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Computational Modeling of Brain Network Vulnerability in Cancer-Related Cognitive Impairment: A Graph Theoretical Study
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Clinical Applications of Brain Mappin
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Presenter Author:
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| Name:
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Mohamad Amir Kakaee
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Author:
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| Name 1:
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Mohamad Amir Kakaee - Corresponding - Presenter
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| Affiliation 1:
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shahid beheshti university of medical sciences
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Abstract
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Original Research
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introduction :
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Cancer-related cognitive impairment (CRCI), or "chemo-brain," affects executive functions and memory in up to 75% of survivors. While clinical studies have identified regional brain changes, the systemic topological vulnerability of the brain remains under-explored. Recent literature from 2024 and 2025 emphasizes the need for objective, network-based biomarkers to predict cognitive decline. High-degree "hubs" in the brain facilitate rapid information transfer; however, they are highly metabolic and susceptible to treatment-induced neurotoxicity. This study employs graph theory to simulate how the loss of these hubs leads to a total collapse of cognitive integration.
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Methods:
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We constructed a scale-free network model (N=100 nodes) using the Barabási–Albert algorithm to represent the human connectome's "rich-club" architecture. To simulate CRCI pathology, we executed a targeted attack simulation, where nodes were removed based on their degree centrality (highest to lowest). The primary metric assessed was Global Efficiency (E_{global}), which measures the network's capacity for parallel information processing. The simulation was performed in a Python environment using the NetworkX library to ensure mathematical reproducibility.
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Results:
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Results
The computational simulation revealed a robust baseline global efficiency of 0.4366 in the healthy state. Upon the targeted removal of strategic hubs (20% of the network), the efficiency plummeted to 0.0312. This represents a total integrative loss of 92.86%. The decay curve demonstrated a critical tipping point, where the network transitioned from a highly connected small-world system to a fragmented state, losing nearly all capacity for long-range communication.
Discussion
The significant efficiency loss (92.86%) supports the "Hub Vulnerability Hypothesis" in CRCI. Our model demonstrates that the brain's communication backbone is extremely sensitive to systemic neurotoxicity. Unlike random damage, targeted hub disruption mimics the effect of chemotherapy on high-traffic areas like the Frontoparietal and Default Mode Networks. This fragmentation explains the clinical "brain fog" experienced by patients, as the neural architecture can no longer synchronize distant cortical regions. These results suggest that preserving network hubs is essential for maintaining cognitive health during oncology treatments.
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Conclusion:
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This study provides a quantitative, original framework for understanding CRCI as a systemic network disorder. Our findings prove that the collapse of global efficiency is a mathematical consequence of hub degradation. While limited by its in-silico nature, this research offers a reproducible method for predicting network failure. Future precision oncology should incorporate brain mapping to identify and protect critical hubs, ensuring better cognitive outcomes for cancer survivors.
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| Attachment:
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| Keywords:
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CRCI ,Chemo-brain,
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| Key message:
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