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: PosixPath('/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/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 0x7f469533c8e0>

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: 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 0x7f46953f5040>

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 0x7f46955ad370>

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 0x7f46afbc1eb0>

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])

[get_dataset_dir] Dataset created in /home/runner/nilearn_data/haxby2001
[fetch_single_file] Downloading data from
https://www.nitrc.org/frs/download.php/7868/mask.nii.gz ...
[fetch_single_file]  ...done. (0 seconds, 0 min)

[fetch_single_file] Downloading data from
http://data.pymvpa.org/datasets/haxby2001/MD5SUMS ...
[fetch_single_file]  ...done. (0 seconds, 0 min)

[fetch_single_file] Downloading data from
http://data.pymvpa.org/datasets/haxby2001/subj1-2010.01.14.tar.gz ...
[_chunk_report_] Downloaded 17350656 of 314803244 bytes (5.5%%,   17.4s
remaining)
[_chunk_report_] Downloaded 54288384 of 314803244 bytes (17.2%%,    9.8s
remaining)
[_chunk_report_] Downloaded 93011968 of 314803244 bytes (29.5%%,    7.3s
remaining)
[_chunk_report_] Downloaded 132554752 of 314803244 bytes (42.1%%,    5.6s
remaining)
[_chunk_report_] Downloaded 171368448 of 314803244 bytes (54.4%%,    4.3s
remaining)
[_chunk_report_] Downloaded 210788352 of 314803244 bytes (67.0%%,    3.0s
remaining)
[_chunk_report_] Downloaded 233324544 of 314803244 bytes (74.1%%,    2.5s
remaining)
[_chunk_report_] Downloaded 258310144 of 314803244 bytes (82.1%%,    1.8s
remaining)
[_chunk_report_] Downloaded 292855808 of 314803244 bytes (93.0%%,    0.7s
remaining)
[fetch_single_file]  ...done. (10 seconds, 0 min)

[uncompress_file] Extracting data from
/home/runner/nilearn_data/haxby2001/b2fd65a88d22090da62c3fb828be840e/subj1-2010.
01.14.tar.gz...
[uncompress_file] .. done.

[fetch_single_file] Downloading data from
http://data.pymvpa.org/datasets/haxby2001/subj2-2010.01.14.tar.gz ...
[_chunk_report_] Downloaded 22601728 of 291168628 bytes (7.8%%,   12.1s
remaining)
[_chunk_report_] Downloaded 60678144 of 291168628 bytes (20.8%%,    7.7s
remaining)
[_chunk_report_] Downloaded 99254272 of 291168628 bytes (34.1%%,    5.9s
remaining)
[_chunk_report_] Downloaded 138461184 of 291168628 bytes (47.6%%,    4.5s
remaining)
[_chunk_report_] Downloaded 173899776 of 291168628 bytes (59.7%%,    3.4s
remaining)
[_chunk_report_] Downloaded 212680704 of 291168628 bytes (73.0%%,    2.3s
remaining)
[_chunk_report_] Downloaded 251387904 of 291168628 bytes (86.3%%,    1.1s
remaining)
[_chunk_report_] Downloaded 289988608 of 291168628 bytes (99.6%%,    0.0s
remaining)
[fetch_single_file]  ...done. (8 seconds, 0 min)

[uncompress_file] Extracting data from
/home/runner/nilearn_data/haxby2001/b2fd65a88d22090da62c3fb828be840e/subj2-2010.
01.14.tar.gz...
[uncompress_file] .. 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()

---

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: 299 MB