在中使用隐藏的 (半) 马尔科夫模型的DMN连接的动态时间模式
Dimitra Amoiridou1, Ioannis Kakkos1,2, Kostakis Gkiatis3
1Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9, Iroon Polytechniou Str, Zografos, 15780 Athens, Greece.
Cognitive neurodynamics
|November 17, 2025
概括
患者表现出改变的默认模式网络 (DMN) 连接动态. 隐藏的半马尔科夫模型揭示了DMN中长时间的低连接状态和减少的状态过渡灵活性,为提供了新的见解.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 医疗成像医学成像
背景情况:
- 是一种神经系统疾病,由反复发作来定义.
- 改变默认模式网络 (DMN) 连接性与病理学和发作传播有关.
- 了解DMN时间动态对于研究至关重要.
研究的目的:
- 调查患者与健康对照群的DMN功能连接的时间模式.
- 评估隐藏的半马尔科夫模型 (HSMMs) 在特征动态功能连接 (dFC) 变化的有效性.
- 确定特定的dFC指标,表明中DMN时间组织受损.
主要方法:
- 采用数据驱动模型,包括隐藏的马尔科夫模型 (HMM) 和HSMM与玛和鱼逗留分布,以分析dFC.
- 衍生动态指标:部分占用率,切换率和大脑状态的平均寿命.
- 在患者和健康对照者之间比较DMN连接状态的时间性质.
主要成果:
- 患者在低连接度的DMN状态中表现出长时间的停留时间,并且在状态过渡中灵活性降低.
- 与标准HMM相比,HSMM,特别是Gamma变种,在检测这些DMN连接性改变方面表现出更高的灵敏度.
- 特定群体的过渡模式表明中DMN状态的时间进展受到干扰.
结论:
- HSMM是捕捉功能性大脑状态的变化和中DMN动态的有效工具.
- 这些发现为中DMN的动态重组提供了新的见解.
- 这项研究强调了灵活的逗留建模在神经系统疾病的dFC分析中的重要性.
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