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()

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

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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x740d1d73e6d0>

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

/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/numpy/core/fromnumeric.py:758: 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)

<nilearn.plotting.displays._projectors.OrthoProjector object at 0x740d1d798d90>

Plotting anatomical images with function plot_anat

Visualizing anatomical image of haxby dataset

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

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x740d1ad21c70>

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)

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

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x740d1a9d8eb0>

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, copy_header=True)

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

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x740d1d199700>

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

plotting.show()

Estimated memory usage: 1085 MB