系统性低频振荡对fMRI识别能力的影响 - - 对拒绝和指纹检测的影响
概括
全球信号回归 (GSR) 有效地消除了fMRI数据中的系统低频振荡 (sLFOs),改善了功能连接分析. 部分全局信号回归 (pGSR) 在需要不那么激进的无声化时提供了一个替代方案.
科学领域:
- 神经成像是一种神经成像.
- 功能磁共振成像 (fMRI) 是一种功能性磁共振成像技术.
- 信号处理 信号处理
背景情况:
- 众所周知,全身低频振荡 (sLFOs) 在fMRI中会污染血液氧气水平依赖 (BOLD) 信号.
- 这种污染可能会扰乱神经活动和功能连接的准确评估.
- 有效的消毒策略对于可靠的fMRI分析至关重要.
研究的目的:
- 评估sLFO校正对fMRI中基于功能连接的识别能力的影响.
- 为了比较全球信号回归 (GSR) 和部分全球信号回归 (pGSR) 在隔离sLFO的有效性,同时保持神经低频振荡 (nLFO).
- 为了确定fMRI指纹和连接性研究的最佳denoising策略.
主要方法:
- 利用了来自人类结合体项目的数据.
- 实施和比较全球信号回归 (GSR) 和部分全球信号回归 (pGSR) 方法.
- 通过基于功能连接的可识别性,评估每个方法隔离sLFO和保存神经低频振荡 (nLFO) 的能力.
主要成果:
- 与零模型和pGSR相比,GSR的识别能力明显更高.
- 这表明GSR提供了优越的sLFO清除.
- 发现识别能力主要是由神经元件驱动的,而不是可测量的噪音.
结论:
- 全球信号回归 (GSR) 是一种有效的策略,可以在fMRI中消除sLFO.
- GSR提高了fMRI指纹和连接性研究的可靠性.
- 局部全球信号回归 (pGSR) 是一种可行的替代方案,可以在外围信号估计准确时进行不那么激进的无声化.
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