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Disambiguating the role of blood flow and global signal with partial information decomposition
NeuroImage ( IF 4.7 ) Pub Date : 2020-06-01 , DOI: 10.1016/j.neuroimage.2020.116699
Nigel Colenbier 1 , Frederik Van de Steen 1 , Lucina Q Uddin 2 , Russell A Poldrack 3 , Vince D Calhoun 4 , Daniele Marinazzo 1
Affiliation  

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.

中文翻译:

用部分信息分解消除血流和全局信号的​​作用

全局信号 (GS) 是静息状态功能磁共振成像 (rs-fMRI) 中无处不在的结构,与干扰相关,但根据定义包含大部分神经元信号。全局信号回归 (GSR) 有效地消除了生理噪声和其他伪影的影响,但同时它以不可预测的方式改变了相关模式。考虑到潜在的生理学(主要是血液到达时间)进行 GSR 已被证明是有益的。从这些观察中,我们的目标是:1)表征 GSR 对大型数据集中网络级功能连接的影响;2) 评估全球信号和船舶的互补作用;3) 使用局部信息分解的框架进一步研究全球信号和船舶的联合动态,以及它们各自对皮质区域动力学的影响。我们观察到 GSR 以非均匀方式影响连接组中的内在连接网络。此外,通过使用部分信息分解估计血流和全局信号的​​预测信息,我们观察到这两种信号在内部连接网络中以不同的数量存在。模拟表明,血液到达时间的差异可以在很大程度上解释这种现象,同时使用血流动力学和钙小鼠记录我们能够确认血管效应的存在,因为钙记录缺乏血流动力学信息。通过这些结果,我们确认了 GSR 的网络特定效应以及考虑血流以改进去噪方法的重要性。此外,
更新日期:2020-06-01
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