A real-world example of a searchlight analysis with nilearn

searchlight.py

from nilearn.decoding import Searchlight
from nilearn.datasets import load_mni152_brain_mask
from nilearn.image import new_img_like 
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from skearn.svm import LinearSVC
from sklearn.model_selection import LeaveOneGroupOut

imgs = # 4D NIfTI image or list of 3D NIfTI images
y = # array-like of condition labels for each volume in `imgs` 
run_labels = # array-like of run labels for each volume of `imgs`

pipeline = make_pipeline(StandardScaler(), LinearSVC())

brain_mask = load_mni152_brain_mask()
searchlight = Searchlight(mask_img=brain_mask, radius=4, estimator=pipeline, 
                          cv=LeaveOneGroupOut())
searchlight.fit(imgs, y, groups=run_labels)
results = new_img_like(brain_mask, searclight.scores_)