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

Anke Meyer-Baese; Kerstin Juetten; Uwe Meyer-Baese; Andreas Stadlbauer; Thomas Kinfe; Chuh-Hyoun Na

doi:10.1117/12.2645973

3 October 2022 Dynamical graph networks may aid in phenotyping prognostically different brain tumor types

Anke Meyer-Baese, Kerstin Juetten, Uwe Meyer-Baese, Andreas Stadlbauer, Thomas Kinfe, Chuh-Hyoun Na

Proceedings Volume 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; 122040E (2022) https://doi.org/10.1117/12.2645973
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

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.

Citation Download Citation

Anke Meyer-Baese, Kerstin Juetten, Uwe Meyer-Baese, Andreas Stadlbauer, Thomas Kinfe, and Chuh-Hyoun Na "Dynamical graph networks may aid in phenotyping prognostically different brain tumor types", Proc. SPIE 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, 122040E (3 October 2022); https://doi.org/10.1117/12.2645973

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KEYWORDS

Brain

Tumors

Anisotropy

Image segmentation

Magnetic resonance imaging

Matrices

Neural networks

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