Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Neuroimage. 2012 Feb 15;59(4):3128-38. doi: 10.1016/j.neuroimage.2011.11.028. Epub 2011 Nov 17.
Near-infrared spectroscopy (NIRS) signals have been shown to correlate with resting-state BOLD-fMRI data across the whole brain volume, particularly at frequencies below 0.1Hz. While the physiological origins of this correlation remain unclear, its existence may have a practical application in minimizing the background physiological noise present in BOLD-fMRI recordings. We performed simultaneous, resting-state fMRI and 28-channel NIRS in seven adult subjects in order to assess the utility of NIRS signals in the regression of physiological noise from fMRI data. We calculated the variance of the residual error in a general linear model of the baseline fMRI signal, and the reduction of this variance achieved by including NIRS signals in the model. In addition, we introduced a sequence of simulated hemodynamic response functions (HRFs) into the resting-state fMRI data of each subject in order to quantify the effectiveness of NIRS signals in optimizing the recovery of that HRF. For comparison, these calculations were also performed using a pulse and respiration RETROICOR model. Our results show that the use of 10 or more NIRS channels can reduce variance in the residual error by as much as 36% on average across the whole cortex. However the same number of low-pass filtered white noise regressors is shown to produce a reduction of 19%. The RETROICOR model obtained a variance reduction of 6.4%. Our HRF simulation showed that the mean-squared error (MSE) between the recovered and true HRFs is reduced by 21% on average when 10 NIRS channels are applied and by introducing an optimized time lag between the NIRS and fMRI time series, a single NIRS channel can provide an average MSE reduction of 14%. The RETROICOR model did not provide a significant change in MSE. By each of the metrics calculated, NIRS recording is shown to be of significant benefit to the regression of low-frequency physiological noise from fMRI data.
近红外光谱 (NIRS) 信号已被证明与全脑体积的静息状态 BOLD-fMRI 数据相关，特别是在低于 0.1Hz 的频率下。虽然这种相关性的生理起源尚不清楚，但它的存在可能在最小化 BOLD-fMRI 记录中存在的背景生理噪声方面具有实际应用。我们在七名成年受试者中同时进行了静息状态 fMRI 和 28 通道 NIRS，以评估 NIRS 信号在从 fMRI 数据中回归生理噪声中的效用。我们计算了基线 fMRI 信号的一般线性模型中残差误差的方差，以及通过将 NIRS 信号纳入模型来减少该方差。此外，我们向每位受试者的静息状态 fMRI 数据中引入了一系列模拟的血液动力学响应函数 (HRF)，以量化 NIRS 信号在优化该 HRF 恢复方面的有效性。为了比较，还使用脉冲和呼吸 RETROICOR 模型进行了这些计算。我们的结果表明，使用 10 个或更多的 NIRS 通道可以使整个皮层的残差误差方差平均降低 36%。然而，相同数量的低通滤波白噪声回归器显示出降低 19%的效果。RETROICOR 模型获得了 6.4%的方差降低。我们的 HRF 模拟表明，当应用 10 个 NIRS 通道并在 NIRS 和 fMRI 时间序列之间引入优化的时滞时，恢复和真实 HRF 之间的均方误差 (MSE) 平均降低 21%，单个 NIRS 通道可以提供平均 MSE 降低 14%。RETROICOR 模型没有提供 MSE 的显著变化。通过计算的每个指标，NIRS 记录都被证明对从 fMRI 数据中回归低频生理噪声有显著的益处。
