
136	-47.25	-50.62	31.88	3.29	49
137	15.75	13.12	43.12	3.29	49

shoe

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.291
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	19.25	73.12	20.62	4.65	49
2	22.75	-9.38	35.62	4.48	49
3	19.25	-20.62	-28.12	4.08	49
4	12.25	5.62	31.88	3.97	49
5	26.25	35.62	28.12	3.97	49
6	61.25	28.12	31.88	3.87	98
7	15.75	16.88	58.12	3.83	49
8	-5.25	-76.88	-28.12	3.74	49
9	36.75	46.88	-16.88	3.71	49
10	-19.25	-28.12	35.62	3.64	49
11	26.25	-46.88	-24.38	3.61	49
12	-12.25	39.38	65.62	3.61	49
13	-29.75	9.38	35.62	3.60	49
14	-8.75	84.38	20.62	3.60	49
15	-54.25	-65.62	16.88	3.59	49
16	-26.25	-13.12	-43.12	3.58	49
17	19.25	-28.12	-54.38	3.57	49
18	-12.25	1.88	-69.38	3.56	49
19	47.25	-28.12	-5.62	3.55	49
20	22.75	-69.38	-28.12	3.53	49
21	33.25	9.38	61.88	3.50	49
22	-8.75	20.62	73.12	3.49	49
23	22.75	61.88	31.88	3.49	49
24	40.25	-54.38	39.38	3.47	49
25	1.75	-24.38	-39.38	3.46	49
26	5.25	-13.12	24.38	3.46	49
27	-68.25	-20.62	24.38	3.45	49
28	-12.25	39.38	24.38	3.42	49
29	50.75	-35.62	-35.62	3.41	49
30	-1.75	-28.12	-73.12	3.39	49
31	29.75	24.38	-28.12	3.38	49
32	22.75	-54.38	-43.12	3.35	49

cat

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.291
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	12.25	-61.88	-31.88	5.00	295
2	-40.25	-65.62	13.12	4.41	147
3	5.25	-61.88	-24.38	4.34	98
4	-29.75	39.38	54.38	4.32	49
5	-12.25	-58.12	-16.88	4.29	49
6	-12.25	-76.88	31.88	4.27	98
7	43.75	-5.62	16.88	4.11	196
8	22.75	-24.38	-58.12	4.05	49
9	19.25	-31.88	-69.38	4.01	98
10	43.75	1.88	9.38	3.94	196
11	5.25	-80.62	9.38	3.90	98
12	61.25	-31.88	20.62	3.86	49
13	-12.25	-20.62	-58.12	3.86	98
14	1.75	35.62	-13.12	3.86	49
15	-29.75	35.62	50.62	3.83	49
16	-36.75	-50.62	-39.38	3.82	98
17	33.25	-1.88	46.88	3.80	49
18	50.75	1.88	-24.38	3.79	49
19	47.25	1.88	-46.88	3.79	49
20	50.75	28.12	24.38	3.78	49
21	-5.25	-50.62	-13.12	3.76	49
22	22.75	-61.88	-54.38	3.76	49
23	-12.25	-73.12	-31.88	3.73	49
24	36.75	28.12	43.12	3.72	98
25	36.75	69.38	5.62	3.71	49
26	-1.75	50.62	-9.38	3.70	49
27	5.25	-31.88	54.38	3.66	49
28	36.75	28.12	-20.62	3.62	49
29	-26.25	-80.62	9.38	3.61	98
30	8.75	-65.62	-24.38	3.60	49
31	-8.75	-46.88	-28.12	3.60	49
32	50.75	9.38	-35.62	3.60	49
33	43.75	5.62	1.88	3.59	49
34	-36.75	46.88	39.38	3.58	49
35	15.75	-80.62	-31.88	3.57	49
36	-54.25	-50.62	5.62	3.56	49
37	36.75	9.38	-35.62	3.55	49
38	29.75	-35.62	-50.62	3.53	49
39	-43.75	-65.62	5.62	3.53	49
40	15.75	-50.62	-39.38	3.50	49
41	61.25	-31.88	43.12	3.50	49
42	12.25	-54.38	-43.12	3.49	49
43	5.25	-61.88	-39.38	3.49	49
44	33.25	-54.38	43.12	3.49	49
45	-64.75	-9.38	-24.38	3.48	49
46	-54.25	-50.62	39.38	3.48	49
47	-40.25	-46.88	50.62	3.48	98
48	8.75	16.88	-16.88	3.48	98
49	-29.75	-46.88	5.62	3.47	49
50	12.25	-39.38	-31.88	3.44	49
51	-40.25	-5.62	50.62	3.44	49
52	64.75	20.62	9.38	3.43	49
53	5.25	-24.38	-73.12	3.42	49
54	12.25	-43.12	1.88	3.42	49
55	-19.25	16.88	69.38	3.42	49
56	-29.75	-54.38	-35.62	3.42	49
57	36.75	28.12	35.62	3.42	49
58	-64.75	-31.88	16.88	3.38	49
59	5.25	43.12	50.62	3.37	49
60	15.75	39.38	-20.62	3.37	49
61	29.75	9.38	-31.88	3.36	49
62	-36.75	-50.62	-20.62	3.35	49
63	-47.25	-43.12	43.12	3.35	49
64	-40.25	-46.88	16.88	3.35	49
65	-43.75	-1.88	-28.12	3.34	49
66	-40.25	-1.88	-31.88	3.34	49
67	12.25	5.62	5.62	3.34	49
68	-64.75	-16.88	24.38	3.33	49
69	-36.75	-76.88	5.62	3.33	98
70	43.75	31.88	39.38	3.33	49
71	-47.25	-50.62	46.88	3.33	49
72	-40.25	-69.38	35.62	3.31	49
73	-29.75	35.62	58.12	3.30	49
74	-22.75	76.88	5.62	3.29	49
75	47.25	13.12	43.12	3.29	49

scissors

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.291
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-50.75	20.62	-24.38	6.02	935
2	-12.25	20.62	-46.88	5.79	49
3	-15.75	16.88	-46.88	5.74	295
4	-19.25	84.38	20.62	5.60	295
5	-12.25	16.88	-50.62	5.51	49
6	-26.25	24.38	28.12	5.28	147
7	29.75	5.62	-5.62	5.26	147
8	36.75	20.62	-31.88	5.18	246
9	47.25	24.38	-1.88	5.12	147
10	12.25	13.12	-43.12	5.09	393
11	-22.75	-58.12	-50.62	5.08	393
11a	-22.75	-46.88	-50.62	4.60
12	-19.25	61.88	50.62	5.05	49
13	-50.75	-9.38	-20.62	5.01	246
14	50.75	31.88	-31.88	4.97	49
15	-19.25	31.88	-35.62	4.91	196
16	1.75	-1.88	-61.88	4.86	295
17	33.25	5.62	-46.88	4.82	49
18	-12.25	1.88	-28.12	4.76	49
19	12.25	-61.88	-46.88	4.76	49
20	1.75	-28.12	-61.88	4.68	49
21	12.25	-35.62	-69.38	4.66	147
22	22.75	-58.12	-50.62	4.61	49
23	-54.25	-39.38	-31.88	4.60	49
24	1.75	13.12	-16.88	4.54	49
25	47.25	24.38	9.38	4.54	49
26	26.25	-46.88	1.88	4.54	49
27	40.25	5.62	31.88	4.53	98
28	-15.75	-43.12	-46.88	4.53	98
29	40.25	-58.12	-5.62	4.50	147
30	19.25	16.88	-46.88	4.50	49
31	5.25	24.38	-46.88	4.47	442
31a	-1.75	28.12	-39.38	3.59
32	-5.25	-16.88	-39.38	4.45	98
33	50.75	24.38	-35.62	4.41	147
34	12.25	80.62	35.62	4.41	98
35	33.25	-35.62	-35.62	4.37	98
36	1.75	1.88	31.88	4.37	196
37	-12.25	76.88	20.62	4.36	98
38	47.25	-5.62	20.62	4.36	295
39	19.25	-46.88	-35.62	4.34	49
40	29.75	-1.88	24.38	4.33	49
41	-12.25	9.38	-58.12	4.29	98
42	54.25	5.62	50.62	4.29	49
43	15.75	-35.62	-73.12	4.28	49
44	19.25	80.62	1.88	4.28	98
45	54.25	1.88	43.12	4.27	98
46	15.75	76.88	28.12	4.26	49
47	-29.75	76.88	20.62	4.26	49
48	-54.25	9.38	-16.88	4.26	49
49	-1.75	-31.88	9.38	4.26	49
50	-22.75	-5.62	-20.62	4.25	49
51	-40.25	61.88	5.62	4.24	98
52	-47.25	24.38	20.62	4.22	49
53	26.25	13.12	-39.38	4.22	49
54	-1.75	-39.38	5.62	4.21	98
55	-8.75	28.12	54.38	4.17	196
56	40.25	-1.88	61.88	4.17	246
56a	40.25	-9.38	58.12	3.83
57	36.75	31.88	-39.38	4.17	246
58	-36.75	-1.88	-20.62	4.17	98
59	-5.25	-9.38	31.88	4.14	49
60	22.75	-54.38	-43.12	4.14	49
61	-29.75	20.62	-46.88	4.13	49
62	22.75	-1.88	9.38	4.11	49
63	-26.25	80.62	16.88	4.11	246
64	29.75	-46.88	-61.88	4.10	49
65	15.75	-28.12	-54.38	4.10	49
66	33.25	24.38	-43.12	4.08	98
67	-40.25	-16.88	-65.62	4.07	49
68	-47.25	31.88	-28.12	4.05	49
69	-26.25	76.88	24.38	4.05	147
70	19.25	-5.62	-35.62	4.04	49
71	54.25	1.88	35.62	4.03	196
72	12.25	24.38	-35.62	4.03	98
73	-26.25	-13.12	1.88	4.02	49
74	-5.25	80.62	31.88	4.02	147
75	-19.25	50.62	13.12	4.02	49
76	40.25	-43.12	-5.62	4.01	49
77	43.75	-31.88	-13.12	4.01	49
78	15.75	5.62	9.38	4.01	49
79	-33.25	-65.62	-16.88	4.00	49
80	26.25	-50.62	-54.38	4.00	98
81	33.25	-5.62	-1.88	3.99	49
82	40.25	-39.38	58.12	3.98	49
83	22.75	-43.12	-61.88	3.98	98
84	-19.25	20.62	24.38	3.98	49
85	57.75	-20.62	28.12	3.97	49
86	-5.25	84.38	1.88	3.96	49
87	40.25	-24.38	-35.62	3.96	49
88	-19.25	9.38	-35.62	3.95	49
89	12.25	80.62	-5.62	3.94	49
90	-5.25	-28.12	-76.88	3.94	147
91	50.75	50.62	20.62	3.93	49
92	19.25	-16.88	-54.38	3.93	49
93	33.25	-39.38	24.38	3.93	49
94	40.25	28.12	-43.12	3.92	98
95	-26.25	16.88	28.12	3.92	49
96	1.75	39.38	-16.88	3.92	49
97	-26.25	-24.38	20.62	3.91	49
98	-15.75	-9.38	28.12	3.91	49
99	-8.75	24.38	-35.62	3.90	98
100	50.75	-16.88	46.88	3.89	49
101	-15.75	76.88	31.88	3.89	49
102	1.75	-28.12	-76.88	3.89	49
103	47.25	16.88	-20.62	3.89	49
104	19.25	-28.12	73.12	3.87	49
105	57.75	-13.12	39.38	3.87	49
106	-8.75	84.38	20.62	3.87	98
107	29.75	76.88	13.12	3.87	49
108	40.25	13.12	-39.38	3.87	49
109	33.25	-69.38	-13.12	3.87	49
110	19.25	54.38	50.62	3.85	49
111	-12.25	-50.62	-50.62	3.85	49
112	-19.25	-1.88	-35.62	3.85	49
113	29.75	-50.62	-50.62	3.84	49
114	12.25	-61.88	9.38	3.84	49
115	-29.75	13.12	61.88	3.83	196
116	5.25	-1.88	5.62	3.83	98
117	15.75	-16.88	46.88	3.83	49
118	-1.75	-1.88	-76.88	3.82	98
119	-36.75	65.62	20.62	3.82	98
120	33.25	-39.38	-20.62	3.81	49
121	29.75	65.62	43.12	3.79	49
122	-50.75	-1.88	20.62	3.79	49
123	50.75	-39.38	-16.88	3.79	49
124	-12.25	-43.12	-13.12	3.78	49
125	-22.75	58.12	-20.62	3.77	246
126	5.25	39.38	-20.62	3.77	49
127	-26.25	31.88	-39.38	3.76	49
128	-1.75	-13.12	-54.38	3.76	98
129	-33.25	-58.12	-31.88	3.75	49
130	15.75	88.12	9.38	3.75	98
131	43.75	13.12	-28.12	3.75	98
132	26.25	-16.88	-16.88	3.75	49
133	-19.25	31.88	-28.12	3.74	49
134	5.25	9.38	-58.12	3.73	49
135	-8.75	9.38	65.62	3.73	49
136	-29.75	-69.38	9.38	3.73	98
137	-64.75	-5.62	5.62	3.71	98
138	12.25	-31.88	-46.88	3.71	49
139	15.75	50.62	-9.38	3.71	49
140	-40.25	-9.38	-5.62	3.70	49
141	33.25	-20.62	43.12	3.70	49
142	-12.25	-50.62	-13.12	3.69	49
143	26.25	-13.12	13.12	3.69	49
144	-5.25	80.62	13.12	3.68	49
145	-26.25	16.88	-16.88	3.67	49
146	19.25	-1.88	-1.88	3.66	49
147	19.25	16.88	-35.62	3.66	49
148	-1.75	73.12	35.62	3.66	49
149	29.75	-54.38	-61.88	3.65	49
150	1.75	-9.38	1.88	3.65	49
151	15.75	73.12	20.62	3.65	49
152	33.25	-50.62	-20.62	3.65	49
153	12.25	-35.62	-24.38	3.65	49
154	15.75	24.38	69.38	3.64	49
155	-47.25	-5.62	-28.12	3.64	49
156	-1.75	1.88	13.12	3.64	49
157	-40.25	69.38	13.12	3.63	49
158	12.25	-28.12	-31.88	3.62	49
159	8.75	1.88	24.38	3.62	49
160	29.75	-9.38	9.38	3.62	49
161	-15.75	-65.62	5.62	3.61	49
162	-26.25	-80.62	-5.62	3.61	49
163	33.25	5.62	16.88	3.60	49
164	33.25	-5.62	24.38	3.60	98
165	57.75	-1.88	5.62	3.60	49
166	15.75	-50.62	-35.62	3.60	49
167	33.25	-39.38	-43.12	3.60	49
168	33.25	24.38	61.88	3.60	49
169	-1.75	-35.62	-65.62	3.58	49
170	22.75	-20.62	20.62	3.58	49
171	-8.75	24.38	43.12	3.58	49
172	40.25	-13.12	46.88	3.58	49
173	-5.25	-20.62	54.38	3.58	98
174	-1.75	-13.12	31.88	3.57	49
175	-47.25	20.62	43.12	3.57	98
176	-33.25	69.38	20.62	3.56	98
177	57.75	-24.38	5.62	3.56	49
178	5.25	-65.62	20.62	3.56	49
179	26.25	-16.88	-35.62	3.55	49
180	-1.75	-46.88	-1.88	3.55	98
181	-50.75	13.12	20.62	3.55	49
182	40.25	69.38	16.88	3.55	49
183	1.75	16.88	-20.62	3.55	49
184	-29.75	20.62	20.62	3.54	49
185	-29.75	31.88	-9.38	3.54	49
186	40.25	-16.88	9.38	3.54	49
187	-12.25	88.12	9.38	3.54	49
188	-5.25	5.62	39.38	3.54	49
189	-1.75	-13.12	-5.62	3.54	49
190	-8.75	80.62	5.62	3.54	98
191	-50.75	-13.12	5.62	3.54	49
192	57.75	16.88	16.88	3.53	49
193	1.75	13.12	31.88	3.53	98
194	-33.25	39.38	54.38	3.53	49
195	-5.25	28.12	61.88	3.52	49
196	-43.75	-24.38	35.62	3.52	49
197	-43.75	31.88	-24.38	3.52	49
198	1.75	65.62	46.88	3.52	49
199	-5.25	5.62	-69.38	3.52	49
200	-22.75	-39.38	-54.38	3.51	49
201	33.25	-28.12	-39.38	3.51	49
202	-1.75	-5.62	-80.62	3.51	98
203	26.25	28.12	-43.12	3.51	49
204	33.25	31.88	-20.62	3.50	49
205	-33.25	73.12	9.38	3.50	49
206	-1.75	20.62	54.38	3.50	49
207	12.25	-1.88	43.12	3.50	49
208	57.75	-28.12	9.38	3.50	49
209	-12.25	1.88	9.38	3.49	49
210	-40.25	43.12	-31.88	3.49	49
211	43.75	-9.38	-16.88	3.49	49
212	47.25	-9.38	54.38	3.49	49
213	-57.75	-5.62	-1.88	3.48	49
214	50.75	1.88	46.88	3.48	49
215	15.75	88.12	16.88	3.47	49
216	54.25	9.38	28.12	3.47	98
217	-5.25	39.38	58.12	3.46	49
218	50.75	16.88	-5.62	3.46	49
219	15.75	46.88	-20.62	3.46	49
220	50.75	54.38	-5.62	3.46	49
221	33.25	-13.12	50.62	3.45	49
222	57.75	-24.38	-13.12	3.45	49
223	-22.75	-9.38	39.38	3.45	49
224	-19.25	-5.62	16.88	3.45	49
225	19.25	-5.62	61.88	3.45	49
226	-43.75	16.88	39.38	3.45	49
227	-1.75	-73.12	-31.88	3.44	49
228	-15.75	1.88	28.12	3.43	49
229	-54.25	20.62	-16.88	3.43	49
230	40.25	16.88	61.88	3.43	49
231	8.75	-1.88	28.12	3.43	49
232	47.25	-9.38	28.12	3.42	49
233	29.75	9.38	16.88	3.42	49
234	12.25	9.38	-54.38	3.42	98
235	33.25	-13.12	-61.88	3.42	49
236	-5.25	1.88	-76.88	3.42	49
237	33.25	-31.88	-43.12	3.42	49
238	-8.75	20.62	58.12	3.42	49
239	-36.75	69.38	16.88	3.42	49
240	-22.75	-88.12	-1.88	3.42	49
241	33.25	-1.88	-24.38	3.41	49
242	26.25	-61.88	-16.88	3.41	49
243	-12.25	46.88	-16.88	3.41	49
244	-47.25	-16.88	-39.38	3.40	49
245	33.25	24.38	31.88	3.40	49
246	43.75	-61.88	5.62	3.40	49
247	-40.25	-16.88	-20.62	3.40	49
248	50.75	-16.88	58.12	3.40	49
249	22.75	-20.62	-5.62	3.40	49
250	1.75	76.88	16.88	3.40	49
251	33.25	-20.62	-58.12	3.40	49
252	22.75	-35.62	-9.38	3.39	49
253	36.75	1.88	65.62	3.39	49
254	-22.75	-31.88	-43.12	3.39	49
255	-15.75	-46.88	-43.12	3.39	49
256	36.75	39.38	16.88	3.38	49
257	47.25	-16.88	31.88	3.38	49
258	36.75	-65.62	-13.12	3.38	49
259	12.25	-24.38	-50.62	3.38	49
260	8.75	76.88	16.88	3.37	49
261	29.75	-58.12	-46.88	3.37	49
262	-47.25	20.62	-16.88	3.37	49
263	22.75	-43.12	54.38	3.37	49
264	-5.25	-1.88	-28.12	3.37	98
265	-8.75	-5.62	-58.12	3.36	49
266	57.75	-9.38	43.12	3.35	49
267	-22.75	39.38	31.88	3.35	49
268	-8.75	5.62	73.12	3.35	49
269	1.75	-39.38	-20.62	3.34	49
270	54.25	-16.88	-13.12	3.34	98
271	-36.75	24.38	-39.38	3.34	49
272	-43.75	24.38	-1.88	3.34	49
273	47.25	-5.62	50.62	3.34	49
274	57.75	-31.88	46.88	3.33	49
275	50.75	-54.38	9.38	3.33	49
276	-33.25	28.12	-39.38	3.33	49
277	5.25	35.62	-9.38	3.32	49
278	5.25	-50.62	-5.62	3.31	49
279	-57.75	-9.38	-13.12	3.31	49
280	40.25	-43.12	54.38	3.31	49
281	-40.25	-31.88	39.38	3.30	49
282	22.75	-13.12	-20.62	3.30	49
283	33.25	39.38	-16.88	3.30	49
284	1.75	-9.38	-58.12	3.30	49
285	8.75	46.88	-20.62	3.30	49
286	-50.75	20.62	20.62	3.30	49
287	-43.75	16.88	58.12	3.30	49
288	-19.25	-13.12	-46.88	3.30	49
289	43.75	-50.62	-5.62	3.29	49
290	12.25	1.88	-69.38	3.29	49
291	1.75	-35.62	-13.12	3.29	49
292	-33.25	9.38	35.62	3.29	49

Built using Nilearn. Source code on GitHub. File bugs & feature requests here.

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

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

# or we can save as an html file
# from pathlib import Path
# output_dir = Path.cwd() / "results" / "plot_haxby_glm_decoding"
# output_dir.mkdir(exist_ok=True, parents=True)
# report.save_as_html(output_dir / 'report.html')

Build the decoding pipeline

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

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

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

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

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

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

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

from sklearn.model_selection import LeaveOneGroupOut

from nilearn.decoding import Decoder

decoder = Decoder(
    estimator="svc",
    mask=haxby_dataset.mask,
    standardize=False,
    screening_percentile=5,
    cv=LeaveOneGroupOut(),
)
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}"
)

/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/image/resampling.py:489: UserWarning:

The provided image has no sform in its header. Please check the provided file. Results may not be as expected.

Classification accuracy: 0.7589 / Chance level: 0.125

Estimated memory usage: 1095 MB