Saad Ziad S, DeYoe Edgar A, Ropella Kristina M
Department of Biomedical Engineering, Marquette University, Milwaukee, WI, USA.
Neuroimage. 2003 Feb;18(2):494-504. doi: 10.1016/s1053-8119(02)00024-1.
We present an efficient algorithm using the Hilbert Transform for estimating the delay of the BOLD response to neuronal stimulation. With minimal additional computations, the algorithm estimates parameters generated in the widely used cross-correlation method and simplifies the interpolation required to estimate the response delay from the cross-correlation function. We examined errors in the Hilbert-based delay estimate associated with the use of DFT on short-duration discrete signals and proposed a method for minimizing these errors. Furthermore, we compared the delay estimates obtained with the Hilbert method to those obtained using the onset of the BOLD response. The Hilbert method resulted in less variance in the delay estimate despite the potential for higher variability in the latter part of the BOLD response. This improved delay estimate was attributed to the reduced sensitivity of the Hilbert method to noise contamination compared to the onset method.
我们提出了一种使用希尔伯特变换的高效算法，用于估计血氧水平依赖（BOLD）响应相对于神经元刺激的延迟。通过最少的额外计算，该算法估计了广泛使用的互相关方法中生成的参数，并简化了从互相关函数估计响应延迟所需的插值。我们研究了在短持续时间离散信号上使用离散傅里叶变换（DFT）时基于希尔伯特的延迟估计中的误差，并提出了一种将这些误差最小化的方法。此外，我们将用希尔伯特方法获得的延迟估计与使用BOLD响应起始点获得的延迟估计进行了比较。尽管BOLD响应后期可能存在较高的变异性，但希尔伯特方法在延迟估计中产生的方差较小。这种改进的延迟估计归因于与起始点方法相比，希尔伯特方法对噪声污染的敏感性较低。
