将静态功能连接分析的不同运动校正方法与功能近红外光谱数据进行比较
Costanza Iester1, Laura Bonzano1,2, Monica Biggio1
1University of Genoa, Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Genoa, Italy.
功能近红外光谱学 (fNIRS) 中的运动器件需要量身定制的校正. 休息状态数据的最佳方法取决于文物类型和严重程度,在不同的污染水平下,特定的策略优于其他策略.
科学领域:
- 神经成像是一种神经成像.
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 运动工件在功能近红外光谱 (fNIRS) 数据采集中构成了重大挑战.
- 在静止状态的fNIRS数据中减轻运动工件的有效策略仍未得到充分探索.
研究的目的:
- 评估各种运动工件校正技术对静止状态fNIRS的功能连接分析的影响.
- 根据运动污染的类型和程度来确定最佳的校正方法.
主要方法:
- 利用半模拟的静止状态fNIRS数据集与受控的尖峰状和基线转移运动工件.
- 应用了15个不同的预处理管道,采用了不同的运动校正策略.
- 通过对集团级功能连接的三个定量指标评估管道性能.
主要成果:
- 多种校正方法在低污染水平下被证明是有效的.
- 管道的可靠性随着运动工件污染的增加而显著下降.
- 抛弃的预处理后处理对于基线移动最小的数据集是最佳的,而当两种文物类型都存在时,预处理的抛弃是优越的.
结论:
- 在fNIRS中运动工件校正的有效性高度依赖于工件的特定特征.
- 一个适合所有人的方法是不够的;为强大的休息状态功能连接分析,需要定制策略.
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