"Analyzing the Progression of Alzheimer's Disease in Human Brain Networ" by Anjan Chowdhury, Swarup Chattopadhyay et al.
 

Analyzing the Progression of Alzheimer's Disease in Human Brain Networks

Document Type

Conference Article

Publication Title

Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2023

Abstract

In this study, we present a new hybrid model that integrates the anatomical and topological characteristics of a brain network. The aim is to effectively capture the structural and/or topological alterations that take place in networks as individuals transition from a healthy control state to the stage of Alzheimer's disease. The utilisation of a brain atlas allows for the assessment of the Euclidean distance between two specific regions of interest (ROIs) inside the brain. This distance is considered a metric of anatomical distance, providing a quantitative representation of an anatomical characteristic. Conversely, the measurement of topological similarity, which assesses a characteristic of topology, is determined by calculating the cosine distance between nodes following the embedding of the whole real brain network into a vector space of dimensionality d. The empirical findings obtained using real-brain network data indicate that the hybridization approach well captures the observed topological variations during the transition from a healthy cognitive state (HC) to Alzheimer's disease (AD).

First Page

415

Last Page

418

DOI

10.1145/3625007.3627496

Publication Date

11-6-2023

Comments

Open Access, Bronze

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