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', vmax=None, 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’}, optional

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

Default=’ortho’.

colorbarbool, optional

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

cbar_tick_format: str, optional

Controls how to format the tick labels of the colorbar. Ex: use “%i” to display as integers. Default is ‘%.2g’ for scientific notation.

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), optional

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, optional

The title displayed on the figure. Default=None.

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, optional

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

draw_crossbool, optional

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

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’, optional

Specifies whether the colorbar should range from -vmax to vmax or from vmin to vmax. Setting to ‘auto’ will select the latter if the range of the whole image is either positive or negative.

Note

The colormap will always range from -vmax to vmax.

Default=’auto’.

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’.

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’.

Examples using `nilearn.plotting.plot_stat_map`#

3D and 4D niimgs: handling and visualizing

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

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

Controlling the contrast of the background when plotting

Plotting tools in nilearn

Making a surface plot of a 3D statistical map

More plotting tools from nilearn

FREM on Jimura et al “mixed gambles” dataset.

FREM on Jimura et al "mixed gambles" dataset.

Decoding with FREM: face vs house object recognition

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

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

Searchlight analysis of face vs house recognition

Different classifiers in decoding the Haxby dataset

Voxel-Based Morphometry on Oasis dataset

Encoding models for visual stimuli from Miyawaki et al. 2008

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

Producing single subject maps of seed-to-voxel correlation

Regions extraction using dictionary learning and functional connectomes

Example of explicit fixed effects fMRI model fitting

Default Mode Network extraction of ADHD dataset

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

Single-subject data (two sessions) in native space

Simple example of two-session fMRI model fitting

Predicted time series and residuals

Understanding parameters of the first-level model

Second-level fMRI model: true positive proportion in clusters

Statistical testing of a second-level analysis

Voxel-Based Morphometry on OASIS dataset

Example of generic design in second-level models

Negating an image with math_img

Comparing the means of 2 images

Regions Extraction of Default Mode Networks using Smith Atlas

Resample an image to a template

Simple example of NiftiMasker use

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

Computing a Region of Interest (ROI) mask manually

Multivariate decompositions: Independent component analysis of fMRI

Massively univariate analysis of a calculation task from the Localizer dataset

NeuroVault meta-analysis of stop-go paradigm studies.

NeuroVault cross-study ICA maps.

Massively univariate analysis of face vs house recognition

Advanced decoding using scikit learn

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

nilearn.plotting.plot_stat_map#

Examples using nilearn.plotting.plot_stat_map#

Examples using `nilearn.plotting.plot_stat_map`#