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.

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(
    "First subject anatomical nifti image (3D) is "
    f"at: {haxby_dataset.anat[0]}"
)
print(
    "First subject functional nifti image (4D) is "
    f"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()
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

Plotting statistical maps with function 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]
)

# 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,
)
  • plot demo plotting
  • plot demo plotting
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f30c8c1e690>

Making interactive visualizations with function view_img#

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

view = plotting.view_img(stat_img, threshold=3)
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
view
/opt/hostedtoolcache/Python/3.12.2/x64/lib/python3.12/site-packages/numpy/core/fromnumeric.py:771: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.
  a.partition(kth, axis=axis, kind=kind, order=order)


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

Plotting statistical maps in a glass brain with function 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)
plot demo plotting
<nilearn.plotting.displays._projectors.OrthoProjector object at 0x7f30c8c1c8f0>

Plotting anatomical images with function plot_anat#

Visualizing anatomical image of haxby dataset

plot demo plotting
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f30b28bb230>

Plotting ROIs (here the mask) with function 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)

plot demo plotting
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f30b288f740>

Plotting EPI image with function 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")
plot demo plotting
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f30b7e7a000>

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

Total running time of the script: (0 minutes 11.122 seconds)

Estimated memory usage: 951 MB

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