9.3.9. 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:

1. Download the Haxby dataset.

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

3. Analyze the decoding performance using a classifier.

To run this example, you must launch IPython via ipython --matplotlib in a terminal, or use the Jupyter notebook.

9.3.9.1. 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

9.3.9.2. 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]

9.3.9.3. 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']

9.3.9.4. 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=.008,
                      smoothing_fwhm=4,
                      memory='nilearn_cache')

9.3.9.5. 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)

Out:

________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3550>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>, { '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 - 1.3s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.256013, ...,  0.308334]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.038572, ...,  0.855077]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.070285, ..., -1.222001]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.1724  , ...,  0.033508]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.96724 , ..., -1.474856]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.432136, ..., -1.197805]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.420626, ..., -0.443207]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.310409, ..., 0.196496]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a81d3c10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6aad03c10>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>, { '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 - 1.3s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.705774, ..., -0.934083]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.476069, ..., -1.29404 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.210752, ..., -1.441079]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.341707, ...,  2.094528]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.097247, ..., -0.496306]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.118812, ...,  1.58503 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.801345, ..., -1.648133]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.621172, ..., -0.678871]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f55dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a7f31280>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>, { '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 - 1.3s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.733742, ..., -0.435687]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.502666, ..., 1.789333]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.690828, ...,  0.731519]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.403789, ...,  0.257657]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.472767, ..., -1.160892]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.07507 , ..., -2.203006]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.445359, ..., -0.36161 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.034083, ..., 0.35299 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f06130>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a7f93370>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>, { '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 - 1.4s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.596132, ..., -1.910225]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.217544, ..., -0.003551]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.337751, ..., 0.789979]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.161412, ..., -0.265537]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.538844, ..., -0.797649]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.589617, ..., -0.790328]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.306331, ..., -0.003053]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.20477 , ..., -0.804061]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a80fc970>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>, { '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 - 1.4s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.161983, ..., -0.068078]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.467058, ..., -0.494102]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.008358, ..., 0.250096]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.127065, ..., -0.241985]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.066426, ..., 0.493324]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.049242, ..., -1.008997]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.336253, ..., -0.596381]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.707037, ..., 1.358865]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3b62280>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a80e1ac0>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>, { '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 - 1.4s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.197916, ...,  1.581644]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.576467, ...,  0.392603]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.969252, ...,  1.15444 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.716041, ...,  1.094486]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.280537, ...,  0.542739]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.331329, ..., -0.019244]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.298501, ..., -0.355821]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.232015, ..., -0.420629]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3820>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a7dbf9a0>, <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>, { '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 - 1.4s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.490124, ..., -0.665442]), <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.374685, ..., -0.980248]), <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.654133, ..., -0.288692]), <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.244312, ..., -1.462072]), <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.665202, ...,  1.793466]), <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.036326, ..., 0.693956]), <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.672634, ..., -0.24818 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.099764, ..., -1.722951]), <nibabel.nifti1.Nifti1Image object at 0x7fc695c78160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a7d7fa00>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>, { '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 - 1.2s, 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.      ],
       ...,
       [-0.259023, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.033872, ..., -0.317176]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.98478 , ..., -0.770334]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.127461, ..., 0.929068]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.489347, ..., -0.230229]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.673052, ...,  0.6757  ]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.260282, ..., -0.346342]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.775541, ..., 1.603123]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.391727, ..., 0.653312]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a3d6eeb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a40f3610>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>, { '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 - 1.4s, 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.      ],
       ...,
       [-0.259023, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.620911, ...,  2.309993]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.44548 , ..., 0.576334]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.483355, ..., -0.068969]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.166576, ..., 0.713549]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.612744, ..., 1.441012]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.254934, ..., -1.288018]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.134169, ..., -0.621367]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.234171, ..., -1.497943]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a4009340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a81b8d00>, <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>, { '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 - 1.4s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.01344 , ...,  1.283233]), <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.79114 , ..., 1.031069]), <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.583575, ..., 0.488828]), <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.265741, ..., -0.623721]), <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.20672, ...,  1.09668]), <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.045178, ...,  0.822339]), <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.463833, ..., -0.151504]), <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.230311, ...,  0.198537]), <nibabel.nifti1.Nifti1Image object at 0x7fc6aad0c0d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a7d9f130>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>, { '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 - 1.3s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.080165, ..., -3.497239]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-3.083184, ..., -3.071235]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.724401, ..., -2.892927]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.044506, ..., 0.285176]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.257372, ..., 1.802962]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.485481, ...,  0.284696]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.032135, ..., -1.290339]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.417132, ..., -1.258094]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a823aac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc6a8005be0>, <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>, { '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 - 1.3s, 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)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.545625, ..., -1.041515]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.152042, ..., 1.145641]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.55236 , ..., 0.696758]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.684509, ...,  0.226524]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.423531, ..., -0.641954]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.726779, ..., 0.687642]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.809306, ...,  0.496974]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.116436, ..., -0.811166]), <nibabel.nifti1.Nifti1Image object at 0x7fc6a7f3daf0>)
___________________________________________________________unmask - 0.1s, 0.0min

9.3.9.6. 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)	0.008
hrf_model	glover
noise_model	ar1
scaling_axis	0
signal_scaling	True
slice_time_ref	0
smoothing_fwhm	4
standardize	False
subject_label	None
t_r (s)	2.5
target_affine	None
target_shape	None

Design Matrix:

Contrasts

Mask

Stat Maps with Cluster Tables

bottle

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.3
Cluster size threshold (voxels)	0
Minimum distance (mm)	8

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-36.75	-46.88	-46.88	6.61	147
2	-54.25	13.12	5.62	6.47	98
3	-1.75	-39.38	13.12	6.45	196
4	-57.75	-39.38	9.38	6.17	246
5	-40.25	-35.62	-35.62	6.12	393
6	33.25	-43.12	-43.12	6.11	1722
6a	22.75	-35.62	-43.12	5.78
6b	15.75	-46.88	-39.38	4.33
6c	12.25	-46.88	-46.88	4.11
7	-19.25	-24.38	-43.12	5.99	492
7a	-19.25	-31.88	-39.38	5.47
8	29.75	-20.62	-24.38	5.98	393
9	-43.75	28.12	-9.38	5.96	196
10	8.75	-50.62	-35.62	5.88	492
11	19.25	-54.38	-43.12	5.78	1476
11a	12.25	-58.12	-28.12	5.44
11b	8.75	-58.12	-39.38	4.44
12	40.25	16.88	-20.62	5.73	49
13	22.75	-46.88	-61.88	5.70	492
13a	22.75	-58.12	-61.88	5.38
14	26.25	-46.88	-46.88	5.69	147
15	-1.75	-50.62	-39.38	5.66	147
16	-26.25	-16.88	-24.38	5.64	147
17	12.25	-28.12	-73.12	5.60	246
18	-50.75	35.62	-16.88	5.58	246
19	-33.25	-73.12	31.88	5.56	49
20	-15.75	-28.12	-43.12	5.55	49
21	19.25	-76.88	-13.12	5.50	492
22	-12.25	-24.38	-76.88	5.46	196
23	-47.25	-13.12	13.12	5.42	98
24	26.25	-20.62	-13.12	5.41	344
25	-29.75	9.38	-28.12	5.38	246
26	-57.75	-16.88	1.88	5.34	98
27	22.75	-43.12	9.38	5.33	246
28	-47.25	-61.88	-5.62	5.29	98
29	-40.25	-39.38	31.88	5.27	344
29a	-29.75	-39.38	31.88	4.36
30	-19.25	-50.62	-39.38	5.26	147
31	-12.25	5.62	-16.88	5.25	49
32	-29.75	-43.12	-50.62	5.24	393
33	8.75	-91.88	5.62	5.24	49
34	1.75	50.62	-16.88	5.19	344
35	-50.75	-50.62	20.62	5.17	147
36	43.75	-50.62	-5.62	5.17	935
36a	47.25	-43.12	-1.88	4.41
36b	50.75	-50.62	-5.62	4.15
37	26.25	9.38	-16.88	5.16	196
38	5.25	-76.88	24.38	5.15	49
39	-29.75	-84.38	1.88	5.14	49
40	26.25	-69.38	-16.88	5.14	246
41	-54.25	-31.88	-13.12	5.14	49
42	-47.25	-9.38	5.62	5.10	295
43	33.25	5.62	-13.12	5.10	246
44	-40.25	24.38	-9.38	5.08	49
45	12.25	-84.38	-28.12	5.07	98
46	-29.75	-80.62	-24.38	5.05	49
47	-54.25	-46.88	-5.62	5.02	98
48	26.25	31.88	-31.88	5.02	147
49	12.25	-13.12	-28.12	5.02	98
50	29.75	1.88	-9.38	5.01	98
51	-47.25	-39.38	-20.62	5.01	885
51a	-50.75	-39.38	-9.38	4.50
52	-19.25	-58.12	-9.38	5.00	196
53	33.25	20.62	-20.62	5.00	98
54	5.25	-61.88	-20.62	4.97	295
55	-33.25	13.12	28.12	4.93	246
56	-47.25	-28.12	13.12	4.90	147
57	1.75	-43.12	-1.88	4.90	49
58	-22.75	-58.12	-46.88	4.89	147
59	-15.75	-76.88	28.12	4.88	196
60	33.25	9.38	-24.38	4.88	98
61	-61.25	-5.62	20.62	4.87	147
62	-40.25	-20.62	-31.88	4.87	49
63	-50.75	16.88	-1.88	4.86	147
64	-15.75	-50.62	-1.88	4.86	49
65	40.25	-35.62	-24.38	4.86	2067
65a	43.75	-28.12	-9.38	4.70
65b	40.25	-20.62	-13.12	4.57
65c	47.25	-35.62	-20.62	4.13
66	22.75	-20.62	-35.62	4.84	98
67	36.75	28.12	-35.62	4.84	49
68	-15.75	16.88	43.12	4.82	147
69	-43.75	50.62	16.88	4.82	49
70	12.25	-65.62	-24.38	4.81	246
71	-47.25	-28.12	5.62	4.81	98
72	1.75	-58.12	-24.38	4.80	98
73	29.75	-50.62	-50.62	4.77	196
74	19.25	-43.12	-20.62	4.75	98
75	36.75	-65.62	-13.12	4.75	344
76	-1.75	-58.12	-54.38	4.73	49
77	19.25	35.62	28.12	4.72	49
78	-40.25	-61.88	-16.88	4.70	295
79	-22.75	1.88	-1.88	4.69	49
80	22.75	-16.88	-9.38	4.69	98
81	33.25	31.88	-9.38	4.69	98
82	-8.75	-46.88	-65.62	4.68	246
83	5.25	-73.12	16.88	4.67	344
83a	-1.75	-65.62	16.88	3.95
84	22.75	-20.62	-28.12	4.67	295
84a	12.25	-20.62	-31.88	4.46
85	-15.75	-1.88	-69.38	4.65	49
86	-47.25	35.62	16.88	4.65	196
87	12.25	20.62	35.62	4.64	49
88	-26.25	-20.62	-13.12	4.64	98
89	-29.75	13.12	-13.12	4.63	147
90	-15.75	-31.88	-61.88	4.63	49
91	-47.25	-54.38	-9.38	4.62	344
92	50.75	43.12	-5.62	4.61	49
93	40.25	31.88	13.12	4.59	49
94	12.25	-54.38	-46.88	4.59	98
95	-15.75	-39.38	-50.62	4.58	49
96	-5.25	-31.88	58.12	4.57	49
97	-1.75	58.12	13.12	4.57	49
98	22.75	-35.62	54.38	4.57	246
99	-12.25	-58.12	-16.88	4.56	49
100	-12.25	-13.12	-46.88	4.55	246
101	-19.25	-76.88	-28.12	4.55	147
102	8.75	-20.62	-13.12	4.55	147
103	1.75	-76.88	35.62	4.55	49
104	-36.75	-76.88	1.88	4.55	246
105	-61.25	-20.62	13.12	4.54	49
106	-1.75	-9.38	-20.62	4.54	49
107	-5.25	-13.12	-69.38	4.53	49
108	-57.75	-9.38	9.38	4.53	295
108a	-61.25	-1.88	9.38	4.20
109	-47.25	-28.12	43.12	4.52	49
110	-47.25	16.88	5.62	4.52	98
111	8.75	-16.88	46.88	4.52	49
112	-12.25	-58.12	-46.88	4.52	49
113	-36.75	43.12	24.38	4.51	98
114	-22.75	-76.88	-16.88	4.51	49
115	-29.75	-58.12	-9.38	4.51	49
116	1.75	13.12	9.38	4.50	49
117	15.75	-1.88	-16.88	4.50	196
118	33.25	-9.38	-58.12	4.50	98
119	50.75	-5.62	5.62	4.49	49
120	33.25	-50.62	-20.62	4.49	246
121	-50.75	9.38	-16.88	4.48	196
122	40.25	35.62	-1.88	4.48	49
123	8.75	-50.62	-20.62	4.48	49
124	-1.75	-46.88	1.88	4.46	98
125	-47.25	20.62	-13.12	4.46	49
126	15.75	9.38	43.12	4.46	98
127	-40.25	-65.62	-5.62	4.45	49
128	-15.75	9.38	-20.62	4.45	98
129	12.25	-9.38	13.12	4.45	49
130	50.75	1.88	43.12	4.44	98
131	-26.25	-54.38	46.88	4.43	98
132	-15.75	-76.88	20.62	4.43	49
133	-50.75	-16.88	24.38	4.42	49
134	-50.75	-5.62	-5.62	4.42	49
135	40.25	5.62	-24.38	4.42	49
136	-50.75	9.38	9.38	4.41	49
137	-50.75	16.88	-13.12	4.41	147
138	-40.25	9.38	-28.12	4.41	147
139	19.25	-1.88	28.12	4.38	49
140	33.25	24.38	39.38	4.38	49
141	-15.75	24.38	-9.38	4.38	49
142	22.75	-61.88	-46.88	4.38	147
143	-40.25	-46.88	1.88	4.38	147
144	-12.25	54.38	-9.38	4.37	98
145	26.25	28.12	-20.62	4.37	98
146	57.75	35.62	28.12	4.37	98
147	5.25	-88.12	-1.88	4.37	49
148	-15.75	13.12	50.62	4.36	98
149	-36.75	28.12	31.88	4.36	246
150	68.25	-13.12	-9.38	4.36	246
150a	68.25	-1.88	-9.38	4.29
151	1.75	-5.62	-13.12	4.35	49
152	-47.25	-24.38	-16.88	4.35	98
153	-26.25	13.12	46.88	4.35	49
154	5.25	-35.62	-58.12	4.35	98
155	36.75	-73.12	5.62	4.34	98
156	-33.25	-9.38	-13.12	4.34	344
157	-15.75	-9.38	28.12	4.34	295
158	-19.25	-54.38	-61.88	4.33	196
159	-8.75	5.62	-24.38	4.33	49
160	19.25	-35.62	69.38	4.32	49
161	-29.75	-80.62	-9.38	4.32	49
162	19.25	-9.38	20.62	4.32	49
163	-1.75	-1.88	1.88	4.32	196
164	-15.75	-54.38	43.12	4.31	49
165	-40.25	-28.12	-20.62	4.31	49
166	19.25	-31.88	16.88	4.31	49
167	15.75	-1.88	-1.88	4.31	49
168	-15.75	-16.88	24.38	4.30	98
169	-36.75	58.12	35.62	4.29	49
170	12.25	-80.62	13.12	4.29	147
171	-1.75	-24.38	9.38	4.29	49
172	-1.75	-54.38	-9.38	4.28	98
173	-43.75	43.12	-28.12	4.28	98
174	5.25	-58.12	-54.38	4.28	49
175	54.25	-24.38	-5.62	4.28	344
176	-57.75	-31.88	-5.62	4.28	49
177	15.75	50.62	58.12	4.27	98
178	36.75	-61.88	-5.62	4.27	49
179	33.25	-50.62	-28.12	4.27	49
180	-5.25	31.88	-20.62	4.26	147
181	15.75	-35.62	31.88	4.26	98
182	-47.25	-65.62	1.88	4.26	196
183	-54.25	-20.62	-24.38	4.25	49
184	26.25	-80.62	-9.38	4.25	49
185	22.75	16.88	5.62	4.24	147
186	5.25	-65.62	-13.12	4.23	196
187	-47.25	24.38	20.62	4.23	98
188	-47.25	-58.12	13.12	4.23	98
189	40.25	9.38	-20.62	4.23	98
190	19.25	28.12	31.88	4.23	49
191	12.25	50.62	-1.88	4.23	98
192	47.25	-54.38	-16.88	4.23	49
193	12.25	61.88	5.62	4.22	49
194	19.25	-9.38	5.62	4.22	49
195	-57.75	-31.88	24.38	4.22	98
196	-19.25	-20.62	-35.62	4.21	98
197	19.25	-84.38	-20.62	4.21	49
198	12.25	43.12	-16.88	4.21	49
199	-1.75	-9.38	-46.88	4.21	49
200	15.75	-24.38	-35.62	4.21	246
201	-5.25	-24.38	-20.62	4.20	147
202	-1.75	-50.62	-1.88	4.20	49
203	15.75	-73.12	-31.88	4.20	49
204	-47.25	-28.12	28.12	4.20	49
205	36.75	-31.88	-9.38	4.20	246
206	12.25	5.62	43.12	4.19	49
207	-1.75	76.88	5.62	4.19	49
208	-29.75	20.62	-24.38	4.18	49
209	47.25	-54.38	-1.88	4.18	49
210	1.75	-5.62	-54.38	4.18	49
211	40.25	-58.12	5.62	4.17	98
212	26.25	5.62	16.88	4.17	246
213	19.25	-5.62	43.12	4.17	98
214	-22.75	-69.38	-13.12	4.16	196
215	-1.75	-58.12	-28.12	4.16	98
216	19.25	65.62	9.38	4.16	98
217	-61.25	-16.88	9.38	4.16	49
218	36.75	43.12	-28.12	4.15	49
219	-1.75	-39.38	1.88	4.15	98
220	12.25	1.88	28.12	4.15	147
221	-15.75	-20.62	-58.12	4.15	49
222	29.75	-28.12	-24.38	4.15	49
223	19.25	16.88	28.12	4.15	49
224	40.25	-35.62	-43.12	4.15	49
225	-22.75	-50.62	54.38	4.15	49
226	-22.75	16.88	5.62	4.15	49
227	5.25	-80.62	-1.88	4.14	49
228	-36.75	20.62	35.62	4.14	246
229	-26.25	-73.12	-35.62	4.14	98
230	-43.75	24.38	-1.88	4.14	98
231	-8.75	9.38	24.38	4.13	196
232	8.75	20.62	-24.38	4.13	147
233	-57.75	5.62	13.12	4.13	98
234	26.25	-5.62	-1.88	4.12	49
235	-54.25	1.88	13.12	4.12	49
236	-33.25	-31.88	-65.62	4.12	98
237	-8.75	-46.88	-31.88	4.12	49
238	-1.75	-50.62	-24.38	4.12	147
239	-12.25	54.38	54.38	4.11	98
240	-47.25	46.88	20.62	4.11	49
241	-15.75	-1.88	16.88	4.10	49
242	-29.75	24.38	13.12	4.10	98
243	-57.75	-46.88	-1.88	4.10	246
244	-50.75	-20.62	9.38	4.10	98
245	19.25	-13.12	28.12	4.10	49
246	22.75	46.88	-16.88	4.10	49
247	19.25	-39.38	-13.12	4.09	49
248	-19.25	65.62	39.38	4.09	49
249	-1.75	-9.38	-5.62	4.09	49
250	-8.75	-39.38	-31.88	4.08	49
251	-40.25	-16.88	-50.62	4.08	49
252	-43.75	-39.38	-16.88	4.07	147
253	19.25	13.12	-1.88	4.07	98
254	22.75	5.62	-1.88	4.07	49
255	-8.75	-39.38	-39.38	4.06	442
255a	-12.25	-46.88	-43.12	3.77
256	-19.25	61.88	5.62	4.06	49
257	54.25	-20.62	39.38	4.06	49
258	54.25	-9.38	20.62	4.06	147
259	22.75	-69.38	1.88	4.06	147
260	-19.25	-5.62	-24.38	4.04	393
261	-1.75	1.88	13.12	4.04	49
262	-40.25	16.88	24.38	4.03	98
263	33.25	5.62	28.12	4.03	49
264	-36.75	43.12	1.88	4.02	147
265	19.25	-69.38	-13.12	4.02	49
266	12.25	-9.38	5.62	4.02	49
267	12.25	-88.12	5.62	4.02	98
268	-26.25	-76.88	35.62	4.02	49
269	-47.25	-43.12	9.38	4.02	147
270	-50.75	39.38	9.38	4.02	49
271	33.25	-24.38	-13.12	4.01	49
272	40.25	-43.12	-28.12	4.01	49
273	29.75	-24.38	-16.88	4.01	49
274	26.25	-20.62	-65.62	4.00	98
275	-43.75	20.62	20.62	4.00	49
276	-15.75	-73.12	-13.12	4.00	49
277	-1.75	13.12	-20.62	3.99	98
278	57.75	-39.38	5.62	3.99	49
279	-29.75	-65.62	-20.62	3.99	49
280	-19.25	-50.62	16.88	3.99	49
281	5.25	-61.88	-46.88	3.98	49
282	22.75	31.88	9.38	3.98	49
283	-19.25	-65.62	-20.62	3.98	49
284	15.75	-16.88	-58.12	3.98	49
285	19.25	-13.12	-28.12	3.98	98
286	-47.25	-13.12	-54.38	3.97	98
287	-47.25	-39.38	16.88	3.97	49
288	1.75	-1.88	31.88	3.97	98
289	36.75	-39.38	-69.38	3.97	49
290	-1.75	-35.62	-61.88	3.96	98
291	-8.75	39.38	5.62	3.96	49
292	26.25	20.62	-24.38	3.96	98
293	-5.25	-84.38	-20.62	3.96	49
294	8.75	76.88	35.62	3.95	147
295	-22.75	31.88	-24.38	3.95	49
296	-26.25	46.88	5.62	3.95	147
297	-22.75	-9.38	13.12	3.95	49
298	-33.25	13.12	13.12	3.94	98
299	5.25	-43.12	-9.38	3.94	49
300	-47.25	-13.12	35.62	3.93	49
301	-68.25	-24.38	1.88	3.93	147
302	-8.75	-54.38	-31.88	3.93	49
303	-54.25	-28.12	-16.88	3.92	49
304	50.75	-54.38	-9.38	3.92	49
305	68.25	-31.88	9.38	3.91	196
306	12.25	-1.88	-69.38	3.91	98
307	19.25	16.88	16.88	3.91	147
308	-33.25	-16.88	-5.62	3.91	49
309	5.25	-28.12	-9.38	3.91	49
310	-5.25	46.88	54.38	3.90	98
311	-43.75	16.88	58.12	3.90	49
312	-33.25	1.88	-16.88	3.90	147
313	15.75	88.12	9.38	3.90	98
314	5.25	-65.62	54.38	3.90	147
315	-64.75	-13.12	-24.38	3.90	49
316	29.75	-43.12	-58.12	3.90	49
317	1.75	-43.12	-16.88	3.90	295
317a	12.25	-39.38	-13.12	3.81
318	-47.25	-58.12	-28.12	3.90	49
319	33.25	5.62	35.62	3.89	98
320	50.75	-13.12	5.62	3.89	49
321	19.25	-24.38	16.88	3.89	49
322	-33.25	-69.38	39.38	3.89	49
323	15.75	-73.12	-20.62	3.89	49
324	64.75	-43.12	5.62	3.89	49
325	15.75	-46.88	20.62	3.88	49
326	-36.75	-31.88	-46.88	3.88	49
327	-12.25	-20.62	-61.88	3.88	49
328	-15.75	-73.12	39.38	3.88	98
329	-43.75	50.62	28.12	3.87	49
330	12.25	-13.12	31.88	3.87	98
331	19.25	-84.38	24.38	3.87	49
332	-5.25	-46.88	-16.88	3.87	147
333	-8.75	-65.62	-43.12	3.87	49
334	1.75	-58.12	9.38	3.87	49
335	-47.25	1.88	-28.12	3.87	49
336	8.75	46.88	-5.62	3.87	49
337	26.25	-80.62	1.88	3.87	49
338	-33.25	35.62	-24.38	3.86	98
339	-19.25	-13.12	61.88	3.86	49
340	12.25	-35.62	73.12	3.86	98
341	43.75	31.88	5.62	3.86	49
342	-57.75	1.88	-13.12	3.86	98
343	8.75	13.12	50.62	3.85	98
344	-26.25	5.62	-13.12	3.85	49
345	8.75	-24.38	9.38	3.85	49
346	-12.25	-20.62	-46.88	3.85	49
347	-26.25	1.88	43.12	3.85	98
348	12.25	24.38	61.88	3.85	49
349	-47.25	-61.88	20.62	3.85	49
350	19.25	-58.12	-13.12	3.85	49
351	-22.75	-5.62	-1.88	3.85	49
352	26.25	-61.88	31.88	3.85	49
353	-29.75	35.62	-9.38	3.85	49
354	47.25	13.12	-20.62	3.85	49
355	1.75	-9.38	-50.62	3.85	49
356	-15.75	-50.62	-31.88	3.84	196
357	-12.25	-31.88	-50.62	3.84	196
358	50.75	50.62	-1.88	3.84	49
359	29.75	-50.62	54.38	3.83	147
360	61.25	1.88	24.38	3.83	49
361	1.75	-31.88	1.88	3.82	49
362	36.75	-54.38	-20.62	3.82	49
363	26.25	-16.88	-58.12	3.82	49
364	-15.75	-46.88	-5.62	3.81	49
365	47.25	20.62	5.62	3.81	49
366	22.75	-28.12	69.38	3.81	98
367	40.25	-20.62	20.62	3.81	49
368	-43.75	-65.62	24.38	3.81	49
369	-19.25	-61.88	20.62	3.81	49
370	15.75	16.88	-16.88	3.80	49
371	29.75	-31.88	39.38	3.80	147
372	-12.25	46.88	28.12	3.80	49
373	12.25	69.38	5.62	3.80	49
374	15.75	-20.62	-20.62	3.80	49
375	22.75	-24.38	1.88	3.79	49
376	-29.75	-20.62	-69.38	3.79	49
377	-47.25	-1.88	-20.62	3.79	49
378	43.75	-69.38	-1.88	3.79	49
379	5.25	-28.12	73.12	3.79	49
380	19.25	35.62	1.88	3.78	98
381	47.25	13.12	5.62	3.78	98
382	-15.75	-5.62	61.88	3.78	98
383	-8.75	84.38	20.62	3.77	147
384	26.25	24.38	13.12	3.77	98
385	19.25	-9.38	-5.62	3.77	49
386	15.75	-16.88	5.62	3.77	49
387	-1.75	-28.12	58.12	3.76	49
388	-29.75	-1.88	-1.88	3.75	49
389	26.25	1.88	13.12	3.75	49
390	29.75	-58.12	-46.88	3.74	49
391	-40.25	35.62	31.88	3.74	49
392	36.75	50.62	1.88	3.74	98
393	-1.75	80.62	1.88	3.74	49
394	-57.75	-9.38	16.88	3.74	147
395	40.25	-9.38	-31.88	3.74	49
396	1.75	24.38	31.88	3.73	49
397	33.25	43.12	35.62	3.73	49
398	43.75	9.38	-39.38	3.73	49
399	26.25	13.12	9.38	3.73	49
400	33.25	-9.38	-9.38	3.73	49
401	40.25	-28.12	-1.88	3.73	49
402	22.75	-76.88	-35.62	3.73	98
403	-8.75	-46.88	-24.38	3.73	49
404	1.75	-20.62	-35.62	3.73	49
405	19.25	54.38	1.88	3.73	49
406	-26.25	-31.88	16.88	3.72	49
407	47.25	31.88	-24.38	3.72	49
408	-54.25	13.12	20.62	3.72	147
409	-33.25	-43.12	-61.88	3.72	49
410	19.25	-16.88	16.88	3.72	49
411	-19.25	-84.38	1.88	3.72	49
412	47.25	16.88	-39.38	3.72	49
413	40.25	-31.88	54.38	3.71	98
414	61.25	-31.88	39.38	3.71	49
415	-54.25	-24.38	-39.38	3.71	49
416	26.25	-20.62	65.62	3.71	49
417	36.75	-73.12	-9.38	3.71	49
418	29.75	-1.88	-20.62	3.71	49
419	15.75	-31.88	-50.62	3.71	49
420	-15.75	5.62	65.62	3.71	49
421	1.75	9.38	-16.88	3.70	49
422	-22.75	-20.62	-31.88	3.70	49
423	8.75	-73.12	35.62	3.70	49
424	36.75	-54.38	-28.12	3.70	49
425	43.75	-39.38	-1.88	3.70	49
426	-33.25	-35.62	-9.38	3.70	49
427	-50.75	28.12	46.88	3.69	49
428	-15.75	-43.12	1.88	3.69	49
429	-1.75	-43.12	-31.88	3.69	98
430	50.75	-16.88	28.12	3.68	49
431	-15.75	20.62	-43.12	3.68	98
432	43.75	-5.62	-13.12	3.68	49
433	29.75	-16.88	-61.88	3.68	49
434	57.75	-20.62	28.12	3.68	49
435	-47.25	46.88	28.12	3.68	49
436	15.75	-20.62	73.12	3.68	49
437	1.75	65.62	-16.88	3.68	98
438	-22.75	13.12	-24.38	3.67	49
439	-33.25	16.88	50.62	3.67	98
440	-15.75	-43.12	-43.12	3.67	98
441	-12.25	-9.38	-24.38	3.67	49
442	-1.75	20.62	-16.88	3.67	98
443	-26.25	-28.12	-31.88	3.67	49
444	-15.75	-69.38	28.12	3.66	49
445	-47.25	9.38	-9.38	3.66	49
446	-15.75	-1.88	-31.88	3.66	49
447	-29.75	-1.88	-24.38	3.66	98
448	26.25	1.88	-13.12	3.66	49
449	33.25	-24.38	-58.12	3.66	49
450	-40.25	-58.12	9.38	3.66	98
451	1.75	20.62	-39.38	3.65	98
452	47.25	16.88	39.38	3.65	49
453	-26.25	-35.62	54.38	3.65	49
454	22.75	-16.88	43.12	3.65	49
455	12.25	24.38	-39.38	3.65	49
456	-1.75	-16.88	-28.12	3.64	49
457	33.25	-39.38	-58.12	3.64	49
458	26.25	-20.62	20.62	3.64	49
459	19.25	5.62	-13.12	3.64	49
460	33.25	43.12	24.38	3.63	49
461	19.25	-9.38	35.62	3.63	49
462	-54.25	-20.62	-9.38	3.63	49
463	-5.25	-39.38	5.62	3.63	49
464	22.75	-54.38	61.88	3.62	49
465	19.25	-35.62	-20.62	3.62	147
466	-22.75	-80.62	1.88	3.62	49
467	40.25	-35.62	50.62	3.62	49
468	22.75	-1.88	5.62	3.62	49
469	43.75	-69.38	9.38	3.61	98
470	33.25	-58.12	-16.88	3.61	49
471	8.75	-65.62	20.62	3.61	49
472	26.25	-9.38	13.12	3.61	98
473	1.75	65.62	-9.38	3.61	49
474	-12.25	88.12	16.88	3.61	49
475	-47.25	61.88	1.88	3.61	49
476	-26.25	13.12	13.12	3.61	49
477	-33.25	-1.88	13.12	3.61	49
478	-8.75	-61.88	-46.88	3.61	49
479	40.25	28.12	-39.38	3.61	49
480	43.75	24.38	-5.62	3.61	49
481	-50.75	-58.12	1.88	3.61	49
482	26.25	-76.88	-16.88	3.60	49
483	-1.75	-9.38	-28.12	3.60	49
484	-5.25	-54.38	-54.38	3.60	49
485	-47.25	-9.38	43.12	3.60	49
486	-22.75	43.12	-28.12	3.59	49
487	-36.75	-16.88	-46.88	3.59	49
488	19.25	-31.88	-16.88	3.58	49
489	47.25	39.38	13.12	3.58	98
490	29.75	16.88	35.62	3.58	49
491	22.75	46.88	13.12	3.58	49
492	-29.75	-80.62	20.62	3.58	49
493	68.25	-28.12	16.88	3.57	49
494	-19.25	43.12	-1.88	3.57	49
495	19.25	-24.38	-39.38	3.57	49
496	-8.75	-9.38	-20.62	3.57	49
497	26.25	-80.62	-28.12	3.57	49
498	15.75	20.62	43.12	3.56	49
499	19.25	-46.88	31.88	3.56	49
500	22.75	73.12	5.62	3.56	49
501	50.75	-43.12	9.38	3.56	49
502	-57.75	-24.38	5.62	3.56	49
503	-1.75	-9.38	31.88	3.56	49
504	-15.75	-50.62	39.38	3.56	49
505	-57.75	39.38	16.88	3.56	98
506	12.25	-9.38	50.62	3.56	49
507	-12.25	-13.12	-73.12	3.55	98
508	-26.25	-61.88	31.88	3.55	49
509	-33.25	39.38	54.38	3.55	49
510	54.25	1.88	35.62	3.55	49
511	29.75	-31.88	-31.88	3.55	49
512	-33.25	65.62	20.62	3.55	49
513	-54.25	-9.38	-5.62	3.54	98
514	36.75	50.62	-16.88	3.54	49
515	15.75	28.12	-39.38	3.54	49
516	1.75	-1.88	-20.62	3.53	49
517	12.25	-65.62	1.88	3.53	49
518	29.75	65.62	43.12	3.53	49
519	47.25	-35.62	16.88	3.53	49
520	-40.25	-50.62	28.12	3.53	49
521	33.25	-28.12	65.62	3.53	49
522	5.25	5.62	-69.38	3.53	49
523	26.25	-58.12	-13.12	3.53	49
524	-1.75	-1.88	-5.62	3.52	49
525	-1.75	-46.88	-65.62	3.52	49
526	12.25	-54.38	-54.38	3.52	49
527	-5.25	43.12	-16.88	3.52	49
528	12.25	-9.38	-24.38	3.52	49
529	-50.75	-1.88	16.88	3.52	49
530	12.25	-35.62	9.38	3.52	49
531	36.75	35.62	16.88	3.52	147
532	19.25	20.62	20.62	3.52	49
533	-15.75	-69.38	-43.12	3.52	49
534	-33.25	-58.12	-1.88	3.52	49
535	-40.25	-69.38	31.88	3.52	49
536	-54.25	-13.12	13.12	3.51	98
537	8.75	28.12	-24.38	3.51	49
538	36.75	-24.38	-31.88	3.50	49
539	-36.75	-13.12	9.38	3.50	49
540	-54.25	-20.62	-50.62	3.50	49
541	36.75	20.62	31.88	3.50	49
542	8.75	-76.88	9.38	3.50	49
543	-47.25	-24.38	-5.62	3.49	49
544	-5.25	-61.88	20.62	3.49	49
545	-36.75	61.88	9.38	3.49	49
546	15.75	76.88	35.62	3.49	49
547	-50.75	-16.88	-13.12	3.49	98
548	5.25	-73.12	-16.88	3.49	49
549	15.75	28.12	-5.62	3.49	49
550	19.25	-28.12	-24.38	3.49	49
551	19.25	-50.62	-73.12	3.49	49
552	-36.75	35.62	-13.12	3.49	49
553	1.75	-16.88	-24.38	3.49	49
554	-29.75	35.62	-1.88	3.48	49
555	12.25	-84.38	-5.62	3.48	49
556	33.25	-35.62	-5.62	3.48	49
557	50.75	39.38	-9.38	3.48	49
558	22.75	-5.62	20.62	3.48	49
559	-50.75	46.88	9.38	3.48	98
560	54.25	1.88	-31.88	3.48	49
561	22.75	-9.38	-50.62	3.48	49
562	40.25	-31.88	-58.12	3.48	49
563	40.25	-76.88	-9.38	3.47	49
564	-36.75	-31.88	20.62	3.47	49
565	36.75	1.88	20.62	3.47	49
566	40.25	-9.38	1.88	3.47	49
567	22.75	13.12	35.62	3.47	98
568	33.25	5.62	50.62	3.47	49
569	-50.75	5.62	-9.38	3.47	49
570	40.25	-35.62	-1.88	3.47	49
571	-50.75	-9.38	-13.12	3.46	49
572	-29.75	24.38	5.62	3.46	49
573	-26.25	-35.62	-9.38	3.46	49
574	-15.75	-16.88	-28.12	3.46	98
575	-47.25	-50.62	5.62	3.46	49
576	-36.75	-69.38	5.62	3.46	49
577	26.25	-65.62	46.88	3.45	49
578	22.75	5.62	5.62	3.45	98
579	19.25	-5.62	13.12	3.45	49
580	-5.25	9.38	9.38	3.45	98
581	-33.25	1.88	31.88	3.45	49
582	-19.25	-50.62	-46.88	3.45	49
583	-1.75	-16.88	-58.12	3.45	49
584	36.75	24.38	-13.12	3.45	49
585	-1.75	-35.62	-13.12	3.45	49
586	-15.75	9.38	35.62	3.45	147
587	-43.75	-35.62	-9.38	3.45	49
588	22.75	-9.38	43.12	3.45	49
589	33.25	50.62	24.38	3.45	49
590	1.75	-65.62	-43.12	3.45	49
591	-29.75	16.88	-5.62	3.44	49
592	43.75	-31.88	-39.38	3.44	49
593	26.25	9.38	24.38	3.44	49
594	-54.25	24.38	24.38	3.44	49
595	43.75	1.88	-1.88	3.44	49
596	29.75	-16.88	58.12	3.44	49
597	54.25	-16.88	1.88	3.44	98
598	-36.75	-69.38	13.12	3.44	49
599	-5.25	-43.12	69.38	3.44	49
600	15.75	-50.62	9.38	3.43	49
601	-26.25	-31.88	-58.12	3.43	49
602	15.75	1.88	35.62	3.43	49
603	-29.75	-43.12	58.12	3.43	49
604	-1.75	43.12	-20.62	3.43	49
605	-19.25	-65.62	9.38	3.43	49
606	12.25	-35.62	-61.88	3.43	49
607	-33.25	-9.38	-46.88	3.43	49
608	-1.75	-16.88	24.38	3.43	49
609	47.25	28.12	-39.38	3.43	49
610	-36.75	16.88	1.88	3.43	49
611	47.25	-9.38	5.62	3.42	49
612	-19.25	1.88	58.12	3.42	49
613	-26.25	-24.38	-5.62	3.42	49
614	36.75	-31.88	13.12	3.42	49
615	12.25	50.62	9.38	3.42	49
616	8.75	-50.62	-5.62	3.41	49
617	-33.25	-35.62	46.88	3.41	49
618	19.25	43.12	-5.62	3.41	49
619	12.25	-1.88	24.38	3.41	49
620	22.75	39.38	20.62	3.40	49
621	-1.75	-39.38	-16.88	3.40	49
622	-15.75	-1.88	1.88	3.40	49
623	-54.25	-16.88	-5.62	3.40	49
624	1.75	-84.38	-16.88	3.40	49
625	-47.25	43.12	9.38	3.40	49
626	5.25	-9.38	-20.62	3.40	49
627	-57.75	1.88	20.62	3.40	49
628	-43.75	13.12	16.88	3.40	98
629	5.25	80.62	31.88	3.40	49
630	-64.75	-35.62	-1.88	3.40	49
631	-1.75	-43.12	-9.38	3.40	49
632	-54.25	-9.38	20.62	3.40	49
633	1.75	-69.38	46.88	3.40	49
634	-12.25	-5.62	-20.62	3.39	49
635	-8.75	43.12	24.38	3.39	49
636	12.25	-9.38	43.12	3.39	49
637	12.25	-35.62	-16.88	3.39	49
638	50.75	-16.88	35.62	3.39	49
639	-5.25	1.88	-58.12	3.39	49
640	12.25	-35.62	65.62	3.39	49
641	33.25	-13.12	65.62	3.38	49
642	19.25	-69.38	20.62	3.38	49
643	19.25	-31.88	-54.38	3.38	49
644	29.75	-39.38	-54.38	3.38	49
645	-54.25	-35.62	-5.62	3.37	49
646	-47.25	-16.88	-24.38	3.37	49
647	-36.75	9.38	54.38	3.37	49
648	50.75	-50.62	9.38	3.37	49
649	-26.25	-5.62	65.62	3.37	49
650	-33.25	-39.38	-28.12	3.36	49
651	33.25	43.12	-16.88	3.36	49
652	43.75	-58.12	-1.88	3.36	49
653	-47.25	16.88	-24.38	3.36	49
654	-19.25	58.12	39.38	3.36	49
655	-40.25	-5.62	35.62	3.36	49
656	-36.75	61.88	16.88	3.36	49
657	-1.75	-28.12	20.62	3.35	98
658	-8.75	-54.38	9.38	3.35	49
659	12.25	-69.38	5.62	3.35	49
660	-19.25	-43.12	31.88	3.35	98
661	-1.75	-76.88	1.88	3.35	49
662	33.25	-20.62	-9.38	3.35	49
663	-12.25	20.62	73.12	3.35	49
664	-8.75	13.12	-13.12	3.35	49
665	61.25	-35.62	35.62	3.35	49
666	43.75	-9.38	46.88	3.34	49
667	1.75	28.12	-5.62	3.34	49
668	47.25	5.62	-1.88	3.34	98
669	15.75	-5.62	28.12	3.34	49
670	15.75	-13.12	-73.12	3.34	49
671	54.25	1.88	9.38	3.34	49
672	22.75	9.38	46.88	3.34	49
673	40.25	-5.62	-9.38	3.33	49
674	61.25	5.62	31.88	3.33	49
675	29.75	-1.88	-54.38	3.33	49
676	1.75	-9.38	-16.88	3.33	49
677	19.25	-13.12	73.12	3.33	49
678	-12.25	16.88	20.62	3.33	49
679	15.75	46.88	46.88	3.33	49
680	-43.75	-61.88	9.38	3.33	49
681	47.25	-20.62	20.62	3.33	49
682	-5.25	-61.88	54.38	3.33	49
683	-1.75	-69.38	-13.12	3.32	49
684	50.75	-20.62	-43.12	3.32	49
685	54.25	-39.38	-24.38	3.32	49
686	29.75	-43.12	-20.62	3.32	49
687	-12.25	-76.88	-9.38	3.32	49
688	-15.75	20.62	20.62	3.32	49
689	-33.25	9.38	16.88	3.32	49
690	-29.75	9.38	61.88	3.31	49
691	40.25	13.12	-46.88	3.31	49
692	1.75	-58.12	-13.12	3.31	49
693	1.75	1.88	-16.88	3.31	49
694	12.25	-50.62	39.38	3.31	49
695	-36.75	5.62	-9.38	3.31	49
696	26.25	16.88	-20.62	3.31	49
697	12.25	-61.88	16.88	3.31	49
698	22.75	-50.62	35.62	3.31	49
699	19.25	-46.88	-5.62	3.31	49
700	12.25	-50.62	-1.88	3.30	49
701	-12.25	-16.88	-65.62	3.30	49
702	-19.25	24.38	31.88	3.30	49
703	47.25	5.62	31.88	3.30	49
704	5.25	46.88	13.12	3.30	49
705	-22.75	-28.12	31.88	3.30	49
706	33.25	-76.88	-28.12	3.30	49
707	22.75	20.62	16.88	3.29	49
708	5.25	-16.88	-20.62	3.29	49
709	-54.25	-46.88	13.12	3.29	49

house

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.3
Cluster size threshold (voxels)	0
Minimum distance (mm)	8

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	22.75	20.62	9.38	5.62	639
2	-22.75	24.38	13.12	5.01	147
3	47.25	-54.38	-16.88	5.00	98
4	22.75	-5.62	20.62	4.99	49
5	22.75	13.12	20.62	4.88	98
6	-15.75	24.38	9.38	4.79	639
7	33.25	28.12	28.12	4.64	98
8	-22.75	-39.38	-1.88	4.59	246
8a	-22.75	-28.12	-1.88	4.38
9	12.25	24.38	20.62	4.57	147
10	-29.75	28.12	5.62	4.56	49
11	-22.75	16.88	5.62	4.39	98
12	1.75	31.88	-13.12	4.33	49
13	-22.75	5.62	24.38	4.29	147
14	19.25	-16.88	24.38	4.28	98
15	-15.75	-35.62	-1.88	4.25	98
16	19.25	5.62	43.12	4.25	98
17	-12.25	-43.12	-50.62	4.25	49
18	8.75	-24.38	24.38	4.21	49
19	-50.75	9.38	-28.12	4.20	49
20	-33.25	-24.38	35.62	4.19	49
21	-12.25	-16.88	-20.62	4.18	49
22	-8.75	35.62	43.12	4.16	49
23	-22.75	39.38	46.88	4.15	147
24	26.25	-24.38	-69.38	4.14	49
25	1.75	13.12	-16.88	4.14	49
26	50.75	50.62	-1.88	4.14	49
27	22.75	-31.88	1.88	4.11	49
28	-12.25	13.12	1.88	4.11	98
29	-15.75	20.62	20.62	4.10	49
30	8.75	-9.38	31.88	4.09	98
31	-12.25	-28.12	-76.88	4.09	49
32	-15.75	-46.88	20.62	4.08	98
33	26.25	-61.88	31.88	4.05	49
34	36.75	-54.38	-20.62	4.01	49
35	15.75	-54.38	5.62	4.00	49
36	-12.25	1.88	-61.88	4.00	98
37	-33.25	-58.12	35.62	3.99	49
38	-54.25	-20.62	-9.38	3.97	49
39	-26.25	9.38	20.62	3.97	98
40	-15.75	20.62	1.88	3.96	49
41	15.75	-28.12	24.38	3.92	49
42	15.75	-39.38	13.12	3.92	49
43	22.75	-16.88	16.88	3.91	49
44	22.75	16.88	5.62	3.90	49
45	33.25	-43.12	-35.62	3.89	49
46	-29.75	16.88	9.38	3.89	49
47	8.75	-16.88	31.88	3.89	49
48	-40.25	-58.12	28.12	3.89	49
49	26.25	9.38	-31.88	3.87	49
50	43.75	-1.88	20.62	3.86	49
51	22.75	-43.12	-31.88	3.84	49
52	-22.75	20.62	20.62	3.83	98
53	22.75	-43.12	9.38	3.83	49
54	-15.75	31.88	5.62	3.83	49
55	36.75	-13.12	-31.88	3.83	49
56	-54.25	-13.12	-1.88	3.82	49
57	5.25	-20.62	-13.12	3.82	49
58	-29.75	-31.88	35.62	3.81	49
59	26.25	13.12	13.12	3.79	49
60	-26.25	13.12	16.88	3.78	49
61	33.25	5.62	-39.38	3.75	49
62	5.25	-65.62	54.38	3.73	49
63	-15.75	-20.62	-28.12	3.73	49
64	26.25	-1.88	13.12	3.72	49
65	-15.75	58.12	5.62	3.72	49
66	26.25	5.62	9.38	3.72	49
67	-15.75	-50.62	24.38	3.71	49
68	40.25	31.88	-24.38	3.71	49
69	50.75	-31.88	-50.62	3.70	49
70	-8.75	1.88	28.12	3.68	98
71	33.25	54.38	28.12	3.67	49
72	-47.25	-76.88	9.38	3.67	49
73	33.25	13.12	-5.62	3.66	49
74	8.75	20.62	20.62	3.65	49
75	-33.25	-73.12	-31.88	3.64	98
76	12.25	-31.88	-73.12	3.63	49
77	5.25	31.88	50.62	3.63	49
78	-12.25	-39.38	46.88	3.63	49
79	-19.25	61.88	20.62	3.62	49
80	40.25	-39.38	13.12	3.62	49
81	40.25	-13.12	-43.12	3.62	49
82	-22.75	-24.38	46.88	3.61	49
83	-33.25	9.38	5.62	3.61	49
84	33.25	-76.88	-24.38	3.60	49
85	29.75	-50.62	-39.38	3.60	49
86	-19.25	31.88	20.62	3.60	49
87	-1.75	-61.88	-39.38	3.60	49
88	15.75	-1.88	-16.88	3.59	49
89	-33.25	13.12	-1.88	3.58	49
90	26.25	9.38	-24.38	3.57	49
91	-36.75	-39.38	-46.88	3.57	49
92	8.75	43.12	-5.62	3.56	49
93	8.75	-61.88	-50.62	3.56	49
94	-5.25	39.38	-20.62	3.56	98
95	22.75	-13.12	20.62	3.56	49
96	40.25	-50.62	-54.38	3.56	98
97	-1.75	-58.12	-28.12	3.55	49
98	-43.75	-9.38	39.38	3.55	49
99	33.25	28.12	-46.88	3.54	49
100	-15.75	31.88	24.38	3.54	49
101	57.75	-54.38	1.88	3.53	49
102	-40.25	-1.88	24.38	3.52	49
103	33.25	9.38	1.88	3.52	49
104	1.75	-46.88	-5.62	3.52	49
105	22.75	-39.38	46.88	3.52	49
106	-26.25	1.88	13.12	3.51	49
107	-12.25	-5.62	-13.12	3.51	98
108	-36.75	-65.62	-16.88	3.51	49
109	-26.25	-50.62	-46.88	3.51	49
110	26.25	-73.12	31.88	3.50	49
111	26.25	-13.12	54.38	3.50	49
112	15.75	-50.62	9.38	3.50	49
113	-36.75	24.38	13.12	3.50	49
114	-33.25	61.88	39.38	3.50	49
115	19.25	-73.12	-16.88	3.49	98
116	-40.25	-69.38	20.62	3.49	49
117	-12.25	-43.12	-16.88	3.48	49
118	64.75	1.88	35.62	3.48	49
119	22.75	-76.88	20.62	3.47	49
120	-22.75	-9.38	-65.62	3.47	49
121	-50.75	31.88	9.38	3.47	49
122	-8.75	-24.38	1.88	3.44	49
123	19.25	-58.12	20.62	3.44	49
124	50.75	13.12	9.38	3.43	49
125	57.75	-16.88	16.88	3.43	49
126	-64.75	-5.62	28.12	3.43	49
127	-15.75	-24.38	-9.38	3.43	49
128	43.75	20.62	16.88	3.43	49
129	-64.75	-24.38	-20.62	3.42	49
130	5.25	-43.12	-1.88	3.42	49
131	1.75	54.38	-9.38	3.42	49
132	-8.75	-46.88	-31.88	3.42	49
133	-8.75	-28.12	5.62	3.42	49
134	-15.75	-61.88	43.12	3.42	49
135	29.75	1.88	13.12	3.42	49
136	36.75	28.12	39.38	3.41	49
137	50.75	-43.12	-1.88	3.41	49
138	29.75	-65.62	-24.38	3.41	49
139	22.75	-9.38	13.12	3.41	49
140	36.75	-50.62	-24.38	3.40	49
141	36.75	-61.88	-13.12	3.40	49
142	-15.75	-31.88	-50.62	3.40	49
143	-40.25	-28.12	-24.38	3.40	49
144	-40.25	-46.88	39.38	3.40	49
145	22.75	-73.12	-13.12	3.39	49
146	22.75	-9.38	35.62	3.39	49
147	22.75	-65.62	20.62	3.39	49
148	29.75	-16.88	46.88	3.39	49
149	-26.25	20.62	-1.88	3.38	49
150	15.75	-1.88	69.38	3.38	49
151	-22.75	-58.12	24.38	3.37	49
152	57.75	16.88	-1.88	3.37	49
153	-40.25	24.38	35.62	3.37	49
154	26.25	5.62	-28.12	3.36	49
155	-15.75	16.88	13.12	3.35	49
156	12.25	1.88	28.12	3.35	49
157	-5.25	28.12	13.12	3.35	49
158	15.75	24.38	-13.12	3.34	49
159	-8.75	28.12	28.12	3.34	49
160	-12.25	-58.12	-46.88	3.34	49
161	-12.25	-65.62	-39.38	3.34	49
162	19.25	24.38	5.62	3.34	49
163	-22.75	-16.88	24.38	3.33	49
164	-12.25	43.12	46.88	3.33	49
165	-15.75	-69.38	46.88	3.33	49
166	26.25	-16.88	-9.38	3.32	49
167	-26.25	-31.88	-69.38	3.32	49
168	26.25	-50.62	-35.62	3.32	49
169	-36.75	-24.38	1.88	3.32	49
170	19.25	-35.62	1.88	3.32	49
171	36.75	20.62	-13.12	3.31	49
172	8.75	-80.62	-5.62	3.31	49
173	12.25	-5.62	-5.62	3.30	49
174	-40.25	50.62	1.88	3.30	49
175	-15.75	-28.12	-5.62	3.29	49
176	29.75	-54.38	-24.38	3.29	49

chair

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.3
Cluster size threshold (voxels)	0
Minimum distance (mm)	8

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	40.25	-46.88	13.12	4.86	147
2	-36.75	-76.88	1.88	4.23	49
3	12.25	35.62	-5.62	4.20	49
4	-50.75	39.38	16.88	4.17	49
5	-50.75	-39.38	-1.88	3.94	147
6	-54.25	-43.12	46.88	3.90	49
7	33.25	-58.12	-1.88	3.89	49
8	-29.75	-43.12	-58.12	3.88	49
9	-36.75	-61.88	43.12	3.84	49
10	19.25	20.62	20.62	3.81	49
11	12.25	5.62	-24.38	3.77	98
12	22.75	24.38	16.88	3.76	49
13	33.25	43.12	-16.88	3.75	49
14	26.25	-46.88	-35.62	3.75	49
15	26.25	16.88	16.88	3.73	49
16	-15.75	-1.88	-16.88	3.70	49
17	-5.25	43.12	50.62	3.67	49
18	33.25	13.12	-1.88	3.66	49
19	-57.75	-9.38	-1.88	3.62	49
20	-22.75	-31.88	-69.38	3.61	49
21	-22.75	24.38	13.12	3.57	49
22	-57.75	13.12	1.88	3.52	49
23	33.25	-58.12	-16.88	3.50	49
24	-29.75	20.62	46.88	3.48	49
25	12.25	16.88	-20.62	3.44	49
26	33.25	28.12	-31.88	3.43	49
27	36.75	-24.38	20.62	3.43	49
28	43.75	-28.12	-13.12	3.40	49
29	29.75	13.12	-43.12	3.39	49
30	12.25	28.12	-16.88	3.38	49
31	-22.75	-43.12	-31.88	3.37	49
32	-1.75	5.62	31.88	3.37	49
33	33.25	9.38	-13.12	3.36	49
34	33.25	-20.62	-58.12	3.35	49
35	-15.75	-35.62	-24.38	3.34	49
36	29.75	-43.12	-35.62	3.34	49
37	-22.75	9.38	24.38	3.34	49
38	-36.75	-46.88	-54.38	3.33	49
39	1.75	28.12	-35.62	3.32	49
40	36.75	-28.12	-9.38	3.29	49

scrambledpix

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.3
Cluster size threshold (voxels)	0
Minimum distance (mm)	8

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	1.75	24.38	-13.12	5.74	49
2	-22.75	-46.88	43.12	4.82	98
3	-15.75	61.88	-9.38	4.79	147
4	-29.75	-61.88	-31.88	4.65	49
5	50.75	16.88	-28.12	4.14	147
6	-22.75	-54.38	-9.38	3.94	49
7	5.25	43.12	-28.12	3.92	49
8	-22.75	-9.38	20.62	3.82	49
9	40.25	-16.88	-39.38	3.80	49
10	-64.75	-31.88	39.38	3.78	49
11	-26.25	-69.38	39.38	3.59	49
12	22.75	-24.38	46.88	3.56	49
13	22.75	46.88	-5.62	3.54	49
14	-1.75	5.62	31.88	3.53	49
15	-40.25	-16.88	-54.38	3.46	49
16	33.25	35.62	-28.12	3.44	49
17	-15.75	-43.12	-16.88	3.41	49
18	26.25	-24.38	-69.38	3.40	49
19	1.75	13.12	31.88	3.34	49
20	33.25	-20.62	43.12	3.33	49

face

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.3
Cluster size threshold (voxels)	0
Minimum distance (mm)	8

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	1.75	24.38	-13.12	5.17	98
2	-50.75	-50.62	13.12	5.09	196
2a	-47.25	-58.12	13.12	3.37
3	-47.25	-43.12	-35.62	4.74	49
4	68.25	-13.12	-9.38	4.74	147
5	-47.25	-39.38	16.88	4.47	98
6	29.75	-46.88	35.62	4.40	49
7	29.75	-54.38	46.88	4.38	49
8	-33.25	50.62	16.88	4.37	147
9	-57.75	-16.88	1.88	4.37	147
10	-36.75	31.88	16.88	4.22	98
11	-12.25	-58.12	-16.88	4.22	49
12	-26.25	24.38	-13.12	4.16	147
13	-40.25	46.88	13.12	4.14	98
14	22.75	-50.62	-24.38	4.11	49
15	-22.75	39.38	13.12	4.09	49
16	40.25	28.12	39.38	3.98	98
17	-15.75	13.12	73.12	3.97	98
18	5.25	20.62	65.62	3.95	49
19	5.25	-24.38	-20.62	3.93	49
20	-47.25	-50.62	20.62	3.88	49
21	43.75	35.62	35.62	3.83	98
22	-47.25	-28.12	28.12	3.80	147
23	-1.75	31.88	61.88	3.79	98
24	-54.25	-43.12	20.62	3.77	49
25	-8.75	13.12	65.62	3.77	49
26	1.75	-43.12	-1.88	3.75	49
27	-8.75	-46.88	-31.88	3.73	49
28	-47.25	-61.88	-5.62	3.72	49
29	-19.25	-73.12	39.38	3.72	49
30	-26.25	-88.12	-9.38	3.71	98
31	12.25	28.12	61.88	3.71	49
32	-50.75	-35.62	43.12	3.70	49
33	-36.75	-43.12	39.38	3.70	147
34	-47.25	24.38	20.62	3.69	49
35	-36.75	-31.88	-46.88	3.69	49
36	-47.25	-54.38	-9.38	3.69	49
37	40.25	43.12	16.88	3.69	49
38	-57.75	-9.38	-1.88	3.67	49
39	-57.75	31.88	31.88	3.67	49
40	5.25	-58.12	-24.38	3.66	98
41	-36.75	31.88	35.62	3.65	147
42	33.25	-46.88	-39.38	3.64	49
43	-43.75	-31.88	35.62	3.62	49
44	-40.25	-43.12	-16.88	3.60	49
45	-12.25	-76.88	28.12	3.60	49
46	-8.75	-28.12	61.88	3.58	49
47	19.25	-16.88	39.38	3.56	49
48	26.25	-43.12	35.62	3.56	49
49	43.75	-54.38	9.38	3.53	49
50	19.25	-46.88	-20.62	3.53	49
51	-29.75	31.88	-9.38	3.52	49
52	40.25	-39.38	-24.38	3.51	49
53	36.75	61.88	28.12	3.51	49
54	-15.75	-73.12	16.88	3.50	49
55	8.75	-1.88	9.38	3.50	49
56	-47.25	-46.88	16.88	3.49	49
57	-22.75	-46.88	-50.62	3.49	49
58	22.75	31.88	28.12	3.49	49
59	12.25	-58.12	-20.62	3.48	49
60	5.25	-65.62	13.12	3.48	49
61	-47.25	-16.88	-35.62	3.48	49
62	-19.25	-61.88	-9.38	3.47	49
63	-15.75	-13.12	24.38	3.47	98
64	-26.25	-43.12	-43.12	3.47	49
65	-12.25	-39.38	58.12	3.46	49
66	-1.75	39.38	-20.62	3.46	49
67	26.25	-43.12	-31.88	3.46	49
68	33.25	-61.88	-13.12	3.45	49
69	5.25	31.88	-35.62	3.45	49
70	-40.25	50.62	20.62	3.43	49
71	-40.25	-35.62	-35.62	3.43	49
72	-50.75	-28.12	35.62	3.42	49
73	15.75	-54.38	-20.62	3.41	49
74	36.75	-43.12	5.62	3.40	49
75	-33.25	39.38	54.38	3.39	49
76	-15.75	-54.38	50.62	3.38	49
77	29.75	-35.62	35.62	3.38	49
78	-43.75	-39.38	-9.38	3.36	49
79	-29.75	28.12	13.12	3.35	49
80	-1.75	-20.62	24.38	3.35	49
81	-40.25	20.62	54.38	3.35	49
82	33.25	-58.12	-16.88	3.34	49
83	-47.25	-31.88	5.62	3.34	49
84	57.75	-35.62	43.12	3.33	49
85	-29.75	28.12	39.38	3.32	49
86	-47.25	-46.88	-31.88	3.32	49
87	26.25	-54.38	-20.62	3.32	49
88	-33.25	31.88	46.88	3.32	49
89	-8.75	-58.12	-43.12	3.30	49
90	-33.25	-58.12	20.62	3.29	49

shoe

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.3
Cluster size threshold (voxels)	0
Minimum distance (mm)	8

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	22.75	-9.38	35.62	4.74	49
2	61.25	24.38	31.88	4.22	147
3	36.75	46.88	-16.88	3.95	49
4	50.75	-35.62	-35.62	3.83	49
5	22.75	-69.38	-28.12	3.82	49
6	19.25	-20.62	-28.12	3.81	49
7	19.25	73.12	20.62	3.76	49
8	-54.25	-65.62	16.88	3.70	49
9	-19.25	20.62	43.12	3.69	49
10	-47.25	-16.88	-24.38	3.66	49
11	-1.75	-28.12	-73.12	3.65	49
12	19.25	-28.12	-54.38	3.59	49
13	15.75	-24.38	-73.12	3.57	49
14	-68.25	-20.62	24.38	3.56	49
15	-68.25	-9.38	9.38	3.53	49
16	26.25	-46.88	-24.38	3.52	49
17	-1.75	-20.62	24.38	3.52	49
18	15.75	16.88	58.12	3.45	49
19	36.75	28.12	39.38	3.45	49
20	33.25	9.38	61.88	3.43	49
21	-12.25	1.88	-69.38	3.37	49
22	26.25	-61.88	13.12	3.36	49
23	-29.75	9.38	35.62	3.36	49
24	1.75	-24.38	-39.38	3.36	49
25	-12.25	39.38	65.62	3.36	49
26	12.25	5.62	31.88	3.33	49
27	64.75	-1.88	31.88	3.32	49
28	26.25	35.62	28.12	3.30	49

cat

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.3
Cluster size threshold (voxels)	0
Minimum distance (mm)	8

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-12.25	-76.88	31.88	4.68	98
2	43.75	-5.62	16.88	4.53	196
3	-15.75	-20.62	-58.12	4.45	98
4	-40.25	-65.62	13.12	4.36	49
5	1.75	35.62	-13.12	4.29	49
6	-12.25	-58.12	-16.88	4.27	49
7	5.25	-61.88	-24.38	4.27	98
8	43.75	5.62	1.88	4.26	344
9	-1.75	50.62	-9.38	4.25	246
10	5.25	-24.38	-73.12	4.22	49
11	22.75	-61.88	-54.38	4.04	49
12	15.75	-16.88	46.88	4.02	49
13	36.75	9.38	-35.62	3.99	49
14	19.25	-31.88	-69.38	3.98	49
15	22.75	-24.38	-58.12	3.98	49
16	-64.75	-9.38	-24.38	3.98	49
17	36.75	69.38	5.62	3.91	98
18	12.25	-61.88	-31.88	3.86	98
19	1.75	28.12	-35.62	3.85	49
20	47.25	1.88	-46.88	3.82	49
21	8.75	-65.62	-24.38	3.82	49
22	-29.75	39.38	54.38	3.81	49
23	15.75	-31.88	-61.88	3.76	49
24	5.25	-80.62	9.38	3.75	49
25	43.75	24.38	9.38	3.72	49
26	40.25	-5.62	-5.62	3.70	49
27	50.75	28.12	24.38	3.70	49
28	22.75	-58.12	-61.88	3.70	49
29	-40.25	-46.88	16.88	3.66	49
30	5.25	43.12	50.62	3.63	49
31	-12.25	-73.12	-31.88	3.62	49
32	-26.25	-80.62	13.12	3.57	98
33	50.75	1.88	-24.38	3.57	49
34	-54.25	-46.88	5.62	3.57	98
35	-40.25	-69.38	35.62	3.53	49
36	36.75	28.12	-20.62	3.53	49
37	8.75	16.88	-16.88	3.53	49
38	5.25	20.62	50.62	3.53	49
39	-47.25	-31.88	-43.12	3.52	49
40	-40.25	-5.62	50.62	3.50	49
41	-5.25	-50.62	-13.12	3.50	49
42	-29.75	-46.88	5.62	3.48	49
43	29.75	1.88	-31.88	3.47	49
44	-8.75	-35.62	-24.38	3.44	49
45	61.25	-31.88	43.12	3.44	49
46	5.25	-31.88	-69.38	3.43	49
47	-22.75	65.62	39.38	3.42	98
48	36.75	28.12	43.12	3.38	147
49	-8.75	-46.88	-28.12	3.36	49
50	12.25	-39.38	-31.88	3.36	49
51	-8.75	-28.12	-61.88	3.35	49
52	61.25	-31.88	20.62	3.35	49
53	-40.25	-31.88	-58.12	3.34	49
54	40.25	-20.62	-39.38	3.32	49
55	5.25	-73.12	-20.62	3.32	49
56	-29.75	-20.62	-35.62	3.32	49
57	15.75	39.38	-20.62	3.31	49
58	-36.75	-50.62	-39.38	3.31	49
59	-47.25	-43.12	43.12	3.31	49
60	26.25	-65.62	13.12	3.30	49

scissors

Contrast Plot

Cluster Table

Height control	fpr
α	0.001
Threshold (computed)	3.3
Cluster size threshold (voxels)	0
Minimum distance (mm)	8

Cluster ID	X	Y	Z	Peak Stat	Cluster Size (mm3)
1	-50.75	20.62	-24.38	6.55	1132
2	-19.25	84.38	20.62	6.20	1476
2a	-26.25	80.62	16.88	4.90
2b	-15.75	76.88	31.88	4.42
3	-26.25	24.38	28.12	5.83	147
4	36.75	20.62	-31.88	5.58	246
5	29.75	5.62	-5.62	5.32	98
6	-12.25	20.62	-46.88	5.25	344
7	12.25	-35.62	-69.38	5.14	98
8	15.75	76.88	28.12	5.05	49
9	5.25	24.38	-46.88	5.04	393
10	-12.25	76.88	20.62	5.02	935
10a	-5.25	84.38	1.88	4.38
10b	-5.25	84.38	20.62	4.38
10c	-5.25	80.62	13.12	4.22
11	-1.75	1.88	-61.88	5.02	295
12	-19.25	61.88	50.62	5.00	49
13	12.25	13.12	-43.12	4.98	49
14	33.25	5.62	-46.88	4.92	49
15	1.75	-28.12	-61.88	4.88	49
16	-54.25	-39.38	-31.88	4.79	49
17	-22.75	-54.38	-50.62	4.66	295
18	12.25	-61.88	-46.88	4.56	49
19	12.25	80.62	-5.62	4.49	49
20	36.75	28.12	-35.62	4.46	295
21	-26.25	-13.12	1.88	4.45	49
22	-15.75	-43.12	-46.88	4.42	98
23	-29.75	20.62	-46.88	4.41	98
24	-12.25	9.38	-58.12	4.41	98
25	26.25	-46.88	1.88	4.36	49
26	-5.25	80.62	31.88	4.32	196
27	15.75	-35.62	-73.12	4.32	49
28	50.75	24.38	-35.62	4.30	98
29	40.25	-58.12	-5.62	4.26	98
30	-19.25	31.88	-35.62	4.26	147
31	12.25	13.12	-50.62	4.26	344
32	-40.25	69.38	13.12	4.25	49
33	47.25	24.38	-1.88	4.23	49
34	1.75	1.88	31.88	4.22	147
35	1.75	13.12	-16.88	4.20	49
36	1.75	-1.88	-76.88	4.20	98
37	-40.25	-9.38	-5.62	4.17	49
38	15.75	88.12	16.88	4.16	147
39	-12.25	1.88	-28.12	4.15	49
40	15.75	-16.88	46.88	4.15	49
41	47.25	24.38	9.38	4.14	49
42	-5.25	-9.38	31.88	4.13	49
43	40.25	28.12	-43.12	4.12	49
44	1.75	76.88	16.88	4.11	196
45	50.75	31.88	-31.88	4.10	49
46	1.75	-5.62	-80.62	4.08	98
47	1.75	-28.12	-76.88	4.07	49
48	-54.25	9.38	-16.88	4.05	49
49	50.75	20.62	-1.88	4.05	49
50	-40.25	61.88	5.62	4.05	49
51	-26.25	-24.38	20.62	4.04	49
52	-47.25	-5.62	-28.12	4.03	49
53	-50.75	-9.38	-20.62	4.03	196
54	-22.75	-5.62	-20.62	4.01	49
55	-5.25	-28.12	-76.88	4.01	49
56	54.25	5.62	50.62	4.01	98
57	40.25	5.62	31.88	4.00	49
58	29.75	76.88	13.12	3.98	49
59	22.75	-54.38	-43.12	3.98	49
60	-26.25	-1.88	-9.38	3.96	98
61	50.75	50.62	20.62	3.93	49
62	-12.25	88.12	9.38	3.91	49
63	-36.75	-1.88	-20.62	3.91	147
64	-29.75	-69.38	9.38	3.91	98
65	47.25	-1.88	20.62	3.90	147
66	19.25	-16.88	-54.38	3.90	49
67	57.75	-24.38	5.62	3.89	49
68	-19.25	50.62	13.12	3.88	49
69	33.25	24.38	-43.12	3.88	98
70	12.25	80.62	35.62	3.87	98
71	-26.25	-80.62	-5.62	3.86	49
72	-26.25	31.88	-39.38	3.86	49
73	-68.25	-5.62	5.62	3.86	98
74	-36.75	65.62	20.62	3.85	98
75	-40.25	-16.88	-65.62	3.85	49
76	22.75	-58.12	-50.62	3.85	49
77	19.25	80.62	1.88	3.85	98
78	-5.25	1.88	-76.88	3.85	49
79	12.25	-61.88	9.38	3.84	49
80	-36.75	69.38	16.88	3.83	49
81	15.75	50.62	-9.38	3.83	49
82	33.25	-35.62	-35.62	3.83	49
83	22.75	-1.88	9.38	3.83	49
84	-5.25	5.62	-69.38	3.82	49
85	5.25	13.12	31.88	3.82	98
86	-33.25	-65.62	-16.88	3.82	49
87	-5.25	-16.88	-39.38	3.80	49
88	22.75	43.12	-9.38	3.80	49
89	-50.75	-13.12	5.62	3.80	49
90	43.75	-61.88	5.62	3.78	49
91	19.25	-1.88	-1.88	3.76	49
92	22.75	-35.62	-9.38	3.75	49
93	12.25	24.38	-35.62	3.75	98
94	-8.75	24.38	-35.62	3.74	49
95	-40.25	43.12	-31.88	3.74	49
96	40.25	-24.38	-35.62	3.73	49
97	-8.75	16.88	-43.12	3.72	49
98	15.75	88.12	9.38	3.72	98
99	29.75	-1.88	24.38	3.72	49
100	54.25	1.88	43.12	3.70	49
101	1.75	65.62	46.88	3.70	49
102	22.75	-20.62	-5.62	3.70	49
103	29.75	58.12	-16.88	3.69	49
104	-1.75	73.12	35.62	3.69	49
105	40.25	-39.38	58.12	3.68	49
106	26.25	-50.62	-54.38	3.68	49
107	-8.75	28.12	54.38	3.68	196
108	-22.75	-39.38	-54.38	3.67	49
109	-1.75	-35.62	-65.62	3.66	98
110	1.75	39.38	-16.88	3.65	49
111	8.75	-9.38	-76.88	3.65	49
112	33.25	-5.62	-1.88	3.64	98
113	-1.75	28.12	-39.38	3.64	49
114	1.75	80.62	24.38	3.62	98
115	19.25	-46.88	-35.62	3.62	49
116	1.75	-9.38	1.88	3.61	49
117	-47.25	24.38	20.62	3.61	49
118	36.75	24.38	-28.12	3.60	49
119	22.75	-43.12	-61.88	3.59	49
120	-22.75	58.12	-20.62	3.59	98
121	43.75	-31.88	-13.12	3.57	49
122	-22.75	54.38	-13.12	3.55	49
123	1.75	-5.62	-20.62	3.54	49
124	-22.75	39.38	31.88	3.54	49
125	-29.75	31.88	-9.38	3.53	49
126	57.75	16.88	16.88	3.53	49
127	15.75	-76.88	-16.88	3.52	49
128	15.75	46.88	-20.62	3.52	49
129	33.25	-39.38	24.38	3.52	49
130	-43.75	-24.38	35.62	3.52	49
131	-12.25	65.62	-9.38	3.52	49
132	-33.25	39.38	54.38	3.51	49
133	-26.25	16.88	28.12	3.51	49
134	29.75	-1.88	13.12	3.50	49
135	12.25	1.88	-69.38	3.50	49
136	19.25	54.38	50.62	3.49	49
137	8.75	76.88	16.88	3.49	49
138	-26.25	-28.12	31.88	3.49	49
139	29.75	-9.38	9.38	3.48	49
140	22.75	58.12	-5.62	3.48	49
141	-19.25	-20.62	-1.88	3.48	49
142	-12.25	-43.12	-13.12	3.48	49
143	-26.25	65.62	35.62	3.48	49
144	15.75	76.88	20.62	3.47	98
145	-5.25	-1.88	-28.12	3.47	49
146	-33.25	69.38	24.38	3.46	98
147	-19.25	-5.62	16.88	3.46	49
148	-26.25	61.88	-5.62	3.46	49
149	-22.75	69.38	35.62	3.46	49
150	29.75	-46.88	-61.88	3.46	49
151	-8.75	20.62	31.88	3.45	49
152	33.25	24.38	61.88	3.45	49
153	40.25	-1.88	61.88	3.44	98
154	-33.25	43.12	-13.12	3.44	49
155	54.25	35.62	35.62	3.44	49
156	22.75	-20.62	20.62	3.44	49
157	-19.25	31.88	-28.12	3.43	49
158	-61.25	-46.88	9.38	3.43	49
159	19.25	16.88	-46.88	3.43	49
160	-1.75	-46.88	-1.88	3.43	49
161	43.75	-9.38	-16.88	3.43	49
162	57.75	-31.88	46.88	3.42	49
163	-19.25	-1.88	-35.62	3.42	49
164	22.75	20.62	24.38	3.42	49
165	-19.25	9.38	-35.62	3.42	49
166	-22.75	20.62	-46.88	3.41	49
167	-57.75	-5.62	-1.88	3.41	49
168	-5.25	39.38	58.12	3.40	49
169	-47.25	-16.88	-13.12	3.40	49
170	-29.75	-13.12	-35.62	3.40	49
171	33.25	-39.38	-43.12	3.40	49
172	1.75	35.62	-24.38	3.40	49
173	29.75	9.38	16.88	3.39	49
174	15.75	-28.12	-54.38	3.38	49
175	22.75	-39.38	13.12	3.38	49
176	29.75	-20.62	-5.62	3.38	49
177	40.25	-43.12	-5.62	3.37	49
178	33.25	-39.38	-20.62	3.37	49
179	-26.25	9.38	20.62	3.37	49
180	50.75	-16.88	-13.12	3.36	49
181	-26.25	43.12	-28.12	3.36	49
182	15.75	-39.38	24.38	3.36	49
183	-12.25	1.88	9.38	3.35	49
184	26.25	-16.88	-35.62	3.35	49
185	33.25	5.62	16.88	3.35	49
186	-19.25	-1.88	-9.38	3.35	49
187	15.75	-50.62	46.88	3.35	49
188	-57.75	-9.38	-13.12	3.35	49
189	-15.75	-65.62	5.62	3.35	49
190	15.75	5.62	9.38	3.35	49
191	29.75	-1.88	1.88	3.35	49
192	8.75	1.88	24.38	3.35	49
193	-33.25	28.12	-39.38	3.34	49
194	26.25	1.88	24.38	3.34	49
195	-22.75	-88.12	-1.88	3.34	49
196	-29.75	-16.88	-1.88	3.34	49
197	33.25	-31.88	-43.12	3.34	49
198	-1.75	-13.12	31.88	3.33	49
199	40.25	13.12	-39.38	3.33	49
200	33.25	-20.62	43.12	3.33	49
201	22.75	-9.38	-5.62	3.32	49
202	29.75	-1.88	50.62	3.32	49
203	5.25	-1.88	5.62	3.32	49
204	-12.25	-50.62	-50.62	3.31	49
205	-15.75	1.88	28.12	3.31	49
206	-33.25	73.12	9.38	3.31	49
207	1.75	-1.88	-24.38	3.31	49
208	29.75	-50.62	-50.62	3.30	49
209	8.75	16.88	-39.38	3.30	49
210	-19.25	-20.62	-20.62	3.30	49
211	26.25	13.12	-39.38	3.30	49
212	50.75	-16.88	46.88	3.30	49
213	29.75	54.38	-5.62	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()

9.3.9.7. 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 nilearn.decoding import Decoder
from sklearn.model_selection import LeaveOneGroupOut
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. / len(np.unique(conditions))
print('Classification accuracy: {:.4f} / Chance level: {}'.format(
    classification_accuracy, chance_level))

Out:

Classification accuracy: 0.6890 / Chance level: 0.125