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('Path to MNI152 template: %r' % MNI152_FILE_PATH)
Path to MNI152 template: '/usr/share/miniconda3/envs/testenv/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)
plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f6b23b33310>

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 0x7f6b23b88eb0>

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 0x7f6b23a7b880>

We could also pass it to the smoothing function

plot nilearn 101
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f6b23ad2ca0>

Saving results to a file#

We can save any in-memory object as follows:

more_smooth_anat_img.to_filename('more_smooth_anat_img.nii.gz')

Finally, calling plotting.show() 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 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 6.525 seconds)

Estimated memory usage: 227 MB

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