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.view_connectome¶

nilearn.plotting.view_connectome(adjacency_matrix, node_coords, edge_threshold=None, edge_cmap=<matplotlib.colors.LinearSegmentedColormap object>, symmetric_cmap=True, linewidth=6.0, node_color='auto', node_size=3.0, colorbar=True, colorbar_height=0.5, colorbar_fontsize=25, title=None, title_fontsize=25)[source]¶

Insert a 3d plot of a connectome into an HTML page.

Parameters:

adjacency_matrixnumpy.ndarray of shape=(n_nodes, n_nodes): The weights of the edges.
node_coordsnumpy.ndarray of shape=(n_nodes, 3): The coordinates of the nodes in MNI space.
node_colorcolor or sequence of colors, default=’auto’: Color(s) of the nodes.
edge_thresholdstr, number or None, default=None: If None, no thresholding. If it is a number only connections of amplitude greater than threshold will be shown. If it is a string it must finish with a percent sign, e.g. “25.3%”, and only connections of amplitude above the given percentile will be shown.
edge_cmapstr or matplotlib colormap, default=cm.bwr: Colormap to use.
symmetric_cmapbool, default=True: Make colormap symmetric (ranging from -vmax to vmax).
linewidthfloat, default=6.0: Width of the lines that show connections.
node_sizefloat, default=3.0: Size of the markers showing the seeds in pixels.
colorbarbool, default=True: Add a colorbar.
colorbar_heightfloat, default=0.5: Height of the colorbar, relative to the figure height.
colorbar_fontsizeint, default=25: Fontsize of the colorbar tick labels.
titlestr or None, default=None: Title for the plot.
title_fontsizeint, default=25: Fontsize of the title.

Returns:

ConnectomeViewplot of the connectome.

It can be saved as an html page or rendered (transparently) by the Jupyter notebook. Useful methods are :

‘resize’ to resize the plot displayed in a Jupyter notebook
‘save_as_html’ to save the plot to a file
‘open_in_browser’ to save the plot and open it in a web browser.

Examples using `nilearn.plotting.view_connectome`¶

Loading and plotting of a cortical surface atlas

Computing a connectome with sparse inverse covariance

Extracting signals of a probabilistic atlas of functional regions

Extract signals on spheres and plot a connectome

nilearn.plotting.view_connectome¶

Examples using nilearn.plotting.view_connectome¶

Examples using `nilearn.plotting.view_connectome`¶