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.

# 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(
    f"First subject functional nifti image (4D) is located 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)

Decoding on the different masks

The classifier used here is a support vector classifier (svc). We use 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 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
        ]

We make a simple bar plot to summarize the results

import matplotlib.pyplot as plt

plt.figure(constrained_layout=True)

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")

show()

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. doi:10.1126/science.1063736.

Estimated memory usage: 0 MB