Disruption of Small World Properties in Functional Brain Networks of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neuro degenerative disorder characterized by cognitive decline and wide spread alterations in brain connectivity. In recent years, graph theory has emerged as a powerful framework for modeling functional brain networks and quantifying their topological properties. In this study we investigate disruptions in small world organizations of functional brain networks in Alzheimer’s disease using graph theoretic measures. Functional connectivity networks are constructed from resting state brain regions and edges denote pairwise functional interactions. Key network metrics including clustering coefficient, characteristic path length and global efficiency are computed and compared between Alzheimer’s patients and health control subjects. The results reveal a significant reduction in local clustering increased path length and decreased global efficiency in AD networks indicating impaired balance between functional segregation and integration. Furthermore, small worldness analysis demonstrates a clear breakdown of optimal network organization in Alzheimer’s disease. These findings provide quantitative evidence of functional brain network disorganization in AD and highlight graph theoretic measures as potential biomarkers for neuro degenerative disorders.