Decoding of a dataset after GLM fit for signal extraction#

Full step-by-step example of fitting a GLM to perform a decoding experiment. We use the data from one subject of the Haxby dataset.

More specifically:

  1. Download the Haxby dataset.

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

  3. Analyze the decoding performance using a classifier.

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 sessions
sessions = behavioral['chunks'].values
unique_sessions = behavioral['chunks'].unique()

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

Build a proper event structure for each session#

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

Instantiate and run FirstLevelModel#

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

z_maps = []
conditions_label = []
session_label = []

# Instantiate the glm
from nilearn.glm.first_level import FirstLevelModel
glm = FirstLevelModel(t_r=TR,
                      mask_img=haxby_dataset.mask,
                      high_pass=.008,
                      smoothing_fwhm=4,
                      memory='nilearn_cache')

Run the glm on data from each session#

events[session].trial_type.unique()
from nilearn.image import index_img
for session in unique_sessions:
    # grab the fmri data for that particular session
    fmri_session = index_img(func_filename, sessions == session)

    # fit the glm
    glm.fit(fmri_session, events=events[session])

    # set up contrasts: one per condition
    conditions = events[session].trial_type.unique()
    for condition_ in conditions:
        z_maps.append(glm.compute_contrast(condition_))
        conditions_label.append(condition_)
        session_label.append(session)
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f122cb34610>, <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>, { 'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.114769, ..., -2.149296],
       ...,
       [ 2.367151, ...,  0.779998]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 2.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.44475 , ...,  0.379275]), <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.013524, ...,  0.844135]), <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.217486, ..., -1.430348]), <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.106474, ..., -0.182434]), <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.747494, ..., -1.660679]), <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.080159, ..., -1.32614 ]), <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.253894, ..., -0.452682]), <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.240914, ..., 0.244136]), <nibabel.nifti1.Nifti1Image object at 0x7f12412ee8c0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1241359fc0>, <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>, { '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 - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.871458, ..., -0.990755]), <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.627194, ..., -1.290147]), <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.153013, ..., -1.320123]), <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.15748 , ...,  2.082416]), <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.997775, ..., -0.754066]), <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.109412, ..., 1.330079]), <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.030863, ..., -1.731439]), <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.559734, ..., -0.720924]), <nibabel.nifti1.Nifti1Image object at 0x7f1241359db0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f124168da20>, <nibabel.nifti1.Nifti1Image object at 0x7f124168e620>, { '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([[ 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 0x7f124168e620>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.457214, ..., 1.537178]), <nibabel.nifti1.Nifti1Image object at 0x7f124168e620>)
___________________________________________________________unmask - 0.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.803925, ...,  0.570463]), <nibabel.nifti1.Nifti1Image object at 0x7f124168e620>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.614932, ...,  0.232909]), <nibabel.nifti1.Nifti1Image object at 0x7f124168e620>)
___________________________________________________________unmask - 0.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.527175, ..., -1.062723]), <nibabel.nifti1.Nifti1Image object at 0x7f124168e620>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.126756, ..., -2.274819]), <nibabel.nifti1.Nifti1Image object at 0x7f124168e620>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.635166, ..., -0.395548]), <nibabel.nifti1.Nifti1Image object at 0x7f124168e620>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.250365, ..., 0.364311]), <nibabel.nifti1.Nifti1Image object at 0x7f124168e620>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f123f910550>, <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>, { 'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.4s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-2.026206, ...,  5.974948],
       ...,
       [ 2.616334, ...,  0.104535]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.503142, ..., -1.639351]), <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.271132, ..., -0.047089]), <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.318104, ..., 0.724813]), <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.073279, ..., -0.316956]), <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.380183, ..., -0.690685]), <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.631912, ..., -0.753286]), <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.155784, ..., -0.065658]), <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.186135, ..., -0.69267 ]), <nibabel.nifti1.Nifti1Image object at 0x7f124135b340>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f125ded01c0>, <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>, { '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([[ 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 - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.158342, ..., -0.068131]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.396497, ..., -0.424937]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.091867, ...,  0.463109]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.054041, ..., -0.122921]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.025223, ...,  0.562991]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.001653, ..., -0.968729]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.274665, ..., -0.667   ]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.564329, ..., 1.496068]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded3fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f123f910280>, <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>, { '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([[-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 - 1.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.987059, ...,  1.41717 ]), <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.24774 , ...,  0.674399]), <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.731234, ...,  1.341998]), <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.714869, ...,  1.182988]), <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.222674, ...,  0.480354]), <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.366899, ..., -0.091153]), <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.2708  , ..., -0.247146]), <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.251249, ..., -0.413063]), <nibabel.nifti1.Nifti1Image object at 0x7f123ca19f30>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1241358be0>, <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>, { 'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.4s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[129.51173 , ..., -15.279282],
       ...,
       [-18.911755, ...,  21.839058]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.290412, ..., -0.609221]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>)
___________________________________________________________unmask - 0.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.090297, ..., -0.822602]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.747918, ..., -0.108861]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.095788, ..., -1.376995]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.807425, ...,  1.826947]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.017351, ..., 0.622242]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.625042, ..., -0.231224]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.056424, ..., -1.672737]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358fa0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f122cb0bf70>, <nibabel.nifti1.Nifti1Image object at 0x7f123faded40>, { 'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-15.915996, ...,  22.07737 ],
       ...,
       [-16.981215, ...,   3.372383]], dtype=float32),
array([[ 0.      , ...,  1.      ],
       ...,
       [-0.200737, ...,  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 0x7f123faded40>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.168487, ..., -0.636238]), <nibabel.nifti1.Nifti1Image object at 0x7f123faded40>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.145684, ..., 0.932773]), <nibabel.nifti1.Nifti1Image object at 0x7f123faded40>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.580823, ..., -0.455655]), <nibabel.nifti1.Nifti1Image object at 0x7f123faded40>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.685537, ...,  0.715791]), <nibabel.nifti1.Nifti1Image object at 0x7f123faded40>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.245273, ..., -0.099707]), <nibabel.nifti1.Nifti1Image object at 0x7f123faded40>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.79538 , ..., 1.913842]), <nibabel.nifti1.Nifti1Image object at 0x7f123faded40>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.519925, ..., 0.629218]), <nibabel.nifti1.Nifti1Image object at 0x7f123faded40>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f123af00af0>, <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>, { '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([[-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 - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.425611, ...,  2.348025]), <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.31867 , ..., 0.408223]), <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.534932, ..., -0.150519]), <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.149007, ..., 0.640215]), <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.640699, ..., 1.50369 ]), <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.246384, ..., -1.346316]), <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.162243, ..., -0.519251]), <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.203695, ..., -1.335337]), <nibabel.nifti1.Nifti1Image object at 0x7f12412efdf0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f12412b9060>, <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>, { '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([[-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 - 2.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.034515, ...,  1.612397]), <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.62798 , ..., 1.160445]), <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.506632, ..., 0.459388]), <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.261016, ..., -0.747236]), <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.231796, ...,  1.098904]), <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.148582, ...,  0.999934]), <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.548262, ...,  0.09934 ]), <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.222824, ...,  0.318977]), <nibabel.nifti1.Nifti1Image object at 0x7f12412b8be0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1272a1ac50>, <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>, { 'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 4,
  'standardize': False,
  'standardize_confounds': True,
  't_r': 2.5,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 1.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-19.66533 , ...,  -6.299562],
       ...,
       [-24.647343, ...,   2.331865]], dtype=float32),
array([[0., ..., 1.],
       ...,
       [0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.175763, ..., -3.429485]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-3.146358, ..., -2.947626]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.806852, ..., -2.720554]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.008926, ...,  0.4544  ]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.279543, ..., 1.828183]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.463642, ...,  0.26599 ]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.058735, ..., -1.191442]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.390268, ..., -1.112207]), <nibabel.nifti1.Nifti1Image object at 0x7f125ded17e0>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f124135a200>, <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>, { '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([[-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 - 1.7s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.340751, ..., -1.056108]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.043261, ..., 1.144442]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.517954, ..., 0.611394]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-5.797134e-01, ...,  4.317655e-06]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.398581, ..., -0.488427]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.714396, ..., 0.869941]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.924894, ...,  0.723724]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>)
___________________________________________________________unmask - 0.2s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.145297, ..., -0.821272]), <nibabel.nifti1.Nifti1Image object at 0x7f1241358c70>)
___________________________________________________________unmask - 0.2s, 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 Session 1.

Contrasts

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

Mask

Model did not supply a mask image.

Stat Maps with Cluster Tables

bottle

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

Cluster Table
Height control fpr
α 0.0
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
49 -40.25 24.38 -9.38 5.04 49
50 33.25 20.62 -20.62 5.03 98
51 8.75 -91.88 5.62 5.03 49
52 -26.25 -54.38 -46.88 5.02 147
53 26.25 31.88 -31.88 5.01 147
54 -26.25 -20.62 -13.12 5.00 98
55 -61.25 -5.62 20.62 5.00 344
56 33.25 9.38 -24.38 5.00 98
57 1.75 -58.12 -24.38 4.99 98
58 -40.25 -39.38 31.88 4.97 344
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house