Note
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A introduction tutorial to fMRI decoding#
Here is a simple tutorial on decoding with nilearn. It reproduces the Haxby 2001 study on a face vs cat discrimination task in a mask of the ventral stream.
J.V. Haxby et al. “Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex”, Science vol 293 (2001), p 2425.-2430.
This tutorial is meant as an introduction to the various steps of a decoding
analysis using Nilearn meta-estimator: nilearn.decoding.Decoder
It is not a minimalistic example, as it strives to be didactic. It is not meant to be copied to analyze new data: many of the steps are unnecessary.
Retrieve and load the fMRI data from the Haxby study#
First download the data#
The nilearn.datasets.fetch_haxby
function will download the
Haxby dataset if not present on the disk, in the nilearn data directory.
It can take a while to download about 310 Mo of data from the Internet.
from nilearn import datasets
# By default 2nd subject will be fetched
haxby_dataset = datasets.fetch_haxby()
# 'func' is a list of filenames: one for each subject
fmri_filename = haxby_dataset.func[0]
# print basic information on the dataset
print(f"First subject functional nifti images (4D) are at: {fmri_filename}")
First subject functional nifti images (4D) are at: /home/runner/work/nilearn/nilearn/nilearn_data/haxby2001/subj2/bold.nii.gz
Visualizing the fmri volume#
One way to visualize a fmri volume is
using nilearn.plotting.plot_epi
.
We will visualize the previously fetched fmri
data from Haxby dataset.
Because fmri data are 4D (they consist of many 3D EPI images),
we cannot plot them directly using nilearn.plotting.plot_epi
(which accepts just 3D input).
Here we are using nilearn.image.mean_img
to
extract a single 3D EPI image from the fmri data.
from nilearn import plotting
from nilearn.image import mean_img
plotting.view_img(mean_img(fmri_filename), threshold=None)
/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:771: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
a.partition(kth, axis=axis, kind=kind, order=order)