Decoding of a dataset after GLM fit for signal extraction

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

More specifically:

  1. Download the Haxby dataset.

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

  3. Analyze the decoding performance using a classifier.

Fetch example Haxby dataset

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

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

from nilearn import datasets

haxby_dataset = datasets.fetch_haxby()

# repetition has to be known
t_r = 2.5
[get_dataset_dir] Dataset found in /home/runner/nilearn_data/haxby2001

Load the behavioral data

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

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

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

Build a proper event structure for each run

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

Instantiate and run FirstLevelModel

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

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

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

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

Run the GLM on data from each run

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

for run in unique_runs:
    # grab the fmri data for that particular run
    fmri_run = index_img(func_filename, runs == run)

    # fit the GLM
    glm.fit(fmri_run, events=events[run])

    # set up contrasts: one per condition
    conditions = events[run].trial_type.unique()
    for condition_ in conditions:
        z_maps.append(glm.compute_contrast(condition_))
        conditions_label.append(condition_)
        run_label.append(run)
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0917f1b80>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.114769, ..., -2.149296],
       ...,
       [ 2.367151, ...,  0.779998]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.575977, ...,  0.481522]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.00779 , ...,  0.924282]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.349128, ..., -1.50145 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.902378, ..., -0.291882]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.624816, ..., -1.682436]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.838935, ..., -1.528093]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.06136 , ..., -0.289828]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.983539, ..., 0.291568]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b5af4d90>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 12.660587, ..., -13.536042],
       ...,
       [ -3.254408, ..., -33.842804]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.940878, ..., -0.740886]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.56852 , ..., -1.374435]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.141601, ..., -1.208286]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.922613, ...,  2.268412]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.904959, ..., -1.104956]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.242388, ..., 1.270843]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.197666, ..., -1.944003]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.6546  , ..., -0.644223]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b4d44a00>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 5.205584, ..., 26.587189],
       ...,
       [-6.836576, ..., 10.676956]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.441012, ..., -0.384872]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.457669, ..., 1.351326]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.697867, ...,  0.601435]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.763713, ...,  0.209919]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.447911, ..., -1.13823 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.162822, ..., -2.114926]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.785086, ..., -0.260924]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.456513, ..., 0.23571 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b505d730>, <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-2.026206, ...,  5.974948],
       ...,
       [ 2.616334, ...,  0.104535]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.424686, ..., -1.190333]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.365571, ..., -0.28288 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.288251, ..., 0.627725]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.327741, ..., -0.321921]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.152188, ..., -0.366314]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.629164, ..., -0.735212]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.873351, ..., -0.14857 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.061121, ..., -0.426454]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b52c41c0>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 53.033577, ..., -55.45955 ],
       ...,
       [-51.57195 , ..., -55.994713]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.09849 , ..., -0.026579]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.329567, ..., -0.355602]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.134325, ...,  0.590796]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.998496, ..., -0.158513]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.0983  , ...,  0.684567]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.003411, ..., -1.001089]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.28481 , ..., -0.796836]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.349998, ..., 1.77109 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b591c2b0>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-27.150482, ...,  -5.81308 ],
       ...,
       [-30.204891, ...,   7.417917]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.773093, ...,  1.506396]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.945765, ...,  0.964351]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.489424, ...,  1.36385 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.60046 , ...,  1.292458]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.115105, ...,  0.360045]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.341327, ..., -0.115557]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.0463  , ..., -0.011997]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.304337, ..., -0.465647]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff088ee3f10>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[129.51173 , ..., -15.279282],
       ...,
       [-18.911755, ...,  21.839058]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.091521, ..., -0.624162]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.375261, ..., -0.645241]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.852582, ..., 0.144768]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.861249, ..., -1.104223]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.147067, ...,  1.938447]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.043015, ..., 0.397187]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.518244, ..., -0.247356]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.292525e-03, ..., -1.546823e+00]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0a9e08e20>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-15.915996, ...,  22.07737 ],
       ...,
       [-16.981215, ...,   3.372383]], dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.352245, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.011367, ..., -0.044286]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.378178, ..., -0.435127]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.249686, ..., 1.006757]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.627259, ..., -0.703344]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.673   , ...,  0.634889]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.170346, ..., -0.003907]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.74249 , ..., 2.416095]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.813068, ..., 0.397476]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a550>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.292987, ..., 18.392956],
       ...,
       [-3.935719, ...,  0.602484]], dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.352245, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.400515, ...,  2.602952]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.497881, ..., 0.140209]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.499281, ..., -0.49038 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.038187, ..., 0.57069 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.622503, ..., 1.679756]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.211798, ..., -1.574836]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.143717, ..., -0.364448]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.156681, ..., -1.209168]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0902a1d00>, <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-5.223948, ..., -5.959582],
       ...,
       [-7.677519, ..., 16.024363]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.00555 , ..., 1.955666]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.488905, ..., 1.063614]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.126069, ..., 0.347685]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.147577, ..., -0.722242]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.137613, ...,  1.219668]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.30493 , ...,  1.036247]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.64672 , ...,  0.256269]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.01524, ...,  0.32111]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0a6b35160>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-19.66533 , ...,  -6.299562],
       ...,
       [-24.647343, ...,   2.331865]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.497712, ..., -3.241636]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.884836, ..., -2.579124]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.361796, ..., -2.404684]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.15611 , ...,  0.717663]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.124367, ..., 1.85051 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.322726, ...,  0.187135]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.943745, ..., -1.223389]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.298717, ..., -1.02394 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff091819cd0>, <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-1.095605, ..., 16.449202],
       ...,
       [ 2.59974 , ..., -2.179998]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.028052, ..., -1.076144]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.07071 , ...,  1.117228]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.278396, ..., 0.666023]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.606351, ..., -0.186859]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.228201, ..., -0.299271]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.760997, ..., 0.931553]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.005349, ...,  0.835504]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.093039, ..., -0.791928]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min

Generating a report

Since we have already computed the FirstLevelModel and have the contrast, we can quickly create a summary report.

from nilearn.image import mean_img
from nilearn.reporting import make_glm_report

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

report  # This report can be viewed in a notebook

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

First Level Model

Model details:

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

Design Matrix:

Plot of Design Matrix used in Run 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) 3.291
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -54.25 13.12 5.62 6.49 98
2 29.75 -20.62 -24.38 6.49 836
2a 12.25 -20.62 -31.88 4.58
3 26.25 -20.62 -13.12 6.13 295
4 -36.75 -46.88 -46.88 6.11 147
5 43.75 -50.62 -5.62 6.03 1476
5a 47.25 -43.12 -1.88 4.74
5b 40.25 -28.12 -1.88 3.99
6 -1.75 -39.38 13.12 5.95 147
7 33.25 -43.12 -43.12 5.94 1181
7a 26.25 -35.62 -43.12 5.67
8 22.75 -54.38 -39.38 5.91 885
8a 8.75 -58.12 -39.38 4.15
9 -43.75 28.12 -9.38 5.90 196
10 -12.25 -24.38 -76.88 5.86 196
11 -19.25 -24.38 -43.12 5.80 590
11a -19.25 -31.88 -39.38 5.31
12 26.25 -46.88 -46.88 5.75 147
13 -57.75 -39.38 9.38 5.74 246
14 33.25 5.62 -13.12 5.69 442
14a 26.25 9.38 -16.88 5.46
15 22.75 -46.88 -61.88 5.68 442
16 19.25 -76.88 -13.12 5.60 590
17 22.75 -43.12 9.38 5.57 196
18 -29.75 9.38 -28.12 5.56 246
19 26.25 -69.38 -16.88 5.49 689
19a 36.75 -65.62 -13.12 4.91
19b 33.25 -58.12 -16.88 4.13
20 47.25 -24.38 -13.12 5.47 3248
20a 40.25 -35.62 -24.38 5.23
20b 33.25 -50.62 -20.62 4.80
20c 43.75 -16.88 -13.12 4.67
21 1.75 50.62 -16.88 5.46 295
22 -26.25 -16.88 -24.38 5.38 147
23 12.25 -84.38 -28.12 5.31 98
24 -29.75 -43.12 -50.62 5.30 442
25 1.75 -43.12 -1.88 5.29 98
26 -15.75 -28.12 -43.12 5.24 49
27 12.25 -13.12 -28.12 5.23 98
28 -47.25 -13.12 13.12 5.21 295
29 -19.25 -50.62 -39.38 5.19 639
29a -8.75 -35.62 -39.38 4.36
29b -12.25 -46.88 -39.38 3.96
30 -40.25 -35.62 -35.62 5.17 344
31 12.25 -28.12 -73.12 5.16 147
32 8.75 -50.62 -35.62 5.13 590
33 29.75 1.88 -9.38 5.11 98
34 19.25 -31.88 16.88 5.10 49
35 -1.75 -50.62 -39.38 5.08 98
36 -50.75 35.62 -16.88 5.08 196
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487 -19.25 -61.88 20.62 3.59 49
488 57.75 35.62 28.12 3.58 98
489 33.25 -35.62 -35.62 3.58 49
490 -19.25 -9.38 -65.62 3.58 49
491 -40.25 -46.88 -5.62 3.58 49
492 5.25 -80.62 -1.88 3.58 49
493 -15.75 -5.62 1.88 3.58 98
494 26.25 -61.88 -16.88 3.58 98
495 29.75 -1.88 -20.62 3.58 49
496 5.25 5.62 -69.38 3.57 49
497 5.25 -9.38 -20.62 3.57 49
498 15.75 -13.12 -73.12 3.57 49
499 -57.75 -9.38 16.88 3.57 98
500 -1.75 1.88 69.38 3.56 49
501 -43.75 13.12 13.12 3.56 98
502 26.25 31.88 43.12 3.56 49
503 -33.25 73.12 20.62 3.56 49
504 36.75 20.62 31.88 3.56 49
505 -12.25 13.12 58.12 3.56 49
506 -43.75 58.12 20.62 3.56 49
507 -29.75 -24.38 -9.38 3.55 49
508 -33.25 -16.88 -5.62 3.55 49
509 -12.25 5.62 35.62 3.55 147
510 19.25 -31.88 -16.88 3.55 49
511 -61.25 -16.88 9.38 3.55 49
512 -54.25 -9.38 -5.62 3.55 98
513 50.75 -13.12 5.62 3.54 49
514 -26.25 -35.62 54.38 3.54 49
515 26.25 -54.38 -13.12 3.54 98
516 -1.75 46.88 54.38 3.54 49
517 47.25 28.12 -39.38 3.54 49
518 -29.75 43.12 39.38 3.54 49
519 -47.25 43.12 9.38 3.54 49
520 57.75 -13.12 16.88 3.54 49
521 -40.25 13.12 -43.12 3.53 49
522 22.75 69.38 5.62