Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.plotting.plot_roi¶

nilearn.plotting.plot_roi(roi_img, bg_img=<MNI152Template>, cut_coords=None, output_file=None, display_mode='ortho', figure=None, axes=None, title=None, annotate=True, draw_cross=True, black_bg='auto', threshold=0.5, alpha=0.7, cmap='gist_ncar', dim='auto', colorbar=True, cbar_tick_format='%.2g', vmin=None, vmax=None, resampling_interpolation='nearest', view_type='continuous', linewidths=2.5, radiological=False, **kwargs)[source]¶

Plot cuts of an ROI/mask image.

By default 3 cuts: Frontal, Axial, and Lateral.

Parameters:

roi_imgNiimg-like object

See Input and output: neuroimaging data representation. The ROI/mask image, it could be binary mask or an atlas or ROIs with integer values.

bg_imgNiimg-like object, optional

See Input and output: neuroimaging data representation. The background image to plot on top of. If nothing is specified, the MNI152 template will be used. To turn off background image, just pass “bg_img=None”. Default=MNI152TEMPLATE.

cut_coordsNone, a tuple of float, or int, optional

The MNI coordinates of the point where the cut is performed.

If display_mode is ‘ortho’ or ‘tiled’, this should be a 3-tuple: (x, y, z)
For display_mode == “x”, “y”, or “z”, then these are the coordinates of each cut in the corresponding direction.
If None is given, the cuts are calculated automatically.
If display_mode is ‘mosaic’, and the number of cuts is the same for all directions, cut_coords can be specified as an integer. It can also be a length 3 tuple specifying the number of cuts for every direction if these are different.

Note

If display_mode is “x”, “y” or “z”, cut_coords can be an integer, in which case it specifies the number of cuts to perform.

output_filestr or pathlib.Path or None, optional

The name of an image file to export the plot to. Valid extensions are .png, .pdf, .svg. If output_file is not None, the plot is saved to a file, and the display is closed.

display_mode{“ortho”, “tiled”, “mosaic”, “x”, “y”, “z”, “yx”, “xz”, “yz”}, default=”ortho”

Choose the direction of the cuts:

"x": sagittal
"y": coronal
"z": axial
"ortho": three cuts are performed in orthogonal directions
"tiled": three cuts are performed and arranged in a 2x2 grid
"mosaic": three cuts are performed along multiple rows and columns

figureint, or matplotlib.figure.Figure, or None, optional

Matplotlib figure used or its number. If None is given, a new figure is created.

axesmatplotlib.axes.Axes, or 4 tuple of float: (xmin, ymin, width, height), default=None

The axes, or the coordinates, in matplotlib figure space, of the axes used to display the plot. If None, the complete figure is used.

titlestr, or None, default=None

The title displayed on the figure.

annotatebool, default=True

If annotate is True, positions and left/right annotation are added to the plot.

draw_crossbool, default=True

If draw_cross is True, a cross is drawn on the plot to indicate the cut position.

black_bgbool, or “auto”, optional

If True, the background of the image is set to be black. If you wish to save figures with a black background, you will need to pass facecolor=”k”, edgecolor=”k” to matplotlib.pyplot.savefig. Default=’auto’.

thresholdint or float, None, or ‘auto’, optional

If None is given, the image is not thresholded. If number is given, it must be non-negative. The specified value is used to threshold the image: values below the threshold (in absolute value) are plotted as transparent. If “auto” is given, the threshold is determined based on the score obtained using percentile value “80%” on the absolute value of the image data. Default=0.5.

alphafloat between 0 and 1, default=0.7

Alpha sets the transparency of the color inside the filled contours.

cmapmatplotlib.colors.Colormap, or str, or pandas.DataFrame, optional

The colormap to use. Either a string which is a name of a matplotlib colormap, or a matplotlib colormap object, or a BIDS compliant look-up table passed as a pandas dataframe. If the look up table does not contain a color column, then the default colormap of this function will be used. Default=`gist_ncar`.

dimfloat, or “auto”, optional

Dimming factor applied to background image. By default, automatic heuristics are applied based upon the background image intensity. Accepted float values, where a typical span is between -2 and 2 (-2 = increase contrast; 2 = decrease contrast), but larger values can be used for a more pronounced effect. 0 means no dimming. Default=’auto’.

colorbarbool, optional

If True, display a colorbar on the right of the plots. Default=True

cbar_tick_formatstr, default=”%i”

Controls how to format the tick labels of the colorbar. Ex: use “%.2g” to use scientific notation.

vminfloat or obj:int or None, optional

Lower bound of the colormap. The values below vmin are masked. If None, the min of the image is used. Passed to matplotlib.pyplot.imshow.

vmaxfloat or obj:int or None, optional

Upper bound of the colormap. The values above vmax are masked. If None, the max of the image is used. Passed to matplotlib.pyplot.imshow.

resampling_interpolationstr, optional

Interpolation to use when resampling the image to the destination space. Can be:

"continuous": use 3rd-order spline interpolation
"nearest": use nearest-neighbor mapping.

Note

"nearest" is faster but can be noisier in some cases.

Default=’nearest’.

view_type{‘continuous’, ‘contours’}, default=’continuous’

By default view_type == ‘continuous’, rois are shown as continuous colors. If view_type == ‘contours’, maps are shown as contours. For this type, label denoted as 0 is considered as background and not shown.

linewidthsfloat, optional

Set the boundary thickness of the contours. Only reflects when view_type=contours. Default=2.5.

radiologicalbool, default=False

Invert x axis and R L labels to plot sections as a radiological view. If False (default), the left hemisphere is on the left of a coronal image. If True, left hemisphere is on the right.

kwargsextra keyword arguments, optional

Extra keyword arguments ultimately passed to matplotlib.pyplot.imshow via add_overlay.

Returns:

displayOrthoSlicer or None: An instance of the OrthoSlicer class. If output_file is defined, None is returned.

Raises:

ValueError: if the specified threshold is a negative number

Examples using `nilearn.plotting.plot_roi`¶

A introduction tutorial to fMRI decoding

Basic Atlas plotting

NeuroImaging volumes visualization

Plotting tools in nilearn

Visualizing multiscale functional brain parcellations

Clustering methods to learn a brain parcellation from fMRI

Understanding parameters of the first-level model

Breaking an atlas of labels in separated regions

Computing a Region of Interest (ROI) mask manually

Simple example of NiftiMasker use

Understanding NiftiMasker and mask computation

nilearn.plotting.plot_roi¶

Examples using nilearn.plotting.plot_roi¶

Examples using `nilearn.plotting.plot_roi`¶