8.3.6. Searchlight analysis of face vs house recognition

Searchlight analysis requires fitting a classifier a large amount of times. As a result, it is an intrinsically slow method. In order to speed up computing, in this example, Searchlight is run only on one slice on the fMRI (see the generated figures). Load Haxby dataset

import pandas as pd
from nilearn import datasets
from nilearn.image import new_img_like, load_img

# We fetch 2nd subject from haxby datasets (which is default)
haxby_dataset = datasets.fetch_haxby()

# print basic information on the dataset
print('Anatomical nifti image (3D) is located at: %s' % haxby_dataset.mask)
print('Functional nifti image (4D) is located at: %s' % haxby_dataset.func[0])

fmri_filename = haxby_dataset.func[0]
labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
y = labels['labels']
session = labels['chunks']


Anatomical nifti image (3D) is located at: /home/kshitij/nilearn_data/haxby2001/mask.nii.gz
Functional nifti image (4D) is located at: /home/kshitij/nilearn_data/haxby2001/subj2/bold.nii.gz Restrict to faces and houses

from nilearn.image import index_img
condition_mask = y.isin(['face', 'house'])

fmri_img = index_img(fmri_filename, condition_mask)
y, session = y[condition_mask], session[condition_mask] Prepare masks

  • mask_img is the original mask
  • process_mask_img is a subset of mask_img, it contains the voxels that should be processed (we only keep the slice z = 26 and the back of the brain to speed up computation)
import numpy as np

mask_img = load_img(haxby_dataset.mask)

# .astype() makes a copy.
process_mask = mask_img.get_data().astype(np.int)
picked_slice = 29
process_mask[..., (picked_slice + 1):] = 0
process_mask[..., :picked_slice] = 0
process_mask[:, 30:] = 0
process_mask_img = new_img_like(mask_img, process_mask) Searchlight computation

# Make processing parallel
# /!\ As each thread will print its progress, n_jobs > 1 could mess up the
#     information output.
n_jobs = 1

# Define the cross-validation scheme used for validation.
# Here we use a KFold cross-validation on the session, which corresponds to
# splitting the samples in 4 folds and make 4 runs using each fold as a test
# set once and the others as learning sets
from sklearn.model_selection import KFold
cv = KFold(n_splits=4)

import nilearn.decoding
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = nilearn.decoding.SearchLight(
    radius=5.6, n_jobs=n_jobs,
    verbose=1, cv=cv)
searchlight.fit(fmri_img, y) F-scores computation

from nilearn.input_data import NiftiMasker

# For decoding, standardizing is often very important
nifti_masker = NiftiMasker(mask_img=mask_img, sessions=session,
                           standardize=True, memory='nilearn_cache',
fmri_masked = nifti_masker.fit_transform(fmri_img)

from sklearn.feature_selection import f_classif
f_values, p_values = f_classif(fmri_masked, y)
p_values = -np.log10(p_values)
p_values[p_values > 10] = 10
p_unmasked = nifti_masker.inverse_transform(p_values).get_data() Visualization

Use the fmri mean image as a surrogate of anatomical data

from nilearn import image
mean_fmri = image.mean_img(fmri_img)

from nilearn.plotting import plot_stat_map, plot_img, show
searchlight_img = new_img_like(mean_fmri, searchlight.scores_)

# Because scores are not a zero-center test statistics, we cannot use
# plot_stat_map
plot_img(searchlight_img, bg_img=mean_fmri,
         title="Searchlight", display_mode="z", cut_coords=[-9],
         vmin=.42, cmap='hot', threshold=.2, black_bg=True)

# F_score results
p_ma = np.ma.array(p_unmasked, mask=np.logical_not(process_mask))
f_score_img = new_img_like(mean_fmri, p_ma)
plot_stat_map(f_score_img, mean_fmri,
              title="F-scores", display_mode="z",

  • ../../_images/sphx_glr_plot_haxby_searchlight_001.png
  • ../../_images/sphx_glr_plot_haxby_searchlight_002.png

Total running time of the script: ( 0 minutes 41.258 seconds)

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