In Ali-Mahzarnia/brainconn2: Plotting Tool for Brain Connectivity Data

knitr::opts_chunk$set(eval=TRUE)
knitr::opts_knit$set(root.dir = '..')

Basic use of brainconn()

The brainconn() function allows users to input a binary/weighted and directed/directed (i.e. symmetric) connectivity matrix which can be plotted in MNI space onto several brain atlases provided with the package, or a user defined custom atlas. The layout of the nodes and edges is created using ggraph, which does most of the heavy lifting here. The background is a custom_annotation using ggplot2.

The minimum user input requirements for the brainconn() function is a connectivity matrix. This can be read into r as a matrix or data.fame. Some example connectivity matrices can be found in the data/example folder. The number of rows & columns should match the number of regions in the selected atlas. Custom atlases can also be used, see [Using custom atlases].

By default, the view is a top view, the node.color is by network.

library(brainconn)
x <- example_unweighted_undirected
brainconn(atlas ="schaefer300_n7", conmat=x, node.size = 3, view="ortho")

You can change the default view, node, edge and background properties as well as show all nodes of the atlas, rather than just the ones that have connected edges.

brainconn(atlas ="schaefer300_n7", conmat=example_unweighted_undirected, view="left", node.size = 3, node.color = "hotpink", edge.width = 2, edge.color="darkblue", edge.alpha = 0.8, all.nodes = T, show.legend = F)

If the connectivity matrix you input is not a binary matrix, i.e. the values are weighted, by default braincon() will modulate the edge.width by the weighted values. scale.edge.width can be used to scale the edge width.

x <- example_weighted_undirected

brainconn(atlas ="schaefer300_n7", conmat=x, node.size = 5,view="bottom", scale.edge.width = c(1,3), background.alpha = 0.4, show.legend = F)

You can also color the edges by weight using the edge.color.weighted option.

brainconn(atlas ="schaefer300_n7", conmat=example_weighted_undirected, node.size = 7,view="bottom", edge.width = 2, edge.color.weighted = T, show.legend = T)

If the input is a directed matrix (i.e. non-symmetrical), the edges will be displayed as directed arrows, with the edge.width and edge.color serving the same purpose:

x <- example_unweighted_directed

brainconn(atlas ="schaefer300_n7", conmat=x, node.size = 4, view="right", edge.alpha = 0.6)

Weighted and directed matrix example:

x <- example_weighted_directed

brainconn(atlas ="schaefer300_n7", conmat=x, view="front", edge.color.weighted=T)

You can auto add labels, but this doesn't work to great if theirs a large number of edges.

brainconn(atlas ="schaefer300_n7", conmat=example_unweighted_undirected, labels = T, label.size = 2, node.size = 3)

If you want to weight the nodes by a feature such as degree, you can provide a vector the length of the number of ROIs in the parcellation to node.size:

x <- example_unweighted_undirected
x_igraph <- igraph::graph_from_adjacency_matrix(as.matrix(example_unweighted_undirected)) #convert connectivity matrix into an graph object.
d <- igraph::degree(x_igraph) #use igraph::degree to obtain a vector of nodal degree
d <- d[d != 0] #remove nodes with no edges 
brainconn(atlas ="schaefer300_n7", conmat=x, node.size = d)

Basic use of brainconn3D()

Currently, brainconn3D() is only able to visualize binary undirected connectivity matrices.

p <- brainconn3D(atlas ="schaefer300_n7", conmat=example_unweighted_undirected, show.legend = F)
p

Modifiable features include node.color, node.size, edge.size & edge.width, brain surface opacity and d.factor (a multiplication factor to control the spread of nodes.)

p <- brainconn3D(atlas = "schaefer300_n7", conmat=example_unweighted_undirected, edge.width = 6, edge.color = "green", node.size = 8, node.color = "red", show.legend = F)
p

The d.factor can be adjusted for different purposes. The default is set to 1.05.

p <- brainconn3D(atlas ="schaefer300_n7", conmat=example_unweighted_undirected, edge.width = 3, edge.color = "brown", node.size = 3, d.factor = 1.3, all.nodes = T, opacity = 0.3, show.legend = F)
p

Using custom atlases

To use you own atlas, you will require a data.frame with a minimum of four column: "ROI.name", "x.mni", "y.mni", "z.mni". You can also include a "network" column for node-coloring purposes. The x y and z.mni columns are the centroid coordinates for each roi in MNI space. These coordinates will sometimes be published with the atlas, and can also be obtained from the atlas .nii file. One way of doing this would be with FSL e.g.fslstats roi.nii -c.

To check that your custom atlas matches the requirements for brainconn() or brainconn3d(), you can use the check_atlas() function:

custom_atlas <- custom_atlas_example
check_atlas(custom_atlas)

brainconn(atlas = custom_atlas, conmat = example_unweighted_undirected,
          node.color = "black")

Ali-Mahzarnia/brainconn2 documentation built on Feb. 19, 2024, 4:16 a.m.