Clustering-based model reduction for glioma graph networks

SPIE Medical Imaging: Clinical and Biomedical Imaging, pp. 77

Abstract

We analyze structural and functional connectivity graphs for both healthy and glioma subjects by clustering-based model reduction to obtain a detailed understanding of these complex graphs. The reduced-order network is obtained by dividing the connected brain regions or nodes into disjoint clusters. Further, each cluster is replaced by a single new node in a reduced-size graph network. Several partitions are analyzed and evaluated based on model reduction errors.

Authors 9

1. Petar Mlinarić

   Virginia Tech

   Affiliation as printed

   Virginia Polytechnic Institute and State Univ. (United States)

2. Zlatko Sokolikj

   Florida State University

   Affiliation as printed

   Florida State Univ. (United States)

3. Gokul Talla

   Florida State University

   Affiliation as printed

   Florida State Univ. (United States)

4. Uwe H. Meyer-Baese

   Florida State University

   Affiliation as printed

   Florida State Univ. (United States)

5. Andreas Stadlbauer

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Friedrich-Alexander Univ. (Germany)

6. Hagen Malberg

   Technische Universität Dresden

   Affiliation as printed

   Technical Univ. Dresden (Germany)

7. Kerstin Juetten Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen Univ. (Germany)

8. Benedikt Wiestler

   Technical University of Munich

   Affiliation as printed

   Technical Univ. Munich (Germany)

9. Anke Meyer‐Baese

   Technical University of Munich

   Affiliation as printed

   Technical Univ. Munich (Germany)