4. Plotting brain images

In this section, we detail the general tools to visualize neuroimaging volumes with nilearn.

Nilearn comes with plotting function to display brain maps coming from Nifti-like images, in the nilearn.plotting module.

Code examples

Nilearn has a whole section of the example gallery on plotting.

A small tour of the plotting functions can be found in the example Plotting tools in nilearn.

Finally, note that, as always in the nilearn documentation, clicking on a figure will take you to the code that generates it.

4.1. Different plotting functions

Nilearn has a set of plotting functions to plot brain volumes that are fined tuned to specific applications. Amongst other things, they use different heuristics to find cutting coordinates.

	plot_anat Plotting an anatomical image
	plot_epi Plotting an EPI, or T2* image
	plot_glass_brain Glass brain visualization. By default plots maximum intensity projection of the absolute values. To plot positive and negative values set plot_abs parameter to False.
	plot_stat_map Plotting a statistical map, like a T-map, a Z-map, or an ICA, with an optional background
	plot_roi Plotting ROIs, or a mask, with an optional background
	plot_connectome Plotting a connectome Functions for automatic extraction of coords based on brain parcellations useful for plot_connectome are demonstrated in Example: Comparing connectomes on different reference atlases
	plot_markers Plotting network nodes (markers) Function for automatic plotting of network nodes (markers) and color coding them according to provided nodal measure (i.e. connection strength) as demonstrated in Example: Comparing connectomes on different reference atlases
	plot_prob_atlas Plotting 4D probabilistic atlas maps
	plot_carpet Plotting voxel intensities across time.
plot_img	plot_img General-purpose function, with no specific presets

Warning

Opening too many figures without closing

Each call to a plotting function creates a new figure by default. When used in non-interactive settings, such as a script or a program, these are not displayed, but still accumulate and eventually lead to slowing the execution and running out of memory.

To avoid this, you must close the plot as follow:

>>> from nilearn import plotting
>>> display = plotting.plot_stat_map(img)
>>> display.close()

See also

Controlling the contrast of the background when plotting

4.2. Different display modes

	display_mode=’ortho’, cut_coords=[36, -27, 60] Ortho slicer: 3 cuts along the x, y, z directions
	display_mode=’z’, cut_coords=5 Cutting in the z direction, specifying the number of cuts
	display_mode=’x’, cut_coords=[-36, 36] Cutting in the x direction, specifying the exact cuts
	display_mode=’y’, cut_coords=1 Cutting in the y direction, with only 1 cut, that is automatically positioned
	display_mode=’z’, cut_coords=1, colorbar=False Cutting in the z direction, with only 1 cut, that is automatically positioned
	display_mode=’xz’, cut_coords=[36, 60] Cutting in the x and z direction, with cuts manually positioned
	display_mode=’yx’, cut_coords=[-27, 36] Cutting in the y and x direction, with cuts manually positioned
	display_mode=’yz’, cut_coords=[-27, 60] Cutting in the y and z direction, with cuts manually positioned
	display_mode=’tiled’, cut_coords=[36, -27, 60] Tiled slicer: 3 cuts along the x, y, z directions, arranged in a 2x2 grid
	display_mode=’mosaic’ Mosaic slicer: multiple cuts along the x, y, z directions, with cuts automatically positioned by default
	Glass brain display_mode=’lzr’ Glass brain and Connectome provide additional display modes due to the possibility of doing hemispheric projections. Check out: ‘l’, ‘r’, ‘lr’, ‘lzr’, ‘lyr’, ‘lzry’, ‘lyrz’.
	Glass brain display_mode=’lyrz’ Glass brain and Connectome provide additional display modes due to the possibility of doing hemispheric projections. Check out: ‘l’, ‘r’, ‘lr’, ‘lzr’, ‘lyr’, ‘lzry’, ‘lyrz’.

4.3. Available Colormaps

Nilearn plotting library ships with a set of extra colormaps, as seen in the image below

These colormaps can be used as any other matplotlib colormap.

4.4. Adding overlays, edges, contours, contour fillings, markers, scale bar

To add overlays, contours, or edges, use the return value of the plotting functions. Indeed, these return a display object, such as the nilearn.plotting.displays.OrthoSlicer. This object represents the plot, and has methods to add overlays, contours or edge maps:

display = plotting.plot_epi(...)

	display.add_edges(img) Add a plot of the edges of img, where edges are extracted using a Canny edge-detection routine. This is typically useful to check registration. Note that img should have some visible sharp edges. Typically an EPI img does not, but a T1 does.
	display.add_contours(img, levels=[.5], colors=’r’) Add a plot of the contours of img, where contours are computed for constant values, specified in ‘levels’. This is typically useful to outline a mask, or ROI on top of another map. Example: Plot Haxby masks
	display.add_contours(img, filled=True, alpha=0.7, levels=[0.5], colors=’b’) Add a plot of img with contours filled with colors
	display.add_overlay(img, cmap=plotting.cm.purple_green, threshold=3) Add a new overlay on the existing figure Example: Visualizing a probabilistic atlas: the default mode in the MSDL atlas
	display.add_markers(coords, marker_color=’y’, marker_size=100) Add seed based MNI coordinates as spheres on top of statistical image or EPI image. This is useful for seed based regions specific interpretation of brain images. Example: Producing single subject maps of seed-to-voxel correlation
	display.annotate(scalebar=True) Adds annotations such as a scale bar, or the cross of the cut coordinates Example: More plotting tools from nilearn

4.5. Displaying or saving to an image file

To display the figure when running a script, you need to call nilearn.plotting.show: (this is just an alias to matplotlib.pyplot.show):

>>> from nilearn import plotting
>>> plotting.show()

The simplest way to output an image file from the plotting functions is to specify the output_file argument:

>>> from nilearn import plotting
>>> plotting.plot_stat_map(img, output_file='pretty_brain.png')

In this case, the display is closed automatically and the plotting function returns None.

The display object returned by the plotting function has a savefig method that can be used to save the plot to an image file:

>>> from nilearn import plotting
>>> display = plotting.plot_stat_map(img)
>>> display.savefig('pretty_brain.png')
# Don't forget to close the display
>>> display.close()

4.6. Surface plotting

Plotting functions required to plot surface data or statistical maps on a brain surface.

New in version 0.3.

	plot_surf_roi Plotting surface atlases on a brain surface Example: Loading and plotting of a cortical surface atlas
	plot_surf_stat_map Plotting statistical maps onto a brain surface Example: Seed-based connectivity on the surface

4.7. Interactive plots

Nilearn also has functions for making interactive plots that can be seen in a web browser.

New in version 0.5: Interactive plotting is new in nilearn 0.5

For 3D surface plots of statistical maps or surface atlases, use view_img_on_surf and view_surf. Both produce a 3D plot on the cortical surface. The difference is that view_surf takes as input a surface map and a cortical mesh, whereas view_img_on_surf takes as input a volume statistical map, and projects it on the cortical surface before making the plot.

For 3D plots of a connectome, use view_connectome. To see only markers, use view_markers.

4.7.1. 3D Plots of statistical maps or atlases on the cortical surface

view_img_on_surf: Surface plot using a 3D statistical map:

>>> from nilearn import plotting, datasets
>>> img = datasets.fetch_localizer_button_task()['tmap']
>>> view = plotting.view_img_on_surf(img, threshold='90%', surf_mesh='fsaverage')

If you are running a notebook, displaying view will embed an interactive plot (this is the case for all interactive plots produced by nilearn’s “view” functions):

If you are not using a notebook, you can open the plot in a browser like this:

>>> view.open_in_browser()

This will open this 3D plot in your web browser:

Or you can save it to an html file:

>>> view.save_as_html("surface_plot.html")

view_surf: Surface plot using a surface map and a cortical mesh:

>>> from nilearn import plotting, datasets
>>> destrieux = datasets.fetch_atlas_surf_destrieux()
>>> fsaverage = datasets.fetch_surf_fsaverage()
>>> view = plotting.view_surf(fsaverage['infl_left'], destrieux['map_left'],
...                           cmap='gist_ncar', symmetric_cmap=False)
...
>>> view.open_in_browser()

4.7.2. 3D Plots of connectomes

view_connectome: 3D plot of a connectome:

>>> view = plotting.view_connectome(correlation_matrix, coords, edge_threshold='90%')
>>> view.open_in_browser()

4.7.3. 3D Plots of markers

view_markers: showing markers (e.g. seed locations) in 3D:

>>> from nilearn import plotting
>>> dmn_coords = [(0, -52, 18), (-46, -68, 32), (46, -68, 32), (1, 50, -5)]
>>> view = plotting.view_markers(
>>>       dmn_coords, ['red', 'cyan', 'magenta', 'orange'], marker_size=10)
>>> view.open_in_browser()

4.7.4. Interactive visualization of statistical map slices

view_img: open stat map in a Brainsprite viewer (https://github.com/simexp/brainsprite.js):

>>> from nilearn import plotting, datasets
>>> img = datasets.fetch_localizer_button_task()['tmap']
>>> html_view = plotting.view_img(img, threshold=2, vmax=4, cut_coords=[-42, -16, 52],
...                                     title="Motor contrast")

in a Jupyter notebook, if html_view is not requested, the viewer will be inserted in the notebook:

Or you can open a viewer in your web browser if you are not in a notebook:

>>> html_view.open_in_browser()

Finally, you can also save the viewer as a stand-alone html file:

>>> html_view.save_as_html('viewer.html')