4.1. Plotting brain images

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

4.1.1. 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
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()     

4.1.2. 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=’x’, cut_coords=1 Cutting in the x direction, with only 1 cut, that is automatically positionned
	display_mode=’z’, cut_coords=1, colorbar=False Cutting in the z direction, with only 1 cut, that is automatically positionned
	display_mode=’xz’, cut_coords=(36, 60) Cutting in the x and z direction, with cuts manually positionned
	display_mode=’yx’, cut_coords=(-27, 36) Cutting in the y and x direction, with cuts manually positionned
	display_mode=’yz’, cut_coords=(-27, 60) Cutting in the y and z direction, with cuts manually positionned

4.1.3. Adding overlays, edges and contours

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: example_manipulating_visualizing_plot_haxby_masks.py
add_overlay	display.add_overlay(img, cmap=plotting.cm.purple_green, threshold=3) Add a new overlay on the existing figure Example: example_manipulating_visualizing_plot_probabilistic_atlas.py

4.1.4. Saving to an image file

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()