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 0x7fc01cdf3f40>, <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>, { '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 - 1.1s, 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 - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.256013, ...,  0.308334]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.038572, ...,  0.855077]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.070285, ..., -1.222001]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.1724  , ...,  0.033508]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.96724 , ..., -1.474856]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.432136, ..., -1.197805]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.420626, ..., -0.443207]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.310409, ..., 0.196496]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4aed00>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc01dbeef10>, <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>, { '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([[ 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 - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.705774, ..., -0.934083]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.476069, ..., -1.29404 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.210752, ..., -1.441079]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.341707, ...,  2.094528]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.097247, ..., -0.496306]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.118812, ...,  1.58503 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.801345, ..., -1.648133]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.621172, ..., -0.678871]), <nibabel.nifti1.Nifti1Image object at 0x7fc01e4ae1c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc017468370>, <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>, { '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 - 1.1s, 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 - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.733742, ..., -0.435687]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.502666, ..., 1.789333]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.690828, ...,  0.731519]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.403789, ...,  0.257657]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.472767, ..., -1.160892]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.07507 , ..., -2.203006]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.445359, ..., -0.36161 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.034083, ..., 0.35299 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1a340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc01c0b53d0>, <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>, { '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 - 1.1s, 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 - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.596132, ..., -1.910225]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.217544, ..., -0.003551]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.337751, ..., 0.789979]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.161412, ..., -0.265537]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.538844, ..., -0.797649]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.589617, ..., -0.790328]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.306331, ..., -0.003053]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.20477 , ..., -0.804061]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b28b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc017488070>, <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>, { '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 - 1.1s, 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 - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.161983, ..., -0.068078]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.467058, ..., -0.494102]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.008358, ..., 0.250096]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.127065, ..., -0.241985]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.066426, ..., 0.493324]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.049242, ..., -1.008997]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.336253, ..., -0.596381]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.707037, ..., 1.358865]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250472b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc017492850>, <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>, { '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 - 1.1s, 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 - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.197916, ...,  1.581644]), <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.576467, ...,  0.392603]), <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.969252, ...,  1.15444 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.716041, ...,  1.094486]), <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.280537, ...,  0.542739]), <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.331329, ..., -0.019244]), <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.298501, ..., -0.355821]), <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.232015, ..., -0.420629]), <nibabel.nifti1.Nifti1Image object at 0x7fc017492370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc01b7871f0>, <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>, { 'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[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 - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.490124, ..., -0.665442]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.374685, ..., -0.980248]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.654133, ..., -0.288692]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.244312, ..., -1.462072]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.665202, ...,  1.793466]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.036326, ..., 0.693956]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.672634, ..., -0.24818 ]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.099764, ..., -1.722951]), <nibabel.nifti1.Nifti1Image object at 0x7fc01ac1ad60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc01b780be0>, <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>, { '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 - 1.1s, 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 - 1.5s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.033872, ..., -0.317176]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.98478 , ..., -0.770334]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.127461, ..., 0.929068]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.489347, ..., -0.230229]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.673052, ...,  0.6757  ]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.260282, ..., -0.346342]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.775541, ..., 1.603123]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.391727, ..., 0.653312]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b2460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc017479910>, <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>, { '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 - 1.1s, 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 - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.620911, ...,  2.309993]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.44548 , ..., 0.576334]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.483355, ..., -0.068969]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.166576, ..., 0.713549]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.612744, ..., 1.441012]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.254934, ..., -1.288018]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.134169, ..., -0.621367]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.234171, ..., -1.497943]), <nibabel.nifti1.Nifti1Image object at 0x7fc0250474f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc01c0d7520>, <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>, { 'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-5.223948, ..., -5.959582],
       ...,
       [-7.677519, ..., 16.024363]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1)
__________________________________________________________run_glm - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.01344 , ...,  1.283233]), <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.79114 , ..., 1.031069]), <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.583575, ..., 0.488828]), <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.265741, ..., -0.623721]), <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.20672, ...,  1.09668]), <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.045178, ...,  0.822339]), <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.463833, ..., -0.151504]), <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.230311, ...,  0.198537]), <nibabel.nifti1.Nifti1Image object at 0x7fc01acfca00>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc01c0aaf40>, <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>, { '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([[-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 - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.080165, ..., -3.497239]), <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-3.083184, ..., -3.071235]), <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.724401, ..., -2.892927]), <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.044506, ..., 0.285176]), <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.257372, ..., 1.802962]), <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.485481, ...,  0.284696]), <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.032135, ..., -1.290339]), <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.417132, ..., -1.258094]), <nibabel.nifti1.Nifti1Image object at 0x7fc01b787970>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fc019d06340>, <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>, { '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 - 1.1s, 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 - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.545625, ..., -1.041515]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.152042, ..., 1.145641]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.55236 , ..., 0.696758]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.684509, ...,  0.226524]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.423531, ..., -0.641954]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.726779, ..., 0.687642]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.809306, ...,  0.496974]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.116436, ..., -0.811166]), <nibabel.nifti1.Nifti1Image object at 0x7fc0174b24f0>)
___________________________________________________________unmask - 0.2s, 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:

Plot of Design Matrix used in Session 1.

Contrasts

Plot of the contrast: bottle. Plot of the contrast: house. Plot of the contrast: chair. Plot of the contrast: scrambledpix. Plot of the contrast: face. Plot of the contrast: shoe. Plot of the contrast: cat. Plot of the contrast: scissors.

Mask

Model did not supply a mask image.

Stat Maps with Cluster Tables

bottle

Stat map plot for the contrast: bottle
Contrast Plot Plot of the contrast: bottle.

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
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528 12.25 -9.38 -24.38 3.52 49
529 -50.75 -1.88 16.88 3.52 49
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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
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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
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570 40.25 -35.62 -1.88 3.47 49
571 -50.75 -9.38 -13.12 3.46 49
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591 -29.75 16.88 -5.62 3.44 49
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593 26.25 9.38 24.38 3.44 49
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595 43.75 1.88 -1.88 3.44 49
596 29.75 -16.88 58.12 3.44 49
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600 15.75 -50.62 9.38 3.43 49
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606 12.25 -35.62 -61.88 3.43 49
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609 47.25 28.12 -39.38 3.43 49
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612 -19.25 1.88 58.12 3.42 49
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614 36.75 -31.88 13.12 3.42 49
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629 5.25 80.62 31.88 3.40 49
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632 -54.25 -9.38 20.62 3.40 49
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662 33.25 -20.62 -9.38 3.35 49
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669 15.75 -5.62 28.12 3.34 49
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671 54.25 1.88 9.38 3.34 49
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684 50.75 -20.62 -43.12 3.32 49
685 54.25 -39.38 -24.38 3.32 49
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688 -15.75 20.62 20.62 3.32 49
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692 1.75 -58.12 -13.12 3.31 49
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700 12.25 -50.62 -1.88 3.30 49
701 -12.25 -16.88 -65.62 3.30 49
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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

Stat map plot for the contrast: house
Contrast Plot