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/3.12.4/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

plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fbbc5338cb0>

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:
<nibabel.nifti1.Nifti1Image object at 0x7fbbbb1da960>

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

plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fbbbb1da660>

We could also pass it to the smoothing function

plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fbbbfa114c0>

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 found in /home/runner/work/nilearn/nilearn/nilearn_data/haxby2001

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

This is a 4D image containing one volume per subject

(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}")
    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 is necessary to display the figure when running as a script outside IPython

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.

Total running time of the script: (0 minutes 5.912 seconds)

Estimated memory usage: 169 MB

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