Note

Go to the end to download the full example code or to run this example in your browser via Binder

ROI-based decoding analysis in Haxby et al. dataset#

In this script we reproduce the data analysis conducted by Haxby et al.[1].

Specifically, we look at decoding accuracy for different objects in three different masks: the full ventral stream (mask_vt), the house selective areas (mask_house) and the face selective areas (mask_face), that have been defined via a standard GLM-based analysis.

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

# Fetch data using nilearn dataset fetcher
from nilearn import datasets
from nilearn.plotting import show

Load and prepare the data#

# by default we fetch 2nd subject data for analysis
haxby_dataset = datasets.fetch_haxby()
func_filename = haxby_dataset.func[0]

# Print basic information on the dataset
print(
    "First subject anatomical nifti image (3D) located is "
    f"at: {haxby_dataset.anat[0]}"
)
print(
    "First subject functional nifti image (4D) is located "
    f"at: {func_filename}"
)

# load labels
import pandas as pd

# Load nilearn NiftiMasker, the practical masking and unmasking tool
from nilearn.maskers import NiftiMasker

labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
stimuli = labels["labels"]
# identify resting state labels in order to be able to remove them
task_mask = stimuli != "rest"

# find names of remaining active labels
categories = stimuli[task_mask].unique()

# extract tags indicating to which acquisition run a tag belongs
run_labels = labels["chunks"][task_mask]

# apply the task_mask to  fMRI data (func_filename)
from nilearn.image import index_img

task_data = index_img(func_filename, task_mask)

First subject anatomical nifti image (3D) located is at: /home/himanshu/nilearn_data/haxby2001/subj2/anat.nii.gz
First subject functional nifti image (4D) is located at: /home/himanshu/nilearn_data/haxby2001/subj2/bold.nii.gz

Decoding on the different masks#

The classifier used here is a support vector classifier (svc). We use class:nilearn.decoding.Decoder and specify the classifier.

import numpy as np

# Make a data splitting object for cross validation
from sklearn.model_selection import LeaveOneGroupOut

from nilearn.decoding import Decoder

cv = LeaveOneGroupOut()

We use nilearn.decoding.Decoder to estimate a baseline.

mask_names = ["mask_vt", "mask_face", "mask_house"]

mask_scores = {}
mask_chance_scores = {}

for mask_name in mask_names:
    print(f"Working on {mask_name}")
    # For decoding, standardizing is often very important
    mask_filename = haxby_dataset[mask_name][0]
    masker = NiftiMasker(mask_img=mask_filename, standardize="zscore_sample")
    mask_scores[mask_name] = {}
    mask_chance_scores[mask_name] = {}

    for category in categories:
        print(f"Processing {mask_name} {category}")
        classification_target = stimuli[task_mask] == category
        # Specify the classifier to the decoder object.
        # With the decoder we can input the masker directly.
        # We are using the svc_l1 here because it is intra subject.
        decoder = Decoder(
            estimator="svc_l1",
            cv=cv,
            mask=masker,
            scoring="roc_auc",
            standardize="zscore_sample",
        )
        decoder.fit(task_data, classification_target, groups=run_labels)
        mask_scores[mask_name][category] = decoder.cv_scores_[1]
        mean = np.mean(mask_scores[mask_name][category])
        std = np.std(mask_scores[mask_name][category])
        print(f"Scores: {mean:1.2f} +- {std:1.2f}")

        dummy_classifier = Decoder(
            estimator="dummy_classifier",
            cv=cv,
            mask=masker,
            scoring="roc_auc",
            standardize="zscore_sample",
        )
        dummy_classifier.fit(
            task_data, classification_target, groups=run_labels
        )
        mask_chance_scores[mask_name][category] = dummy_classifier.cv_scores_[
            1
        ]

Working on mask_vt
Processing mask_vt scissors
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Scores: 0.92 +- 0.05
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Processing mask_vt face
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Scores: 0.98 +- 0.03
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Processing mask_vt cat
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Scores: 0.96 +- 0.04
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Processing mask_vt shoe
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Scores: 0.92 +- 0.07
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Processing mask_vt house
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Scores: 1.00 +- 0.00
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Processing mask_vt scrambledpix
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Scores: 0.99 +- 0.01
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Processing mask_vt bottle
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Scores: 0.89 +- 0.08
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Processing mask_vt chair
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Scores: 0.93 +- 0.04
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
Working on mask_face
Processing mask_face scissors
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Scores: 0.70 +- 0.16
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Processing mask_face face
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Scores: 0.90 +- 0.06
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Processing mask_face cat
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Scores: 0.76 +- 0.12
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Processing mask_face shoe
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Scores: 0.73 +- 0.17
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Processing mask_face house
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Scores: 0.71 +- 0.16
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Processing mask_face scrambledpix
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Scores: 0.87 +- 0.09
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Processing mask_face bottle
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Scores: 0.68 +- 0.17
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Processing mask_face chair
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Scores: 0.63 +- 0.10
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:760: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters.
  warnings.warn(
Working on mask_house
Processing mask_house scissors
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Scores: 0.83 +- 0.08
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Processing mask_house face
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Scores: 0.90 +- 0.07
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Processing mask_house cat
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Scores: 0.86 +- 0.09
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Processing mask_house shoe
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Scores: 0.82 +- 0.12
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Processing mask_house house
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Scores: 1.00 +- 0.00
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Processing mask_house scrambledpix
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Scores: 0.96 +- 0.05
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Processing mask_house bottle
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Scores: 0.86 +- 0.10
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Processing mask_house chair
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(
Scores: 0.90 +- 0.10
/home/himanshu/Desktop/nilearn_work/nilearn/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.
  warnings.warn(
/home/himanshu/Desktop/nilearn_work/nilearn/nilearn/decoding/decoder.py:742: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned to be used on such data.
  selector = check_feature_screening(

We make a simple bar plot to summarize the results#

import matplotlib.pyplot as plt

plt.figure()

tick_position = np.arange(len(categories))
plt.xticks(tick_position, categories, rotation=45)

for color, mask_name in zip("rgb", mask_names):
    score_means = [
        np.mean(mask_scores[mask_name][category]) for category in categories
    ]
    plt.bar(
        tick_position, score_means, label=mask_name, width=0.25, color=color
    )

    score_chance = [
        np.mean(mask_chance_scores[mask_name][category])
        for category in categories
    ]
    plt.bar(
        tick_position,
        score_chance,
        width=0.25,
        edgecolor="k",
        facecolor="none",
    )

    tick_position = tick_position + 0.2

plt.ylabel("Classification accuracy (AUC score)")
plt.xlabel("Visual stimuli category")
plt.ylim(0.3, 1)
plt.legend(loc="lower right")
plt.title("Category-specific classification accuracy for different masks")
plt.tight_layout()

show()

Category-specific classification accuracy for different masks

References#

[1]
James V. Haxby, M. Ida Gobbini, Maura L. Furey, Alumit Ishai, Jennifer L. Schouten, and Pietro Pietrini. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539):2425–2430, 2001. URL: https://science.sciencemag.org/content/293/5539/2425, arXiv:https://science.sciencemag.org/content/293/5539/2425.full.pdf, doi:10.1126/science.1063736.

Total running time of the script: (5 minutes 23.048 seconds)

Estimated memory usage: 1368 MB

Gallery generated by Sphinx-Gallery