Chang Catie, Cunningham John P, Glover Gary H
Department of Electrical Engineering, Stanford University, Lucas MRI/S Center, Stanford, CA 94305-5488, USA.
Neuroimage. 2009 Feb 1;44(3):857-69. doi: 10.1016/j.neuroimage.2008.09.029. Epub 2008 Oct 7.
It has previously been shown that low-frequency fluctuations in both respiratory volume and cardiac rate can induce changes in the blood-oxygen level dependent (BOLD) signal. Such physiological noise can obscure the detection of neural activation using fMRI, and it is therefore important to model and remove the effects of this noise. While a hemodynamic response function relating respiratory variation (RV) and the BOLD signal has been described [Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008b. The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40, 644-654.], no such mapping for heart rate (HR) has been proposed. In the current study, the effects of RV and HR are simultaneously deconvolved from resting state fMRI. It is demonstrated that a convolution model including RV and HR can explain significantly more variance in gray matter BOLD signal than a model that includes RV alone, and an average HR response function is proposed that well characterizes our subject population. It is observed that the voxel-wise morphology of the deconvolved RV responses is preserved when HR is included in the model, and that its form is adequately modeled by Birn et al.'s previously-described respiration response function. Furthermore, it is shown that modeling out RV and HR can significantly alter functional connectivity maps of the default-mode network.
先前的研究表明，呼吸量和心率的低频波动均可诱发血氧水平依赖（BOLD）信号的变化。这种生理噪声会干扰使用功能磁共振成像（fMRI）检测神经激活，因此对这种噪声的影响进行建模并消除非常重要。虽然已经描述了一种将呼吸变化（RV）与BOLD信号相关联的血液动力学响应函数[Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008b. 呼吸响应函数：与呼吸变化相关的fMRI信号波动的时间动态。《神经影像学》40, 644 - 654。]，但尚未提出针对心率（HR）的此类映射。在当前研究中，从静息态fMRI中同时解卷积出RV和HR的影响。结果表明，与仅包含RV的模型相比，包含RV和HR的卷积模型能够解释灰质BOLD信号中显著更多的方差，并提出了一个平均HR响应函数，该函数很好地表征了我们的受试者群体。研究发现，当模型中包含HR时，解卷积后的RV响应的体素形态得以保留，并且其形式可以由Birn等人先前描述的呼吸响应函数充分建模。此外，研究表明，对RV和HR进行建模消除可以显著改变默认模式网络的功能连接图谱。
