Controlling the contrast of the background when plotting

The dim argument controls the contrast of the background.

dim modifies the contrast of this image: dim=0 leaves the image unchanged, negative values of dim enhance it, and positive values decrease it (dim the background).

This dim argument may also be useful for the plot_roi function used to display ROIs on top of a background image.

Retrieve the data: the localizer dataset with contrast maps

from nilearn import datasets

localizer_dataset = datasets.fetch_localizer_button_task(legacy_format=False)
# Contrast map of motor task
localizer_tmap_filename = localizer_dataset.tmap
# Subject specific anatomical image
localizer_anat_filename = localizer_dataset.anat

Plotting with enhancement of background image with dim=-.5

from nilearn import plotting
plotting.plot_stat_map(localizer_tmap_filename,
                       bg_img=localizer_anat_filename,
                       cut_coords=(36, -27, 66),
                       threshold=3, title="dim=-.5",
                       dim=-.5)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff8facd7670>

Plotting with no change of contrast in background image with dim=0

plotting.plot_stat_map(localizer_tmap_filename,
                       bg_img=localizer_anat_filename,
                       cut_coords=(36, -27, 66),
                       threshold=3, title="dim=0",
                       dim=0)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff8eaee7a30>

Plotting with decrease of contrast in background image with dim=.5

plotting.plot_stat_map(localizer_tmap_filename,
                       bg_img=localizer_anat_filename,
                       cut_coords=(36, -27, 66),
                       threshold=3, title="dim=.5",
                       dim=.5)

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff8faec5000>

Plotting with more decrease in contrast with dim=1

plotting.plot_stat_map(localizer_tmap_filename,
                       bg_img=localizer_anat_filename,
                       cut_coords=(36, -27, 66),
                       threshold=3, title="dim=1",
                       dim=1)

plotting.show()

Estimated memory usage: 9 MB