Note

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

Decoding of a dataset after GLM fit for signal extraction

Full step-by-step example of fitting a GLM to perform a decoding experiment. 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/remi/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 0x757c29d2fa30>, <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  '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([[-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 - 2.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.575977, ...,  0.481522]), <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.00779 , ...,  0.924282]), <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.349128, ..., -1.50145 ]), <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.902378, ..., -0.291882]), <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.624816, ..., -1.682436]), <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.838935, ..., -1.528093]), <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.06136 , ..., -0.289828]), <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.983539, ..., 0.291568]), <nibabel.nifti1.Nifti1Image object at 0x757c29d2f700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c2ebf4a30>, <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  '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, random_state=None)
__________________________________________________________run_glm - 3.1s, 0.1min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.940878, ..., -0.740886]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.56852 , ..., -1.374435]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.141601, ..., -1.208286]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.922613, ...,  2.268412]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.904959, ..., -1.104956]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.242388, ..., 1.270843]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.197666, ..., -1.944003]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.6546  , ..., -0.644223]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c48999bb0>, <nibabel.nifti1.Nifti1Image object at 0x757c48999610>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  '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([[ 5.205584, ..., 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 - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.441012, ..., -0.384872]), <nibabel.nifti1.Nifti1Image object at 0x757c48999610>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.457669, ..., 1.351326]), <nibabel.nifti1.Nifti1Image object at 0x757c48999610>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.697867, ...,  0.601435]), <nibabel.nifti1.Nifti1Image object at 0x757c48999610>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.763713, ...,  0.209919]), <nibabel.nifti1.Nifti1Image object at 0x757c48999610>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.447911, ..., -1.13823 ]), <nibabel.nifti1.Nifti1Image object at 0x757c48999610>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.162822, ..., -2.114926]), <nibabel.nifti1.Nifti1Image object at 0x757c48999610>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.785086, ..., -0.260924]), <nibabel.nifti1.Nifti1Image object at 0x757c48999610>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.456513, ..., 0.23571 ]), <nibabel.nifti1.Nifti1Image object at 0x757c48999610>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c4b5268b0>, <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-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 - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.424686, ..., -1.190333]), <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.365571, ..., -0.28288 ]), <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.288251, ..., 0.627725]), <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.327741, ..., -0.321921]), <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.152188, ..., -0.366314]), <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.629164, ..., -0.735212]), <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.873351, ..., -0.14857 ]), <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.061121, ..., -0.426454]), <nibabel.nifti1.Nifti1Image object at 0x757c35a26700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c486f9760>, <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  '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([[ 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 - 2.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.09849 , ..., -0.026579]), <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.329567, ..., -0.355602]), <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.134325, ...,  0.590796]), <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.998496, ..., -0.158513]), <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.0983  , ...,  0.684567]), <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.003411, ..., -1.001089]), <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.28481 , ..., -0.796836]), <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.349998, ..., 1.77109 ]), <nibabel.nifti1.Nifti1Image object at 0x757c5e5a1250>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c2ebf41f0>, <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  '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 - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-27.150482, ...,  -5.81308 ],
       ...,
       [-30.204891, ...,   7.417917]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 4.4s, 0.1min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.773093, ...,  1.506396]), <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.945765, ...,  0.964351]), <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.489424, ...,  1.36385 ]), <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.60046 , ...,  1.292458]), <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.115105, ...,  0.360045]), <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.341327, ..., -0.115557]), <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.0463  , ..., -0.011997]), <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.304337, ..., -0.465647]), <nibabel.nifti1.Nifti1Image object at 0x757c4de048b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c4b193b20>, <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  '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, random_state=None)
__________________________________________________________run_glm - 2.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.091521, ..., -0.624162]), <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.375261, ..., -0.645241]), <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.852582, ..., 0.144768]), <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.861249, ..., -1.104223]), <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.147067, ...,  1.938447]), <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.043015, ..., 0.397187]), <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.518244, ..., -0.247356]), <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.292525e-03, ..., -1.546823e+00]), <nibabel.nifti1.Nifti1Image object at 0x757c5e54f340>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c48b56c70>, <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  '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([[-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 - 2.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.011367, ..., -0.044286]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.378178, ..., -0.435127]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.249686, ..., 1.006757]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.627259, ..., -0.703344]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.673   , ...,  0.634889]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.170346, ..., -0.003907]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.74249 , ..., 2.416095]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.813068, ..., 0.397476]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526400>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c07be6fd0>, <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.292987, ..., 18.392956],
       ...,
       [-3.935719, ...,  0.602484]], dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.352245, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.400515, ...,  2.602952]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.497881, ..., 0.140209]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.499281, ..., -0.49038 ]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.038187, ..., 0.57069 ]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.622503, ..., 1.679756]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.211798, ..., -1.574836]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.143717, ..., -0.364448]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.156681, ..., -1.209168]), <nibabel.nifti1.Nifti1Image object at 0x757c4b526af0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c4b482af0>, <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  '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 - 2.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.00555 , ..., 1.955666]), <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.488905, ..., 1.063614]), <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.126069, ..., 0.347685]), <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.147577, ..., -0.722242]), <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.137613, ...,  1.219668]), <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.30493 , ...,  1.036247]), <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.64672 , ...,  0.256269]), <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.01524, ...,  0.32111]), <nibabel.nifti1.Nifti1Image object at 0x757c4b158790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c4b193b50>, <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-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 - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.497712, ..., -3.241636]), <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.884836, ..., -2.579124]), <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.361796, ..., -2.404684]), <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.15611 , ...,  0.717663]), <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.124367, ..., 1.85051 ]), <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.322726, ...,  0.187135]), <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.943745, ..., -1.223389]), <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.298717, ..., -1.02394 ]), <nibabel.nifti1.Nifti1Image object at 0x757c4b193460>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x757c4b31a8e0>, <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>, { 'clean_kwargs': {},
  'cmap': 'CMRmap_r',
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-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 - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.028052, ..., -1.076144]), <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.07071 , ...,  1.117228]), <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.278396, ..., 0.666023]), <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.606351, ..., -0.186859]), <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.228201, ..., -0.299271]), <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.760997, ..., 0.931553]), <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.005349, ...,  0.835504]), <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.093039, ..., -0.791928]), <nibabel.nifti1.Nifti1Image object at 0x757c4b31ae20>)
___________________________________________________________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_modelcosine
drift_order1
high_pass (Hz)0.01
hrf_modelglover
noise_modelar1
signal_scaling0
slice_time_ref0.0
smoothing_fwhm4
standardizeFalse
subject_labelNone
t_r (s)2.5
target_affineNone
target_shapeNone

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 controlfpr
α0.001
Threshold (computed)3.291
Cluster size threshold (voxels)0
Minimum distance (mm)8.0

Cluster IDXYZPeak StatCluster Size (mm3)
1-54.2513.125.626.4998
229.75-20.62-24.386.49836
2a12.25-20.62-31.884.58
326.25-20.62-13.126.13295
4-36.75-46.88-46.886.11147
543.75-50.62-5.626.031476
5a47.25-43.12-1.884.74
5b40.25-28.12-1.883.99
6-1.75-39.3813.125.95147
733.25-43.12-43.125.941181
7a26.25-35.62-43.125.67
822.75-54.38-39.385.91885
8a8.75-58.12-39.384.15
9-43.7528.12-9.385.90196
10-12.25-24.38-76.885.86196
11-19.25-24.38-43.125.80590
11a-19.25-31.88-39.385.31
1226.25-46.88-46.885.75147
13-57.75-39.389.385.74246
1433.255.62-13.125.69442
14a26.259.38-16.885.46
1522.75-46.88-61.885.68442
1619.25-76.88-13.125.60590
1722.75-43.129.385.57196
18-29.759.38-28.125.56246
1926.25-69.38-16.885.49689
19a36.75-65.62-13.124.91
19b33.25-58.12-16.884.13
2047.25-24.38-13.125.473248
20a40.25-35.62-24.385.23
20b33.25-50.62-20.624.80
20c43.75-16.88-13.124.67
211.7550.62-16.885.46295
22-26.25-16.88-24.385.38147
2312.25-84.38-28.125.3198
24-29.75-43.12-50.625.30442
251.75-43.12-1.885.2998
26-15.75-28.12-43.125.2449
2712.25-13.12-28.125.2398
28-47.25-13.1213.125.21295
29-19.25-50.62-39.385.19639
29a-8.75-35.62-39.384.36
29b-12.25-46.88-39.383.96
30-40.25-35.62-35.625.17344
3112.25-28.12-73.125.16147
328.75-50.62-35.625.13590
3329.751.88-9.385.1198
3419.25-31.8816.885.1049
35-1.75-50.62-39.385.0898
36-50.7535.62-16.885.08196
37-26.25-54.3846.885.0698
38-54.25-31.88-13.125.0649
39-8.75-46.88-65.625.05295
401.75-58.12-24.385.0598
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523-12.25-76.88-9.383.5349
524-1.75-50.62-16.883.5249
525-5.2543.12-16.883.5249
526-5.251.881.883.5249
527-22.751.8828.123.5249
528-1.759.389.383.5249
52922.75-54.3861.883.5249
53019.25-84.38-20.623.5249
53140.25-28.12-69.383.5298
53240.2539.3828.123.5249
53315.7520.6243.123.5149
53422.75-24.381.883.5149
53522.75-1.885.623.5149
53654.25-16.8835.623.5198
537-47.25-1.88-20.623.5149
53843.759.38-39.383.5149
53919.25-58.12-13.123.5149
5401.75-58.129.383.5049
541-29.75-65.62-20.623.5049
542-50.75-9.38-13.123.5049
54315.75-73.12-20.623.5049
54440.2565.6231.883.5049
54526.25-5.62-1.883.5049
54640.25-39.38-13.123.5049
547-8.75-13.125.623.5049
5485.25-16.8869.383.4949
54919.2546.8839.383.4949
55026.25-20.6265.623.4949
551-29.75-80.6220.623.4849
55261.25-35.6235.623.4849
55336.7550.62-16.883.4849
55440.25-35.6250.623.4849
555-1.75-5.62-73.123.4849
55619.25-31.88-54.383.4749
557-33.251.8831.883.4798
55847.255.6231.883.4749
55915.751.8858.123.4798
560-36.75-46.889.383.4798
56115.75-24.38-73.123.4749
562-36.7561.889.383.4749
56329.7543.1216.883.4649
564-8.7539.3831.883.4649
565-40.25-69.3831.883.4649
5665.25-50.62-5.623.4649
567-47.251.88-28.123.4549
56812.2569.38-13.123.4549
56933.2539.3828.123.4549
570-43.7535.6231.883.4598
571-1.7520.62-16.883.45147
57247.2516.8839.383.4549
57336.75-46.889.383.4549
574-19.25-50.62-46.883.4549
57536.75-54.38-28.123.4449
5761.7528.12-5.623.4449
57726.251.88-13.123.4449
578-1.75-28.1258.123.4449
57919.25-61.88-46.883.4498
58012.255.6213.123.4449
581-64.7520.625.623.4449
58240.25-9.38-31.883.4449
583-1.75-9.38-28.123.4349
5845.25-16.88-20.623.4349
58519.25-13.1228.123.4349
586-64.75-35.62-1.883.4349
5871.75-1.88-20.623.4349
58833.25-24.38-58.123.4349
589-5.25-73.1228.123.4349
59033.25-43.12-35.623.4349
591-19.25-16.889.383.4249
59226.2580.6224.383.4249
5938.7513.1216.883.4249
59454.25-28.1224.383.4249
595-22.75-28.1231.883.4249
59654.25-28.1231.883.4249
5971.7584.381.883.4249
59847.2531.88-24.383.4249
599-12.25-50.62-24.383.4249
600-29.7524.385.623.4149
601-36.75-5.62-20.623.4149
602-15.75-73.1239.383.4149
603-15.7535.625.623.4149
60447.25-31.88-39.383.4198
60526.25-16.88-58.123.4149
60612.25-9.38-24.383.4149
607-19.25-43.1231.883.4149
608-19.25-31.88-69.383.4149
609-43.7550.6228.123.4049
61040.2513.12-46.883.4049
61119.25-46.8831.883.4049
61212.25-9.3843.123.4049
61340.25-9.3828.123.4049
61433.255.6250.623.4049
61515.75-16.885.623.4049
61622.7554.3816.883.4049
617-22.75-54.3839.383.4049
61850.75-20.62-43.123.3949
61912.25-1.8824.383.3949
620-29.75-20.62-69.383.3949
62112.25-35.62-16.883.3949
62212.25-84.38-5.623.3949
623-29.75-1.88-1.883.3849
624-36.75-50.62-16.883.3849
625-5.25-31.88-13.123.3849
62626.25-1.8820.623.3849
627-1.755.62-39.383.3849
628-50.751.889.383.3849
629-22.7524.3820.623.3849
63022.7539.3820.623.3849
63119.25-61.88-20.623.3849
632-12.2516.88-50.623.3849
633-8.75-9.38-20.623.3849
6348.7524.3839.383.3849
63540.25-35.62-58.123.3898
636-33.25-1.8813.123.3849
637-50.75-1.8816.883.3798
638-36.75-16.88-46.883.3749
63947.25-20.6220.623.3749
64022.75-9.3843.123.3749
6411.751.88-16.883.3649
64219.25-28.12-24.383.3649
643-22.75-28.12-65.623.3649
644-22.75-28.12-13.123.3698
645-19.25-43.1246.883.3649
646-5.2576.885.623.3649
64768.25-28.1216.883.3649
648-8.75-35.6265.623.3649
649-33.25-24.38-65.623.3549
650-22.7554.38-20.623.3549
6518.75-5.62-24.383.3549
6525.2546.8813.123.3549
653-26.25-43.1254.383.3549
65415.7535.625.623.3549
655-33.25-58.12-1.883.3549
656-8.75-54.38-31.883.3549
65726.2528.12-43.123.3498
658-29.7539.3835.623.3449
6598.75-76.889.383.3449
660-29.75-1.88-46.883.3449
66112.25-35.6265.623.3449
66254.251.889.383.3449
663-22.7520.62-1.883.3449
66433.25-76.88-28.123.3449
665-1.75-1.88-5.623.3449
666-26.2516.889.383.3349
66747.25-35.6216.883.3349
668-15.75-46.88-24.383.3349
669-47.25-61.8820.623.3349
670-29.75-46.88-28.123.3349
671-22.75-54.38-35.623.3349
672-5.251.88-58.123.3349
6735.25-61.88-46.883.3349
67433.25-46.8854.383.3349
67526.25-80.621.883.3349
676-43.75-35.62-1.883.3349
67736.75-24.38-31.883.3349
67826.25-5.62-13.123.3249
679-36.75-73.12-9.383.3249
680-47.25-1.88-13.123.3249
68133.25-35.62-46.883.3249
682-50.7513.1235.623.3249
683-12.25-13.12-73.123.3249
684-50.75-58.121.883.3249
6858.7520.62-35.623.3249
68622.7520.6216.883.3249
687-36.75-46.88-24.383.3298
688-5.25-54.38-54.383.3249
689-29.759.3861.883.3149
69015.75-46.8820.623.3149
69119.25-9.38-5.623.3149
69215.75-39.38-35.623.3149
69336.75-24.389.383.3149
694-1.75-16.88-58.123.3149
695-19.25-50.6239.383.3149
696-19.25-54.3813.123.3149
69754.25-16.88-1.883.3049
698-12.2520.6213.123.3049
699-36.759.3854.383.3049
70029.75-50.6254.383.3049
70129.75-46.88-31.883.3049
702-12.25-35.62-58.123.3049
70315.75-16.8854.383.3049
704-26.2550.6228.123.3049
70561.251.8824.383.3049
70615.75-16.88-58.123.3049
7071.75-69.3846.883.2949
708-8.7554.3843.123.2949
709-26.25-31.88-58.123.2949
7105.25-73.12-16.883.2949
71115.7528.1213.123.2949
712-12.2513.1265.623.2949

house

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

Cluster Table
Height controlfpr
α0.001
Threshold (computed)3.291
Cluster size threshold (voxels)0
Minimum distance (mm)8.0

Cluster IDXYZPeak StatCluster Size (mm3)
1-22.75-39.38-1.885.50393
222.7520.629.385.43442
2a22.7524.3816.884.16
3-22.7524.3813.125.2998
422.7513.1220.625.2598
547.25-54.38-16.885.2098
633.25-43.12-35.624.65295
7-29.7528.125.624.6049
822.75-5.6220.624.5749
9-33.25-58.1235.624.5349
1036.75-54.38-20.624.5249
1112.2524.3820.624.51147
12-15.7524.389.384.48492
12a-15.7528.1216.884.03
13-50.759.38-28.124.4449
14-40.25-58.1228.124.4198
1522.75-31.881.884.3949
1622.75-43.12-31.884.3849
1733.255.62-39.384.3749
1826.255.629.384.3549
191.7513.12-16.884.3349
208.75-16.8831.884.3349
21-22.7516.885.624.2798
2233.2528.1228.124.2549
2326.259.38-31.884.2349
24-22.755.6224.384.23295
251.75-46.88-5.624.2149
26-26.2539.3846.884.2198
2719.255.6243.124.2198
2843.75-1.8820.624.1998
298.75-24.3824.384.1649
3019.25-13.1224.384.1698
3115.75-39.3813.124.1349
32-12.25-16.88-20.624.1249
33-29.7516.889.384.1149
34-22.7520.6220.624.10147
35-12.25-28.12-76.884.0849
3626.25-61.8831.884.0749
3715.75-54.385.624.0549
38-54.25-20.62-9.384.0349
39-15.75-46.8820.624.0298
4026.25-1.8813.124.0049
41-15.75-35.62-1.883.99147
42-12.25-43.12-16.883.9749
43-29.75-31.8835.623.9649
44-8.7535.6243.123.9049
4526.2513.1213.123.88147
468.75-9.3831.883.8898
475.25-43.12-1.883.8798
48-15.75-50.6224.383.8749
49-22.75-9.38-65.623.8649
50-15.75-24.38-9.383.8598
51-26.25-50.62-46.883.8449
52-8.75-43.12-9.383.8449
5322.75-43.129.383.8349
54-8.75-28.125.623.8249
55-12.25-43.12-50.623.8249
56-15.75-31.88-50.623.8249
5722.75-39.3846.883.8149
5822.75-76.8820.623.8149
59-40.25-69.3820.623.8049
60-29.75-9.38-31.883.7849
61-15.75-28.12-5.623.7849
62-12.25-39.3846.883.7749
635.2531.8850.623.7749
64-22.75-58.1224.383.7549
65-15.7516.8813.123.7549
66-1.75-61.88-39.383.7549
6750.75-31.88-50.623.7449
6815.7528.1213.123.7349
6950.7550.62-1.883.7349
7033.25-50.62-24.383.72147
7129.751.8813.123.7149
721.7531.88-13.123.6949
7326.259.38-24.383.6949
7415.75-28.12-5.623.6849
75-43.7558.129.383.6749
76-8.755.6228.123.6798
7726.25-13.1254.383.6649
7833.25-76.88-24.383.6449
79-33.25-24.3835.623.6449
8019.25-76.88-16.883.6398
81-40.2550.621.883.6349
8240.2531.88-24.383.6249
8333.2543.1250.623.6249
8426.25-24.38-69.383.6249
8533.259.381.883.6249
86-26.2520.62-1.883.6249
8722.75-65.6220.623.6049
88-15.7520.621.883.6049
89-15.7520.6220.623.5949
90-33.25-73.12-31.883.5849
9122.75-73.12-13.123.5849
9257.75-54.381.883.5749
9329.75-43.12-20.623.5649
94-22.75-24.3846.883.5649
9540.25-13.12-43.123.5649
96-29.75-13.12-20.623.5549
97-40.25-28.12-24.383.5549
98-43.75-9.3839.383.5549
99-26.25-50.6246.883.5449
100-26.2513.1216.883.5449
101-29.7550.6220.623.5449
102-22.75-13.1246.883.5449
103-36.7524.3813.123.5349
10422.75-9.3813.123.5349
105-12.251.88-61.883.5298
1065.259.3839.383.5249
10729.75-54.38-24.383.5149
10822.75-43.1231.883.5149
1095.25-65.6254.383.5049
110-50.7531.889.383.4949
111-12.259.385.623.4949
11222.751.8820.623.4849
11312.25-5.62-5.623.4849
11450.75-43.12-1.883.4749
11515.75-1.8869.383.4649
11626.25-73.1231.883.4649
11736.75-61.88-13.123.4649
118-1.7539.38-20.623.4598
119-26.25-54.3843.123.4549
12029.75-46.88-24.383.4549
121-57.7520.6213.123.4549
122-15.75-50.62-9.383.4449
123-26.25-20.6216.883.4449
12419.2524.385.623.4449
125-26.251.8813.123.4449
126-36.75-61.8835.623.4449
12761.25-1.8813.123.4349
12857.75-16.8816.883.4349
12915.751.8858.123.4349
130-36.75-65.62-16.883.4349
13122.75-46.88-20.623.4249
13212.25-31.88-73.123.4249
13322.75-16.8816.883.4249
13433.2554.3828.123.4149
135-33.259.385.623.4149
13643.7535.6246.883.4149
13715.75-28.1224.383.4149
138-26.25-76.88-9.383.3949
13922.75-13.1220.623.3949
140-40.25-46.8839.383.3949
141-33.2561.8839.383.3849
142-36.75-69.3831.883.3898
1438.75-61.88-50.623.3849
14422.755.6224.383.3849
1455.25-20.62-13.123.3849
14619.25-35.62-28.123.3849
147-15.75-61.8843.123.3749
148-33.2513.12-1.883.3749
14922.75-46.88-5.623.3749
150-15.75-20.62-28.123.3749
15112.25-13.1254.383.3749
15229.755.6239.383.3649
15326.25-20.6224.383.3649
15415.75-73.12-28.123.3649
155-8.7561.88-16.883.3649
156-36.75-50.62-16.883.3649
157-64.75-24.38-20.623.3549
158-57.7516.885.623.3449
159-15.7558.125.623.3449
160-26.2516.8850.623.3449
161-15.75-65.6246.883.3449
16240.25-39.3813.123.3449
16336.75-13.12-31.883.3449
16426.25-16.88-9.383.3449
16515.7516.8850.623.3449
166-54.25-13.12-1.883.3449
16740.25-50.62-58.123.3449
168-8.75-24.381.883.3349
169-22.75-16.8824.383.3349
170-1.7528.1261.883.3349
17126.25-58.12-16.883.3398
1728.75-39.381.883.3349
17354.259.38-31.883.3349
174-22.7535.62-24.383.3249
1758.75-80.62-5.623.3249
17640.25-16.88-46.883.3249
177-15.75-5.62-13.123.3149
178-47.25-76.889.383.3149
179-40.25-1.8824.383.3049
18033.25-28.12-13.123.3049
1818.75-58.12-24.383.2949
18219.25-50.62-13.123.2949
18312.25-73.12-13.123.2949

chair

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

Cluster Table
Height controlfpr
α0.001
Threshold (computed)3.291
Cluster size threshold (voxels)0
Minimum distance (mm)8.0

Cluster IDXYZPeak StatCluster Size (mm3)
140.25-46.8813.125.02147
212.2535.62-5.624.88196
3-50.7539.3816.884.6549
4-15.75-1.88-16.884.2149
533.25-58.12-1.884.1649
6-36.75-76.881.884.1549
7-29.7520.6246.884.1249
8-54.25-43.1246.884.0949
912.2528.12-16.884.0849
1012.255.62-24.384.0698
1133.2543.12-16.883.9949
1233.25-58.12-16.883.9749
13-22.75-43.12-31.883.9798
148.75-35.62-35.623.9449
1526.25-46.88-35.623.9349
16-5.2543.1250.623.8949
1712.2516.88-20.623.8849
18-36.75-61.8843.123.8549
19-50.75-39.38-1.883.83147
20-15.7520.6213.123.8149
21-22.751.885.623.7849
22-57.75-9.38-1.883.7349
23-1.755.6231.883.6949
24-57.7513.121.883.6798
25-36.75-46.8850.623.6549
2633.2513.12-1.883.6449
2736.75-24.3820.623.6249
2854.2520.625.623.6149
2922.7524.3816.883.6149
3033.25-20.62-58.123.5549
31-22.75-9.3820.623.5349
32-22.7524.3813.123.5349
3329.75-43.12-35.623.5249
3412.2516.88-50.623.5149
3512.25-1.8869.383.5149
36-15.75-39.38-54.383.5049
3726.25-69.3831.883.5098
38-22.75-31.88-69.383.4949
3936.75-1.8828.123.4849
40-22.7550.629.383.4749
41-22.75-39.38-28.123.4749
4226.2516.8816.883.4649
43-22.759.3824.383.4649
4433.2513.1261.883.4449
4519.2520.6220.623.4398
4612.259.38-1.883.4349
4736.7520.62-43.123.4249
4819.2576.8831.883.4149
491.7524.38-39.383.4049
5029.759.38-16.883.3649
51-26.25-1.88-50.623.3649
5233.259.38-13.123.3549
53-68.251.885.623.3349
54-15.75-39.38-24.383.3349
555.25-1.8831.883.3349
5622.75-46.88-24.383.3249
5722.75-24.38-61.883.3149
5843.75-28.12-13.123.3049

scrambledpix

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

Cluster Table
Height controlfpr
α0.001
Threshold (computed)3.291
Cluster size threshold (voxels)0
Minimum distance (mm)8.0

Cluster IDXYZPeak StatCluster Size (mm3)
11.7524.38-13.125.0649
2-29.75-61.88-31.884.9649
3-22.75-46.8843.124.9198
4-15.7561.88-9.384.69147
55.2543.12-28.124.4949
6-22.75-9.3820.624.2949
7-1.75-35.625.624.1798
850.7516.88-28.124.12147
9-1.755.6231.884.0449
1040.25-16.88-39.383.9349
11-15.75-43.12-16.883.8549
12-64.75-31.8839.383.7749
13-1.75-46.88-9.383.7549
1450.755.62-35.623.6849
15-22.75-54.38-9.383.64147
1622.75-24.3846.883.5949
1754.25-9.3816.883.5549
1819.2543.1213.123.5149
1922.7546.88-5.623.5049
20-29.75-65.6220.623.4649
21-22.75-28.1239.383.4249
2233.25-20.6243.123.4049
23-40.25-16.88-54.383.3949
24-26.25-69.3839.383.3949
2515.75-39.38-31.883.3849
2657.7513.12-9.383.3849
27-5.25-50.6261.883.3349
2826.25-20.6243.123.3349
29-22.75-9.38-65.623.3149
30-26.25-65.6216.883.3149

face

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

Cluster Table
Height controlfpr
α0.001
Threshold (computed)3.291
Cluster size threshold (voxels)0
Minimum distance (mm)8.0

Cluster IDXYZPeak StatCluster Size (mm3)
1-50.75-50.6213.125.43196
1a-47.25-58.1213.124.43
2-47.25-43.12-35.625.0849
3-33.2550.6216.884.92442
3a-40.2546.8813.124.22
4-47.25-39.3816.884.8998
5-12.25-58.12-16.884.8498
668.25-9.38-9.384.82147
7-36.7531.8816.884.66147
8-1.75-39.385.624.6498
9-15.7513.1273.124.60196
1033.25-61.88-13.124.57196
11-43.75-31.8835.624.4698
121.75-43.12-1.884.4549
13-47.2524.3820.624.4549
1440.25-39.38-24.384.3049
15-22.7539.3813.124.2749
1622.75-50.62-24.384.2749
17-47.25-50.6220.624.2249
18-47.25-54.38-9.384.1949
19-15.75-13.1224.384.1898
20-29.7524.38-13.124.15147
21-40.25-43.12-16.884.1449
2240.25-43.12-9.384.1349
23-1.7528.1261.884.0798
24-1.75-58.12-16.884.0749
25-57.75-16.881.884.0698
2629.75-46.8835.624.0249
275.25-58.12-24.384.00393
28-36.75-31.88-46.884.0049
29-8.7513.1265.623.9949
3033.25-46.88-39.383.9998
311.75-43.12-16.883.98196
32-36.7531.8835.623.95442
3322.75-1.8858.123.9498
34-19.25-73.1239.383.9398
351.7524.38-13.123.9249
36-43.75-43.12-9.383.9098
37-8.75-39.38-5.623.8749
38-47.25-46.889.383.8698
395.2520.6265.623.8649
4040.2543.1216.883.8549
41-26.25-43.12-43.123.8349
4219.25-46.88-20.623.81196
43-36.75-43.1239.383.80147
44-8.75-28.1261.883.7949
45-8.75-46.88-31.883.7949
4643.7535.6239.383.76147
47-22.75-46.88-50.623.7549
48-47.25-61.88-5.623.7449
49-47.25-28.1228.123.7449
50-29.7528.1213.123.7349
5129.75-46.88-31.883.7349
5219.25-16.8839.383.7249
53-47.25-9.3813.123.7149
54-47.25-46.88-31.883.6849
5533.2550.6220.623.6749
56-29.75-13.125.623.6749
5736.75-46.889.383.6649
58-50.7520.6220.623.6549
59-47.25-16.88-35.623.6149
6036.75-35.62-20.623.6149
6133.25-50.62-20.623.61147
6233.25-54.3843.123.6049
635.25-24.38-20.623.5949
64-47.25-46.8816.883.5949
65-57.75-9.38-1.883.5849
66-12.25-76.8828.123.5849
6722.7531.8828.123.5749
68-36.755.6243.123.5749
69-33.2539.3854.383.5649
70-1.75-46.88-9.383.5649
7126.25-28.1224.383.5649
7229.75-50.62-39.383.5649
7340.2528.1239.383.5698
74-12.25-39.3858.123.5549
7529.75-54.3846.883.5549
76-50.75-43.121.883.53147
77-36.7520.6250.623.5349
7819.25-1.8850.623.5249
79-57.7531.8831.883.5249
80-43.75-43.1246.883.5149
81-29.755.6258.123.5149
8226.251.8813.123.5149
835.25-65.6213.123.4949
8429.75-46.88-43.123.4749
8533.25-43.12-24.383.4749
86-47.25-16.88-43.123.4649
87-36.75-46.8850.623.4549
88-54.25-9.38-31.883.4449
89-5.25-58.12-13.123.4449
90-33.2531.8846.883.4349
91-26.25-84.38-9.383.4398
9212.2528.1261.883.4249
93-15.7516.8816.883.4149
94-33.2516.8850.623.4149
95-5.2561.88-9.383.4149
96-47.25-61.88-28.123.4149
97-33.25-9.38-28.123.4149
98-40.2550.629.383.4149
99-19.25-61.88-9.383.4049
10022.75-24.38-58.123.3949
101-29.7535.62-1.883.3949
102-54.25-20.62-39.383.3949
10340.25-46.88-20.623.3949
1048.75-58.12-28.123.3849
105-47.25-46.88-1.883.3849
10636.7554.3824.383.3849
107-29.7531.88-9.383.3749
10840.25-35.62-43.123.3749
109-50.75-58.12-13.123.3649
11036.7561.8828.123.3549
111-47.2558.12-1.883.3549
112-47.2524.3828.123.3449
113-47.25-9.385.623.3449
114-33.25-43.1243.123.3449
115-50.75-35.6243.123.3449
116-47.25-46.88-58.123.3449
11768.25-28.1216.883.3349
11847.2535.6213.123.3349
11919.255.6213.123.3249
12029.75-46.88-20.623.3249
121-8.75-13.1220.623.3249
1225.2531.88-35.623.3249
12315.75-54.38-20.623.3249
12443.759.3850.623.3249
12515.755.629.383.3149
1268.75-1.889.383.3149
12733.2520.6243.123.3149
12843.75-54.389.383.3049
129-40.2539.3843.123.3049
130-47.25-58.12-31.883.3049
13112.25-88.125.623.3049
13240.25-69.38-16.883.3049
133-43.75-31.88-9.383.2949
13450.751.8843.123.2949
13543.7516.8831.883.2949
136-47.25-50.6231.883.2949
13715.7513.1243.123.2949

shoe

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

Cluster Table
Height controlfpr
α0.001
Threshold (computed)3.291
Cluster size threshold (voxels)0
Minimum distance (mm)8.0

Cluster IDXYZPeak StatCluster Size (mm3)
119.2573.1220.624.6549
222.75-9.3835.624.4849
319.25-20.62-28.124.0849
412.255.6231.883.9749
526.2535.6228.123.9749
661.2528.1231.883.8798
715.7516.8858.123.8349
8-5.25-76.88-28.123.7449
936.7546.88-16.883.7149
10-19.25-28.1235.623.6449
1126.25-46.88-24.383.6149
12-12.2539.3865.623.6149
13-29.759.3835.623.6049
14-8.7584.3820.623.6049
15-54.25-65.6216.883.5949
16-26.25-13.12-43.123.5849
1719.25-28.12-54.383.5749
18-12.251.88-69.383.5649
1947.25-28.12-5.623.5549
2022.75-69.38-28.123.5349
2133.259.3861.883.5049
22-8.7520.6273.123.4949
2322.7561.8831.883.4949
2440.25-54.3839.383.4749
251.75-24.38-39.383.4649
265.25-13.1224.383.4649
27-68.25-20.6224.383.4549
28-12.2539.3824.383.4249
2950.75-35.62-35.623.4149
30-1.75-28.12-73.123.3949
3129.7524.38-28.123.3849
3222.75-54.38-43.123.3549

cat

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

Cluster Table
Height controlfpr
α0.001
Threshold (computed)3.291
Cluster size threshold (voxels)0
Minimum distance (mm)8.0

Cluster IDXYZPeak StatCluster Size (mm3)
112.25-61.88-31.885.00295
2-40.25-65.6213.124.41147
35.25-61.88-24.384.3498
4-29.7539.3854.384.3249
5-12.25-58.12-16.884.2949
6-12.25-76.8831.884.2798
743.75-5.6216.884.11196
822.75-24.38-58.124.0549
919.25-31.88-69.384.0198
1043.751.889.383.94196
115.25-80.629.383.9098
1261.25-31.8820.623.8649
13-12.25-20.62-58.123.8698
141.7535.62-13.123.8649
15-29.7535.6250.623.8349
16-36.75-50.62-39.383.8298
1733.25-1.8846.883.8049
1850.751.88-24.383.7949
1947.251.88-46.883.7949
2050.7528.1224.383.7849
21-5.25-50.62-13.123.7649
2222.75-61.88-54.383.7649
23-12.25-73.12-31.883.7349
2436.7528.1243.123.7298
2536.7569.385.623.7149
26-1.7550.62-9.383.7049
275.25-31.8854.383.6649
2836.7528.12-20.623.6249
29-26.25-80.629.383.6198
308.75-65.62-24.383.6049
31-8.75-46.88-28.123.6049
3250.759.38-35.623.6049
3343.755.621.883.5949
34-36.7546.8839.383.5849
3515.75-80.62-31.883.5749
36-54.25-50.625.623.5649
3736.759.38-35.623.5549
3829.75-35.62-50.623.5349
39-43.75-65.625.623.5349
4015.75-50.62-39.383.5049
4161.25-31.8843.123.5049
4212.25-54.38-43.123.4949
435.25-61.88-39.383.4949
4433.25-54.3843.123.4949
45-64.75-9.38-24.383.4849
46-54.25-50.6239.383.4849
47-40.25-46.8850.623.4898
488.7516.88-16.883.4898
49-29.75-46.885.623.4749
5012.25-39.38-31.883.4449
51-40.25-5.6250.623.4449
5264.7520.629.383.4349
535.25-24.38-73.123.4249
5412.25-43.121.883.4249
55-19.2516.8869.383.4249
56-29.75-54.38-35.623.4249
5736.7528.1235.623.4249
58-64.75-31.8816.883.3849
595.2543.1250.623.3749
6015.7539.38-20.623.3749
6129.759.38-31.883.3649
62-36.75-50.62-20.623.3549
63-47.25-43.1243.123.3549
64-40.25-46.8816.883.3549
65-43.75-1.88-28.123.3449
66-40.25-1.88-31.883.3449
6712.255.625.623.3449
68-64.75-16.8824.383.3349
69-36.75-76.885.623.3398
7043.7531.8839.383.3349
71-47.25-50.6246.883.3349
72-40.25-69.3835.623.3149
73-29.7535.6258.123.3049
74-22.7576.885.623.2949
7547.2513.1243.123.2949

scissors

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

Cluster Table
Height controlfpr
α0.001
Threshold (computed)3.291
Cluster size threshold (voxels)0
Minimum distance (mm)8.0

Cluster IDXYZPeak StatCluster Size (mm3)
1-50.7520.62-24.386.02935
2-12.2520.62-46.885.7949
3-15.7516.88-46.885.74295
4-19.2584.3820.625.60295
5-12.2516.88-50.625.5149
6-26.2524.3828.125.28147
729.755.62-5.625.26147
836.7520.62-31.885.18246
947.2524.38-1.885.12147
1012.2513.12-43.125.09393
11-22.75-58.12-50.625.08393
11a-22.75-46.88-50.624.60
12-19.2561.8850.625.0549
13-50.75-9.38-20.625.01246
1450.7531.88-31.884.9749
15-19.2531.88-35.624.91196
161.75-1.88-61.884.86295
1733.255.62-46.884.8249
18-12.251.88-28.124.7649
1912.25-61.88-46.884.7649
201.75-28.12-61.884.6849
2112.25-35.62-69.384.66147
2222.75-58.12-50.624.6149
23-54.25-39.38-31.884.6049
241.7513.12-16.884.5449
2547.2524.389.384.5449
2626.25-46.881.884.5449
2740.255.6231.884.5398
28-15.75-43.12-46.884.5398
2940.25-58.12-5.624.50147
3019.2516.88-46.884.5049
315.2524.38-46.884.47442
31a-1.7528.12-39.383.59
32-5.25-16.88-39.384.4598
3350.7524.38-35.624.41147
3412.2580.6235.624.4198
3533.25-35.62-35.624.3798
361.751.8831.884.37196
37-12.2576.8820.624.3698
3847.25-5.6220.624.36295
3919.25-46.88-35.624.3449
4029.75-1.8824.384.3349
41-12.259.38-58.124.2998
4254.255.6250.624.2949
4315.75-35.62-73.124.2849
4419.2580.621.884.2898
4554.251.8843.124.2798
4615.7576.8828.124.2649
47-29.7576.8820.624.2649
48-54.259.38-16.884.2649
49-1.75-31.889.384.2649
50-22.75-5.62-20.624.2549
51-40.2561.885.624.2498
52-47.2524.3820.624.2249
5326.2513.12-39.384.2249
54-1.75-39.385.624.2198
55-8.7528.1254.384.17196
5640.25-1.8861.884.17246
56a40.25-9.3858.123.83
5736.7531.88-39.384.17246
58-36.75-1.88-20.624.1798
59-5.25-9.3831.884.1449
6022.75-54.38-43.124.1449
61-29.7520.62-46.884.1349
6222.75-1.889.384.1149
63-26.2580.6216.884.11246
6429.75-46.88-61.884.1049
6515.75-28.12-54.384.1049
6633.2524.38-43.124.0898
67-40.25-16.88-65.624.0749
68-47.2531.88-28.124.0549
69-26.2576.8824.384.05147
7019.25-5.62-35.624.0449
7154.251.8835.624.03196
7212.2524.38-35.624.0398
73-26.25-13.121.884.0249
74-5.2580.6231.884.02147
75-19.2550.6213.124.0249
7640.25-43.12-5.624.0149
7743.75-31.88-13.124.0149
7815.755.629.384.0149
79-33.25-65.62-16.884.0049
8026.25-50.62-54.384.0098
8133.25-5.62-1.883.9949
8240.25-39.3858.123.9849
8322.75-43.12-61.883.9898
84-19.2520.6224.383.9849
8557.75-20.6228.123.9749
86-5.2584.381.883.9649
8740.25-24.38-35.623.9649
88-19.259.38-35.623.9549
8912.2580.62-5.623.9449
90-5.25-28.12-76.883.94147
9150.7550.6220.623.9349
9219.25-16.88-54.383.9349
9333.25-39.3824.383.9349
9440.2528.12-43.123.9298
95-26.2516.8828.123.9249
961.7539.38-16.883.9249
97-26.25-24.3820.623.9149
98-15.75-9.3828.123.9149
99-8.7524.38-35.623.9098
10050.75-16.8846.883.8949
101-15.7576.8831.883.8949
1021.75-28.12-76.883.8949
10347.2516.88-20.623.8949
10419.25-28.1273.123.8749
10557.75-13.1239.383.8749
106-8.7584.3820.623.8798
10729.7576.8813.123.8749
10840.2513.12-39.383.8749
10933.25-69.38-13.123.8749
11019.2554.3850.623.8549
111-12.25-50.62-50.623.8549
112-19.25-1.88-35.623.8549
11329.75-50.62-50.623.8449
11412.25-61.889.383.8449
115-29.7513.1261.883.83196
1165.25-1.885.623.8398
11715.75-16.8846.883.8349
118-1.75-1.88-76.883.8298
119-36.7565.6220.623.8298
12033.25-39.38-20.623.8149
12129.7565.6243.123.7949
122-50.75-1.8820.623.7949
12350.75-39.38-16.883.7949
124-12.25-43.12-13.123.7849
125-22.7558.12-20.623.77246
1265.2539.38-20.623.7749
127-26.2531.88-39.383.7649
128-1.75-13.12-54.383.7698
129-33.25-58.12-31.883.7549
13015.7588.129.383.7598
13143.7513.12-28.123.7598
13226.25-16.88-16.883.7549
133-19.2531.88-28.123.7449
1345.259.38-58.123.7349
135-8.759.3865.623.7349
136-29.75-69.389.383.7398
137-64.75-5.625.623.7198
13812.25-31.88-46.883.7149
13915.7550.62-9.383.7149
140-40.25-9.38-5.623.7049
14133.25-20.6243.123.7049
142-12.25-50.62-13.123.6949
14326.25-13.1213.123.6949
144-5.2580.6213.123.6849
145-26.2516.88-16.883.6749
14619.25-1.88-1.883.6649
14719.2516.88-35.623.6649
148-1.7573.1235.623.6649
14929.75-54.38-61.883.6549
1501.75-9.381.883.6549
15115.7573.1220.623.6549
15233.25-50.62-20.623.6549
15312.25-35.62-24.383.6549
15415.7524.3869.383.6449
155-47.25-5.62-28.123.6449
156-1.751.8813.123.6449
157-40.2569.3813.123.6349
15812.25-28.12-31.883.6249
1598.751.8824.383.6249
16029.75-9.389.383.6249
161-15.75-65.625.623.6149
162-26.25-80.62-5.623.6149
16333.255.6216.883.6049
16433.25-5.6224.383.6098
16557.75-1.885.623.6049
16615.75-50.62-35.623.6049
16733.25-39.38-43.123.6049
16833.2524.3861.883.6049
169-1.75-35.62-65.623.5849
17022.75-20.6220.623.5849
171-8.7524.3843.123.5849
17240.25-13.1246.883.5849
173-5.25-20.6254.383.5898
174-1.75-13.1231.883.5749
175-47.2520.6243.123.5798
176-33.2569.3820.623.5698
17757.75-24.385.623.5649
1785.25-65.6220.623.5649
17926.25-16.88-35.623.5549
180-1.75-46.88-1.883.5598
181-50.7513.1220.623.5549
18240.2569.3816.883.5549
1831.7516.88-20.623.5549
184-29.7520.6220.623.5449
185-29.7531.88-9.383.5449
18640.25-16.889.383.5449
187-12.2588.129.383.5449
188-5.255.6239.383.5449
189-1.75-13.12-5.623.5449
190-8.7580.625.623.5498
191-50.75-13.125.623.5449
19257.7516.8816.883.5349
1931.7513.1231.883.5398
194-33.2539.3854.383.5349
195-5.2528.1261.883.5249
196-43.75-24.3835.623.5249
197-43.7531.88-24.383.5249
1981.7565.6246.883.5249
199-5.255.62-69.383.5249
200-22.75-39.38-54.383.5149
20133.25-28.12-39.383.5149
202-1.75-5.62-80.623.5198
20326.2528.12-43.123.5149
20433.2531.88-20.623.5049
205-33.2573.129.383.5049
206-1.7520.6254.383.5049
20712.25-1.8843.123.5049
20857.75-28.129.383.5049
209-12.251.889.383.4949
210-40.2543.12-31.883.4949
21143.75-9.38-16.883.4949
21247.25-9.3854.383.4949
213-57.75-5.62-1.883.4849
21450.751.8846.883.4849
21515.7588.1216.883.4749
21654.259.3828.123.4798
217-5.2539.3858.123.4649
21850.7516.88-5.623.4649
21915.7546.88-20.623.4649
22050.7554.38-5.623.4649
22133.25-13.1250.623.4549
22257.75-24.38-13.123.4549
223-22.75-9.3839.383.4549
224-19.25-5.6216.883.4549
22519.25-5.6261.883.4549
226-43.7516.8839.383.4549
227-1.75-73.12-31.883.4449
228-15.751.8828.123.4349
229-54.2520.62-16.883.4349
23040.2516.8861.883.4349
2318.75-1.8828.123.4349
23247.25-9.3828.123.4249
23329.759.3816.883.4249
23412.259.38-54.383.4298
23533.25-13.12-61.883.4249
236-5.251.88-76.883.4249
23733.25-31.88-43.123.4249
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Built using Nilearn. Source code on GitHub. File bugs & feature requests here.


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

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

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

Build the decoding pipeline

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

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

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

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

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

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

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

from sklearn.model_selection import LeaveOneGroupOut

from nilearn.decoding import Decoder

decoder = Decoder(
    estimator="svc",
    mask=haxby_dataset.mask,
    standardize=False,
    screening_percentile=5,
    cv=LeaveOneGroupOut(),
)
decoder.fit(z_maps, conditions_label, groups=run_label)

# Return the corresponding mean prediction accuracy compared to chance
# for classifying one-vs-all items.

classification_accuracy = np.mean(list(decoder.cv_scores_.values()))
chance_level = 1.0 / len(np.unique(conditions))
print(
    f"Classification accuracy: {classification_accuracy:.4f} / "
    f"Chance level: {chance_level}"
)

/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/image/resampling.py:489: UserWarning:

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

Classification accuracy: 0.7589 / Chance level: 0.125

Total running time of the script: (3 minutes 25.511 seconds)

Estimated memory usage: 1052 MB

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