Is Granger causality a viable technique for analyzing fMRI data?
Is Granger causality a viable technique for analyzing fMRI data?
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DOI：
10.1371/journal.pone.0067428
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Ding M
中科院分区：
文献类型：
--
作者：
Wen X;Rangarajan G;Ding M
多变量神经数据为评估大脑网络中的相互作用提供了基础。在众多的连通性指标中，格兰杰因果关系（GC）已被证明是统计直观，易于实施，并产生有意义的结果。虽然它的应用功能磁共振成像（fMRI）的数据越来越多，已经确定了几个因素，似乎阻碍其神经的可解释性：（a）在不同的大脑区域，（B）低采样率的血流动力学反应功能（HRF）的潜伏期差异，（c）噪声。认识到在基础和临床神经科学中，通常是因变量的变化（例如，GC）之间的实验条件和之间的正常和病理是感兴趣的，我们解决的问题，是否存在系统的GC之间的关系，在功能磁共振成像水平和在神经水平。将模拟神经信号与典型的HRF进行卷积，下采样，并添加噪声以生成模拟的fMRI数据。随着模型中耦合参数的变化，计算了fMRI GC和神经GC，并检验了它们之间的关系。发现了三个主要结果：（1）HRF卷积后的GC是神经GC的单调递增函数;（2）当使用真实的fMRI时间分辨率和噪声水平时，这种单调性可以可靠地检测为正相关;（3）尽管由于HRF潜伏期差异的存在，单调性的可检测性下降，但在校正潜伏期差异后，可检测性基本恢复。这些结果表明，格兰杰因果关系是一个可行的技术分析功能磁共振成像数据时，适当的问题制定。
Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC) has proven to be statistically intuitive, easy to implement, and generate meaningful results. Although its application to functional MRI (fMRI) data is increasing, several factors have been identified that appear to hinder its neural interpretability: (a) latency differences in hemodynamic response function (HRF) across different brain regions, (b) low-sampling rates, and (c) noise. Recognizing that in basic and clinical neuroscience, it is often the change of a dependent variable (e.g., GC) between experimental conditions and between normal and pathology that is of interest, we address the question of whether there exist systematic relationships between GC at the fMRI level and that at the neural level. Simulated neural signals were convolved with a canonical HRF, down-sampled, and noise-added to generate simulated fMRI data. As the coupling parameters in the model were varied, fMRI GC and neural GC were calculated, and their relationship examined. Three main results were found: (1) GC following HRF convolution is a monotonically increasing function of neural GC; (2) this monotonicity can be reliably detected as a positive correlation when realistic fMRI temporal resolution and noise level were used; and (3) although the detectability of monotonicity declined due to the presence of HRF latency differences, substantial recovery of detectability occurred after correcting for latency differences. These results suggest that Granger causality is a viable technique for analyzing fMRI data when the questions are appropriately formulated.
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DOI：
10.1523/jneurosci.1776-08.2008
发表时间：
2008-10-01
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
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影响因子：
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DOI：
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发表时间：
2004-06-29
影响因子：
11.1
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通讯作者：
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影响因子：
5.7
作者：
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DOI：
10.1016/s0926-6410(01)00037-4
发表时间：
2001-08-01
期刊：
COGNITIVE BRAIN RESEARCH
影响因子：
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作者：
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通讯作者：
Papageorgiou, C