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.
8.10.12. nilearn.plotting.plot_connectome¶
nilearn.plotting.plot_connectome(adjacency_matrix, node_coords, node_color='auto', node_size=50, edge_cmap=<matplotlib.colors.LinearSegmentedColormap object>, edge_vmin=None, edge_vmax=None, edge_threshold=None, output_file=None, display_mode='ortho', figure=None, axes=None, title=None, annotate=True, black_bg=False, alpha=0.7, edge_kwargs=None, node_kwargs=None, colorbar=False)¶Plot connectome on top of the brain glass schematics.
The plotted image should be in MNI space for this function to work properly.
In the case of ‘l’ and ‘r’ directions (for hemispheric projections), markers in the coordinate x == 0 are included in both hemispheres.
Parameters: adjacency_matrix : numpy array of shape (n, n)
represents the link strengths of the graph. Assumed to be a symmetric matrix.
node_coords : numpy array_like of shape (n, 3)
3d coordinates of the graph nodes in world space.
node_color : color or sequence of colors
color(s) of the nodes. If string is given, all nodes are plotted with same color given in string.
node_size : scalar or array_like
size(s) of the nodes in points^2.
edge_cmap : colormap
colormap used for representing the strength of the edges.
edge_vmin : float, optional, default: None
edge_vmax : float, optional, default: None
If not None, either or both of these values will be used to as the minimum and maximum values to color edges. If None are supplied the maximum absolute value within the given threshold will be used as minimum (multiplied by -1) and maximum coloring levels.
edge_threshold : str or number
If it is a number only the edges with a value greater than edge_threshold will be shown. If it is a string it must finish with a percent sign, e.g. “25.3%”, and only the edges with a abs(value) above the given percentile will be shown.
output_file : string, 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 : string, optional. Default is ‘ortho’.
Choose the direction of the cuts: ‘x’ - sagittal, ‘y’ - coronal, ‘z’ - axial, ‘l’ - sagittal left hemisphere only, ‘r’ - sagittal right hemisphere only, ‘ortho’ - three cuts are performed in orthogonal directions. Possible values are: ‘ortho’, ‘x’, ‘y’, ‘z’, ‘xz’, ‘yx’, ‘yz’, ‘l’, ‘r’, ‘lr’, ‘lzr’, ‘lyr’, ‘lzry’, ‘lyrz’.
figure : integer or matplotlib figure, optional
Matplotlib figure used or its number. If None is given, a new figure is created.
axes : matplotlib axes or 4 tuple of 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.
title : string, optional
The title displayed on the figure.
annotate : boolean, optional
If annotate is True, positions and left/right annotation are added to the plot.
black_bg : boolean, 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.
alpha : float between 0 and 1
Alpha transparency for the brain schematics.
edge_kwargs : dict
will be passed as kwargs for each edge matlotlib Line2D.
node_kwargs : dict
will be passed as kwargs to the plt.scatter call that plots all the nodes in one go
colorbar : bool, optional
If True, display a colorbar on the right of the plots. By default it is False.
See also
nilearn.plotting.find_parcellation_cut_coords- Extraction of node coords on brain parcellations.
nilearn.plotting.find_probabilistic_atlas_cut_coords- Extraction of node coords on brain probabilisitic atlases.
