Abstract #2842
Line graphs and vector weights: a novel paradigm for brain network analysis
Peter Savadjiev 1 , Carl-Fredrik Westin 2 , and Yogesh Rathi 1
1
Psychiatry Neuroimaging Laboratory, Brigham
and Women's Hospital, Harvard Medical School, Boston,
MA, United States,
2
Laboratory
for Mathematics in Imaging, Brigham and Women's
Hospital, Harvard Medical School, Boston, MA, United
States
Graph theoretical representations of brain networks
model the organization of gray matter units. We
introduce a novel Dual graph formalism, in which the
role of edges and vertices is inverted relative to the
original (Primal) graph. This transformation shifts the
emphasis of brain network analysis from gray matter
units to their underlying connections. It applies
standard graph theoretical operations to discover the
organization of connections, as opposed to gray matter
centers. Furthermore, it facilitates the
characterization of each connection by a vector of
several features. This is one solution to the problem of
vector weights in standard brain network analysis.
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