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相关概念视频

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

556
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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相关实验视频

Updated: Jul 15, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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改进了动态功能连接性估计,采用了交替隐藏的马尔科夫模型.

Zhiying Long1, Xuanping Liu2, Yantong Niu2

  • 1School of Artificial Intelligence, Beijing Normal University, Beijing, 100875 China.

Cognitive neurodynamics
|October 3, 2023
PubMed
概括

一种新的交替隐藏马尔科夫模型 (aHMM) 方法改善了fMRI数据中的动态功能连接 (DFC) 分析. aHMM提供了更好的稳健性,并检测了脑小血管疾病和认知障碍患者的微妙大脑状态差异.

关键词:
大脑状态 大脑状态动态 动态 动态 动态功能连接性的功能连接性.这是一个HMMMM.功能磁力共振成像 (fMRI) 是一种

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科学领域:

  • 神经成像是一种神经成像.
  • 计算神经科学是一种神经科学.
  • 生物统计学 生物统计学

背景情况:

  • 动态功能连接 (DFC) 分析揭示了使用fMRI的时间变化的大脑相互作用.
  • 滑动窗口 (SW) 和隐藏的马尔科夫模型 (HMM) 是常见的DFC方法,但SW具有有限的时间分辨率,HMM可以超过fMRI数据.

研究的目的:

  • 引入一个交替的HMM (aHMM) 进行可靠的DFC估计.
  • 使用模拟和真实fMRI数据将aHMM与SW和HMM进行比较.
  • 研究大脑小血管疾病与失忆症和轻度认知障碍 (CSVD-aMCI) 的DFC变化.

主要方法:

  • 提出了一个交替的HMM (aHMM),初始化HMM与SW连接,并使用一个交替的程序来减少参数.
  • 在模拟和人类结合体项目的fMRI数据上验证了aHMM.
  • 将aHMM应用于CSVD-aMCI患者和对照组的fMRI数据.

主要成果:

  • 与SW和HMM相比,aHMM在噪声,参数数量和样本大小方面表现出优越的稳定性.
  • CSVD-aMCI患者表现出大脑状态的改变,在弱连接状态中花费更多时间,在强连接状态中花费更少时间.
  • 与对照组相比,CSVD-aMCI患者的连接幅度较低,波动较高,HMM或SW无法检测到差异.

结论:

  • aHMM是一种比SW和HMM更稳健和敏感的DFC分析方法.
  • aHMM有效地识别了DFC时间属性和连接性波动的跨组差异.
  • 这些发现凸显了aHMM在神经系统疾病 (CSVD-aMCI) 等神经系统疾病中临床应用的潜力.