在杂的动态网络中区分不同的透模式,并应用于发作
Xiaojing Zhu1, Heather Shappell2, Mark A Kramer1
1Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America.
PLoS computational biology
|June 16, 2023
概括
发作涉及类似于透的脑网络变化. 这项研究引入了新的模型来识别发作中的不同透类型,可能引导个性化治疗.
科学领域:
- 临床神经科学 临床神经科学
- 复杂系统科学 复杂系统科学
- 计算神经科学是一种神经科学.
背景情况:
- 发作与突然,广泛的合大脑活动有关,形成功能网络.
- 抓获期间的网络变化类似于透,这是复杂网络中巨型组件出现的现象.
- 现有的透模型往往忽视了现实世界大脑网络的动态和杂性质.
研究的目的:
- 开发新的统计模型,以描述在发作期间在动态,杂的大脑网络中的透.
- 区分在人类发作中发生的不同透模式.
- 建立一个框架来推断潜在的透机制,并告知治疗策略.
主要方法:
- 开发随机图形隐藏的马尔科夫模型 (RG-HMMs) 用于杂的,不断发展的网络.
- 从亚样本网络数据进行参数估计的预期最大化 (EM) 算法的实现.
- 应用假设测试来区分扣押数据中的透机制.
主要成果:
- 证明了在人类发作期间可以发生不同类型的透现象.
- 成功地应用了RG-HMM和EM算法来分析动态抓获网络.
- 确定了不同的透模式,表明了不同的底层网络动态.
结论:
- 在发作中观察到的透类型可能会为机制提供关键的见解.
- 这一框架为开发针对的量身定制治疗策略提供了潜力.
- 进步了对神经系统疾病 (如) 中复杂网络动态的理解.
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