April 24, 2010
brain connectivity of DTI fibers can be obtained as an
The algorithm was first introduced in OHBM  then
refiend in .  performs the ε-neighbor network consruction on a
template so the resulting networks have identical node
positions in the template.  modfied the
method to be applicable to arbitrary connectivty matrix. Here
we present the ε-neighbor
algorithm and visualization and analysis tools.
have been tested
Matlab versions 7.5 on a Mac computer (intel processor)
memory and MATLAB 7.5.
you are using the Matlab codes/sample data given here
for your publication,
please reference .
Fiber bundle visualization
July 7, 2010
the FA map of the template used in  . We have to
swap x- and
y-coordinates since the MATLAB convention is slightly
the usual NII-format. You need to unzip nii.zip
file from the link below.
To obtain the two end points of tracts and color them as read, we run
The resulting image is shown in Figure 1.
Figure 1. White matter fiber bundles obtained from a streamline-based algorithm. The tracts are sparsely subsampled for better visualization. The end points are colored as red. The surface is the isosurface of the template FA map so some tracts are expected to be outside of the surface. The ε-neighbor method will use the proximity of the ends points in constructing the network graph.
July 7, 2010
ε-neighbor algorithm is run directly on
the list of
fiber bundles SL.mat.
is the adjacency matrix, prob
is the fiber concentration density, which is computed by
number of tracts that is connecting to the given node within
is the coordinates of the constructed nodes. Figure 2 shows
resulting graphs superimposed on top of the FA template and
corresponding adjacency matrices.
figure; imagesc(-adj); colormap('bone');
Figure 2. Scalable 3D connectivity graphs constructed from the proposed algorithm and the corresponding adjacency matrices. The nodes are indexed by numbers. From the left to right, graphs are at 20, 10 and 6mm resolution.
April 28, 2010
April 22, 2010