Graph-Based Analysis of Brain Connectivity Alterations Induced by Tumors

Abstract

Brain tumors cause major alterations in connectivity by disrupting the anatomical and functional networks of the brain. These disruptions can lead to significant changes in brain communication, affecting both local and global network properties. Better treatment planning and early diagnosis depend on an understanding of these structural changes. In this study, we suggest a new graph-based method for examining the structural alterations in brain connections brought on by malignancies. we describe the brain as a network, where nodes stand for various brain regions and edges indicate anatomical or functional relationships. We quantify and characterize tumor-induced alterations in network topology using sophisticated graph theory metrics, including degree centrality, betweenness centrality, modularity, and clustering coefficients. There are notable differences in the patterns of connection between brains with tumors and those without. Notably, global measurements like as modularity and clustering coefficients show abnormalities in the general structure of the brain network, while regions close to tumors frequently display aberrant centrality values. These results demonstrate the promise of graph-based techniques as a quantitative and systematic tool for identifying changes in brain connection associated with tumors, providing important information for clinical evaluation and early detection.