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
TR = 2.5

Load the behavioral data#

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

# Record these as an array of runs
runs = behavioral["chunks"].values
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 = TR * np.arange(n_scans)
    # each event last the full TR
    duration = TR * np.ones(n_scans)
    # Define the events object
    events_ = pd.DataFrame(
        {
            "onset": frame_times,
            "trial_type": conditions_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=TR,
    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 0x7fb38d37b990>, <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-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 - 2.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.44475 , ...,  0.379275]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.013524, ...,  0.844135]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.217486, ..., -1.430348]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.106474, ..., -0.182434]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.747494, ..., -1.660679]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.080159, ..., -1.32614 ]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.253894, ..., -0.452682]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.240914, ..., 0.244136]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d378790>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3cb356250>, <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.1s, 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 - 2.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.871458, ..., -0.990755]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.627194, ..., -1.290147]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.153013, ..., -1.320123]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.15748 , ...,  2.082416]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.997775, ..., -0.754066]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.109412, ..., 1.330079]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.030863, ..., -1.731439]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.559734, ..., -0.720924]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb23eed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb392153610>, <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 5.205584, ..., 26.587189],
       ...,
       [-6.836576, ..., 10.676956]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 2.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.695564, ..., -0.455092]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.457214, ..., 1.537178]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.803925, ...,  0.570463]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.614932, ...,  0.232909]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.527175, ..., -1.062723]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.126756, ..., -2.274819]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.635166, ..., -0.395548]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.250365, ..., 0.364311]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d37bd10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3cb23d4d0>, <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-2.026206, ...,  5.974948],
       ...,
       [ 2.616334, ...,  0.104535]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 2.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.503142, ..., -1.639351]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.271132, ..., -0.047089]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.318104, ..., 0.724813]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.073279, ..., -0.316956]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.380183, ..., -0.690685]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.631912, ..., -0.753286]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.155784, ..., -0.065658]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.186135, ..., -0.69267 ]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355310>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3852da850>, <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 53.033577, ..., -55.45955 ],
       ...,
       [-51.57195 , ..., -55.994713]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.158342, ..., -0.068131]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.396497, ..., -0.424937]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.091867, ...,  0.463109]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.054041, ..., -0.122921]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.025223, ...,  0.562991]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.001653, ..., -0.968729]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.274665, ..., -0.667   ]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.564329, ..., 1.496068]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb3723d0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3cb3980d0>, <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.2s, 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 - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.987059, ...,  1.41717 ]), <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.24774 , ...,  0.674399]), <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.731234, ...,  1.341998]), <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.714869, ...,  1.182988]), <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.222674, ...,  0.480354]), <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.366899, ..., -0.091153]), <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.2708  , ..., -0.247146]), <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.251249, ..., -0.413063]), <nibabel.nifti1.Nifti1Image object at 0x7fb385763710>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb38bd7bc50>, <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.1s, 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 - 1.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.290412, ..., -0.609221]), <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.090297, ..., -0.822602]), <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.747918, ..., -0.108861]), <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.095788, ..., -1.376995]), <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.807425, ...,  1.826947]), <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.017351, ..., 0.622242]), <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.625042, ..., -0.231224]), <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.056424, ..., -1.672737]), <nibabel.nifti1.Nifti1Image object at 0x7fb38bd79890>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3cb0b82d0>, <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-15.915996, ...,  22.07737 ],
       ...,
       [-16.981215, ...,   3.372383]], dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.200737, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.008536, ..., -0.066075]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.168487, ..., -0.636238]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.145684, ..., 0.932773]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.580823, ..., -0.455655]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.685537, ...,  0.715791]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.245273, ..., -0.099707]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.79538 , ..., 1.913842]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.519925, ..., 0.629218]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb0bbb10>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3b16a71d0>, <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.292987, ..., 18.392956],
       ...,
       [-3.935719, ...,  0.602484]], dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.200737, ...,  1.      ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 2.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.425611, ...,  2.348025]), <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.31867 , ..., 0.408223]), <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.534932, ..., -0.150519]), <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.149007, ..., 0.640215]), <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.640699, ..., 1.50369 ]), <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.246384, ..., -1.346316]), <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.162243, ..., -0.519251]), <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.203695, ..., -1.335337]), <nibabel.nifti1.Nifti1Image object at 0x7fb3852dabd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3cb23ddd0>, <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-5.223948, ..., -5.959582],
       ...,
       [-7.677519, ..., 16.024363]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.034515, ...,  1.612397]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.62798 , ..., 1.160445]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.506632, ..., 0.459388]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.261016, ..., -0.747236]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.231796, ...,  1.098904]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.148582, ...,  0.999934]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.548262, ...,  0.09934 ]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.222824, ...,  0.318977]), <nibabel.nifti1.Nifti1Image object at 0x7fb38d2c7110>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3cad3d690>, <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-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 - 2.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.175763, ..., -3.429485]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-3.146358, ..., -2.947626]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.806852, ..., -2.720554]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.008926, ...,  0.4544  ]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.279543, ..., 1.828183]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.463642, ...,  0.26599 ]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.058735, ..., -1.191442]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.390268, ..., -1.112207]), <nibabel.nifti1.Nifti1Image object at 0x7fb3cb355190>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7fb3921fa590>, <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>, { 'clean_kwargs': {},
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-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([ 0.340751, ..., -1.056108]), <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.043261, ..., 1.144442]), <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.517954, ..., 0.611394]), <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-5.797134e-01, ...,  4.317655e-06]), <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.398581, ..., -0.488427]), <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.714396, ..., 0.869941]), <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.924894, ...,  0.723724]), <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.145297, ..., -0.821272]), <nibabel.nifti1.Nifti1Image object at 0x7fb3921fbed0>)
___________________________________________________________unmask - 0.1s, 0.0min

Generating a report#

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

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

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

report  # This report can be viewed in a notebook

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

First Level Model

Model details:

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

Design Matrix:

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 0.0
Threshold (computed) 3.29
Cluster size threshold (voxels) 0
Minimum distance (mm) 8.0

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -36.75 -46.88 -46.88 6.63 147
2 -54.25 13.12 5.62 6.62 98
3 29.75 -20.62 -24.38 6.33 393
4 -1.75 -39.38 13.12 6.33 196
5 33.25 -43.12 -43.12 6.21 2264
5a 22.75 -31.88 -39.38 5.95
5b 8.75 -50.62 -35.62 5.77
6 -43.75 28.12 -9.38 6.16 196
7 -57.75 -39.38 9.38 6.12 246
8 -19.25 -24.38 -43.12 6.04 541
8a -19.25 -31.88 -39.38 5.59
9 26.25 -20.62 -13.12 5.94 344
10 -40.25 -35.62 -35.62 5.92 344
11 26.25 -46.88 -46.88 5.86 147
12 22.75 -46.88 -61.88 5.83 492
12a 22.75 -58.12 -61.88 5.39
13 19.25 -54.38 -43.12 5.81 1132
14 -12.25 -24.38 -76.88 5.71 196
15 19.25 -76.88 -13.12 5.70 639
16 43.75 -50.62 -5.62 5.64 984
16a 47.25 -43.12 -1.88 4.69
17 22.75 -43.12 9.38 5.64 246
18 -26.25 -16.88 -24.38 5.63 147
19 12.25 -28.12 -73.12 5.62 246
20 -1.75 -50.62 -39.38 5.61 147
21 40.25 16.88 -20.62 5.59 49
22 -29.75 9.38 -28.12 5.53 246
23 26.25 -69.38 -16.88 5.50 787
23a 36.75 -65.62 -13.12 4.90
24 1.75 50.62 -16.88 5.49 344
25 -50.75 35.62 -16.88 5.48 196
26 -15.75 -28.12 -43.12 5.48 49
27 33.25 5.62 -13.12 5.47 492
27a 26.25 9.38 -16.88 5.44
28 12.25 -58.12 -28.12 5.46 393
29 12.25 -84.38 -28.12 5.44 98
30 -47.25 -13.12 13.12 5.42 295
31 -19.25 -50.62 -39.38 5.37 590
31a -8.75 -35.62 -39.38 4.34
31b -12.25 -46.88 -39.38 3.83
32 -33.25 -73.12 31.88 5.34 49
33 -29.75 -43.12 -50.62 5.31 393
34 -54.25 -31.88 -13.12 5.31 49
35 1.75 -43.12 -1.88 5.28 147
36 12.25 -13.12 -28.12 5.25 98
37 -12.25 5.62 -16.88 5.25 49
38 29.75 1.88 -9.38 5.23 98
39 -57.75 -16.88 1.88 5.23 98
40 5.25 -61.88 -20.62 5.22 295
41 -47.25 -39.38 -20.62 5.17 1132
41a -50.75 -39.38 -9.38 4.55
42 40.25 -35.62 -24.38 5.16 2214
42a 47.25 -24.38 -13.12 5.04
42b 47.25 -35.62 -20.62 4.36
43 -29.75 -84.38 1.88 5.16 442
43a -36.75 -76.88 1.88 4.69
44 22.75 -20.62 -35.62 5.09 98
45 19.25 -43.12 -20.62 5.08 147
46 -47.25 -9.38 5.62 5.06 295
47 5.25 -76.88 24.38 5.05 49
48 -33.25 13.12 28.12 5.04 246
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498 -54.25 -35.62 -5.62 3.62 49
499 29.75 -16.88 58.12 3.62 49
500 -54.25 -9.38 -5.62 3.61 98
501 -1.75 -9.38 -28.12 3.61 49
502 29.75 -1.88 -20.62 3.61 49
503 -36.75 -13.12 9.38 3.61 49
504 36.75 35.62 16.88 3.61 196
505 36.75 1.88 20.62 3.61 49
506 33.25 -39.38 -58.12 3.61 49
507 22.75 -16.88 43.12 3.60 49
508 -8.75 -9.38 -20.62 3.60 49
509 19.25 20.62 20.62 3.60 49
510 19.25 -28.12 -24.38 3.59 49
511 -12.25 16.88 20.62 3.59 49
512 -1.75 -9.38 31.88 3.59 49
513 -29.75 16.88 -5.62 3.59 49
514 -33.25 -43.12 -28.12 3.59 98
515 50.75 -16.88 28.12 3.58 49
516 -36.75 16.88 1.88 3.58 49
517 1.75 -84.38 -16.88 3.58 49
518 -47.25 20.62 46.88 3.58 49
519 26.25 -16.88 -58.12 3.58 49
520 -33.25 46.88 35.62 3.58 49
521 15.75 1.88 35.62 3.58 49
522 -5.25 -43.12 69.38 3.57 49
523 8.75 28.12 -24.38 3.57 49
524 15.75 20.62 43.12 3.57 49
525 -29.75 -20.62 -69.38 3.57 49
526 26.25 5.62 5.62 3.56 98
527 5.25 -9.38 -20.62 3.56 49
528 -12.25 88.12 16.88 3.56 49
529 15.75 16.88 -16.88 3.56 49
530 -12.25 -39.38 -5.62 3.56 49
531 19.25 9.38 20.62 3.56 49
532 1.75 -16.88 -24.38 3.56 49
533 -19.25 24.38 31.88 3.56 49
534 -5.25 -54.38 -54.38 3.56 49
535 -47.25 9.38 -9.38 3.56 49
536 -33.25 -1.88 13.12 3.55 49
537 -50.75 5.62 -9.38 3.55 49
538 22.75 73.12 5.62 3.55 98
539 -15.75 -5.62 1.88 3.55 98
540 -5.25 -13.12 -61.88 3.55 49
541 36.75 -31.88 13.12 3.55 49
542 -12.25 5.62 35.62 3.54 147
543 -19.25 43.12 -1.88 3.54 49
544 47.25 28.12 -39.38 3.54 49
545 -22.75 -20.62 -31.88 3.54 49
546 -50.75 -58.12 1.88 3.54 49
547 19.25 -46.88 31.88 3.54 49
548 -26.25 -61.88 35.62 3.53 98
549 47.25 -35.62 16.88 3.53 49
550 15.75 -5.62 28.12 3.53 49
551 12.25 -1.88 24.38 3.53 49
552 61.25 -35.62 35.62 3.52 49
553 5.25 -16.88 -20.62 3.52 49
554 -15.75 -69.38 28.12 3.52 49
555 -5.25 43.12 -16.88 3.52 49
556 15.75 -13.12 -73.12 3.51 49
557 -36.75 -16.88 -46.88 3.51 49
558 43.75 9.38 -39.38 3.51 49
559 5.25 -73.12 -16.88 3.51 49
560 -36.75 61.88 9.38 3.51 49
561 -57.75 -24.38 5.62 3.51 49
562 -8.75 -54.38 9.38 3.50 98
563 12.25 -84.38 -5.62 3.50 49
564 33.25 50.62 50.62 3.50 49
565 -29.75 -80.62 20.62 3.50 49
566 1.75 -65.62 -43.12 3.50 49
567 -5.25 -20.62 1.88 3.50 49
568 12.25 -9.38 -24.38 3.50 49
569 40.25 -31.88 -58.12 3.50 98
570 -1.75 80.62 1.88 3.49 49
571 -47.25 -24.38 -5.62 3.49 49
572 54.25 -16.88 -1.88 3.49 98
573 47.25 5.62 31.88 3.49 49
574 -1.75 9.38 9.38 3.49 98
575 -12.25 -76.88 -9.38 3.49 49
576 68.25 -28.12 16.88 3.49 49
577 -8.75 43.12 24.38 3.49 49
578 8.75 -76.88 9.38 3.49 49
579 -29.75 24.38 5.62 3.49 49
580 22.75 24.38 46.88 3.49 49
581 12.25 -9.38 43.12 3.49 49
582 22.75 -1.88 5.62 3.48 49
583 29.75 -39.38 -54.38 3.48 49
584 -33.25 -58.12 -1.88 3.48 49
585 -33.25 -43.12 -61.88 3.48 49
586 -43.75 13.12 16.88 3.48 98
587 -19.25 -84.38 1.88 3.48 49
588 -15.75 -50.62 39.38 3.48 49
589 40.25 -35.62 39.38 3.48 98
590 12.25 -65.62 1.88 3.48 49
591 -33.25 1.88 31.88 3.48 49
592 33.25 5.62 50.62 3.47 49
593 22.75 -9.38 43.12 3.47 49
594 -1.75 -1.88 -5.62 3.47 49
595 -15.75 -31.88 -16.88 3.47 49
596 26.25 31.88 43.12 3.46 49
597 40.25 -5.62 46.88 3.46 49
598 -19.25 13.12 28.12 3.46 49
599 19.25 -31.88 -54.38 3.46 49
600 47.25 -35.62 -9.38 3.46 49
601 40.25 -39.38 -13.12 3.45 49
602 -5.25 1.88 -58.12 3.45 49
603 12.25 -35.62 -16.88 3.45 49
604 -19.25 -50.62 -46.88 3.45 49
605 12.25 16.88 -50.62 3.45 49
606 1.75 -1.88 -20.62 3.45 49
607 -12.25 -16.88 -65.62 3.45 49
608 -19.25 -43.12 31.88 3.45 49
609 -1.75 -35.62 -13.12 3.45 49
610 -19.25 -61.88 -13.12 3.44 49
611 -1.75 -69.38 -13.12 3.44 49
612 -8.75 54.38 43.12 3.44 49
613 22.75 39.38 20.62 3.44 49
614 -29.75 9.38 61.88 3.44 49
615 -40.25 -50.62 28.12 3.43 49
616 1.75 84.38 1.88 3.43 49
617 57.75 -13.12 16.88 3.43 49
618 -15.75 -16.88 -28.12 3.43 98
619 54.25 -16.88 35.62 3.43 98
620 -1.75 24.38 -39.38 3.43 49
621 -33.25 73.12 20.62 3.43 49
622 54.25 -5.62 13.12 3.43 49
623 26.25 -39.38 35.62 3.43 49
624 -36.75 35.62 -13.12 3.43 49
625 -50.75 -9.38 -13.12 3.42 49
626 50.75 39.38 -9.38 3.42 49
627 40.25 13.12 -46.88 3.42 49
628 -12.25 -13.12 -73.12 3.42 98
629 -12.25 -5.62 -20.62 3.42 49
630 43.75 -9.38 46.88 3.42 49
631 47.25 -20.62 20.62 3.42 49
632 33.25 -20.62 -9.38 3.42 49
633 -50.75 -1.88 16.88 3.41 98
634 26.25 16.88 -20.62 3.41 49
635 22.75 20.62 16.88 3.41 49
636 12.25 -35.62 65.62 3.41 49
637 15.75 28.12 -5.62 3.41 49
638 -50.75 13.12 -39.38 3.41 49
639 -54.25 24.38 24.38 3.41 49
640 -64.75 -35.62 -1.88 3.41 49
641 -1.75 -9.38 69.38 3.41 49
642 -40.25 -5.62 35.62 3.40 49
643 -19.25 -9.38 -65.62 3.40 49
644 -29.75 -1.88 -46.88 3.40 49
645 54.25 1.88 9.38 3.40 49
646 29.75 35.62 39.38 3.40 49
647 19.25 -5.62 13.12 3.39 49
648 5.25 -61.88 -35.62 3.39 49
649 -22.75 43.12 -28.12 3.39 49
650 19.25 -61.88 -20.62 3.39 49
651 12.25 -35.62 -61.88 3.39 49
652 -29.75 -24.38 -9.38 3.38 49
653 47.25 39.38 13.12 3.38 98
654 -57.75 1.88 20.62 3.38 49
655 -1.75 5.62 -39.38 3.38 49
656 -40.25 39.38 43.12 3.37 98
657 -36.75 -69.38 13.12 3.37 49
658 12.25 20.62 -46.88 3.37 49
659 -19.25 -16.88 9.38 3.37 49
660 33.25 -28.12 58.12 3.37 49
661 50.75 -20.62 -43.12 3.37 49
662 -5.25 1.88 1.88 3.37 49
663 40.25 -69.38 9.38 3.37 98
664 8.75 -65.62 20.62 3.36 49
665 -12.25 -20.62 -13.12 3.36 49
666 -5.25 -61.88 20.62 3.36 49
667 -26.25 -31.88 -58.12 3.36 49
668 15.75 35.62 5.62 3.36 49
669 12.25 -69.38 5.62 3.36 49
670 1.75 65.62 -9.38 3.36 49
671 -12.25 -39.38 5.62 3.36 49
672 -43.75 -16.88 13.12 3.36 49
673 47.25 -54.38 13.12 3.36 49
674 12.25 -50.62 39.38 3.36 49
675 22.75 -73.12 -5.62 3.35 49
676 -36.75 -35.62 39.38 3.35 49
677 1.75 1.88 -16.88 3.35 49
678 22.75 -50.62 35.62 3.35 49
679 -22.75 20.62 -1.88 3.35 49
680 -50.75 39.38 16.88 3.35 49
681 -36.75 9.38 54.38 3.35 49
682 -8.75 39.38 -28.12 3.35 49
683 1.75 -69.38 46.88 3.35 49
684 -1.75 -16.88 -58.12 3.35 49
685 5.25 1.88 13.12 3.35 49
686 -47.25 -9.38 43.12 3.35 49
687 -1.75 -5.62 -73.12 3.35 49
688 22.75 -13.12 73.12 3.35 49
689 -15.75 35.62 5.62 3.34 49
690 36.75 24.38 -13.12 3.34 49
691 54.25 -28.12 24.38 3.34 49
692 54.25 -28.12 31.88 3.34 49
693 -5.25 -31.88 1.88 3.34 49
694 -22.75 20.62 -46.88 3.34 49
695 -5.25 -61.88 54.38 3.34 49
696 40.25 -76.88 -9.38 3.34 49
697 -26.25 -24.38 -5.62 3.34 49
698 -36.75 -46.88 9.38 3.34 49
699 -22.75 1.88 28.12 3.34 49
700 1.75 28.12 -5.62 3.34 49
701 -47.25 -50.62 5.62 3.33 49
702 5.25 46.88 13.12 3.33 49
703 22.75 54.38 5.62 3.33 49
704 5.25 -16.88 69.38 3.33 49
705 -22.75 24.38 20.62 3.33 49
706 54.25 1.88 -31.88 3.33 49
707 19.25 -69.38 20.62 3.33 49
708 26.25 -80.62 -28.12 3.33 49
709 -50.75 -35.62 -54.38 3.33 49
710 15.75 -31.88 -50.62 3.33 49
711 29.75 -9.38 9.38 3.33 49
712 -40.25 -46.88 -5.62 3.32 49
713 5.25 -50.62 -5.62 3.32 98
714 -8.75 -13.12 5.62 3.32 49
715 12.25 -58.12 46.88 3.32 49
716 -19.25 -65.62 9.38 3.32 49
717 26.25 -54.38 31.88 3.32 49
718 26.25 -43.12 5.62 3.32 49
719 29.75 -1.88 -54.38 3.32 49
720 -36.75 -31.88 20.62 3.32 49
721 15.75 46.88 46.88 3.32 49
722 29.75 43.12 16.88 3.31 49
723 15.75 -16.88 54.38 3.31 49
724 50.75 -61.88 -13.12 3.31 49
725 -15.75 -46.88 -24.38 3.31 49
726 33.25 -76.88 -28.12 3.31 49
727 -50.75 -16.88 -9.38 3.31 49
728 -40.25 -65.62 -31.88 3.31 49
729 33.25 -35.62 -35.62 3.31 49
730 33.25 31.88 -28.12 3.31 49
731 40.25 -9.38 -13.12 3.31 49
732 -29.75 -46.88 -28.12 3.30 49
733 36.75 -46.88 9.38 3.30 49
734 -15.75 -69.38 -43.12 3.30 49
735 47.25 5.62 -1.88 3.30 49
736 -68.25 -24.38 13.12 3.30 49
737 -22.75 -28.12 -65.62 3.30 49
738 -26.25 -35.62 -43.12 3.30 49
739 22.75 9.38 46.88 3.30 49
740 8.75 13.12 16.88 3.30 49
741 40.25 -31.88 -69.38 3.30 49
742 8.75 24.38 39.38 3.30 49
743 -12.25 -54.38 -9.38 3.29 49
744 -26.25 -5.62 65.62 3.29 49
745 -1.75 -76.88 1.88 3.29 49
746 -64.75 20.62 5.62 3.29 49

house

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

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

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

chair

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

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

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 40.25 -46.88 13.12 4.87 147
2 12.25 35.62 -5.62 4.69 98
3 -50.75 39.38 16.88 4.39 49
4 -36.75 -76.88 1.88 4.35 49
5 -50.75 -39.38 -1.88 4.08 147
6 -15.75 -1.88 -16.88 4.02 49
7 -54.25 -43.12 46.88 4.02 49
8 -29.75 20.62 46.88 3.99 49
9 26.25 -46.88 -35.62 3.98 49
10 33.25 43.12 -16.88 3.95 49
11 33.25 -58.12 -1.88 3.93 49
12 -5.25 43.12 50.62 3.92 49
13 22.75 24.38 16.88 3.88 49
14 33.25 -58.12 -16.88 3.83 49
15 12.25 5.62 -24.38 3.82 49
16 -36.75 -61.88 43.12 3.82 49
17 -22.75 -43.12 -31.88 3.81 98
18 -22.75 24.38 13.12 3.74 49
19 -22.75 -31.88 -69.38 3.74 49
20 12.25 28.12 -16.88 3.72 49
21 8.75 -35.62 -35.62 3.71 49
22 -57.75 -9.38 -1.88 3.70 49
23 -57.75 13.12 1.88 3.70 98
24 12.25 16.88 -20.62 3.68 49
25 19.25 20.62 20.62 3.68 49
26 26.25 16.88 16.88 3.67 49
27 33.25 13.12 -1.88 3.67 49
28 36.75 -24.38 20.62 3.63 49
29 -15.75 20.62 13.12 3.62 49
30 -36.75 -46.88 50.62 3.61 49
31 -1.75 5.62 31.88 3.61 49
32 -22.75 9.38 24.38 3.57 49
33 29.75 -43.12 -35.62 3.56 49
34 -22.75 1.88 5.62 3.54 49
35 -29.75 -43.12 -58.12 3.53 49
36 22.75 13.12 20.62 3.50 49
37 54.25 20.62 5.62 3.48 49
38 33.25 -20.62 -58.12 3.47 49
39 -22.75 50.62 9.38 3.46 49
40 43.75 -28.12 -13.12 3.46 49
41 36.75 -1.88 28.12 3.44 49
42 33.25 9.38 -13.12 3.41 49
43 -15.75 -35.62 -24.38 3.41 98
44 -22.75 -9.38 20.62 3.39 49
45 29.75 9.38 -16.88 3.39 49
46 26.25 -69.38 31.88 3.38 49
47 12.25 9.38 -1.88 3.37 49
48 1.75 28.12 -35.62 3.37 49
49 36.75 -28.12 -9.38 3.36 49
50 -22.75 -39.38 -28.12 3.35 49
51 -15.75 -39.38 -54.38 3.32 49
52 36.75 20.62 -43.12 3.32 49
53 29.75 13.12 -43.12 3.31 49
54 29.75 -20.62 -5.62 3.30 49

scrambledpix

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

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

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 1.75 24.38 -13.12 5.54 49
2 -22.75 -46.88 43.12 4.95 98
3 -15.75 61.88 -9.38 4.82 147
4 -29.75 -61.88 -31.88 4.79 49
5 5.25 43.12 -28.12 4.21 49
6 -22.75 -9.38 20.62 4.16 49
7 50.75 16.88 -28.12 4.13 147
8 40.25 -16.88 -39.38 3.97 49
9 -1.75 5.62 31.88 3.89 49
10 -64.75 -31.88 39.38 3.88 49
11 -22.75 -54.38 -9.38 3.86 49
12 -15.75 -43.12 -16.88 3.74 49
13 -1.75 -35.62 5.62 3.63 49
14 22.75 -24.38 46.88 3.57 49
15 -40.25 -16.88 -54.38 3.56 49
16 22.75 46.88 -5.62 3.56 49
17 -26.25 -69.38 39.38 3.56 49
18 -1.75 -46.88 -9.38 3.54 49
19 50.75 5.62 -35.62 3.50 49
20 19.25 1.88 9.38 3.46 49
21 33.25 -20.62 43.12 3.42 49
22 -19.25 -73.12 39.38 3.40 49
23 -22.75 -9.38 -65.62 3.39 49
24 -29.75 -65.62 20.62 3.39 49
25 -15.75 -61.88 -46.88 3.33 49
26 1.75 13.12 31.88 3.31 49
27 33.25 35.62 -28.12 3.31 49
28 26.25 -20.62 43.12 3.31 49
29 54.25 -9.38 16.88 3.30 49
30 19.25 43.12 13.12 3.30 49
31 26.25 -24.38 -69.38 3.30 49

face

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

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

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -50.75 -50.62 13.12 5.22 196
1a -47.25 -58.12 13.12 3.83
2 -47.25 -43.12 -35.62 4.91 49
3 1.75 24.38 -13.12 4.80 98
4 -47.25 -39.38 16.88 4.76 98
5 68.25 -9.38 -9.38 4.71 147
6 -33.25 50.62 16.88 4.70 196
7 -12.25 -58.12 -16.88 4.59 98
8 -57.75 -16.88 1.88 4.46 147
9 -15.75 13.12 73.12 4.42 196
10 -36.75 31.88 16.88 4.39 98
11 -40.25 46.88 13.12 4.30 98
12 -22.75 39.38 13.12 4.27 49
13 1.75 -43.12 -1.88 4.23 49
14 22.75 -50.62 -24.38 4.19 49
15 29.75 -46.88 35.62 4.15 49
16 -47.25 -50.62 20.62 4.12 49
17 33.25 -61.88 -13.12 4.10 49
18 -43.75 -31.88 35.62 4.07 98
19 -26.25 24.38 -13.12 4.06 147
20 -1.75 -39.38 5.62 4.05 98
21 40.25 -39.38 -24.38 4.05 49
22 29.75 -54.38 46.88 4.03 49
23 -47.25 24.38 20.62 3.99 49
24 -47.25 -54.38 -9.38 3.98 49
25 5.25 20.62 65.62 3.96 49
26 5.25 -58.12 -24.38 3.95 98
27 -8.75 -46.88 -31.88 3.94 49
28 -36.75 -31.88 -46.88 3.94 49
29 -19.25 -73.12 39.38 3.91 98
30 -40.25 -43.12 -16.88 3.91 49
31 -1.75 28.12 61.88 3.89 98
32 -36.75 31.88 35.62 3.86 393
32a -29.75 28.12 39.38 3.52
33 -8.75 13.12 65.62 3.85 49
34 -15.75 -13.12 24.38 3.82 98
35 -36.75 -43.12 39.38 3.78 147
36 -8.75 -28.12 61.88 3.78 49
37 43.75 35.62 35.62 3.78 98
38 -47.25 -61.88 -5.62 3.78 49
39 40.25 28.12 39.38 3.77 98
40 33.25 -46.88 -39.38 3.77 98
41 5.25 -24.38 -20.62 3.76 49
42 40.25 43.12 16.88 3.74 49
43 -47.25 -28.12 28.12 3.74 98
44 -57.75 31.88 31.88 3.73 49
45 -1.75 -58.12 -16.88 3.69 49
46 -43.75 -39.38 -9.38 3.68 98
47 19.25 -46.88 -20.62 3.68 98
48 -12.25 -39.38 58.12 3.66 49
49 40.25 -43.12 -9.38 3.65 49
50 12.25 28.12 61.88 3.65 49
51 -26.25 -43.12 -43.12 3.65 49
52 -47.25 -16.88 -35.62 3.63 49
53 -22.75 -46.88 -50.62 3.62 49
54 -26.25 -88.12 -9.38 3.62 98
55 -1.75 -46.88 -9.38 3.62 49
56 -57.75 -9.38 -1.88 3.61 49
57 -40.25 50.62 20.62 3.61 49
58 33.25 -58.12 -16.88 3.60 49
59 -54.25 -43.12 20.62 3.60 49
60 -50.75 -35.62 43.12 3.60 49
61 19.25 -16.88 39.38 3.59 49
62 -12.25 -76.88 28.12 3.59 49
63 -47.25 -46.88 16.88 3.59 49
64 -19.25 -61.88 -9.38 3.57 49
65 5.25 -65.62 13.12 3.57 49
66 -29.75 -13.12 5.62 3.57 49
67 -29.75 28.12 13.12 3.55 49
68 1.75 -43.12 -16.88 3.52 49
69 -29.75 31.88 -9.38 3.52 49
70 -33.25 39.38 54.38 3.51 49
71 -47.25 -46.88 9.38 3.50 49
72 36.75 -46.88 9.38 3.50 49
73 33.25 -50.62 -20.62 3.49 49
74 -47.25 -46.88 -31.88 3.49 49
75 22.75 31.88 28.12 3.49 49
76 43.75 -54.38 9.38 3.48 49
77 5.25 31.88 -35.62 3.47 49
78 12.25 -58.12 -20.62 3.46 49
79 8.75 -58.12 -28.12 3.46 98
80 -8.75 -39.38 -5.62 3.45 49
81 15.75 -54.38 -20.62 3.44 49
82 22.75 -1.88 58.12 3.43 49
83 33.25 -54.38 43.12 3.43 49
84 5.25 -54.38 -20.62 3.43 49
85 29.75 -50.62 -39.38 3.41 49
86 -40.25 50.62 9.38 3.41 49
87 8.75 -1.88 9.38 3.41 49
88 36.75 61.88 28.12 3.40 49
89 29.75 -46.88 -43.12 3.40 49
90 -33.25 31.88 46.88 3.39 49
91 40.25 -35.62 -43.12 3.36 49
92 -47.25 -9.38 13.12 3.36 49
93 -50.75 20.62 20.62 3.36 49
94 33.25 50.62 20.62 3.36 49
95 -15.75 -73.12 16.88 3.35 49
96 29.75 -46.88 -31.88 3.35 49
97 -50.75 -28.12 35.62 3.34 49
98 15.75 13.12 43.12 3.33 49
99 -1.75 39.38 -20.62 3.33 49
100 57.75 -35.62 43.12 3.33 49
101 26.25 1.88 13.12 3.32 49
102 33.25 -65.62 -16.88 3.32 49
103 -36.75 20.62 50.62 3.31 49
104 -43.75 -43.12 46.88 3.31 49
105 -50.75 -58.12 -13.12 3.31 49
106 40.25 -43.12 5.62 3.30 49
107 26.25 -43.12 35.62 3.30 49
108 -29.75 -50.62 1.88 3.30 49
109 -47.25 -9.38 5.62 3.29 49
110 43.75 9.38 50.62 3.29 49
111 -54.25 -20.62 -39.38 3.29 49
112 29.75 -35.62 35.62 3.29 49

shoe

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

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

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

cat

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

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

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

scissors

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

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

Cluster ID X Y Z Peak Stat Cluster Size (mm3)
1 -50.75 20.62 -24.38 6.11 984
2 -19.25 84.38 20.62 5.80 344
3 -15.75 16.88 -46.88 5.71 442
4 -26.25 24.38 28.12 5.64 147
5 36.75 20.62 -31.88 5.22 246
6 29.75 5.62 -5.62 5.15 147
7 12.25 -35.62 -69.38 5.04 98
8 12.25 13.12 -43.12 5.03 442
9 33.25 5.62 -46.88 4.99 49
10 1.75 -28.12 -61.88 4.99 98
11 -19.25 61.88 50.62 4.98 49
12 -1.75 1.88 -61.88 4.98 295
13 5.25 24.38 -46.88 4.96 492
14 -22.75 -54.38 -50.62 4.86 295
15 15.75 76.88 28.12 4.67 49
16 12.25 -61.88 -46.88 4.67 49
17 47.25 24.38 -1.88 4.62 49
18 -12.25 76.88 20.62 4.54 98
19 -54.25 -39.38 -31.88 4.53 49
20 26.25 -46.88 1.88 4.50 98
21 1.75 13.12 -16.88 4.46 49
22 -19.25 31.88 -35.62 4.46 147
23 -29.75 76.88 20.62 4.45 541
24 50.75 31.88 -31.88 4.44 49
25 -15.75 -43.12 -46.88 4.43 98
26 -12.25 9.38 -58.12 4.38 98
27 -50.75 -9.38 -20.62 4.38 246
28 50.75 20.62 -1.88 4.36 49
29 40.25 5.62 31.88 4.36 98
30 -12.25 1.88 -28.12 4.33 49
31 15.75 -35.62 -73.12 4.32 49
32 1.75 1.88 31.88 4.31 147
33 47.25 -5.62 20.62 4.30 246
34 -29.75 20.62 -46.88 4.28 49
35 40.25 -58.12 -5.62 4.27 98
36 47.25 24.38 9.38 4.26 49
37 22.75 -58.12 -50.62 4.23 49
38 12.25 80.62 -5.62 4.21 49
39 54.25 5.62 50.62 4.19 98
40 -26.25 -13.12 1.88 4.18 49
41 -47.25 31.88 -28.12 4.16 49
42 -5.25 80.62 31.88 4.14 147
43 12.25 24.38 -35.62 4.12 98
44 22.75 -54.38 -43.12 4.12 49
45 -22.75 -5.62 -20.62 4.12 49
46 -5.25 -16.88 -39.38 4.12 98
47 -5.25 -9.38 31.88 4.11 49
48 -40.25 -16.88 -65.62 4.11 49
49 -5.25 84.38 1.88 4.10 49
50 29.75 -1.88 24.38 4.08 49
51 1.75 -1.88 -76.88 4.07 98
52 -5.25 -28.12 -76.88 4.07 98
53 -54.25 9.38 -16.88 4.06 49
54 33.25 24.38 -43.12 4.06 98
55 1.75 -28.12 -76.88 4.04 49
56 15.75 -16.88 46.88 4.00 49
57 50.75 24.38 -35.62 4.00 147
58 -15.75 76.88 31.88 3.99 49
59 -40.25 61.88 5.62 3.98 49
60 19.25 -46.88 -35.62 3.98 49
61 36.75 31.88 -39.38 3.98 246
62 -36.75 -1.88 -20.62 3.97 98
63 33.25 -35.62 -35.62 3.97 98
64 12.25 80.62 35.62 3.97 98
65 40.25 28.12 -43.12 3.97 49
66 -47.25 24.38 20.62 3.96 49
67 -26.25 -24.38 20.62 3.96 49
68 19.25 16.88 -46.88 3.94 49
69 1.75 -5.62 -80.62 3.94 98
70 54.25 1.88 43.12 3.93 49
71 50.75 50.62 20.62 3.93 49
72 22.75 -1.88 9.38 3.92 49
73 -19.25 50.62 13.12 3.92 49
74 -40.25 69.38 13.12 3.92 49
75 -8.75 24.38 -35.62 3.86 98
76 19.25 80.62 1.88 3.86 98
77 22.75 -43.12 -61.88 3.86 98
78 -33.25 -65.62 -16.88 3.85 49
79 -8.75 28.12 54.38 3.85 196
80 40.25 -39.38 58.12 3.84 49
81 19.25 -1.88 -1.88 3.84 49
82 12.25 -61.88 9.38 3.83 49
83 26.25 13.12 -39.38 3.83 49
84 -40.25 -9.38 -5.62 3.82 49
85 -26.25 31.88 -39.38 3.81 49
86 -8.75 84.38 20.62 3.81 98
87 -5.25 5.62 -69.38 3.81 49
88 -1.75 -31.88 9.38 3.80 49
89 -26.25 -80.62 -5.62 3.78 49
90 19.25 -16.88 -54.38 3.78 49
91 -1.75 73.12 35.62 3.77 49
92 15.75 50.62 -9.38 3.77 49
93 33.25 -5.62 -1.88 3.76 49
94 15.75 88.12 16.88 3.76 49
95 -36.75 65.62 20.62 3.76 98
96 -1.75 -35.62 -65.62 3.76 49
97 -5.25 80.62 13.12 3.75 49
98 -47.25 -5.62 -28.12 3.75 49
99 -26.25 16.88 28.12 3.75 49
100 29.75 76.88 13.12 3.74 49
101 40.25 -1.88 61.88 3.74 246
102 -64.75 -5.62 5.62 3.73 98
103 1.75 39.38 -16.88 3.72 49
104 -5.25 1.88 -76.88 3.72 49
105 -29.75 -69.38 9.38 3.72 98
106 43.75 -31.88 -13.12 3.71 49
107 40.25 -43.12 -5.62 3.71 49
108 43.75 -61.88 5.62 3.71 49
109 -1.75 -39.38 5.62 3.70 98
110 40.25 -24.38 -35.62 3.69 49
111 26.25 -50.62 -54.38 3.68 49
112 33.25 -39.38 24.38 3.67 49
113 29.75 -9.38 9.38 3.66 49
114 5.25 13.12 31.88 3.66 147
115 -22.75 -39.38 -54.38 3.65 49
116 -50.75 -13.12 5.62 3.65 49
117 -12.25 -43.12 -13.12 3.64 49
118 -1.75 -46.88 -1.88 3.63 49
119 57.75 -24.38 5.62 3.63 49
120 19.25 54.38 50.62 3.62 49
121 33.25 -39.38 -20.62 3.62 49
122 29.75 -46.88 -61.88 3.62 49
123 40.25 13.12 -39.38 3.61 49
124 -22.75 58.12 -20.62 3.61 98
125 50.75 -16.88 46.88 3.61 49
126 15.75 5.62 9.38 3.60 49
127 29.75 -50.62 -50.62 3.60 49
128 -29.75 31.88 -9.38 3.60 49
129 -50.75 -1.88 20.62 3.60 49
130 15.75 88.12 9.38 3.59 98
131 -43.75 -24.38 35.62 3.59 49
132 -19.25 9.38 -35.62 3.58 49
133 -29.75 13.12 61.88 3.58 98
134 -33.25 39.38 54.38 3.58 49
135 -19.25 -1.88 -35.62 3.58 49
136 -8.75 16.88 -43.12 3.56 49
137 57.75 16.88 16.88 3.56 49
138 8.75 1.88 24.38 3.56 49
139 -12.25 -50.62 -50.62 3.56 49
140 15.75 -28.12 -54.38 3.56 49
141 33.25 5.62 16.88 3.56 49
142 -40.25 43.12 -31.88 3.55 49
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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/env/lib/python3.11/site-packages/nilearn/image/resampling.py:492: UserWarning:

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

Classification accuracy: 0.7173 / Chance level: 0.125

Total running time of the script: (3 minutes 33.323 seconds)

Estimated memory usage: 917 MB

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