The MRI Unit & Division of Child and Adolescent Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA.
J Neurosci Methods. 2012 Mar 30;205(1):28-35. doi: 10.1016/j.jneumeth.2011.12.016. Epub 2011 Dec 29.
Functional Magnetic Resonance Imaging (fMRI), measuring Blood Oxygen Level-Dependent (BOLD), is a widely used tool to reveal spatiotemporal pattern of neural activity in human brain. Standard analysis of fMRI data relies on a general linear model and the model is constructed by convolving the task stimuli with a hypothesized hemodynamic response function (HRF). To capture possible phase shifts in the observed BOLD response, the informed basis functions including canonical HRF and its temporal derivative, have been proposed to extend the hypothesized hemodynamic response in order to obtain a good fitting model. Different t contrasts are constructed from the estimated model parameters for detecting the neural activity between different task conditions. However, the estimated model parameters corresponding to the orthogonal basis functions have different physical meanings. It remains unclear how to combine the neural features detected by the two basis functions and construct t contrasts for further analyses. In this paper, we have proposed a novel method for representing multiple basis functions in complex domain to model the task-driven fMRI data. Using this method, we can treat each pair of model parameters, corresponding respectively to canonical HRF and its temporal derivative, as one complex number for each task condition. Using the specific rule we have defined, we can conveniently perform arithmetical operations on the estimated model parameters and generate different t contrasts. We validate this method using the fMRI data acquired from twenty-two healthy participants who underwent an auditory stimulation task.
功能磁共振成像（fMRI）通过测量血氧水平依赖（BOLD），是一种广泛用于揭示人类大脑神经活动时空模式的工具。功能磁共振成像数据的标准分析依赖于广义线性模型，该模型通过卷积假设的血液动力学响应函数（HRF）构建任务刺激。为了捕捉观察到的 BOLD 响应中的可能相位移动，已经提出了包括规范 HRF 及其时间导数的知情基础函数，以扩展假设的血液动力学响应，从而获得良好的拟合模型。从估计的模型参数中构建不同的 t 对比，以检测不同任务条件下的神经活动。然而，对应于正交基函数的估计模型参数具有不同的物理意义。如何结合两个基函数检测到的神经特征并构建 t 对比以进行进一步分析仍不清楚。在本文中，我们提出了一种新的方法，用于在复域中表示多个基函数，以对任务驱动的 fMRI 数据进行建模。使用这种方法，我们可以将每对对应于规范 HRF 和其时间导数的模型参数分别视为每个任务条件的一个复数。使用我们定义的特定规则，我们可以方便地对估计的模型参数进行算术运算，并生成不同的 t 对比。我们使用从接受听觉刺激任务的 22 名健康参与者获得的 fMRI 数据验证了该方法。
