%matplotlib inline
In this example, we demonstrate how to use plotting options from nilearn essential in visualizing brain image analysis results.
We emphasize the use of parameters such as display_mode and cut_coords
with plotting function :func:nilearn.plotting.plot_stat_map. Also,
we show how to use various features such as add_edges, add_contours,
add_markers essential in visualizing regions of interest images or
mask images overlaying on subject specific anatomical/EPI image.
The display features shown here are inherited from the
:class:nilearn.plotting.displays.OrthoSlicer class.
The parameter display_mode is used to draw brain slices along given
specific directions, where directions can be one of 'ortho',
'tiled','x', 'y', 'z', 'yx', 'xz', 'yz'. whereas parameter cut_coords
is used to specify a limited number of slices to visualize along given
specific slice direction. The parameter cut_coords can also be used
to draw the specific cuts in the slices by giving its particular
coordinates in MNI space accordingly with particular slice direction.
This helps us point to the activation specific location of the brain slices.
See plotting for more details.
from nilearn import datasets
# haxby dataset to have anatomical image, EPI images and masks
haxby_dataset = datasets.fetch_haxby()
haxby_anat_filename = haxby_dataset.anat[0]
haxby_mask_filename = haxby_dataset.mask_vt[0]
haxby_func_filename = haxby_dataset.func[0]
# localizer dataset to have contrast maps
motor_images = datasets.fetch_neurovault_motor_task()
stat_img = motor_images.images[0]
Now, we show from here how to visualize the retrieved datasets using plotting tools from nilearn.
from nilearn import plotting
The first argument is a path to the filename of a contrast map,
optional argument display_mode is given as string 'ortho' to visualize
the map in three specific directions xyz and the optional cut_coords
argument, is here a list of integers denotes coordinates of each slice
in the order [x, y, z]. By default the colorbar argument is set to True
in plot_stat_map.
plotting.plot_stat_map(stat_img, display_mode='ortho',
cut_coords=[36, -27, 60],
title="display_mode='ortho', cut_coords=[36, -27, 60]")
In this type of visualization, the display_mode argument is given as
string 'z' for axial direction and cut_coords as integer 5 without a
list implies that number of cuts in the slices should be maximum of 5.
The coordinates to cut the slices are selected automatically
plotting.plot_stat_map(stat_img, display_mode='z', cut_coords=5,
title="display_mode='z', cut_coords=5")
In this type, display_mode should be given as string 'x' for sagittal
view and coordinates should be given as integers in a list
plotting.plot_stat_map(stat_img, display_mode='x',
cut_coords=[-36, 36],
title="display_mode='x', cut_coords=[-36, 36]")
For coronal view, display_mode is given as string 'y' and cut_coords
as integer 1 not as a list for single cut. The coordinates are selected
automatically
plotting.plot_stat_map(stat_img, display_mode='y', cut_coords=1,
title="display_mode='y', cut_coords=1")
The argument colorbar should be given as False to show plots without
a colorbar on the right side.
plotting.plot_stat_map(stat_img, display_mode='z',
cut_coords=1, colorbar=False,
title="display_mode='z', cut_coords=1, colorbar=False")
argument display_mode='xz' where 'x' for sagittal and 'z' for axial view.
argument cut_coords should match with input number of views therefore two
integers should be given in a list to select the slices to be displayed
plotting.plot_stat_map(stat_img, display_mode='xz',
cut_coords=[36, 60],
title="display_mode='xz', cut_coords=[36, 60]")
display_mode='yx' for coronal and sagittal view and coordinates will be assigned in the order of direction as [x, y, z]
plotting.plot_stat_map(stat_img, display_mode='yx',
cut_coords=[-27, 36],
title="display_mode='yx', cut_coords=[-27, 36]")
plotting.plot_stat_map(stat_img, display_mode='yz',
cut_coords=[-27, 60],
title="display_mode='yz', cut_coords=[-27, 60]")
display_mode='tiled' for sagittal, coronal and axial view
plotting.plot_stat_map(stat_img, display_mode='tiled',
cut_coords=[36, -27, 60],
title="display_mode='tiled'")
display_mode='mosaic' for sagittal, coronal and axial view with default option i.e. cut_coords=None
plotting.plot_stat_map(stat_img, display_mode='mosaic',
title="display_mode='mosaic' default cut_coords")
display_mode='mosaic' for sagittal, coronal and axial view with number of slices specified as integer i.e. cut_coords=3
plotting.plot_stat_map(stat_img, display_mode='mosaic',
cut_coords=3,
title="display_mode='mosaic' with cut_coords=3")
display_mode='mosaic' for sagittal, coronal and axial view with number of slices specified as tuple of length 3
plotting.plot_stat_map(stat_img, display_mode='mosaic',
cut_coords=(5, 4, 10),
title="display_mode='mosaic' with cut_coords as tuple")
In second part, we switch to demonstrating various features add_* from nilearn where each specific feature will be helpful in projecting brain imaging results for further interpretation.
# Import image processing tool for basic processing of functional brain image
from nilearn import image
# Compute voxel-wise mean functional image across time dimension. Now we have
# functional image in 3D assigned in mean_haxby_img
mean_haxby_img = image.mean_img(haxby_func_filename)
add_edges¶Now let us see how to use add_edges, method useful for checking
coregistration by overlaying anatomical image as edges (red) on top of
mean functional image (background), both being of same subject.
# First, we call the `plot_anat` plotting function, with a background image
# as first argument, in this case the mean fMRI image.
display = plotting.plot_anat(mean_haxby_img, title="add_edges")
# We are now able to use add_edges method inherited in plotting object named as
# display. First argument - anatomical image and by default edges will be
# displayed as red 'r', to choose different colors green 'g' and blue 'b'.
display.add_edges(haxby_anat_filename)
add_contours¶Plotting outline of the mask (red) on top of the mean EPI image with
add_contours. This method is useful for region specific interpretation
of brain images
# As seen before, we call the `plot_anat` function with a background image
# as first argument, in this case again the mean fMRI image and argument
# `cut_coords` as list for manual cut with coordinates pointing at masked
# brain regions
display = plotting.plot_anat(mean_haxby_img, title="add_contours",
cut_coords=[-34, -39, -9])
# Now use `add_contours` in display object with the path to a mask image from
# the Haxby dataset as first argument and argument `levels` given as list
# of values to select particular level in the contour to display and argument
# `colors` specified as red 'r' to see edges as red in color.
# See help on matplotlib.pyplot.contour to use more options with this method
display.add_contours(haxby_mask_filename, levels=[0.5], colors='r')
Plotting outline of the mask (blue) with color fillings using same method
add_contours.
display = plotting.plot_anat(mean_haxby_img,
title="add_contours with filled=True",
cut_coords=[-34, -39, -9])
# By default, no color fillings will be shown using `add_contours`. To see
# contours with color fillings use argument filled=True. contour colors are
# changed to blue 'b' with alpha=0.7 sets the transparency of color fillings.
# See help on matplotlib.pyplot.contourf to use more options given that filled
# should be True
display.add_contours(haxby_mask_filename, filled=True, alpha=0.7,
levels=[0.5], colors='b')
add_markers¶Plotting seed regions of interest as spheres using new feature add_markers
with MNI coordinates of interest.
display = plotting.plot_anat(mean_haxby_img, title="add_markers",
cut_coords=[-34, -39, -9])
# Coordinates of seed regions should be specified in first argument and second
# argument `marker_color` denotes color of the sphere in this case yellow 'y'
# and third argument `marker_size` denotes size of the sphere
coords = [(-34, -39, -9)]
display.add_markers(coords, marker_color='y', marker_size=100)
It is possible to alter the default annotations of plots, using the
annotate member function of display objects.
For example, we can add a scale bar at the bottom right of each view:
display = plotting.plot_anat(mean_haxby_img,
title="adding a scale bar",
cut_coords=[-34, -39, -9])
display.annotate(scalebar=True)
Further configuration can be achieved by setting scale_* keyword args.
For instance, changing units to mm or a different scale bar size.
display = plotting.plot_anat(mean_haxby_img,
title="adding a scale bar",
cut_coords=[-34, -39, -9])
display.annotate(scalebar=True, scale_size=25, scale_units='mm')
Finally, saving the plots to file with two different ways
# Contrast maps plotted with function `plot_stat_map` can be saved using an
# inbuilt parameter output_file as filename + .extension as string. Valid
# extensions are .png, .pdf, .svg
plotting.plot_stat_map(stat_img,
title='Using plot_stat_map output_file',
output_file='plot_stat_map.png')
Another way of saving plots is using 'savefig' option from display object
display = plotting.plot_stat_map(stat_img,
title='Using display savefig')
display.savefig('plot_stat_map_from_display.png')
# In non-interactive settings make sure you close your displays
display.close()
plotting.show()