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Alzheimer's Disease: Overview01:26

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Updated: Feb 28, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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使用休息状态fNIRS复杂度图进行阿尔茨海默病阶段评估的可解释网络级生物标志物发现.

Min-Kyoung Kang1, Agatha Elisabet1, So-Hyeon Yoo2

  • 1School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea.

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概括

这项研究提出了一种新的基于图形的方法,用于静止状态fNIRS分析,识别轻度认知障碍和阿尔茨海默病的脑网络生物标志物. 该框架为神经退行变化提供了可重现和可解释的见解.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.功能近红外光谱学 (fNIRS) 是一种图形神经网络的神经网络休息状态的大脑网络信号的复杂性 信号的复杂性

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 数据科学数据科学数据科学

背景情况:

  • 静止状态功能近红外光谱 (fNIRS) 对阿尔茨海默病 (AD) 评估有价值.
  • 现有的fNIRS方法往往缺乏网络层面的洞察力和可重复性.
  • 协调的网络动态对于理解神经退行过程至关重要.

研究的目的:

  • 开发一个可复制和可解释的基于图形的静止状态fNIRS.框架.
  • 为了使网络层面的阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 的生物标志物发现.
  • 超越静态的道分析,转向动态的网络评估.

主要方法:

  • 作为主体级图表,表示静止状态前额头fNIRS信号.
  • 利用滑窗分析来捕获边缘计算的非线性信号复杂度波动.
  • 使用图形神经网络 (GNN) 进行网络模式识别和可解释性分析.

主要成果:

  • 复杂度波动图的方法超过了传统的基于振幅的连接.
  • 确定了统计学意义上的前额叶网络生物标志物,区分MCI与健康衰老 (p=0.001).
  • 在AD中观察到更异质的网络模式,MCI显示更一致的变化.

结论:

  • 为fNIRS分析建立了一个可重复和可解释的框架,重点关注复杂性动态.
  • 网络变化在MCI阶段被最一致地检测到,这表明它的意义.
  • 该框架显示了对神经退行性疾病的纵向监测和临床评估的潜力.