# Network properties of healthy and Alzheimer's brains

## TL;DR

This study compares healthy and Alzheimer's brains' structural connections using small-world network models, revealing potential biomarkers for abnormalities through network quantifiers.

## Contribution

It demonstrates that small-world network models can effectively approximate brain structural connections and identifies increased assortativity as a marker of Alzheimer's disease.

## Key findings

- Similar small-world structures in healthy and Alzheimer's brains
- Increased assortativity in Alzheimer's brain networks
- Network quantifiers can identify structural abnormalities

## Abstract

Small-world structures are often used to describe structural connections in the brain. In this work, we compare the structural connection of cortical areas of a healthy brain and a brain affected by Alzheimer's disease with artificial small-world networks. Based on statistics analysis, we demonstrate that similar small-world networks can be constructed using Newman-Watts procedure. The network quantifiers of both structural matrices are identified inside the probabilistic valley. Despite of similarities between structural connection matrices and sampled small-world networks, increased assortativity can be found in the Alzheimer brain. Our results indicate that network quantifiers can be helpful to identify abnormalities in real structural connection matrices.