Small-world networks propensity in spontaneous speech signals of Alzheimer's disease: visibility graph analysis.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-02-10 DOI:10.1038/s41598-025-88947-9

Mahda Nasrolahzadeh, Zeynab Mohammadpoory, Javad Haddadnia

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Abstract

Exploiting complex network methods to describe dynamical behavior based on speech time series can provide fundamental insights into the function of underlying dynamical processes in Alzheimer's disease (AD). This study scrutinizes the dynamic alterations in Alzheimer's speech through abstract concepts of small-world networks. The visibility graph (VG) of the time series of spontaneous speech is introduced as a quantitative method to differentiate between healthy individuals and those with Alzheimer's. The dynamic speech patterns across three AD and healthy subjects stages are analyzed by examining the small-world feature structure, characterized by a high clustering coefficient (C) and short average path length (L) in the VG. These characteristics are calculated based on degree K. The results demonstrate the practical utility of C and L in identifying the underlying pathological mechanisms of AD. Furthermore, all speech series exhibit small-world topology based on VG, with changes reflecting the brain system's pathology that impacts individuals' language skills.

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阿尔茨海默病自发语音信号中的小世界网络倾向：可见性图分析。

利用复杂网络方法描述基于语音时间序列的动态行为，可以为阿尔茨海默病（AD）潜在动态过程的功能提供基本见解。本研究通过小世界网络的抽象概念仔细观察了阿尔茨海默氏症语言的动态变化。引入自发性语音时间序列的可见性图（VG）作为区分健康个体和阿尔茨海默病患者的定量方法。通过分析小世界特征结构的聚类系数(C)高、平均路径长度(L)短的特点，分析了AD和健康受试者在三个阶段的动态语音模式。这些特征是基于k度计算的。结果表明C和L在识别AD的潜在病理机制方面的实际效用。此外，所有语音系列都表现出基于VG的小世界拓扑结构，其变化反映了影响个体语言技能的大脑系统病理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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