mvpa 1.0.0

Multi-voxel pattern analysis (MVPA) tool for fMRI data

mvpa

mvpa is a multi-voxel pattern analysis tool for fMRI data. Given a subject's fMRI scans for two conditions (e.g. exposure to two different stimuli), this tool can identify distinct patterns of responses for each condition. It's adapted from the method described in Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex, except searchlight spheres are analyzed instead of anatomical ROIs.

Notably, this tool is less computationally expensive than a SVM-based searchlight.

Installation

mvpa can be installed from PyPi:

$ pip install mvpa

Usage Example

Load the Haxby dataset

import pandas as pd
from nilearn.datasets import fetch_haxby

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

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

Restrict to faces and houses

from nilearn.image import index_img

face_mask = y.isin(["face"]) # Condition A
house_mask = y.isin(["house"]) # Condition B

face_niimg = index_img(fmri_filename, face_mask)
house_niimg = index_img(fmri_filename, house_mask)

Prepare input data

from nilearn.image import load_img
from mvpa import even_odd_split

face_niimg_even, face_niimg_odd = even_odd_split(face_niimg)
house_niimg_even, house_niimg_odd = even_odd_split(house_niimg)

A = [(face_niimg_even, face_niimg_odd)]
B = [(house_niimg_even, house_niimg_odd)]
mask_img = load_img(haxby_dataset.mask)

Run correlation searchlight

from mvpa import correlation_searchlight, significance_map

score_maps = correlation_searchlight(A, B, mask_img, radius=3)
# t_map, p_map = significance_map(score_maps, mask_img) # Only used for datasets
                                                        # with multiple subjects

score_maps is a list of paths to NIfTI files, each representing the significance scores for each voxel in a subject's scan.

Visualize results

from nilearn.image import mean_img
from nilearn.plotting import plot_stat_map

score_map = load_img(score_maps[0])
plot_stat_map(score_map, bg_img=mean_img(fmri_filename), colorbar=True,
              display_mode="z")