Note

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

Decoding of a dataset after GLM fit for signal extraction#

Full step-by-step example of fitting a GLM to perform a decoding experiment. We use the data from one subject of the Haxby dataset.

More specifically:

Download the Haxby dataset.
Extract the information to generate a glm representing the blocks of stimuli.
Analyze the decoding performance using a classifier.

Fetch example Haxby dataset#

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

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

from nilearn import datasets

haxby_dataset = datasets.fetch_haxby()

# repetition has to be known
TR = 2.5

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"].values

# Record these as an array of sessions
sessions = behavioral["chunks"].values
unique_sessions = behavioral["chunks"].unique()

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

Build a proper event structure for each session#

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

Instantiate and run FirstLevelModel#

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

z_maps = []
conditions_label = []
session_label = []

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

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

Run the glm on data from each session#

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

for session in unique_sessions:
    # grab the fmri data for that particular session
    fmri_session = index_img(func_filename, sessions == session)

    # fit the glm
    glm.fit(fmri_session, events=events[session])

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

________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1f00de6d0>, <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>, { '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 - 0.9s, 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 - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.44475 , ...,  0.379275]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.013524, ...,  0.844135]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.217486, ..., -1.430348]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.106474, ..., -0.182434]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.747494, ..., -1.660679]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.080159, ..., -1.32614 ]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.253894, ..., -0.452682]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.240914, ..., 0.244136]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f00dddd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1e32c8f50>, <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>, { '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 - 1.5s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.871458, ..., -0.990755]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.627194, ..., -1.290147]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.153013, ..., -1.320123]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.15748 , ...,  2.082416]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.997775, ..., -0.754066]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.109412, ..., 1.330079]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.030863, ..., -1.731439]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.559734, ..., -0.720924]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed781190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1efefc890>, <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>, { '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 - 1.4s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.695564, ..., -0.455092]), <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.457214, ..., 1.537178]), <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.803925, ...,  0.570463]), <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.614932, ...,  0.232909]), <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.527175, ..., -1.062723]), <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.126756, ..., -2.274819]), <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.635166, ..., -0.395548]), <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.250365, ..., 0.364311]), <nibabel.nifti1.Nifti1Image object at 0x7fd1efcb2c50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1f2297d50>, <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>, { '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 - 1.5s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.503142, ..., -1.639351]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.271132, ..., -0.047089]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.318104, ..., 0.724813]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.073279, ..., -0.316956]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.380183, ..., -0.690685]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.631912, ..., -0.753286]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.155784, ..., -0.065658]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.186135, ..., -0.69267 ]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f225e2d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1f2930f50>, <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>, { '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 - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.158342, ..., -0.068131]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.396497, ..., -0.424937]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.091867, ...,  0.463109]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.054041, ..., -0.122921]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.025223, ...,  0.562991]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.001653, ..., -0.968729]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.274665, ..., -0.667   ]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.564329, ..., 1.496068]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e3890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1efe9fe50>, <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>, { '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 - 0.9s, 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 - 1.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.987059, ...,  1.41717 ]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.24774 , ...,  0.674399]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.731234, ...,  1.341998]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.714869, ...,  1.182988]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.222674, ...,  0.480354]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.366899, ..., -0.091153]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.2708  , ..., -0.247146]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.251249, ..., -0.413063]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f26af510>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1e3426b10>, <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>, { '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 - 1.5s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.290412, ..., -0.609221]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.090297, ..., -0.822602]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.747918, ..., -0.108861]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.095788, ..., -1.376995]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.807425, ...,  1.826947]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.017351, ..., 0.622242]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.625042, ..., -0.231224]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.056424, ..., -1.672737]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f2294290>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1f292a2d0>, <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>, { '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 - 0.9s, 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.      ],
       ...,
       [-0.200737, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.5s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.008536, ..., -0.066075]), <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.168487, ..., -0.636238]), <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.145684, ..., 0.932773]), <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.580823, ..., -0.455655]), <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.685537, ...,  0.715791]), <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.245273, ..., -0.099707]), <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.79538 , ..., 1.913842]), <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.519925, ..., 0.629218]), <nibabel.nifti1.Nifti1Image object at 0x7fd1b20b3050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1f240aa10>, <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>, { '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 - 0.9s, 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.      ],
       ...,
       [-0.200737, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.425611, ...,  2.348025]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.31867 , ..., 0.408223]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.534932, ..., -0.150519]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.149007, ..., 0.640215]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.640699, ..., 1.50369 ]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.246384, ..., -1.346316]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.162243, ..., -0.519251]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.203695, ..., -1.335337]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f240b050>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1f227f290>, <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>, { '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 - 1.0s, 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 - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.034515, ...,  1.612397]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.62798 , ..., 1.160445]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.506632, ..., 0.459388]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.261016, ..., -0.747236]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.231796, ...,  1.098904]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.148582, ...,  0.999934]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.548262, ...,  0.09934 ]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.222824, ...,  0.318977]), <nibabel.nifti1.Nifti1Image object at 0x7fd1f27a1d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1efefced0>, <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>, { '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 - 1.5s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.175763, ..., -3.429485]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-3.146358, ..., -2.947626]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.806852, ..., -2.720554]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.008926, ...,  0.4544  ]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.279543, ..., 1.828183]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.463642, ...,  0.26599 ]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.058735, ..., -1.191442]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.390268, ..., -1.112207]), <nibabel.nifti1.Nifti1Image object at 0x7fd1ed34fe10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fd1f2932b10>, <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>, { '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 - 1.4s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.340751, ..., -1.056108]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.043261, ..., 1.144442]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.517954, ..., 0.611394]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-5.797134e-01, ...,  4.317655e-06]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.398581, ..., -0.488427]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.714396, ..., 0.869941]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.924894, ...,  0.723724]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.145297, ..., -0.821272]), <nibabel.nifti1.Nifti1Image object at 0x7fd1e33e0850>)
___________________________________________________________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)
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
fir_delays	[0]
high_pass (Hz)	0.01
hrf_model	glover
noise_model	ar1
scaling_axis	0
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	0.0
Threshold (computed)	3.29
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-36.75	-46.88	-46.88	6.63	147
2	-54.25	13.12	5.62	6.62	98
3	29.75	-20.62	-24.38	6.33	393
4	-1.75	-39.38	13.12	6.33	196
5	33.25	-43.12	-43.12	6.21	2264
5a	22.75	-31.88	-39.38	5.95
5b	8.75	-50.62	-35.62	5.77
6	-43.75	28.12	-9.38	6.16	196
7	-57.75	-39.38	9.38	6.12	246
8	-19.25	-24.38	-43.12	6.04	541
8a	-19.25	-31.88	-39.38	5.59
9	26.25	-20.62	-13.12	5.94	344
10	-40.25	-35.62	-35.62	5.92	344
11	26.25	-46.88	-46.88	5.86	147
12	22.75	-46.88	-61.88	5.83	492
12a	22.75	-58.12	-61.88	5.39
13	19.25	-54.38	-43.12	5.81	1132
14	-12.25	-24.38	-76.88	5.71	196
15	19.25	-76.88	-13.12	5.70	639
16	43.75	-50.62	-5.62	5.64	984
16a	47.25	-43.12	-1.88	4.69
17	22.75	-43.12	9.38	5.64	246
18	-26.25	-16.88	-24.38	5.63	147
19	12.25	-28.12	-73.12	5.62	246
20	-1.75	-50.62	-39.38	5.61	147
21	40.25	16.88	-20.62	5.59	49
22	-29.75	9.38	-28.12	5.53	246
23	26.25	-69.38	-16.88	5.50	787
23a	36.75	-65.62	-13.12	4.90
24	1.75	50.62	-16.88	5.49	344
25	-50.75	35.62	-16.88	5.48	196
26	-15.75	-28.12	-43.12	5.48	49
27	33.25	5.62	-13.12	5.47	492
27a	26.25	9.38	-16.88	5.44
28	12.25	-58.12	-28.12	5.46	393
29	12.25	-84.38	-28.12	5.44	98
30	-47.25	-13.12	13.12	5.42	295
31	-19.25	-50.62	-39.38	5.37	590
31a	-8.75	-35.62	-39.38	4.34
31b	-12.25	-46.88	-39.38	3.83
32	-33.25	-73.12	31.88	5.34	49
33	-29.75	-43.12	-50.62	5.31	393
34	-54.25	-31.88	-13.12	5.31	49
35	1.75	-43.12	-1.88	5.28	147
36	12.25	-13.12	-28.12	5.25	98
37	-12.25	5.62	-16.88	5.25	49
38	29.75	1.88	-9.38	5.23	98
39	-57.75	-16.88	1.88	5.23	98
40	5.25	-61.88	-20.62	5.22	295
41	-47.25	-39.38	-20.62	5.17	1132
41a	-50.75	-39.38	-9.38	4.55
42	40.25	-35.62	-24.38	5.16	2214
42a	47.25	-24.38	-13.12	5.04
42b	47.25	-35.62	-20.62	4.36
43	-29.75	-84.38	1.88	5.16	442
43a	-36.75	-76.88	1.88	4.69
44	22.75	-20.62	-35.62	5.09	98
45	19.25	-43.12	-20.62	5.08	147
46	-47.25	-9.38	5.62	5.06	295
47	5.25	-76.88	24.38	5.05	49
48	-33.25	13.12	28.12	5.04	246
49	-40.25	24.38	-9.38	5.04	49
50	33.25	20.62	-20.62	5.03	98
51	8.75	-91.88	5.62	5.03	49
52	-26.25	-54.38	-46.88	5.02	147
53	26.25	31.88	-31.88	5.01	147
54	-26.25	-20.62	-13.12	5.00	98
55	-61.25	-5.62	20.62	5.00	344
56	33.25	9.38	-24.38	5.00	98
57	1.75	-58.12	-24.38	4.99	98
58	-40.25	-39.38	31.88	4.97	344
58a	-29.75	-39.38	31.88	4.29
59	-47.25	-61.88	-5.62	4.96	49
60	29.75	-50.62	-50.62	4.95	196
61	-15.75	-76.88	28.12	4.94	295
62	-8.75	-46.88	-65.62	4.94	246
63	-43.75	50.62	16.88	4.93	49
64	22.75	-20.62	-28.12	4.90	344
64a	12.25	-20.62	-31.88	4.58
65	-19.25	-58.12	-9.38	4.89	98
66	-54.25	-46.88	-5.62	4.88	98
67	33.25	-50.62	-20.62	4.88	541
67a	29.75	-43.12	-20.62	3.73
68	-40.25	-61.88	-16.88	4.87	295
69	-50.75	-50.62	20.62	4.87	98
70	19.25	35.62	28.12	4.87	49
71	33.25	31.88	-9.38	4.86	98
72	-40.25	-20.62	-31.88	4.80	49
73	-47.25	-54.38	-9.38	4.80	344
74	19.25	-31.88	16.88	4.80	49
75	-15.75	-50.62	-1.88	4.79	49
76	22.75	-16.88	-9.38	4.77	98
77	-15.75	-1.88	-69.38	4.76	49
78	-26.25	-54.38	46.88	4.76	98
79	-12.25	-58.12	-16.88	4.75	49
80	-29.75	-80.62	-24.38	4.72	49
81	40.25	31.88	13.12	4.72	49
82	-50.75	16.88	-1.88	4.72	49
83	-47.25	-28.12	13.12	4.71	98
84	22.75	-35.62	54.38	4.70	295
85	-15.75	16.88	43.12	4.70	147
86	-22.75	1.88	-1.88	4.70	49
87	8.75	-16.88	46.88	4.70	49
88	-1.75	-46.88	1.88	4.69	98
89	36.75	28.12	-35.62	4.68	49
90	-40.25	-28.12	-20.62	4.68	49
91	-15.75	-31.88	-61.88	4.68	49
92	-12.25	-58.12	-46.88	4.67	49
93	-5.25	-13.12	-69.38	4.65	49
94	19.25	-1.88	28.12	4.65	49
95	36.75	-61.88	-5.62	4.64	98
96	-29.75	13.12	-13.12	4.64	295
96a	-26.25	5.62	-13.12	3.88
97	-29.75	-58.12	-9.38	4.63	49
98	-1.75	-9.38	-20.62	4.63	49
99	-50.75	9.38	9.38	4.62	49
100	-40.25	-65.62	-5.62	4.62	98
101	-1.75	-58.12	-54.38	4.62	49
102	-5.25	-31.88	58.12	4.62	49
103	33.25	-9.38	-58.12	4.62	98
104	12.25	-9.38	13.12	4.61	49
105	26.25	28.12	-20.62	4.61	196
106	50.75	1.88	43.12	4.61	98
107	19.25	-65.62	-24.38	4.61	196
108	8.75	-20.62	-13.12	4.60	147
109	-15.75	9.38	-20.62	4.60	98
110	-47.25	-28.12	5.62	4.59	49
111	-15.75	-39.38	-50.62	4.59	49
112	-15.75	-16.88	24.38	4.59	98
113	-36.75	58.12	35.62	4.57	49
114	15.75	-24.38	-35.62	4.55	344
115	-47.25	-24.38	-16.88	4.55	147
116	-33.25	-13.12	-13.12	4.55	344
117	19.25	-39.38	-13.12	4.54	49
118	-26.25	13.12	46.88	4.54	49
119	19.25	-35.62	69.38	4.53	49
120	12.25	43.12	-16.88	4.52	49
121	-1.75	-54.38	-9.38	4.52	98
122	-47.25	-28.12	43.12	4.52	49
123	-47.25	24.38	20.62	4.51	196
124	5.25	-73.12	16.88	4.51	344
124a	-1.75	-65.62	16.88	4.21
125	-15.75	-9.38	28.12	4.51	344
126	12.25	-80.62	13.12	4.51	147
127	-12.25	-13.12	-46.88	4.50	98
128	12.25	20.62	35.62	4.50	49
129	-36.75	43.12	24.38	4.49	98
130	1.75	13.12	9.38	4.48	49
131	-1.75	-46.88	-24.38	4.48	147
132	-47.25	35.62	16.88	4.48	196
133	-1.75	-9.38	-46.88	4.47	196
134	-1.75	-50.62	-1.88	4.46	49
135	22.75	31.88	9.38	4.45	49
136	-29.75	-80.62	-9.38	4.45	98
137	15.75	-1.88	-16.88	4.45	196
138	-40.25	-46.88	1.88	4.44	147
139	40.25	5.62	-24.38	4.44	49
140	47.25	-54.38	-16.88	4.44	49
141	-57.75	5.62	13.12	4.43	98
142	50.75	-5.62	5.62	4.43	49
143	-50.75	-5.62	-5.62	4.42	49
144	68.25	-1.88	-9.38	4.42	196
144a	68.25	-13.12	-9.38	4.41
145	33.25	24.38	39.38	4.42	49
146	-57.75	-9.38	9.38	4.42	344
146a	-61.25	-1.88	9.38	4.25
147	-36.75	20.62	35.62	4.42	295
148	54.25	-24.38	-5.62	4.42	344
149	-43.75	43.12	-28.12	4.41	147
150	-1.75	-1.88	1.88	4.41	246
151	40.25	35.62	-1.88	4.41	49
152	-61.25	-20.62	13.12	4.41	49
153	19.25	16.88	28.12	4.40	49
154	40.25	-35.62	-43.12	4.40	49
155	-50.75	-16.88	24.38	4.39	49
156	15.75	9.38	43.12	4.39	98
157	-22.75	16.88	5.62	4.39	98
158	-1.75	58.12	13.12	4.39	49
159	8.75	16.88	-20.62	4.38	49
160	-43.75	24.38	-1.88	4.38	196
161	-50.75	-20.62	9.38	4.38	98
162	-8.75	-46.88	-31.88	4.37	49
163	-15.75	13.12	50.62	4.36	147
164	-8.75	9.38	24.38	4.36	246
165	-40.25	5.62	-28.12	4.36	147
166	29.75	-24.38	-16.88	4.36	49
167	50.75	43.12	-5.62	4.36	49
168	-47.25	16.88	5.62	4.35	98
169	-47.25	20.62	-13.12	4.35	49
170	40.25	9.38	-20.62	4.35	98
171	-22.75	-54.38	-61.88	4.35	147
172	-15.75	24.38	-9.38	4.34	49
173	36.75	-73.12	5.62	4.34	98
174	-36.75	28.12	31.88	4.34	246
175	12.25	50.62	-1.88	4.34	98
176	-22.75	-9.38	13.12	4.34	49
177	-47.25	-58.12	13.12	4.33	98
178	22.75	46.88	-16.88	4.33	49
179	-15.75	-1.88	16.88	4.33	49
180	-5.25	-24.38	-20.62	4.33	196
181	19.25	-69.38	-13.12	4.32	98
182	33.25	-24.38	-13.12	4.32	49
183	-15.75	-54.38	43.12	4.32	49
184	5.25	-35.62	-58.12	4.31	98
185	-22.75	-69.38	-13.12	4.31	196
186	36.75	-31.88	-9.38	4.31	295
187	1.75	-76.88	35.62	4.31	49
188	36.75	-54.38	-20.62	4.31	49
189	5.25	-88.12	-1.88	4.31	49
190	-1.75	-54.38	-28.12	4.30	98
191	-1.75	13.12	-20.62	4.30	98
192	22.75	16.88	5.62	4.29	147
193	1.75	-5.62	-13.12	4.29	49
194	22.75	-28.12	69.38	4.29	98
195	-15.75	-73.12	-13.12	4.29	98
196	1.75	-5.62	-54.38	4.29	49
197	36.75	43.12	-28.12	4.28	49
198	-22.75	-76.88	-16.88	4.28	49
199	54.25	-20.62	39.38	4.28	49
200	-15.75	-20.62	-58.12	4.27	98
201	22.75	9.38	16.88	4.26	246
202	19.25	-9.38	20.62	4.26	49
203	-50.75	9.38	-16.88	4.26	246
204	-5.25	-46.88	-16.88	4.26	738
204a	12.25	-39.38	-13.12	3.93
205	15.75	50.62	58.12	4.25	49
206	-33.25	13.12	13.12	4.25	196
207	-19.25	-20.62	-35.62	4.24	98
208	33.25	-50.62	-28.12	4.24	49
209	-43.75	16.88	58.12	4.24	49
210	-1.75	-39.38	1.88	4.23	246
211	-19.25	61.88	5.62	4.23	49
212	15.75	-1.88	-1.88	4.23	49
213	47.25	-54.38	-1.88	4.23	147
214	-1.75	1.88	13.12	4.23	49
215	5.25	-58.12	-54.38	4.23	49
216	12.25	24.38	61.88	4.21	98
217	54.25	-9.38	20.62	4.21	196
218	29.75	-28.12	-24.38	4.21	49
219	-1.75	-24.38	9.38	4.21	49
220	8.75	-50.62	-20.62	4.21	98
221	-47.25	-65.62	1.88	4.20	196
222	19.25	-9.38	5.62	4.20	49
223	57.75	35.62	28.12	4.20	98
224	19.25	65.62	9.38	4.19	49
225	-22.75	31.88	-24.38	4.19	98
226	-57.75	-31.88	-5.62	4.19	49
227	1.75	-1.88	31.88	4.19	98
228	-50.75	39.38	9.38	4.19	49
229	5.25	-43.12	-9.38	4.19	49
230	-8.75	84.38	20.62	4.18	147
231	-5.25	31.88	-20.62	4.18	147
232	36.75	5.62	35.62	4.18	147
233	-54.25	1.88	13.12	4.18	49
234	-22.75	-50.62	54.38	4.17	49
235	12.25	1.88	28.12	4.16	147
236	-47.25	-28.12	28.12	4.16	49
237	19.25	16.88	13.12	4.16	98
238	-36.75	43.12	1.88	4.16	147
239	-54.25	-20.62	-24.38	4.16	49
240	-12.25	54.38	-9.38	4.16	98
241	-15.75	-76.88	20.62	4.15	49
242	12.25	5.62	43.12	4.15	49
243	-26.25	1.88	43.12	4.15	98
244	-19.25	-76.88	-28.12	4.15	147
245	33.25	5.62	28.12	4.14	49
246	40.25	-58.12	5.62	4.14	98
247	-8.75	5.62	-24.38	4.14	49
248	15.75	-35.62	31.88	4.14	98
249	-50.75	16.88	-13.12	4.14	147
250	-1.75	-9.38	-5.62	4.13	49
251	12.25	-65.62	-13.12	4.13	196
252	8.75	20.62	-24.38	4.13	49
253	-26.25	50.62	5.62	4.12	147
254	12.25	-9.38	5.62	4.12	49
255	15.75	-35.62	73.12	4.12	196
256	-33.25	-69.38	39.38	4.12	49
257	19.25	-1.88	43.12	4.11	98
258	15.75	-88.12	5.62	4.11	98
259	36.75	50.62	1.88	4.11	98
260	-8.75	-46.88	-24.38	4.11	49
261	12.25	61.88	5.62	4.11	49
262	-22.75	-1.88	-20.62	4.10	393
263	-33.25	-31.88	-65.62	4.10	98
264	-47.25	46.88	20.62	4.10	49
265	19.25	-13.12	-28.12	4.09	98
266	26.25	-61.88	31.88	4.09	49
267	8.75	76.88	35.62	4.08	246
268	-47.25	-43.12	9.38	4.08	147
269	-19.25	-50.62	16.88	4.08	49
270	50.75	-54.38	-9.38	4.08	49
271	40.25	-43.12	-28.12	4.08	49
272	22.75	-69.38	1.88	4.08	147
273	-29.75	20.62	-24.38	4.07	49
274	15.75	-73.12	-31.88	4.07	49
275	29.75	-31.88	39.38	4.07	246
276	-26.25	-73.12	-35.62	4.07	98
277	-15.75	20.62	-43.12	4.07	98
278	40.25	-20.62	20.62	4.06	49
279	-15.75	-46.88	-5.62	4.06	98
280	-50.75	13.12	-5.62	4.06	49
281	36.75	-39.38	-69.38	4.05	49
282	12.25	-46.88	-46.88	4.05	49
283	-26.25	-28.12	-31.88	4.04	49
284	19.25	13.12	-1.88	4.04	98
285	-36.75	16.88	50.62	4.04	147
286	8.75	13.12	50.62	4.03	98
287	26.25	1.88	13.12	4.02	98
288	5.25	-80.62	-1.88	4.01	49
289	26.25	-5.62	-1.88	4.01	49
290	-54.25	-20.62	-9.38	4.01	49
291	-43.75	-65.62	24.38	4.01	49
292	19.25	28.12	31.88	4.01	49
293	43.75	-69.38	-1.88	4.01	98
294	29.75	-58.12	-46.88	4.00	49
295	5.25	-28.12	-9.38	4.00	49
296	19.25	-24.38	16.88	4.00	49
297	-57.75	-46.88	-1.88	3.99	246
297a	-54.25	-39.38	-1.88	3.69
298	22.75	-61.88	-46.88	3.99	147
299	12.25	24.38	-39.38	3.99	147
300	22.75	5.62	-1.88	3.99	49
301	29.75	-43.12	-58.12	3.99	49
302	19.25	-84.38	-20.62	3.98	49
303	-33.25	39.38	54.38	3.98	49
304	-8.75	-39.38	-31.88	3.98	49
305	-40.25	-58.12	9.38	3.97	98
306	-36.75	-31.88	-46.88	3.97	49
307	1.75	24.38	31.88	3.97	49
308	40.25	-31.88	54.38	3.97	98
309	8.75	-43.12	-35.62	3.96	98
310	-29.75	24.38	13.12	3.96	98
311	5.25	-61.88	54.38	3.96	196
312	26.25	-20.62	-65.62	3.96	98
313	33.25	43.12	24.38	3.95	49
314	-15.75	-5.62	61.88	3.95	147
315	-64.75	-13.12	-24.38	3.95	49
316	-40.25	-16.88	-50.62	3.94	49
317	1.75	-20.62	-35.62	3.94	49
318	12.25	69.38	5.62	3.94	49
319	47.25	20.62	5.62	3.94	49
320	26.25	-80.62	-9.38	3.94	49
321	43.75	-39.38	-1.88	3.93	196
322	-33.25	1.88	-16.88	3.92	147
323	-19.25	1.88	58.12	3.92	49
324	-47.25	-13.12	35.62	3.92	49
325	-12.25	46.88	28.12	3.92	49
326	-5.25	-84.38	-20.62	3.91	49
327	-47.25	-39.38	16.88	3.91	49
328	-8.75	39.38	5.62	3.91	49
329	-15.75	-16.88	-43.12	3.91	49
330	64.75	-43.12	5.62	3.91	49
331	-19.25	-13.12	61.88	3.91	49
332	61.25	-31.88	39.38	3.91	49
333	8.75	46.88	-5.62	3.91	49
334	-12.25	-28.12	-50.62	3.91	196
335	-29.75	-1.88	-24.38	3.91	98
336	36.75	-73.12	-9.38	3.90	49
337	8.75	-24.38	9.38	3.90	49
338	19.25	54.38	1.88	3.90	49
339	22.75	13.12	35.62	3.90	196
340	1.75	-58.12	9.38	3.90	49
341	54.25	1.88	35.62	3.90	49
342	15.75	-20.62	73.12	3.90	98
343	40.25	-28.12	-1.88	3.90	49
344	-40.25	16.88	24.38	3.90	98
345	26.25	24.38	13.12	3.89	98
346	-29.75	-65.62	-20.62	3.89	49
347	-54.25	13.12	20.62	3.89	147
348	33.25	-28.12	65.62	3.89	49
349	26.25	-9.38	13.12	3.89	147
350	19.25	35.62	1.88	3.89	98
351	-47.25	-13.12	-54.38	3.89	98
352	12.25	-9.38	31.88	3.88	98
353	47.25	13.12	-20.62	3.88	49
354	57.75	-39.38	5.62	3.88	49
355	-54.25	-28.12	-16.88	3.88	49
356	19.25	-13.12	28.12	3.88	49
357	15.75	88.12	9.38	3.88	98
358	5.25	-28.12	73.12	3.87	49
359	-50.75	28.12	46.88	3.87	49
360	8.75	-73.12	35.62	3.86	49
361	-15.75	-50.62	-31.88	3.86	196
362	-33.25	35.62	-24.38	3.86	98
363	29.75	-31.88	-31.88	3.86	49
364	12.25	-1.88	-69.38	3.86	98
365	-22.75	13.12	-24.38	3.86	49
366	-19.25	65.62	39.38	3.85	49
367	-61.25	-16.88	9.38	3.85	49
368	12.25	-54.38	-54.38	3.85	49
369	-47.25	-58.12	-28.12	3.84	49
370	-33.25	-16.88	-5.62	3.84	49
371	-15.75	-1.88	-31.88	3.84	49
372	-12.25	-20.62	-61.88	3.84	49
373	-12.25	20.62	73.12	3.84	196
373a	-15.75	13.12	73.12	3.74
374	-12.25	-20.62	-46.88	3.83	49
375	-54.25	-24.38	-39.38	3.83	98
376	33.25	43.12	35.62	3.83	49
377	50.75	-13.12	5.62	3.83	49
378	-1.75	-43.12	-31.88	3.83	98
379	19.25	-58.12	-13.12	3.82	49
380	-15.75	-73.12	39.38	3.82	98
381	1.75	-31.88	1.88	3.82	49
382	-15.75	5.62	65.62	3.82	98
383	-1.75	-16.88	-28.12	3.82	49
384	-12.25	54.38	54.38	3.81	98
385	57.75	-20.62	28.12	3.80	49
386	-5.25	46.88	54.38	3.80	98
387	-8.75	-54.38	-31.88	3.80	49
388	19.25	-9.38	-5.62	3.80	49
389	-33.25	-35.62	-9.38	3.80	98
390	15.75	-20.62	-20.62	3.79	49
391	-15.75	-43.12	-43.12	3.79	98
392	33.25	-9.38	-9.38	3.79	49
393	-36.75	-69.38	5.62	3.79	49
394	26.25	20.62	-24.38	3.79	98
395	43.75	31.88	5.62	3.78	49
396	47.25	16.88	-39.38	3.78	98
397	-26.25	-31.88	16.88	3.78	49
398	26.25	-20.62	20.62	3.77	49
399	19.25	-16.88	16.88	3.76	49
400	1.75	65.62	-16.88	3.76	98
401	-57.75	-1.88	-13.12	3.76	98
402	12.25	-9.38	50.62	3.75	49
403	26.25	-20.62	65.62	3.75	49
404	36.75	-54.38	-28.12	3.75	49
405	-1.75	-28.12	58.12	3.75	49
406	-1.75	76.88	5.62	3.75	98
407	1.75	-35.62	-61.88	3.75	98
408	22.75	46.88	13.12	3.75	49
409	-26.25	-35.62	-9.38	3.75	49
410	-57.75	-31.88	24.38	3.75	98
411	22.75	-54.38	61.88	3.74	49
412	-47.25	-1.88	-20.62	3.73	49
413	-33.25	65.62	20.62	3.73	98
414	40.25	-35.62	50.62	3.73	49
415	-54.25	46.88	9.38	3.73	98
416	5.25	-61.88	-46.88	3.72	49
417	-1.75	-43.12	-9.38	3.72	49
418	26.25	-65.62	46.88	3.72	49
419	29.75	16.88	35.62	3.72	49
420	50.75	50.62	-1.88	3.72	49
421	22.75	-76.88	-35.62	3.72	98
422	-19.25	-61.88	20.62	3.72	49
423	29.75	-16.88	-61.88	3.72	49
424	47.25	13.12	5.62	3.72	98
425	19.25	-84.38	24.38	3.72	49
426	43.75	-5.62	-13.12	3.71	49
427	-47.25	1.88	-28.12	3.71	49
428	26.25	-80.62	1.88	3.71	49
429	40.25	28.12	-39.38	3.71	49
430	22.75	-35.62	-20.62	3.71	196
431	-1.75	-28.12	20.62	3.71	98
432	1.75	9.38	-16.88	3.71	49
433	19.25	-50.62	-73.12	3.70	49
434	19.25	-31.88	-16.88	3.70	49
435	-1.75	-46.88	-65.62	3.70	49
436	29.75	-50.62	54.38	3.70	147
437	-22.75	-80.62	1.88	3.70	49
438	26.25	13.12	9.38	3.70	49
439	15.75	-46.88	20.62	3.70	49
440	29.75	65.62	43.12	3.70	49
441	50.75	-50.62	9.38	3.69	49
442	15.75	-73.12	-20.62	3.69	49
443	-22.75	-5.62	-1.88	3.69	49
444	-26.25	-35.62	54.38	3.69	49
445	12.25	-35.62	9.38	3.69	49
446	-54.25	-9.38	20.62	3.69	49
447	-54.25	-20.62	-50.62	3.69	49
448	40.25	-9.38	-31.88	3.69	49
449	-36.75	61.88	16.88	3.69	98
450	15.75	-16.88	-58.12	3.69	49
451	-47.25	-61.88	20.62	3.69	49
452	61.25	1.88	24.38	3.68	49
453	33.25	-43.12	-24.38	3.68	49
454	-1.75	-16.88	24.38	3.68	49
455	-29.75	-43.12	58.12	3.68	98
456	-19.25	-65.62	-20.62	3.68	49
457	-26.25	-76.88	35.62	3.68	49
458	-33.25	-35.62	46.88	3.68	49
459	15.75	-16.88	5.62	3.68	49
460	43.75	24.38	-5.62	3.68	49
461	-47.25	16.88	-24.38	3.68	49
462	33.25	-35.62	-5.62	3.68	49
463	-8.75	-65.62	-43.12	3.67	49
464	1.75	16.88	-39.38	3.67	98
465	1.75	-58.12	-13.12	3.66	49
466	26.25	1.88	-13.12	3.66	49
467	19.25	-9.38	35.62	3.66	49
468	36.75	-24.38	-31.88	3.66	49
469	-40.25	35.62	31.88	3.66	98
470	36.75	20.62	31.88	3.65	49
471	-29.75	35.62	-1.88	3.65	49
472	-40.25	-69.38	31.88	3.65	49
473	-43.75	50.62	28.12	3.65	49
474	36.75	50.62	-16.88	3.65	49
475	22.75	-24.38	1.88	3.65	49
476	68.25	-31.88	9.38	3.65	147
477	5.25	5.62	-69.38	3.65	49
478	33.25	-13.12	65.62	3.65	49
479	-15.75	-43.12	1.88	3.64	49
480	-12.25	-9.38	-24.38	3.64	49
481	47.25	43.12	9.38	3.64	49
482	-47.25	46.88	28.12	3.64	49
483	33.25	43.12	-16.88	3.64	49
484	33.25	-24.38	-58.12	3.64	49
485	-29.75	-46.88	-13.12	3.64	49
486	47.25	31.88	-24.38	3.64	49
487	33.25	50.62	24.38	3.63	98
488	-29.75	35.62	-9.38	3.63	49
489	50.75	-43.12	9.38	3.63	49
490	26.25	-58.12	-13.12	3.63	98
491	22.75	-5.62	20.62	3.63	49
492	-47.25	43.12	9.38	3.63	49
493	-68.25	-24.38	1.88	3.63	98
494	-29.75	-1.88	-1.88	3.63	49
495	47.25	16.88	39.38	3.63	49
496	-1.75	20.62	-16.88	3.63	98
497	19.25	43.12	-5.62	3.62	49
498	-54.25	-35.62	-5.62	3.62	49
499	29.75	-16.88	58.12	3.62	49
500	-54.25	-9.38	-5.62	3.61	98
501	-1.75	-9.38	-28.12	3.61	49
502	29.75	-1.88	-20.62	3.61	49
503	-36.75	-13.12	9.38	3.61	49
504	36.75	35.62	16.88	3.61	196
505	36.75	1.88	20.62	3.61	49
506	33.25	-39.38	-58.12	3.61	49
507	22.75	-16.88	43.12	3.60	49
508	-8.75	-9.38	-20.62	3.60	49
509	19.25	20.62	20.62	3.60	49
510	19.25	-28.12	-24.38	3.59	49
511	-12.25	16.88	20.62	3.59	49
512	-1.75	-9.38	31.88	3.59	49
513	-29.75	16.88	-5.62	3.59	49
514	-33.25	-43.12	-28.12	3.59	98
515	50.75	-16.88	28.12	3.58	49
516	-36.75	16.88	1.88	3.58	49
517	1.75	-84.38	-16.88	3.58	49
518	-47.25	20.62	46.88	3.58	49
519	26.25	-16.88	-58.12	3.58	49
520	-33.25	46.88	35.62	3.58	49
521	15.75	1.88	35.62	3.58	49
522	-5.25	-43.12	69.38	3.57	49
523	8.75	28.12	-24.38	3.57	49
524	15.75	20.62	43.12	3.57	49
525	-29.75	-20.62	-69.38	3.57	49
526	26.25	5.62	5.62	3.56	98
527	5.25	-9.38	-20.62	3.56	49
528	-12.25	88.12	16.88	3.56	49
529	15.75	16.88	-16.88	3.56	49
530	-12.25	-39.38	-5.62	3.56	49
531	19.25	9.38	20.62	3.56	49
532	1.75	-16.88	-24.38	3.56	49
533	-19.25	24.38	31.88	3.56	49
534	-5.25	-54.38	-54.38	3.56	49
535	-47.25	9.38	-9.38	3.56	49
536	-33.25	-1.88	13.12	3.55	49
537	-50.75	5.62	-9.38	3.55	49
538	22.75	73.12	5.62	3.55	98
539	-15.75	-5.62	1.88	3.55	98
540	-5.25	-13.12	-61.88	3.55	49
541	36.75	-31.88	13.12	3.55	49
542	-12.25	5.62	35.62	3.54	147
543	-19.25	43.12	-1.88	3.54	49
544	47.25	28.12	-39.38	3.54	49
545	-22.75	-20.62	-31.88	3.54	49
546	-50.75	-58.12	1.88	3.54	49
547	19.25	-46.88	31.88	3.54	49
548	-26.25	-61.88	35.62	3.53	98
549	47.25	-35.62	16.88	3.53	49
550	15.75	-5.62	28.12	3.53	49
551	12.25	-1.88	24.38	3.53	49
552	61.25	-35.62	35.62	3.52	49
553	5.25	-16.88	-20.62	3.52	49
554	-15.75	-69.38	28.12	3.52	49
555	-5.25	43.12	-16.88	3.52	49
556	15.75	-13.12	-73.12	3.51	49
557	-36.75	-16.88	-46.88	3.51	49
558	43.75	9.38	-39.38	3.51	49
559	5.25	-73.12	-16.88	3.51	49
560	-36.75	61.88	9.38	3.51	49
561	-57.75	-24.38	5.62	3.51	49
562	-8.75	-54.38	9.38	3.50	98
563	12.25	-84.38	-5.62	3.50	49
564	33.25	50.62	50.62	3.50	49
565	-29.75	-80.62	20.62	3.50	49
566	1.75	-65.62	-43.12	3.50	49
567	-5.25	-20.62	1.88	3.50	49
568	12.25	-9.38	-24.38	3.50	49
569	40.25	-31.88	-58.12	3.50	98
570	-1.75	80.62	1.88	3.49	49
571	-47.25	-24.38	-5.62	3.49	49
572	54.25	-16.88	-1.88	3.49	98
573	47.25	5.62	31.88	3.49	49
574	-1.75	9.38	9.38	3.49	98
575	-12.25	-76.88	-9.38	3.49	49
576	68.25	-28.12	16.88	3.49	49
577	-8.75	43.12	24.38	3.49	49
578	8.75	-76.88	9.38	3.49	49
579	-29.75	24.38	5.62	3.49	49
580	22.75	24.38	46.88	3.49	49
581	12.25	-9.38	43.12	3.49	49
582	22.75	-1.88	5.62	3.48	49
583	29.75	-39.38	-54.38	3.48	49
584	-33.25	-58.12	-1.88	3.48	49
585	-33.25	-43.12	-61.88	3.48	49
586	-43.75	13.12	16.88	3.48	98
587	-19.25	-84.38	1.88	3.48	49
588	-15.75	-50.62	39.38	3.48	49
589	40.25	-35.62	39.38	3.48	98
590	12.25	-65.62	1.88	3.48	49
591	-33.25	1.88	31.88	3.48	49
592	33.25	5.62	50.62	3.47	49
593	22.75	-9.38	43.12	3.47	49
594	-1.75	-1.88	-5.62	3.47	49
595	-15.75	-31.88	-16.88	3.47	49
596	26.25	31.88	43.12	3.46	49
597	40.25	-5.62	46.88	3.46	49
598	-19.25	13.12	28.12	3.46	49
599	19.25	-31.88	-54.38	3.46	49
600	47.25	-35.62	-9.38	3.46	49
601	40.25	-39.38	-13.12	3.45	49
602	-5.25	1.88	-58.12	3.45	49
603	12.25	-35.62	-16.88	3.45	49
604	-19.25	-50.62	-46.88	3.45	49
605	12.25	16.88	-50.62	3.45	49
606	1.75	-1.88	-20.62	3.45	49
607	-12.25	-16.88	-65.62	3.45	49
608	-19.25	-43.12	31.88	3.45	49
609	-1.75	-35.62	-13.12	3.45	49
610	-19.25	-61.88	-13.12	3.44	49
611	-1.75	-69.38	-13.12	3.44	49
612	-8.75	54.38	43.12	3.44	49
613	22.75	39.38	20.62	3.44	49
614	-29.75	9.38	61.88	3.44	49
615	-40.25	-50.62	28.12	3.43	49
616	1.75	84.38	1.88	3.43	49
617	57.75	-13.12	16.88	3.43	49
618	-15.75	-16.88	-28.12	3.43	98
619	54.25	-16.88	35.62	3.43	98
620	-1.75	24.38	-39.38	3.43	49
621	-33.25	73.12	20.62	3.43	49
622	54.25	-5.62	13.12	3.43	49
623	26.25	-39.38	35.62	3.43	49
624	-36.75	35.62	-13.12	3.43	49
625	-50.75	-9.38	-13.12	3.42	49
626	50.75	39.38	-9.38	3.42	49
627	40.25	13.12	-46.88	3.42	49
628	-12.25	-13.12	-73.12	3.42	98
629	-12.25	-5.62	-20.62	3.42	49
630	43.75	-9.38	46.88	3.42	49
631	47.25	-20.62	20.62	3.42	49
632	33.25	-20.62	-9.38	3.42	49
633	-50.75	-1.88	16.88	3.41	98
634	26.25	16.88	-20.62	3.41	49
635	22.75	20.62	16.88	3.41	49
636	12.25	-35.62	65.62	3.41	49
637	15.75	28.12	-5.62	3.41	49
638	-50.75	13.12	-39.38	3.41	49
639	-54.25	24.38	24.38	3.41	49
640	-64.75	-35.62	-1.88	3.41	49
641	-1.75	-9.38	69.38	3.41	49
642	-40.25	-5.62	35.62	3.40	49
643	-19.25	-9.38	-65.62	3.40	49
644	-29.75	-1.88	-46.88	3.40	49
645	54.25	1.88	9.38	3.40	49
646	29.75	35.62	39.38	3.40	49
647	19.25	-5.62	13.12	3.39	49
648	5.25	-61.88	-35.62	3.39	49
649	-22.75	43.12	-28.12	3.39	49
650	19.25	-61.88	-20.62	3.39	49
651	12.25	-35.62	-61.88	3.39	49
652	-29.75	-24.38	-9.38	3.38	49
653	47.25	39.38	13.12	3.38	98
654	-57.75	1.88	20.62	3.38	49
655	-1.75	5.62	-39.38	3.38	49
656	-40.25	39.38	43.12	3.37	98
657	-36.75	-69.38	13.12	3.37	49
658	12.25	20.62	-46.88	3.37	49
659	-19.25	-16.88	9.38	3.37	49
660	33.25	-28.12	58.12	3.37	49
661	50.75	-20.62	-43.12	3.37	49
662	-5.25	1.88	1.88	3.37	49
663	40.25	-69.38	9.38	3.37	98
664	8.75	-65.62	20.62	3.36	49
665	-12.25	-20.62	-13.12	3.36	49
666	-5.25	-61.88	20.62	3.36	49
667	-26.25	-31.88	-58.12	3.36	49
668	15.75	35.62	5.62	3.36	49
669	12.25	-69.38	5.62	3.36	49
670	1.75	65.62	-9.38	3.36	49
671	-12.25	-39.38	5.62	3.36	49
672	-43.75	-16.88	13.12	3.36	49
673	47.25	-54.38	13.12	3.36	49
674	12.25	-50.62	39.38	3.36	49
675	22.75	-73.12	-5.62	3.35	49
676	-36.75	-35.62	39.38	3.35	49
677	1.75	1.88	-16.88	3.35	49
678	22.75	-50.62	35.62	3.35	49
679	-22.75	20.62	-1.88	3.35	49
680	-50.75	39.38	16.88	3.35	49
681	-36.75	9.38	54.38	3.35	49
682	-8.75	39.38	-28.12	3.35	49
683	1.75	-69.38	46.88	3.35	49
684	-1.75	-16.88	-58.12	3.35	49
685	5.25	1.88	13.12	3.35	49
686	-47.25	-9.38	43.12	3.35	49
687	-1.75	-5.62	-73.12	3.35	49
688	22.75	-13.12	73.12	3.35	49
689	-15.75	35.62	5.62	3.34	49
690	36.75	24.38	-13.12	3.34	49
691	54.25	-28.12	24.38	3.34	49
692	54.25	-28.12	31.88	3.34	49
693	-5.25	-31.88	1.88	3.34	49
694	-22.75	20.62	-46.88	3.34	49
695	-5.25	-61.88	54.38	3.34	49
696	40.25	-76.88	-9.38	3.34	49
697	-26.25	-24.38	-5.62	3.34	49
698	-36.75	-46.88	9.38	3.34	49
699	-22.75	1.88	28.12	3.34	49
700	1.75	28.12	-5.62	3.34	49
701	-47.25	-50.62	5.62	3.33	49
702	5.25	46.88	13.12	3.33	49
703	22.75	54.38	5.62	3.33	49
704	5.25	-16.88	69.38	3.33	49
705	-22.75	24.38	20.62	3.33	49
706	54.25	1.88	-31.88	3.33	49
707	19.25	-69.38	20.62	3.33	49
708	26.25	-80.62	-28.12	3.33	49
709	-50.75	-35.62	-54.38	3.33	49
710	15.75	-31.88	-50.62	3.33	49
711	29.75	-9.38	9.38	3.33	49
712	-40.25	-46.88	-5.62	3.32	49
713	5.25	-50.62	-5.62	3.32	98
714	-8.75	-13.12	5.62	3.32	49
715	12.25	-58.12	46.88	3.32	49
716	-19.25	-65.62	9.38	3.32	49
717	26.25	-54.38	31.88	3.32	49
718	26.25	-43.12	5.62	3.32	49
719	29.75	-1.88	-54.38	3.32	49
720	-36.75	-31.88	20.62	3.32	49
721	15.75	46.88	46.88	3.32	49
722	29.75	43.12	16.88	3.31	49
723	15.75	-16.88	54.38	3.31	49
724	50.75	-61.88	-13.12	3.31	49
725	-15.75	-46.88	-24.38	3.31	49
726	33.25	-76.88	-28.12	3.31	49
727	-50.75	-16.88	-9.38	3.31	49
728	-40.25	-65.62	-31.88	3.31	49
729	33.25	-35.62	-35.62	3.31	49
730	33.25	31.88	-28.12	3.31	49
731	40.25	-9.38	-13.12	3.31	49
732	-29.75	-46.88	-28.12	3.30	49
733	36.75	-46.88	9.38	3.30	49
734	-15.75	-69.38	-43.12	3.30	49
735	47.25	5.62	-1.88	3.30	49
736	-68.25	-24.38	13.12	3.30	49
737	-22.75	-28.12	-65.62	3.30	49
738	-26.25	-35.62	-43.12	3.30	49
739	22.75	9.38	46.88	3.30	49
740	8.75	13.12	16.88	3.30	49
741	40.25	-31.88	-69.38	3.30	49
742	8.75	24.38	39.38	3.30	49
743	-12.25	-54.38	-9.38	3.29	49
744	-26.25	-5.62	65.62	3.29	49
745	-1.75	-76.88	1.88	3.29	49
746	-64.75	20.62	5.62	3.29	49

house

Contrast Plot

Cluster Table

Height control	fpr
α	0.0
Threshold (computed)	3.29
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	22.75	20.62	9.38	5.62	689
1a	22.75	24.38	16.88	4.40
2	-22.75	24.38	13.12	5.30	147
3	47.25	-54.38	-16.88	5.30	98
4	22.75	13.12	20.62	5.26	98
5	-22.75	-39.38	-1.88	5.15	295
6	22.75	-5.62	20.62	5.01	49
7	-15.75	24.38	9.38	4.77	590
7a	-15.75	28.12	16.88	4.25
8	-29.75	28.12	5.62	4.69	49
9	12.25	24.38	20.62	4.67	147
10	33.25	28.12	28.12	4.59	98
11	-22.75	16.88	5.62	4.54	98
12	-22.75	5.62	24.38	4.47	295
13	33.25	-43.12	-35.62	4.45	344
13a	29.75	-50.62	-39.38	3.75
14	-50.75	9.38	-28.12	4.40	49
15	-33.25	-58.12	35.62	4.38	49
16	36.75	-54.38	-20.62	4.35	49
17	-26.25	39.38	46.88	4.33	147
18	-40.25	-58.12	28.12	4.31	98
19	19.25	-13.12	24.38	4.30	98
20	1.75	13.12	-16.88	4.29	49
21	22.75	-31.88	1.88	4.29	49
22	-15.75	-35.62	-1.88	4.24	147
23	19.25	5.62	43.12	4.23	98
24	8.75	-24.38	24.38	4.23	49
25	-54.25	-20.62	-9.38	4.21	49
26	-12.25	-16.88	-20.62	4.21	49
27	26.25	-61.88	31.88	4.20	49
28	-29.75	16.88	9.38	4.16	49
29	22.75	-43.12	-31.88	4.15	49
30	15.75	-54.38	5.62	4.14	49
31	-22.75	20.62	20.62	4.14	147
32	1.75	31.88	-13.12	4.13	49
33	26.25	5.62	9.38	4.12	49
34	-8.75	35.62	43.12	4.12	49
35	26.25	-24.38	-69.38	4.11	49
36	-15.75	-46.88	20.62	4.09	98
37	8.75	-16.88	31.88	4.07	49
38	-12.25	-28.12	-76.88	4.07	49
39	26.25	9.38	-31.88	4.07	49
40	33.25	5.62	-39.38	4.07	49
41	26.25	13.12	13.12	4.06	98
42	15.75	-39.38	13.12	4.04	49
43	8.75	-9.38	31.88	4.03	98
44	50.75	50.62	-1.88	4.02	49
45	-12.25	-43.12	-50.62	4.01	49
46	26.25	-1.88	13.12	3.97	49
47	22.75	-43.12	9.38	3.97	49
48	-15.75	20.62	20.62	3.96	49
49	-33.25	-24.38	35.62	3.95	49
50	43.75	-1.88	20.62	3.94	98
51	-12.25	1.88	-61.88	3.90	98
52	5.25	31.88	50.62	3.89	49
53	-26.25	13.12	16.88	3.87	98
54	-15.75	20.62	1.88	3.86	49
55	-15.75	-50.62	24.38	3.85	49
56	5.25	-20.62	-13.12	3.84	49
57	-29.75	-31.88	35.62	3.84	49
58	1.75	-46.88	-5.62	3.82	49
59	5.25	-43.12	-1.88	3.81	98
60	-12.25	-39.38	46.88	3.79	49
61	40.25	31.88	-24.38	3.79	49
62	-22.75	-9.38	-65.62	3.78	49
63	-40.25	-69.38	20.62	3.78	49
64	22.75	-16.88	16.88	3.77	49
65	-26.25	-50.62	-46.88	3.76	49
66	-8.75	5.62	28.12	3.76	98
67	-12.25	13.12	1.88	3.74	98
68	-33.25	-73.12	-31.88	3.72	49
69	26.25	9.38	-24.38	3.72	49
70	-12.25	-43.12	-16.88	3.71	49
71	-15.75	-20.62	-28.12	3.70	49
72	29.75	1.88	13.12	3.70	49
73	22.75	-39.38	46.88	3.69	49
74	50.75	-31.88	-50.62	3.69	49
75	19.25	-76.88	-16.88	3.69	98
76	33.25	54.38	28.12	3.68	49
77	-15.75	-31.88	-50.62	3.68	49
78	15.75	-28.12	24.38	3.68	49
79	-22.75	-24.38	46.88	3.67	49
80	22.75	16.88	5.62	3.66	49
81	-26.25	1.88	13.12	3.66	49
82	36.75	-13.12	-31.88	3.66	49
83	-36.75	24.38	13.12	3.64	49
84	12.25	-31.88	-73.12	3.64	49
85	-43.75	-9.38	39.38	3.63	49
86	-8.75	-28.12	5.62	3.63	49
87	-15.75	16.88	13.12	3.62	49
88	33.25	-76.88	-24.38	3.62	49
89	33.25	9.38	1.88	3.62	49
90	26.25	-73.12	31.88	3.62	49
91	26.25	-13.12	54.38	3.62	49
92	50.75	-43.12	-1.88	3.61	49
93	-22.75	-58.12	24.38	3.61	49
94	40.25	-13.12	-43.12	3.61	98
95	-15.75	-24.38	-9.38	3.61	98
96	-15.75	58.12	5.62	3.61	49
97	-43.75	58.12	9.38	3.61	49
98	-33.25	9.38	5.62	3.60	49
99	5.25	-65.62	54.38	3.60	49
100	-50.75	31.88	9.38	3.59	49
101	-15.75	-28.12	-5.62	3.59	49
102	36.75	-50.62	-24.38	3.58	147
103	-33.25	61.88	39.38	3.58	49
104	40.25	-39.38	13.12	3.57	49
105	36.75	-61.88	-13.12	3.57	49
106	57.75	-54.38	1.88	3.57	49
107	-15.75	-5.62	-13.12	3.57	98
108	-15.75	31.88	5.62	3.56	49
109	8.75	-61.88	-50.62	3.56	49
110	22.75	-13.12	20.62	3.56	49
111	-1.75	-61.88	-39.38	3.55	49
112	-33.25	13.12	-1.88	3.55	49
113	-54.25	-13.12	-1.88	3.55	49
114	22.75	-9.38	13.12	3.54	49
115	-47.25	-76.88	9.38	3.53	49
116	33.25	28.12	-46.88	3.53	49
117	-40.25	-1.88	24.38	3.53	49
118	-36.75	-65.62	-16.88	3.53	49
119	-40.25	-46.88	39.38	3.52	49
120	33.25	13.12	-5.62	3.52	49
121	-22.75	-16.88	24.38	3.52	49
122	22.75	1.88	20.62	3.52	49
123	-26.25	20.62	-1.88	3.51	49
124	-29.75	-9.38	-31.88	3.51	49
125	29.75	-54.38	-24.38	3.51	49
126	22.75	-76.88	20.62	3.50	49
127	-15.75	-61.88	43.12	3.49	49
128	-40.25	50.62	1.88	3.48	49
129	-1.75	39.38	-20.62	3.48	98
130	8.75	43.12	-5.62	3.48	49
131	22.75	-73.12	-13.12	3.47	49
132	-8.75	-46.88	-31.88	3.47	49
133	-8.75	-43.12	-9.38	3.47	49
134	15.75	-28.12	-5.62	3.47	49
135	22.75	-65.62	20.62	3.46	49
136	15.75	-50.62	9.38	3.46	49
137	-19.25	61.88	20.62	3.46	49
138	19.25	-35.62	-28.12	3.46	49
139	-64.75	-24.38	-20.62	3.45	49
140	-40.25	-28.12	-24.38	3.44	49
141	-36.75	-39.38	-46.88	3.43	49
142	12.25	-5.62	-5.62	3.43	49
143	-8.75	-24.38	1.88	3.42	49
144	-19.25	31.88	20.62	3.42	49
145	61.25	-1.88	13.12	3.42	49
146	15.75	-1.88	69.38	3.42	49
147	22.75	-43.12	31.88	3.42	49
148	8.75	20.62	20.62	3.41	49
149	26.25	-16.88	-9.38	3.40	49
150	19.25	24.38	5.62	3.39	49
151	-12.25	9.38	5.62	3.39	49
152	15.75	1.88	58.12	3.39	49
153	43.75	20.62	16.88	3.39	49
154	29.75	-43.12	-20.62	3.39	49
155	57.75	-16.88	16.88	3.38	49
156	-36.75	-61.88	35.62	3.38	49
157	40.25	-50.62	-58.12	3.38	98
158	-12.25	43.12	46.88	3.38	49
159	29.75	-16.88	46.88	3.38	49
160	-15.75	31.88	24.38	3.37	49
161	-36.75	-69.38	35.62	3.37	49
162	-26.25	16.88	50.62	3.37	49
163	-29.75	-13.12	-20.62	3.37	49
164	-40.25	24.38	35.62	3.37	49
165	-26.25	-54.38	43.12	3.36	49
166	12.25	-13.12	54.38	3.35	49
167	-26.25	-31.88	-69.38	3.35	49
168	19.25	-35.62	1.88	3.34	49
169	22.75	5.62	24.38	3.34	49
170	-15.75	-65.62	46.88	3.33	98
171	43.75	35.62	46.88	3.33	49
172	-57.75	20.62	13.12	3.33	49
173	-36.75	-50.62	-16.88	3.33	49
174	-12.25	-58.12	-46.88	3.33	49
175	50.75	-9.38	20.62	3.32	49
176	19.25	-58.12	20.62	3.32	49
177	-22.75	-13.12	46.88	3.32	49
178	8.75	-58.12	-24.38	3.32	49
179	26.25	-13.12	13.12	3.31	49
180	-57.75	16.88	5.62	3.31	49
181	26.25	-20.62	24.38	3.31	49
182	29.75	-46.88	-24.38	3.31	49
183	8.75	-80.62	-5.62	3.31	49
184	-36.75	-46.88	-46.88	3.31	49
185	15.75	-1.88	-16.88	3.31	49
186	15.75	24.38	-13.12	3.30	49
187	33.25	-28.12	-13.12	3.30	49
188	-15.75	-61.88	24.38	3.30	49
189	5.25	-35.62	-28.12	3.30	49
190	-26.25	-20.62	-13.12	3.30	49
191	54.25	9.38	-31.88	3.29	49
192	-5.25	-5.62	50.62	3.29	49

chair

Contrast Plot

Cluster Table

Height control	fpr
α	0.0
Threshold (computed)	3.29
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	4.87	147
2	12.25	35.62	-5.62	4.69	98
3	-50.75	39.38	16.88	4.39	49
4	-36.75	-76.88	1.88	4.35	49
5	-50.75	-39.38	-1.88	4.08	147
6	-15.75	-1.88	-16.88	4.02	49
7	-54.25	-43.12	46.88	4.02	49
8	-29.75	20.62	46.88	3.99	49
9	26.25	-46.88	-35.62	3.98	49
10	33.25	43.12	-16.88	3.95	49
11	33.25	-58.12	-1.88	3.93	49
12	-5.25	43.12	50.62	3.92	49
13	22.75	24.38	16.88	3.88	49
14	33.25	-58.12	-16.88	3.83	49
15	12.25	5.62	-24.38	3.82	49
16	-36.75	-61.88	43.12	3.82	49
17	-22.75	-43.12	-31.88	3.81	98
18	-22.75	24.38	13.12	3.74	49
19	-22.75	-31.88	-69.38	3.74	49
20	12.25	28.12	-16.88	3.72	49
21	8.75	-35.62	-35.62	3.71	49
22	-57.75	-9.38	-1.88	3.70	49
23	-57.75	13.12	1.88	3.70	98
24	12.25	16.88	-20.62	3.68	49
25	19.25	20.62	20.62	3.68	49
26	26.25	16.88	16.88	3.67	49
27	33.25	13.12	-1.88	3.67	49
28	36.75	-24.38	20.62	3.63	49
29	-15.75	20.62	13.12	3.62	49
30	-36.75	-46.88	50.62	3.61	49
31	-1.75	5.62	31.88	3.61	49
32	-22.75	9.38	24.38	3.57	49
33	29.75	-43.12	-35.62	3.56	49
34	-22.75	1.88	5.62	3.54	49
35	-29.75	-43.12	-58.12	3.53	49
36	22.75	13.12	20.62	3.50	49
37	54.25	20.62	5.62	3.48	49
38	33.25	-20.62	-58.12	3.47	49
39	-22.75	50.62	9.38	3.46	49
40	43.75	-28.12	-13.12	3.46	49
41	36.75	-1.88	28.12	3.44	49
42	33.25	9.38	-13.12	3.41	49
43	-15.75	-35.62	-24.38	3.41	98
44	-22.75	-9.38	20.62	3.39	49
45	29.75	9.38	-16.88	3.39	49
46	26.25	-69.38	31.88	3.38	49
47	12.25	9.38	-1.88	3.37	49
48	1.75	28.12	-35.62	3.37	49
49	36.75	-28.12	-9.38	3.36	49
50	-22.75	-39.38	-28.12	3.35	49
51	-15.75	-39.38	-54.38	3.32	49
52	36.75	20.62	-43.12	3.32	49
53	29.75	13.12	-43.12	3.31	49
54	29.75	-20.62	-5.62	3.30	49

scrambledpix

Contrast Plot

Cluster Table

Height control	fpr
α	0.0
Threshold (computed)	3.29
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	5.54	49
2	-22.75	-46.88	43.12	4.95	98
3	-15.75	61.88	-9.38	4.82	147
4	-29.75	-61.88	-31.88	4.79	49
5	5.25	43.12	-28.12	4.21	49
6	-22.75	-9.38	20.62	4.16	49
7	50.75	16.88	-28.12	4.13	147
8	40.25	-16.88	-39.38	3.97	49
9	-1.75	5.62	31.88	3.89	49
10	-64.75	-31.88	39.38	3.88	49
11	-22.75	-54.38	-9.38	3.86	49
12	-15.75	-43.12	-16.88	3.74	49
13	-1.75	-35.62	5.62	3.63	49
14	22.75	-24.38	46.88	3.57	49
15	-40.25	-16.88	-54.38	3.56	49
16	22.75	46.88	-5.62	3.56	49
17	-26.25	-69.38	39.38	3.56	49
18	-1.75	-46.88	-9.38	3.54	49
19	50.75	5.62	-35.62	3.50	49
20	19.25	1.88	9.38	3.46	49
21	33.25	-20.62	43.12	3.42	49
22	-19.25	-73.12	39.38	3.40	49
23	-22.75	-9.38	-65.62	3.39	49
24	-29.75	-65.62	20.62	3.39	49
25	-15.75	-61.88	-46.88	3.33	49
26	1.75	13.12	31.88	3.31	49
27	33.25	35.62	-28.12	3.31	49
28	26.25	-20.62	43.12	3.31	49
29	54.25	-9.38	16.88	3.30	49
30	19.25	43.12	13.12	3.30	49
31	26.25	-24.38	-69.38	3.30	49

face

Contrast Plot

Cluster Table

Height control	fpr
α	0.0
Threshold (computed)	3.29
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	5.22	196
1a	-47.25	-58.12	13.12	3.83
2	-47.25	-43.12	-35.62	4.91	49
3	1.75	24.38	-13.12	4.80	98
4	-47.25	-39.38	16.88	4.76	98
5	68.25	-9.38	-9.38	4.71	147
6	-33.25	50.62	16.88	4.70	196
7	-12.25	-58.12	-16.88	4.59	98
8	-57.75	-16.88	1.88	4.46	147
9	-15.75	13.12	73.12	4.42	196
10	-36.75	31.88	16.88	4.39	98
11	-40.25	46.88	13.12	4.30	98
12	-22.75	39.38	13.12	4.27	49
13	1.75	-43.12	-1.88	4.23	49
14	22.75	-50.62	-24.38	4.19	49
15	29.75	-46.88	35.62	4.15	49
16	-47.25	-50.62	20.62	4.12	49
17	33.25	-61.88	-13.12	4.10	49
18	-43.75	-31.88	35.62	4.07	98
19	-26.25	24.38	-13.12	4.06	147
20	-1.75	-39.38	5.62	4.05	98
21	40.25	-39.38	-24.38	4.05	49
22	29.75	-54.38	46.88	4.03	49
23	-47.25	24.38	20.62	3.99	49
24	-47.25	-54.38	-9.38	3.98	49
25	5.25	20.62	65.62	3.96	49
26	5.25	-58.12	-24.38	3.95	98
27	-8.75	-46.88	-31.88	3.94	49
28	-36.75	-31.88	-46.88	3.94	49
29	-19.25	-73.12	39.38	3.91	98
30	-40.25	-43.12	-16.88	3.91	49
31	-1.75	28.12	61.88	3.89	98
32	-36.75	31.88	35.62	3.86	393
32a	-29.75	28.12	39.38	3.52
33	-8.75	13.12	65.62	3.85	49
34	-15.75	-13.12	24.38	3.82	98
35	-36.75	-43.12	39.38	3.78	147
36	-8.75	-28.12	61.88	3.78	49
37	43.75	35.62	35.62	3.78	98
38	-47.25	-61.88	-5.62	3.78	49
39	40.25	28.12	39.38	3.77	98
40	33.25	-46.88	-39.38	3.77	98
41	5.25	-24.38	-20.62	3.76	49
42	40.25	43.12	16.88	3.74	49
43	-47.25	-28.12	28.12	3.74	98
44	-57.75	31.88	31.88	3.73	49
45	-1.75	-58.12	-16.88	3.69	49
46	-43.75	-39.38	-9.38	3.68	98
47	19.25	-46.88	-20.62	3.68	98
48	-12.25	-39.38	58.12	3.66	49
49	40.25	-43.12	-9.38	3.65	49
50	12.25	28.12	61.88	3.65	49
51	-26.25	-43.12	-43.12	3.65	49
52	-47.25	-16.88	-35.62	3.63	49
53	-22.75	-46.88	-50.62	3.62	49
54	-26.25	-88.12	-9.38	3.62	98
55	-1.75	-46.88	-9.38	3.62	49
56	-57.75	-9.38	-1.88	3.61	49
57	-40.25	50.62	20.62	3.61	49
58	33.25	-58.12	-16.88	3.60	49
59	-54.25	-43.12	20.62	3.60	49
60	-50.75	-35.62	43.12	3.60	49
61	19.25	-16.88	39.38	3.59	49
62	-12.25	-76.88	28.12	3.59	49
63	-47.25	-46.88	16.88	3.59	49
64	-19.25	-61.88	-9.38	3.57	49
65	5.25	-65.62	13.12	3.57	49
66	-29.75	-13.12	5.62	3.57	49
67	-29.75	28.12	13.12	3.55	49
68	1.75	-43.12	-16.88	3.52	49
69	-29.75	31.88	-9.38	3.52	49
70	-33.25	39.38	54.38	3.51	49
71	-47.25	-46.88	9.38	3.50	49
72	36.75	-46.88	9.38	3.50	49
73	33.25	-50.62	-20.62	3.49	49
74	-47.25	-46.88	-31.88	3.49	49
75	22.75	31.88	28.12	3.49	49
76	43.75	-54.38	9.38	3.48	49
77	5.25	31.88	-35.62	3.47	49
78	12.25	-58.12	-20.62	3.46	49
79	8.75	-58.12	-28.12	3.46	98
80	-8.75	-39.38	-5.62	3.45	49
81	15.75	-54.38	-20.62	3.44	49
82	22.75	-1.88	58.12	3.43	49
83	33.25	-54.38	43.12	3.43	49
84	5.25	-54.38	-20.62	3.43	49
85	29.75	-50.62	-39.38	3.41	49
86	-40.25	50.62	9.38	3.41	49
87	8.75	-1.88	9.38	3.41	49
88	36.75	61.88	28.12	3.40	49
89	29.75	-46.88	-43.12	3.40	49
90	-33.25	31.88	46.88	3.39	49
91	40.25	-35.62	-43.12	3.36	49
92	-47.25	-9.38	13.12	3.36	49
93	-50.75	20.62	20.62	3.36	49
94	33.25	50.62	20.62	3.36	49
95	-15.75	-73.12	16.88	3.35	49
96	29.75	-46.88	-31.88	3.35	49
97	-50.75	-28.12	35.62	3.34	49
98	15.75	13.12	43.12	3.33	49
99	-1.75	39.38	-20.62	3.33	49
100	57.75	-35.62	43.12	3.33	49
101	26.25	1.88	13.12	3.32	49
102	33.25	-65.62	-16.88	3.32	49
103	-36.75	20.62	50.62	3.31	49
104	-43.75	-43.12	46.88	3.31	49
105	-50.75	-58.12	-13.12	3.31	49
106	40.25	-43.12	5.62	3.30	49
107	26.25	-43.12	35.62	3.30	49
108	-29.75	-50.62	1.88	3.30	49
109	-47.25	-9.38	5.62	3.29	49
110	43.75	9.38	50.62	3.29	49
111	-54.25	-20.62	-39.38	3.29	49
112	29.75	-35.62	35.62	3.29	49

shoe

Contrast Plot

Cluster Table

Height control	fpr
α	0.0
Threshold (computed)	3.29
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	22.75	-9.38	35.62	4.66	98
2	19.25	73.12	20.62	4.19	49
3	61.25	24.38	31.88	4.11	147
4	19.25	-20.62	-28.12	3.98	49
5	22.75	-69.38	-28.12	3.82	49
6	36.75	46.88	-16.88	3.76	49
7	26.25	-46.88	-24.38	3.69	49
8	-1.75	-28.12	-73.12	3.69	49
9	26.25	35.62	28.12	3.66	49
10	12.25	5.62	31.88	3.64	49
11	-54.25	-65.62	16.88	3.64	49
12	-47.25	-16.88	-24.38	3.62	49
13	-68.25	-20.62	24.38	3.61	49
14	33.25	9.38	61.88	3.59	49
15	15.75	16.88	58.12	3.57	49
16	-12.25	39.38	65.62	3.54	49
17	50.75	-35.62	-35.62	3.53	49
18	-19.25	20.62	43.12	3.52	49
19	15.75	-24.38	-73.12	3.51	49
20	-68.25	-9.38	9.38	3.51	49
21	-8.75	20.62	73.12	3.51	49
22	-8.75	84.38	20.62	3.49	49
23	-5.25	-76.88	-28.12	3.48	49
24	-29.75	9.38	35.62	3.48	49
25	19.25	-28.12	-54.38	3.46	49
26	-12.25	1.88	-69.38	3.39	49
27	40.25	-54.38	39.38	3.38	49
28	1.75	-24.38	-39.38	3.37	49
29	47.25	-28.12	-5.62	3.37	49
30	-26.25	-13.12	-43.12	3.35	49
31	-12.25	-46.88	-43.12	3.35	49
32	-8.75	-46.88	-39.38	3.33	49
33	22.75	61.88	31.88	3.33	49
34	-8.75	73.12	31.88	3.31	49

cat

Contrast Plot

Cluster Table

Height control	fpr
α	0.0
Threshold (computed)	3.29
Cluster size threshold (voxels)	0
Minimum distance (mm)	8.0

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-12.25	-76.88	31.88	4.69	98
2	5.25	-61.88	-24.38	4.54	98
3	-40.25	-65.62	13.12	4.48	147
4	-12.25	-58.12	-16.88	4.38	49
5	12.25	-61.88	-31.88	4.35	147
6	43.75	-5.62	16.88	4.27	196
7	1.75	35.62	-13.12	4.21	49
8	-29.75	39.38	54.38	4.18	98
9	-1.75	50.62	-9.38	4.12	246
10	-15.75	-20.62	-58.12	4.11	98
11	22.75	-24.38	-58.12	4.06	49
12	22.75	-61.88	-54.38	4.02	49
13	19.25	-31.88	-69.38	4.00	49
14	43.75	5.62	1.88	4.00	49
15	5.25	-24.38	-73.12	3.96	49
16	36.75	69.38	5.62	3.95	49
17	47.25	1.88	-46.88	3.91	49
18	8.75	-65.62	-24.38	3.89	49
19	-64.75	-9.38	-24.38	3.87	49
20	36.75	9.38	-35.62	3.86	49
21	43.75	5.62	9.38	3.86	246
22	5.25	-80.62	9.38	3.84	98
23	50.75	28.12	24.38	3.78	49
24	-5.25	-50.62	-13.12	3.77	49
25	-29.75	35.62	50.62	3.75	49
26	36.75	28.12	-20.62	3.74	49
27	-12.25	-73.12	-31.88	3.73	49
28	-54.25	-50.62	5.62	3.73	98
29	50.75	1.88	-24.38	3.70	49
30	15.75	-16.88	46.88	3.69	49
31	5.25	43.12	50.62	3.67	49
32	-36.75	-50.62	-39.38	3.64	49
33	-8.75	-46.88	-28.12	3.63	49
34	-40.25	-46.88	16.88	3.63	49
35	61.25	-31.88	43.12	3.63	49
36	8.75	16.88	-16.88	3.63	49
37	-26.25	-80.62	9.38	3.60	98
38	15.75	-31.88	-61.88	3.57	49
39	61.25	-31.88	20.62	3.57	49
40	1.75	28.12	-35.62	3.56	49
41	-40.25	-5.62	50.62	3.55	49
42	36.75	28.12	43.12	3.54	98
43	-40.25	-69.38	35.62	3.54	49
44	-8.75	-35.62	-24.38	3.54	49
45	22.75	-58.12	-61.88	3.50	49
46	40.25	-5.62	-5.62	3.50	49
47	33.25	-54.38	43.12	3.48	49
48	-29.75	-46.88	5.62	3.47	49
49	12.25	5.62	5.62	3.46	49
50	-47.25	-43.12	43.12	3.45	49
51	-40.25	-46.88	50.62	3.44	49
52	12.25	-39.38	-31.88	3.44	49
53	5.25	-31.88	54.38	3.43	49
54	12.25	-54.38	-43.12	3.41	49
55	-43.75	-1.88	-28.12	3.41	49
56	15.75	-50.62	-39.38	3.40	49
57	-22.75	65.62	39.38	3.38	98
58	29.75	-43.12	50.62	3.38	49
59	12.25	-43.12	1.88	3.37	49
60	36.75	28.12	35.62	3.34	49
61	-22.75	35.62	61.88	3.34	49
62	36.75	-9.38	50.62	3.34	49
63	29.75	9.38	-31.88	3.33	49
64	-26.25	-54.38	13.12	3.33	49
65	-47.25	-50.62	46.88	3.33	49
66	5.25	20.62	50.62	3.33	49
67	15.75	-80.62	-31.88	3.31	49
68	5.25	-61.88	-39.38	3.31	49
69	-43.75	28.12	-9.38	3.30	49
70	-43.75	-65.62	5.62	3.30	49
71	-8.75	-28.12	-61.88	3.29	49
72	33.25	-1.88	46.88	3.29	49
73	29.75	1.88	-31.88	3.29	49

scissors

Contrast Plot

Cluster Table

Height control	fpr
α	0.0
Threshold (computed)	3.29
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	6.11	984
2	-19.25	84.38	20.62	5.80	344
3	-15.75	16.88	-46.88	5.71	442
4	-26.25	24.38	28.12	5.64	147
5	36.75	20.62	-31.88	5.22	246
6	29.75	5.62	-5.62	5.15	147
7	12.25	-35.62	-69.38	5.04	98
8	12.25	13.12	-43.12	5.03	442
9	33.25	5.62	-46.88	4.99	49
10	1.75	-28.12	-61.88	4.99	98
11	-19.25	61.88	50.62	4.98	49
12	-1.75	1.88	-61.88	4.98	295
13	5.25	24.38	-46.88	4.96	492
14	-22.75	-54.38	-50.62	4.86	295
15	15.75	76.88	28.12	4.67	49
16	12.25	-61.88	-46.88	4.67	49
17	47.25	24.38	-1.88	4.62	49
18	-12.25	76.88	20.62	4.54	98
19	-54.25	-39.38	-31.88	4.53	49
20	26.25	-46.88	1.88	4.50	98
21	1.75	13.12	-16.88	4.46	49
22	-19.25	31.88	-35.62	4.46	147
23	-29.75	76.88	20.62	4.45	541
24	50.75	31.88	-31.88	4.44	49
25	-15.75	-43.12	-46.88	4.43	98
26	-12.25	9.38	-58.12	4.38	98
27	-50.75	-9.38	-20.62	4.38	246
28	50.75	20.62	-1.88	4.36	49
29	40.25	5.62	31.88	4.36	98
30	-12.25	1.88	-28.12	4.33	49
31	15.75	-35.62	-73.12	4.32	49
32	1.75	1.88	31.88	4.31	147
33	47.25	-5.62	20.62	4.30	246
34	-29.75	20.62	-46.88	4.28	49
35	40.25	-58.12	-5.62	4.27	98
36	47.25	24.38	9.38	4.26	49
37	22.75	-58.12	-50.62	4.23	49
38	12.25	80.62	-5.62	4.21	49
39	54.25	5.62	50.62	4.19	98
40	-26.25	-13.12	1.88	4.18	49
41	-47.25	31.88	-28.12	4.16	49
42	-5.25	80.62	31.88	4.14	147
43	12.25	24.38	-35.62	4.12	98
44	22.75	-54.38	-43.12	4.12	49
45	-22.75	-5.62	-20.62	4.12	49
46	-5.25	-16.88	-39.38	4.12	98
47	-5.25	-9.38	31.88	4.11	49
48	-40.25	-16.88	-65.62	4.11	49
49	-5.25	84.38	1.88	4.10	49
50	29.75	-1.88	24.38	4.08	49
51	1.75	-1.88	-76.88	4.07	98
52	-5.25	-28.12	-76.88	4.07	98
53	-54.25	9.38	-16.88	4.06	49
54	33.25	24.38	-43.12	4.06	98
55	1.75	-28.12	-76.88	4.04	49
56	15.75	-16.88	46.88	4.00	49
57	50.75	24.38	-35.62	4.00	147
58	-15.75	76.88	31.88	3.99	49
59	-40.25	61.88	5.62	3.98	49
60	19.25	-46.88	-35.62	3.98	49
61	36.75	31.88	-39.38	3.98	246
62	-36.75	-1.88	-20.62	3.97	98
63	33.25	-35.62	-35.62	3.97	98
64	12.25	80.62	35.62	3.97	98
65	40.25	28.12	-43.12	3.97	49
66	-47.25	24.38	20.62	3.96	49
67	-26.25	-24.38	20.62	3.96	49
68	19.25	16.88	-46.88	3.94	49
69	1.75	-5.62	-80.62	3.94	98
70	54.25	1.88	43.12	3.93	49
71	50.75	50.62	20.62	3.93	49
72	22.75	-1.88	9.38	3.92	49
73	-19.25	50.62	13.12	3.92	49
74	-40.25	69.38	13.12	3.92	49
75	-8.75	24.38	-35.62	3.86	98
76	19.25	80.62	1.88	3.86	98
77	22.75	-43.12	-61.88	3.86	98
78	-33.25	-65.62	-16.88	3.85	49
79	-8.75	28.12	54.38	3.85	196
80	40.25	-39.38	58.12	3.84	49
81	19.25	-1.88	-1.88	3.84	49
82	12.25	-61.88	9.38	3.83	49
83	26.25	13.12	-39.38	3.83	49
84	-40.25	-9.38	-5.62	3.82	49
85	-26.25	31.88	-39.38	3.81	49
86	-8.75	84.38	20.62	3.81	98
87	-5.25	5.62	-69.38	3.81	49
88	-1.75	-31.88	9.38	3.80	49
89	-26.25	-80.62	-5.62	3.78	49
90	19.25	-16.88	-54.38	3.78	49
91	-1.75	73.12	35.62	3.77	49
92	15.75	50.62	-9.38	3.77	49
93	33.25	-5.62	-1.88	3.76	49
94	15.75	88.12	16.88	3.76	49
95	-36.75	65.62	20.62	3.76	98
96	-1.75	-35.62	-65.62	3.76	49
97	-5.25	80.62	13.12	3.75	49
98	-47.25	-5.62	-28.12	3.75	49
99	-26.25	16.88	28.12	3.75	49
100	29.75	76.88	13.12	3.74	49
101	40.25	-1.88	61.88	3.74	246
102	-64.75	-5.62	5.62	3.73	98
103	1.75	39.38	-16.88	3.72	49
104	-5.25	1.88	-76.88	3.72	49
105	-29.75	-69.38	9.38	3.72	98
106	43.75	-31.88	-13.12	3.71	49
107	40.25	-43.12	-5.62	3.71	49
108	43.75	-61.88	5.62	3.71	49
109	-1.75	-39.38	5.62	3.70	98
110	40.25	-24.38	-35.62	3.69	49
111	26.25	-50.62	-54.38	3.68	49
112	33.25	-39.38	24.38	3.67	49
113	29.75	-9.38	9.38	3.66	49
114	5.25	13.12	31.88	3.66	147
115	-22.75	-39.38	-54.38	3.65	49
116	-50.75	-13.12	5.62	3.65	49
117	-12.25	-43.12	-13.12	3.64	49
118	-1.75	-46.88	-1.88	3.63	49
119	57.75	-24.38	5.62	3.63	49
120	19.25	54.38	50.62	3.62	49
121	33.25	-39.38	-20.62	3.62	49
122	29.75	-46.88	-61.88	3.62	49
123	40.25	13.12	-39.38	3.61	49
124	-22.75	58.12	-20.62	3.61	98
125	50.75	-16.88	46.88	3.61	49
126	15.75	5.62	9.38	3.60	49
127	29.75	-50.62	-50.62	3.60	49
128	-29.75	31.88	-9.38	3.60	49
129	-50.75	-1.88	20.62	3.60	49
130	15.75	88.12	9.38	3.59	98
131	-43.75	-24.38	35.62	3.59	49
132	-19.25	9.38	-35.62	3.58	49
133	-29.75	13.12	61.88	3.58	98
134	-33.25	39.38	54.38	3.58	49
135	-19.25	-1.88	-35.62	3.58	49
136	-8.75	16.88	-43.12	3.56	49
137	57.75	16.88	16.88	3.56	49
138	8.75	1.88	24.38	3.56	49
139	-12.25	-50.62	-50.62	3.56	49
140	15.75	-28.12	-54.38	3.56	49
141	33.25	5.62	16.88	3.56	49
142	-40.25	43.12	-31.88	3.55	49
143	-19.25	20.62	24.38	3.55	49
144	1.75	65.62	46.88	3.55	49
145	-1.75	-13.12	-54.38	3.54	98
146	33.25	-39.38	-43.12	3.53	49
147	-12.25	88.12	9.38	3.53	49
148	33.25	-69.38	-13.12	3.53	49
149	22.75	-35.62	-9.38	3.53	49
150	26.25	-13.12	13.12	3.53	49
151	33.25	24.38	61.88	3.52	49
152	1.75	76.88	16.88	3.52	49
153	-15.75	-9.38	28.12	3.51	49
154	-5.25	-1.88	-28.12	3.51	98
155	1.75	-9.38	1.88	3.51	49
156	-33.25	69.38	20.62	3.51	98
157	5.25	9.38	-58.12	3.50	49
158	15.75	73.12	20.62	3.50	49
159	-36.75	69.38	16.88	3.50	49
160	15.75	-76.88	-16.88	3.49	49
161	-5.25	84.38	9.38	3.49	49
162	43.75	-9.38	-16.88	3.49	49
163	57.75	-20.62	28.12	3.49	49
164	-47.25	20.62	43.12	3.48	49
165	33.25	-50.62	-20.62	3.48	49
166	-15.75	-65.62	5.62	3.47	49
167	22.75	43.12	-9.38	3.47	49
168	50.75	-39.38	-16.88	3.47	49
169	33.25	-20.62	43.12	3.47	49
170	-22.75	54.38	-13.12	3.47	49
171	19.25	-5.62	-35.62	3.47	49
172	-1.75	-13.12	31.88	3.47	49
173	5.25	-1.88	5.62	3.47	98
174	-19.25	31.88	-28.12	3.46	49
175	40.25	-16.88	9.38	3.46	49
176	22.75	-20.62	20.62	3.46	49
177	8.75	-9.38	-76.88	3.45	49
178	-19.25	-5.62	16.88	3.45	49
179	-57.75	-9.38	-13.12	3.45	49
180	50.75	-16.88	58.12	3.45	49
181	57.75	-13.12	39.38	3.45	49
182	-26.25	-1.88	-9.38	3.45	49
183	15.75	46.88	-20.62	3.45	49
184	5.25	-65.62	20.62	3.44	49
185	-12.25	-50.62	-13.12	3.44	49
186	15.75	24.38	69.38	3.44	49
187	-22.75	39.38	31.88	3.44	49
188	22.75	-20.62	-5.62	3.43	49
189	1.75	-5.62	-20.62	3.43	49
190	29.75	58.12	-16.88	3.43	49
191	54.25	1.88	35.62	3.43	98
192	-26.25	-28.12	31.88	3.42	49
193	-33.25	-58.12	-31.88	3.41	49
194	1.75	16.88	-20.62	3.41	49
195	26.25	-16.88	-35.62	3.41	49
196	-22.75	-88.12	-1.88	3.40	49
197	12.25	1.88	-69.38	3.40	49
198	1.75	35.62	-24.38	3.40	49
199	33.25	-31.88	-43.12	3.39	49
200	-8.75	20.62	31.88	3.39	49
201	-12.25	-1.88	-58.12	3.39	49
202	57.75	-31.88	46.88	3.39	49
203	-19.25	73.12	31.88	3.38	49
204	36.75	1.88	65.62	3.38	49
205	-12.25	1.88	9.38	3.38	49
206	-8.75	9.38	65.62	3.38	49
207	-57.75	-5.62	-1.88	3.38	49
208	-26.25	43.12	-28.12	3.38	49
209	40.25	69.38	16.88	3.38	49
210	-8.75	-5.62	-58.12	3.37	49
211	47.25	16.88	-20.62	3.37	49
212	-8.75	24.38	43.12	3.37	49
213	-5.25	39.38	58.12	3.37	49
214	12.25	-35.62	-24.38	3.37	49
215	8.75	46.88	-20.62	3.37	49
216	-1.75	1.88	13.12	3.37	49
217	15.75	-50.62	-35.62	3.36	49
218	-15.75	1.88	28.12	3.36	49
219	26.25	1.88	24.38	3.36	49
220	29.75	-1.88	13.12	3.35	49
221	15.75	-39.38	24.38	3.34	49
222	29.75	9.38	16.88	3.34	49
223	-33.25	73.12	9.38	3.34	49
224	-1.75	-13.12	-5.62	3.33	49
225	-26.25	9.38	20.62	3.32	49
226	-26.25	16.88	-16.88	3.32	49
227	19.25	-5.62	61.88	3.32	49
228	19.25	-28.12	73.12	3.31	49
229	8.75	76.88	16.88	3.31	49
230	-1.75	-54.38	-61.88	3.30	49
231	5.25	39.38	-20.62	3.30	49
232	-43.75	16.88	39.38	3.30	49
233	-61.25	-46.88	9.38	3.30	49
234	12.25	-31.88	-46.88	3.30	49
235	-33.25	28.12	-39.38	3.29	49
236	19.25	16.88	-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 have several other ways to access the report:

# report.save_as_html('report.html')
# report.open_in_browser()

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 sessions which corresponds to a leave-one-session-out

We fit directly this pipeline on the Niimgs outputs of the GLM, with corresponding conditions labels and session 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=session_label)

# Return the corresponding mean prediction accuracy compared to chance

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/remi/github/nilearn/env/lib/python3.11/site-packages/nilearn/image/resampling.py:493: UserWarning:

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

Classification accuracy: 0.7173 / Chance level: 0.125

Total running time of the script: (2 minutes 59.254 seconds)

Estimated memory usage: 917 MB

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