Decoding with ANOVA + SVM: face vs house in the Haxby dataset

This example does a simple but efficient decoding on the Haxby dataset: using a feature selection, followed by an SVM.

Retrieve the files of the Haxby dataset

from nilearn import datasets

# By default 2nd subject will be fetched
haxby_dataset = datasets.fetch_haxby()
func_img = haxby_dataset.func[0]
# print basic information on the dataset
print(f"Mask nifti image (3D) is located at: {haxby_dataset.mask}")
print(f"Functional nifti image (4D) is located at: {func_img}")

Mask nifti image (3D) is located at: /home/runner/work/nilearn/nilearn/nilearn_data/haxby2001/mask.nii.gz
Functional nifti image (4D) is located at: /home/runner/work/nilearn/nilearn/nilearn_data/haxby2001/subj2/bold.nii.gz

Load the behavioral data

import pandas as pd

# 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"]

# Restrict the analysis to faces and places
from nilearn.image import index_img

condition_mask = behavioral["labels"].isin(["face", "house"])
conditions = conditions[condition_mask]
func_img = index_img(func_img, condition_mask)

# Confirm that we now have 2 conditions
print(conditions.unique())

# The number of the session is stored in the CSV file giving the behavioral
# data. We have to apply our session mask, to select only faces and houses.
session_label = behavioral["chunks"][condition_mask]

['face' 'house']

ANOVA pipeline with nilearn.decoding.Decoder object

Nilearn Decoder object aims to provide smooth user experience by acting as a pipeline of several tasks: preprocessing with NiftiMasker, reducing dimension by selecting only relevant features with ANOVA – a classical univariate feature selection based on F-test, and then decoding with different types of estimators (in this example is Support Vector Machine with a linear kernel) on nested cross-validation.

from nilearn.decoding import Decoder

# Here screening_percentile is set to 5 percent
mask_img = haxby_dataset.mask
decoder = Decoder(
    estimator="svc",
    mask=mask_img,
    smoothing_fwhm=4,
    standardize=True,
    screening_percentile=5,
    scoring="accuracy",
)

Fit the decoder and predict

decoder.fit(func_img, conditions)
y_pred = decoder.predict(func_img)

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:494: UserWarning:

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

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:349: FutureWarning:

The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:349: FutureWarning:

The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.

Obtain prediction scores via cross validation

Define the cross-validation scheme used for validation. Here we use a LeaveOneGroupOut cross-validation on the session group which corresponds to a leave a session out scheme, then pass the cross-validator object to the cv parameter of decoder.leave-one-session-out For more details please take a look at: Measuring prediction scores using cross-validation

from sklearn.model_selection import LeaveOneGroupOut

cv = LeaveOneGroupOut()

decoder = Decoder(
    estimator="svc",
    mask=mask_img,
    standardize=True,
    screening_percentile=5,
    scoring="accuracy",
    cv=cv,
)
# Compute the prediction accuracy for the different folds (i.e. session)
decoder.fit(func_img, conditions, groups=session_label)

# Print the CV scores
print(decoder.cv_scores_["face"])

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/image/resampling.py:494: UserWarning:

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

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/joblib/memory.py:349: FutureWarning:

The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.

[1.0, 0.9444444444444444, 1.0, 0.9444444444444444, 1.0, 1.0, 0.9444444444444444, 1.0, 0.6111111111111112, 1.0, 1.0, 1.0]

Visualize the results

Look at the SVC’s discriminating weights using nilearn.plotting.plot_stat_map

weight_img = decoder.coef_img_["face"]
from nilearn.plotting import plot_stat_map, show

plot_stat_map(weight_img, bg_img=haxby_dataset.anat[0], title="SVM weights")

show()

Or we can plot the weights using nilearn.plotting.view_img as a dynamic html viewer

from nilearn.plotting import view_img

view_img(weight_img, bg_img=haxby_dataset.anat[0], title="SVM weights", dim=-1)

/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:784: UserWarning:

Warning: 'partition' will ignore the 'mask' of the MaskedArray.