Controllability and Robustness of Functional and Structural Connectomic Networks in Glioma Patients

Cancers, vol. 15, pp. 2714

Abstract

Previous studies suggest that the topological properties of structural and functional neural networks in glioma patients are altered beyond the tumor location. These alterations are due to the dynamic interactions with large-scale neural circuits. Understanding and describing these interactions may be an important step towards deciphering glioma disease evolution. In this study, we analyze structural and functional brain networks in terms of determining the correlation between network robustness and topological features regarding the default-mode network (DMN), comparing prognostically differing patient groups to healthy controls. We determine the driver nodes of these networks, which are receptive to outside signals, and the critical nodes as the most important elements for controllability since their removal will dramatically affect network controllability. Our results suggest that network controllability and robustness of the DMN is decreased in glioma patients. We found losses of driver and critical nodes in patients, especially in the prognostically less favorable IDH wildtype (IDHwt) patients, which might reflect lesion-induced network disintegration. On the other hand, topological shifts of driver and critical nodes, and even increases in the number of critical nodes, were observed mainly in IDH mutated (IDHmut) patients, which might relate to varying degrees of network plasticity accompanying the chronic disease course in some of the patients, depending on tumor growth dynamics. We hereby implement a novel approach for further exploring disease evolution in brain cancer under the aspects of neural network controllability and robustness in glioma patients.

Authors 7

1. Anke Meyer‐Baese

   Florida State University · Technische Universität Dresden

   Affiliation as printed

   Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA

   Institute for Biomedical Engineering, Technical University of Dresden, 01069 Dresden, Germany

2. Kerstin Jütten Aachen Department of Neurosurgery

   RWTH Aachen University

   Affiliation as printed

   Department of Neurosurgery, RWTH Aachen University, 52074 Aachen, Germany

3. Uwe H. Meyer-Baese

   Florida State University

   Affiliation as printed

   Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310, USA

4. Ali Moradi Amani

   RMIT University

   Affiliation as printed

   School of Engineering, RMIT University, Melbourne, Victoria 3001, Australia

5. Hagen Malberg

   Technische Universität Dresden

   Affiliation as printed

   Institute for Biomedical Engineering, Technical University of Dresden, 01069 Dresden, Germany

6. Andreas Stadlbauer

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Department of Neurosurgery, Friedrich-Alexander University of Erlangen-Nürnberg, 91054 Erlangen, Germany

7. Thomas Mehari Kinfe

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Department of Neurosurgery, Friedrich-Alexander University of Erlangen-Nürnberg, 91054 Erlangen, Germany