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.datasets import fetch_haxby, load_sample_motor_activation_image

# Fetch the Haxby dataset to have EPI images and masks.
haxby_dataset = 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 = load_sample_motor_activation_image()

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

Nilearn plotting functions

Plotting statistical maps: plot_stat_map

from nilearn.plotting import plot_stat_map, show

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

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

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f5fc6472fb0>

It’s also possible to visualize volumes in a LR-flipped “radiological” view by setting radiological=True:

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 0x7f5fc7896f50>

Calling show function from nilearn.plotting package is necessary to display the figure when running as a script outside IPython.

show()

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:771: 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 by setting 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:771: 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.

from nilearn.plotting import plot_glass_brain

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

show()

Plotting anatomical images: plot_anat

Visualizing anatomical image of the Haxby dataset

from nilearn.plotting import plot_anat

plot_anat(haxby_anat_filename, title="plot_anat")

show()

Plotting ROIs (here the mask): plot_roi

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

from nilearn.plotting import plot_roi

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

show()

Plotting EPI image: plot_epi

# Compute the voxel_wise mean of functional images across time,
# basically reducing the functional image from 4D to 3D.
from nilearn.image import mean_img
from nilearn.plotting import plot_epi

mean_haxby_img = mean_img(haxby_func_filename)

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

show()

Thresholding plots

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

Plotting without threshold

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

show()

Plotting threshold set to 1

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

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

show()

Plotting threshold set to 1 with vmin=0

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

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,
)

show()

Plotting threshold set to 1 with vmax=0

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

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,
)

show()

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.

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

show()

Estimated memory usage: 1017 MB