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_stat_map#

nilearn.plotting.plot_stat_map(stat_map_img, bg_img=<MNI152Template>, cut_coords=None, output_file=None, display_mode='ortho', colorbar=True, cbar_tick_format='%.2g', figure=None, axes=None, title=None, threshold=1e-06, annotate=True, draw_cross=True, black_bg='auto', cmap=<matplotlib.colors.LinearSegmentedColormap object>, symmetric_cbar='auto', dim='auto', vmin=None, vmax=None, radiological=False, resampling_interpolation='continuous', **kwargs)[source]#

Plot cuts of an ROI/mask image.

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

Parameters:
stat_map_imgNiimg-like object

See Input and output: neuroimaging data representation. The statistical map image

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 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

colorbarbool, optional

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

cbar_tick_format: str, default=”%.2g” (scientific notation)

Controls how to format the tick labels of the colorbar. Ex: use “%i” to display as integers.

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 tupleof 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.

thresholda number, None, or ‘auto’, optional

If None is given, the image is not thresholded. If a number is given, it 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 magically by analysis of the image. Default=1e-6.

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’.

cmapmatplotlib.colors.Colormap, or str, optional

The colormap to use. Either a string which is a name of a matplotlib colormap, or a matplotlib colormap object.

Note

The colormap must be symmetrical.

Default=`plt.cm.cold_hot`.

symmetric_cbarbool, or “auto”, default=”auto”

Specifies whether the colorbar and colormap should range from -vmax to vmax (or from vmin to -vmin if -vmin is greater than vmax) or from vmin to vmax. Setting to “auto” (the default) will select the former if either vmin or vmax is None and the image has both positive and negative values.

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’.

vminfloat, optional

Lower bound of the colormap. If None, the min of the image is used. Passed to matplotlib.pyplot.imshow.

vmaxfloat, optional

Upper bound of the colormap. 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=’continuous’.

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.

kwargs: extra 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.

See also

nilearn.plotting.plot_anat

To simply plot anatomical images

nilearn.plotting.plot_epi

To simply plot raw EPI images

nilearn.plotting.plot_glass_brain

To plot maps in a glass brain

Notes

Arrays should be passed in numpy convention: (x, y, z) ordered.

For visualization, non-finite values found in passed ‘stat_map_img’ or ‘bg_img’ are set to zero.

Examples using nilearn.plotting.plot_stat_map#

3D and 4D niimgs: handling and visualizing

3D and 4D niimgs: handling and visualizing

Intro to GLM Analysis: a single-run, single-subject fMRI dataset

Intro to GLM Analysis: a single-run, single-subject fMRI dataset

Visualizing a probabilistic atlas: the default mode in the MSDL atlas

Visualizing a probabilistic atlas: the default mode in the MSDL atlas

Controlling the contrast of the background when plotting

Controlling the contrast of the background when plotting

Plotting tools in nilearn

Plotting tools in nilearn

Making a surface plot of a 3D statistical map

Making a surface plot of a 3D statistical map

More plotting tools from nilearn

More plotting tools from nilearn

FREM on Jimura et al “mixed gambles” dataset

FREM on Jimura et al "mixed gambles" dataset

Voxel-Based Morphometry on Oasis dataset with Space-Net prior

Voxel-Based Morphometry on Oasis dataset with Space-Net prior

Decoding with FREM: face vs house vs chair object recognition

Decoding with FREM: face vs house vs chair object recognition

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

Searchlight analysis of face vs house recognition

Searchlight analysis of face vs house recognition

Voxel-Based Morphometry on Oasis dataset

Voxel-Based Morphometry on Oasis dataset

Different classifiers in decoding the Haxby dataset

Different classifiers in decoding the Haxby dataset

Encoding models for visual stimuli from Miyawaki et al. 2008

Encoding models for visual stimuli from Miyawaki et al. 2008

Producing single subject maps of seed-to-voxel correlation

Producing single subject maps of seed-to-voxel correlation

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes

Default Mode Network extraction of ADHD dataset

Default Mode Network extraction of ADHD dataset

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Single-subject data (two runs) in native space

Single-subject data (two runs) in native space

Predicted time series and residuals

Predicted time series and residuals

Simple example of two-runs fMRI model fitting

Simple example of two-runs fMRI model fitting

Understanding parameters of the first-level model

Understanding parameters of the first-level model

Second-level fMRI model: true positive proportion in clusters

Second-level fMRI model: true positive proportion in clusters

Statistical testing of a second-level analysis

Statistical testing of a second-level analysis

Voxel-Based Morphometry on OASIS dataset

Voxel-Based Morphometry on OASIS dataset

Example of generic design in second-level models

Example of generic design in second-level models

Negating an image with math_img

Negating an image with math_img

Comparing the means of 2 images

Comparing the means of 2 images

Resample an image to a template

Resample an image to a template

Regions Extraction of Default Mode Networks using Smith Atlas

Regions Extraction of Default Mode Networks using Smith Atlas

Simple example of NiftiMasker use

Simple example of NiftiMasker use

Region Extraction using a t-statistical map (3D)

Region Extraction using a t-statistical map (3D)

Computing a Region of Interest (ROI) mask manually

Computing a Region of Interest (ROI) mask manually

Multivariate decompositions: Independent component analysis of fMRI

Multivariate decompositions: Independent component analysis of fMRI

Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of a calculation task from the Localizer dataset

NeuroVault meta-analysis of stop-go paradigm studies

NeuroVault meta-analysis of stop-go paradigm studies

NeuroVault cross-study ICA maps

NeuroVault cross-study ICA maps

Massively univariate analysis of face vs house recognition

Massively univariate analysis of face vs house recognition

Advanced decoding using scikit learn

Advanced decoding using scikit learn

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding