Basic nilearn example: manipulating and looking at data

A simple example showing how to load an existing Nifti file and use basic nilearn functionalities.

# Let us use a Nifti file that is shipped with nilearn
from nilearn.datasets import MNI152_FILE_PATH

# Note that the variable MNI152_FILE_PATH is just a path to a Nifti file
print(f"Path to MNI152 template: {MNI152_FILE_PATH!r}")

Path to MNI152 template: '/opt/hostedtoolcache/Python/3.12.3/x64/lib/python3.12/site-packages/nilearn/datasets/data/mni_icbm152_t1_tal_nlin_sym_09a_converted.nii.gz'

A first step: looking at our data

Let’s quickly plot this file:

from nilearn import plotting

plotting.plot_img(MNI152_FILE_PATH)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f853dc1a420>

This is not a very pretty plot. We just used the simplest possible code. There is a whole section of the documentation on making prettier code.

Exercise: Try plotting one of your own files. In the above, MNI152_FILE_PATH is nothing more than a string with a path pointing to a nifti image. You can replace it with a string pointing to a file on your disk. Note that it should be a 3D volume, and not a 4D volume.

Simple image manipulation: smoothing

Let’s use an image-smoothing function from nilearn: nilearn.image.smooth_img

Functions containing ‘img’ can take either a filename or an image as input.

Here we give as inputs the image filename and the smoothing value in mm

from nilearn import image

smooth_anat_img = image.smooth_img(MNI152_FILE_PATH, fwhm=3)

# While we are giving a file name as input, the function returns
# an in-memory object:
smooth_anat_img

<nibabel.nifti1.Nifti1Image object at 0x7f853df3bad0>

This is an in-memory object. We can pass it to nilearn function, for instance to look at it

plotting.plot_img(smooth_anat_img)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f853df38500>

We could also pass it to the smoothing function

more_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)
plotting.plot_img(more_smooth_anat_img)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f853dcdb4a0>

Globbing over multiple 3D volumes

Nilearn also supports reading multiple volumes at once, using glob-style patterns. For instance, we can smooth volumes from many subjects at once and get a 4D image as output.

First let’s fetch Haxby dataset for subject 1 and 2

from nilearn import datasets

haxby = datasets.fetch_haxby(subjects=[1, 2])

Downloading data from http://data.pymvpa.org/datasets/haxby2001/subj1-2010.01.14.tar.gz ...

Downloaded 17309696 of 314803244 bytes (5.5%,   17.4s remaining)
Downloaded 55853056 of 314803244 bytes (17.7%,    9.4s remaining)
Downloaded 93642752 of 314803244 bytes (29.7%,    7.2s remaining)
Downloaded 132014080 of 314803244 bytes (41.9%,    5.6s remaining)
Downloaded 170680320 of 314803244 bytes (54.2%,    4.3s remaining)
Downloaded 209911808 of 314803244 bytes (66.7%,    3.0s remaining)
Downloaded 249126912 of 314803244 bytes (79.1%,    1.9s remaining)
Downloaded 287588352 of 314803244 bytes (91.4%,    0.8s remaining) ...done. (9 seconds, 0 min)
Extracting data from /home/runner/work/nilearn/nilearn/nilearn_data/haxby2001/b6297e3e4eedbdbfb999e3188d364506/subj1-2010.01.14.tar.gz..... done.

Now we can find the anatomical images from both subjects using the * wildcard

from pathlib import Path

anats_all_subjects = (
    Path(datasets.get_data_dirs()[0]) / "haxby2001" / "subj*" / "anat*"
)

Now we can smooth all the anatomical images at once

anats_all_subjects_smooth = image.smooth_img(anats_all_subjects, fwhm=5)

This is a 4D image containing one volume per subject

print(anats_all_subjects_smooth.shape)

(124, 256, 256, 2)

Saving results to a file

We can save any in-memory object as follows:

output_dir = Path.cwd() / "results" / "plot_nilearn_101"
output_dir.mkdir(exist_ok=True, parents=True)
print(f"Output will be saved to: {output_dir}")
anats_all_subjects_smooth.to_filename(
    output_dir / "anats_all_subjects_smooth.nii.gz"
)

Output will be saved to: /home/runner/work/nilearn/nilearn/examples/00_tutorials/results/plot_nilearn_101

Finally, calling plotting.show() is necessary to display the figure when running as a script outside IPython

plotting.show()

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To recap, all the nilearn tools can take data as filenames or glob-style patterns or in-memory objects, and return brain volumes as in-memory objects. These can be passed on to other nilearn tools, or saved to disk.

Estimated memory usage: 185 MB