通过从驱动方法推导预测追踪回归来导出自我调节平原的有效性和可靠性
Joel S Burma1,2,3,4,5,6,7, James K Griffiths1,8, Jonathan D Smirl1,2,3,4,5,6,7
1Cerebrovascular Concussion Lab, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
Physiological reports
|January 23, 2024
概括
对振荡下体负压 (OLBNP) 和立姿势 (SSM) 的投影追踪回归 (PPR) 分析显示,评估大脑自我调节的可靠性和构造有效性不佳. 这些方法并没有始终显示出明确的自我调节平原.
科学领域:
- 生理学 生理学 生理学
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
背景情况:
- 大脑自调节是一个复杂的生理过程,对于维持稳定的脑血流至关重要.
- 由于压力-流量关系的复杂性,量化大脑自我调节具有挑战性.
- 预测追踪回归 (PPR) 是一种用于分析复杂数据模式的统计方法.
研究的目的:
- 为了比较PPR的构造有效性和日间可靠性,应用于振荡式下体负压 (OLBNP) 和式站立机动 (SSM).
- 评估PPR在识别大脑血流中的自我调节高原方面的有效性.
- 在不同的测试会话中评估PPR衍生自调节参数的可靠性.
主要方法:
- 十九名参与者在两个单独的访问中接受了0.05和0.10 Hz的OLBNP和SSMs.
- 使用PPR推导自主调节高原,分析点估计和整个心脏周期的数据.
- 评估日间可靠性是使用类内相关系数 (ICC),布兰德-阿尔特曼图表,变化系数 (CoV) 和最小实差.
主要成果:
- 预期的自我调节曲线与明显的上升和下降斜率仅在约23%的数据中观察到.
- 日间可靠性差到好,Cov估计在50%到70%之间,并且非常宽的95%的协议极限 (LOA).
- 来自SSM的高原比OLBNPs更大,效果大小中等至大,但PPR分析并不总是产生明确的中央高原.
结论:
- 对OLBNP和SSM的PPR分析表明,用于评估大脑自我调节,结构有效性有限,日间可靠性差.
- 目前的发现表明,PPR可能无法可靠地引起明确的自我调节高原,突出显示了量化这种复杂的生理系统所面临的挑战.
- 需要进一步的研究来完善使用动态机动和先进的分析技术准确评估大脑自我调节的方法.
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