探索特定时间尺度的功能性大脑网络及其与衰老和认知表现的关联,在没有痴呆症的健康队列中
Wen-Xiang Tsai1, Shih-Jen Tsai2, Ching-Po Lin3
1Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
NeuroImage
|February 14, 2024
概括
探索大脑信号时间尺度揭示了内在的功能性大脑网络 (fFBNs) 比传统的血液氧气水平依赖 (BOLD) 网络对衰老和认知表现更敏感. 这种分解方法为fMRI研究提供了新的见解.
科学领域:
- 神经成像是一种神经成像.
- 认知神经科学 认知神经科学
- 系统神经科学 系统神经科学
背景情况:
- 功能性大脑网络 (FBNs) 对大脑功能至关重要,在fMRI中通常使用血液氧气水平依赖 (BOLD) 信号相关性来分析.
- 衰老和认知衰退与FBN变化有关,但BOLD信号受到各种生理因素的影响.
- 使用信号分解在不同的时间尺度上研究内在的BOLD信号组件尚未得到充分探索.
研究的目的:
- 为了区分内在FBN与传统的BOLD-FBN.
- 检查这些网络与健康成年人的年龄和认知表现的关联.
- 在FBN分析中评估信号分解的实用性.
主要方法:
- 分析了396名没有痴呆症的健康参与者 (20-85岁).
- 用集体实证模式分解,在各种时间尺度上将BOLD信号分解为内在信号.
- 用BOLD和内在信号构建FBN,并计算全球和本地效率.
主要成果:
- 传统的BOLD-FBNs的整体效率与年龄相关,具体的内在FBNs的贡献.
- 局部效率分析表明,内在FBN在识别与年龄和认知表现相关的大脑区域方面,比传统的BOLD-FBN更有效.
结论:
- 分析BOLD信号时间尺度对于构建FBN很重要.
- 特定的内在FBN与衰老和认知表现有关.
- 基于分解的FBN构建方法为未来的fMRI研究提供了宝贵的见解.
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