Plotting tools in nilearn

Nilearn comes with a set of plotting functions for easy visualization of Nifti-like images such as statistical maps mapped onto anatomical images or onto glass brain representation, anatomical images, functional/EPI images, region specific mask images.

See Plotting brain images for more details.

We will first retrieve data from nilearn provided (general-purpose) datasets.

from nilearn import datasets

# haxby dataset to have EPI images and masks
haxby_dataset = datasets.fetch_haxby()

# print basic information on the dataset
print(
    f"First subject anatomical nifti image (3D) is at: {haxby_dataset.anat[0]}"
)
print(
    f"First subject functional nifti image (4D) is at: {haxby_dataset.func[0]}"
)

haxby_anat_filename = haxby_dataset.anat[0]
haxby_mask_filename = haxby_dataset.mask_vt[0]
haxby_func_filename = haxby_dataset.func[0]

# one motor activation map
stat_img = datasets.load_sample_motor_activation_image()

[fetch_haxby] Dataset found in /home/runner/nilearn_data/haxby2001
First subject anatomical nifti image (3D) is at: /home/runner/nilearn_data/haxby2001/subj2/anat.nii.gz
First subject functional nifti image (4D) is at: /home/runner/nilearn_data/haxby2001/subj2/bold.nii.gz

Nilearn plotting functions

Plotting statistical maps: plot_stat_map

from nilearn import plotting

Visualizing t-map image on EPI template with manual positioning of coordinates using cut_coords given as a list

plotting.plot_stat_map(
    stat_img, threshold=3, title="plot_stat_map", cut_coords=[36, -27, 66]
)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd159354a90>

It’s also possible to visualize volumes in a LR-flipped “radiological” view Just set radiological=True

plotting.plot_stat_map(
    stat_img,
    threshold=3,
    title="plot_stat_map",
    cut_coords=[36, -27, 66],
    radiological=True,
)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd0fe1c50f0>

Making interactive visualizations: view_img

An alternative to plot_stat_map is to use view_img that gives more interactive visualizations in a web browser. See Interactive visualization of statistical map slices for more details.

from nilearn.plotting import view_img

view = view_img(stat_img, threshold=3)
# In a notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
view

/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/numpy/_core/fromnumeric.py:868: UserWarning:

Warning: 'partition' will ignore the 'mask' of the MaskedArray.

uncomment this to open the plot in a web browser: view.open_in_browser()

It’s also possible to visualize volumes in a LR-flipped “radiological” view Just set radiological=True

view_radio = view_img(
    stat_img, threshold=3, title="radiological view", radiological=True
)
view_radio

/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/numpy/_core/fromnumeric.py:868: UserWarning:

Warning: 'partition' will ignore the 'mask' of the MaskedArray.

uncomment this to open the plot in a web browser: view_radio.open_in_browser()

Plotting statistical maps in a glass brain: plot_glass_brain

Now, the t-map image is mapped on glass brain representation where glass brain is always a fixed background template

plotting.plot_glass_brain(stat_img, title="plot_glass_brain", threshold=3)

<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7fd0ee200e20>

Plotting anatomical images: plot_anat

Visualizing anatomical image of haxby dataset

plotting.plot_anat(haxby_anat_filename, title="plot_anat")

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd159262920>

Plotting ROIs (here the mask): plot_roi

Visualizing ventral temporal region image from haxby dataset overlaid on subject specific anatomical image with coordinates positioned automatically on region of interest (roi)

plotting.plot_roi(
    haxby_mask_filename, bg_img=haxby_anat_filename, title="plot_roi"
)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd134ecc070>

Plotting EPI image: plot_epi

# Import image processing tool
from nilearn import image

# Compute the voxel_wise mean of functional images across time.
# Basically reducing the functional image from 4D to 3D
mean_haxby_img = image.mean_img(haxby_func_filename)

# Visualizing mean image (3D)
plotting.plot_epi(mean_haxby_img, title="plot_epi")

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd1350012a0>

A call to plotting.show is needed to display the plots when running in script mode (ie outside IPython)

plotting.show()

/home/runner/work/nilearn/nilearn/examples/01_plotting/plot_demo_plotting.py:140: UserWarning:

You are using the 'agg' matplotlib backend that is non-interactive.
No figure will be plotted when calling matplotlib.pyplot.show() or nilearn.plotting.show().
You can fix this by installing a different backend: for example via
        pip install PyQt6

Thresholding plots

Using threshold value alongside with vmin and vmax parameters enable us to mask certain values in the image.

Plotting without threshold

plotting.plot_stat_map(
    stat_img,
    display_mode="ortho",
    cut_coords=[36, -27, 60],
    title="No plotting threshold",
)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd1593ad690>

Plotting threshold set to 1

When plotting threshold is set to 1, the values between -1 and 1 are masked in the plot.

plotting.plot_stat_map(
    stat_img,
    threshold=1,
    display_mode="ortho",
    cut_coords=[36, -27, 60],
    title="plotting threshold=1",
)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd158b37b20>

Plotting threshold set to 1 with vmin=0

Setting vmin=0, it is possible to plot only positive image values.

plotting.plot_stat_map(
    stat_img,
    threshold=1,
    cmap="inferno",
    display_mode="ortho",
    cut_coords=[36, -27, 60],
    title="plotting threshold=1, vmin=0",
    vmin=0,
)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd15908e140>

Plotting threshold set to 1 with vmax=0

Setting vmax=0, it is possible to plot only negative image values.

plotting.plot_stat_map(
    stat_img,
    threshold=1,
    cmap="inferno",
    display_mode="ortho",
    cut_coords=[36, -27, 60],
    title="plotting threshold=1, vmax=0",
    vmax=0,
)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd134ecf3a0>

Visualizing without a colorbar on the right side

The argument colorbar should be set to False to show plots without a colorbar on the right side.

plotting.plot_stat_map(
    stat_img,
    display_mode="ortho",
    cut_coords=[36, -27, 60],
    colorbar=False,
    title="no colorbar",
)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd15978b610>

Estimated memory usage: 1046 MB