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).

Note

If you are using Nilearn with a version older than 0.9.0, then you should either upgrade your version or import maskers from the input_data module instead of the maskers module.

That is, you should manually replace in the following example all occurrences of:

from nilearn.maskers import NiftiMasker

with:

from nilearn.input_data import NiftiMasker

Load Haxby dataset

import pandas as pd

from nilearn import datasets
from nilearn.image import get_data, load_img, new_img_like

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

# print basic information on the dataset
print(f"Anatomical nifti image (3D) is located at: {haxby_dataset.mask}")
print(f"Functional nifti image (4D) is located at: {haxby_dataset.func[0]}")

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

Anatomical nifti image (3D) is located at: /home/himanshu/nilearn_data/haxby2001/mask.nii.gz
Functional nifti image (4D) is located at: /home/himanshu/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, run = y[condition_mask], run[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 = 29 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 = get_data(mask_img).astype(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)

/home/himanshu/Desktop/nilearn_work/nilearn/examples/02_decoding/plot_haxby_searchlight.py:61: UserWarning:

Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.

Searchlight computation

# Make processing parallel
# /!\ As each thread will print its progress, n_jobs > 1 could mess up the
#     information output.
n_jobs = 2

# Define the cross-validation scheme used for validation.
# Here we use a KFold cross-validation on the run, 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(
    mask_img,
    process_mask_img=process_mask_img,
    radius=5.6,
    n_jobs=n_jobs,
    verbose=1,
    cv=cv,
)
searchlight.fit(fmri_img, y)

/home/himanshu/.local/miniconda3/envs/nilearnpy/lib/python3.12/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.

[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:   25.5s finished

SearchLight(cv=KFold(n_splits=4, random_state=None, shuffle=False),
            mask_img=<nibabel.nifti1.Nifti1Image object at 0x733231f66a50>,
            n_jobs=2,
            process_mask_img=<nibabel.nifti1.Nifti1Image object at 0x733231f66570>,
            radius=5.6, verbose=1)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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F-scores computation

from nilearn.maskers import NiftiMasker

# For decoding, standardizing is often very important
nifti_masker = NiftiMasker(
    mask_img=mask_img,
    runs=run,
    standardize="zscore_sample",
    memory="nilearn_cache",
    memory_level=1,
)
fmri_masked = nifti_masker.fit_transform(fmri_img)

from sklearn.feature_selection import f_classif

_, p_values = f_classif(fmri_masked, y)
p_values = -np.log10(p_values)
p_values[p_values > 10] = 10
p_unmasked = get_data(nifti_masker.inverse_transform(p_values))

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_img, plot_stat_map, 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=0.42,
    cmap="hot",
    threshold=0.2,
    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",
    cut_coords=[-9],
    colorbar=False,
)

show()

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Estimated memory usage: 916 MB