Dynamical graph networks may aid in phenotyping prognostically different brain tumor types

Meyer-Baese A, Juetten K, Meyer-Baese U, Stadlbauer A, Kinfe TM, Na CH (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Proceedings of SPIE Society of Photo-optical Instrumentation Engineers (SPIE)

Publisher: SPIE

Book Volume: 12204

Conference Proceedings Title: Proceedings of SPIE - The International Society for Optical Engineering

Event location: San Diego, CA, USA

ISBN: 9781510653924

DOI: 10.1117/12.2645973

Abstract

Diffuse infiltrative glioma are considered as a systemic brain disorder and produce alterations on cerebral functional and structural integrity beyond the tumor location. These alterations are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for glioma disease evolution. Modern dynamic graph network theory techniques and control theory applied to these structural and functional networks opens a new research avenue for understanding the dynamical properties and differences between healthy controls and glioma patients. It has been shown that controllability is relevant for providing the mechanistic explanation of how the brain navigates between cognitive states. We believe that it is also relevant for describing the connectomic alterations in glioma and the differences among subtypes and healthy controls. The nodes that are needed to control these networks and influence them to any state are called driver nodes. We determined the driver nodes of the Default-Mode Network (DMN) for resting-state functional connectivity (FC) and diffusion-MRI-based structural connectivity (SC) (comprising edge-weight (EW) and fractional anisotropy (FA)) networks in isodehydrogenase mutated (IDHmut) and wildtype (IDHwt) patients and healthy controls. Our results show that healthy controls have a better controllability for both FC and SC, and that structural connectomic dynamical aberrations are more pronounced in glioma patients than functional connectomic alterations.

Authors with CRIS profile

Andreas Stadlbauer Department of Neurosurgery Thomas Mehari Kinfe Professur für Neurochirurgie mit dem Schwerpunkt Funktionelle Stereotaktische Neurochirurgie

Involved external institutions

Florida State University (FSU) US United States (USA) (US) Centrum für Integrierte Onkologie Aachen Bonn Köln Düsseldorf (CIO ABCD) DE Germany (DE) Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) Florida Agricultural and Mechanical University (FAMU) US United States (USA) (US)

How to cite

APA:

Meyer-Baese, A., Juetten, K., Meyer-Baese, U., Stadlbauer, A., Kinfe, T.M., & Na, C.H. (2022). Dynamical graph networks may aid in phenotyping prognostically different brain tumor types. In Giovanni Volpe, Joana B. Pereira, Daniel Brunner, Aydogan Ozcan (Eds.), Proceedings of SPIE - The International Society for Optical Engineering. San Diego, CA, USA: SPIE.

MLA:

Meyer-Baese, Anke, et al. "Dynamical graph networks may aid in phenotyping prognostically different brain tumor types." Proceedings of the 2022 Emerging Topics in Artificial Intelligence, ETAI 2022, San Diego, CA, USA Ed. Giovanni Volpe, Joana B. Pereira, Daniel Brunner, Aydogan Ozcan, SPIE, 2022.

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