Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies.
Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies.
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DOI：
10.1016/j.neuroimage.2016.12.045
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发表时间：
2017-02-15
期刊：
影响因子：
5.7
通讯作者：
Lee JH
中科院分区：
文献类型：
--
作者：
Liu J;Duffy BA;Bernal-Casas D;Fang Z;Lee JH
大量的fMRI研究表明，诱发BOLD反应的时间动力学可以是高度异质性的。在统计分析中未能对异质反应进行建模可能会导致信号检测和表征中的重大错误，并改变神经生物学解释。然而，迄今为止，尚不清楚的是，在大量的选项，哪些方法是强大的对变化的时间动态的BOLD响应在块设计研究。在这里，我们使用具有异质BOLD响应的啮齿动物光遗传学fMRI数据和由实验数据指导的模拟作为一种手段来研究不同分析方法对异质BOLD响应的性能。评价是在一般线性模型（GLM）框架内进行的，包括标准基组以及独立成分分析（伊卡）。分析表明，在存在异质BOLD响应的情况下，传统使用的具有典型基集的GLM会导致BOLD响应的检测和表征出现相当大的错误。我们的研究结果表明，第三和第四阶伽玛基组，第七至第九阶有限脉冲响应（FIR）基组，第五至第九阶B样条基组，和第二至第五阶傅立叶基组是最佳的检测和表征之间的良好平衡，而第一阶傅立叶基组（相干分析）在我们早期的研究中使用显示出良好的检测能力。伊卡大多具有良好的检测和表征能力，但检测到大量的虚假激活与控制功能磁共振成像数据。
A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods’ performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data.