Note
Go to the end to download the full example code. or to run this example in your browser via Binder
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/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
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,
)
<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f2d87221be0>
Making interactive visualizations with function 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.
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/runner/work/nilearn/nilearn/.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 0x7f2d86ed5c10>
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 0x7f2d871a4040>
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 0x7f2d872127f0>
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 0x7f2d629adeb0>
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 12.492 seconds)
Estimated memory usage: 1054 MB