大脑状态的动态在眼睛打开和眼睛关闭之间有所不同,眼睛休息.
Brandon T Ingram1, Stephen D Mayhew2, Andrew P Bagshaw1
1Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
Human brain mapping
|July 11, 2024
概括
这项研究使用隐藏的马尔科夫模型 (HMM) 与同步的EEG-fMRI数据来分析休息期间的大脑状态. 多模式分析改善了空间定义,并揭示了不同大脑状态的类似时间动态.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 人类大脑表现出复杂的时空活动模式,称为大脑状态,即使在静止状态.
- 以前的大脑状态分析依赖于单模数据,限制状态定义和跨模理解.
- 同时的脑电图功能磁共振成像 (EEG-fMRI) 提供了一种多模式的方法来捕捉大脑动态.
研究的目的:
- 将隐藏的马尔科夫模型 (HMM) 应用于并发的EEG-fMRI静止状态数据 (眼睛打开/关闭).
- 从单独的EEG和fMRI分析中确定的大脑状态的空间和时间关系.
- 评估fMRI数据是否可以增强EEG衍生的大脑状态的空间定义.
主要方法:
- 隐藏的马尔科夫模型 (HMM) 应用于眼睛开放和闭眼休息期间的并发EEG-fMRI数据.
- 在EEG和fMRI数据上训练单独的HMM模型.
- 一般线性模型 (GLM) 用于识别EEG状态的BOLD相关值.
- 在状态时间课程上进行的滑动窗口分析,以评估时间动态.
主要成果:
- 脑电图和fMRI HMM 模型都区分了眼睛开放和闭眼的休息条件.
- fMRI模型发现视觉和注意力网络的变化;EEG模型检测到α功率增加.
- fMRI数据成功推断了EEG状态的空间特性,产生了正规的alpha-BOLD相关性.
- 移动窗口分析揭示了不同的状态占用动态和跨模态时间相关性.
结论:
- HMM对于多模式大脑状态分析是有效的.
- 同时的EEG-fMRI提供了一个更全面的定义的大脑状态.
- 在不同模式中识别的脑状态表现出类似的时间动态.
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