Scarpa F, Cutini S, Scatturin P, Dell'Acqua R, Sparacino G
Department of Developmental Psychology, University of Padova, Via Venezia 8, Padova 35131, Italy.
Opt Express. 2010 Dec 6;18(25):26550-68. doi: 10.1364/OE.18.026550.
Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that measures changes in oxy-hemoglobin (ΔHbO) and deoxy-hemoglobin (ΔHbR) concentration associated with brain activity. The signal acquired with fNIRS is naturally affected by disturbances engendering from ongoing physiological activity (e.g., cardiac, respiratory, Mayer wave) and random measurement noise. Despite its several drawbacks, the so-called conventional averaging (CA) is still widely used to estimate the hemodynamic response function (HRF) from noisy signal. One such drawback is related to the number of trials necessary to derive stable HRF functions adopting the CA approach, which must be substantial (N >> 50). In this work, a pre-processing procedure to remove artifacts followed by the application of a non-parametric Bayesian approach is proposed that capitalizes on a priori available knowledge about HRF and noise. Results with the proposed Bayesian approach were compared with CA and with a straightforward band-pass filtering approach. On simulated data, a five times lower estimation error on HRF was obtained with respect to that obtained by CA, and 2.5 times lower than that obtained by band pass filtering. On real data, the improvement achieved by the present method was attested by an increase in the contrast to noise ratio (CNR) and by a reduced variability in single trial estimation. An application of the present Bayesian approach is illustrated that was optimized to monitor changes in hemodynamic activity reflecting variations in visual short-term memory load in humans, which are notoriously hard to detect using functional magnetic resonance imaging (fMRI). In particular, statistical analyses of HRFs recorded during a memory task established with high reliability the crucial role of the intraparietal sulcus and the intra-occipital sulcus in posterior areas of the human brain in visual short-term memory maintenance.
功能近红外光谱技术（fNIRS）是一种神经成像技术，可测量与大脑活动相关的氧合血红蛋白（ΔHbO）和脱氧血红蛋白（ΔHbR）浓度的变化。用fNIRS采集的信号自然会受到持续生理活动（如心脏、呼吸、迈尔波）产生的干扰以及随机测量噪声的影响。尽管存在一些缺点，但所谓的传统平均法（CA）仍被广泛用于从噪声信号中估计血流动力学响应函数（HRF）。其中一个缺点与采用CA方法推导稳定HRF函数所需的试验次数有关，该次数必须足够多（N >> 50）。在这项工作中，我们提出了一种预处理程序来去除伪迹，然后应用非参数贝叶斯方法，该方法利用了关于HRF和噪声的先验可用知识。将所提出的贝叶斯方法的结果与CA以及直接的带通滤波方法进行了比较。在模拟数据上，相对于CA获得的估计误差，HRF的估计误差降低了五倍，比带通滤波获得的估计误差低2.5倍。在真实数据上，对比度噪声比（CNR）的增加和单次试验估计中变异性的降低证明了本方法所取得的改进。本文阐述了当前贝叶斯方法的一个应用，该应用经过优化，用于监测反映人类视觉短期记忆负荷变化的血流动力学活动变化，而使用功能磁共振成像（fMRI）很难检测到这些变化。特别是，对在记忆任务期间记录的HRF进行的统计分析高度可靠地确定了顶内沟和枕内沟在人类大脑后部区域的视觉短期记忆维持中的关键作用。
