A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals
A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals
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DOI：
10.1016/j.neuroimage.2003.09.052
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发表时间：
2004-02-01
期刊：
影响因子：
5.7
通讯作者：
Kawashima, R
中科院分区：
文献类型：
--
作者：
Riera, JJ;Watanabe, J;Kawashima, R
在本文中，提出了一种新程序，该程序允许根据血氧水平依赖性（BOLD）反应来估计血流动力学方法的状态和参数。所提出的方法构成了最近提出的 Friston [Neuroimage 16 (2002) 513] 方法的替代方案，并且比它具有一些优点。该程序基于最近突破性的时间序列分析技术，在本例中，该技术已被用于表征功能磁共振成像 (fMRI) 中的血流动力学响应。这项工作代表了对现有使用非线性血液动力学模型进行系统识别的方法的根本改进，并且由于三个原因而很重要。首先，我们的模型包含生理噪声。以前的模型基于常微分方程，只允许噪声或误差进入观察级别。其次，通过使用创新方法和局部线性化滤波器，不仅可以估计参数，还可以估计产生响应的系统的基本状态。这些状态可以包括由神经元激活、脱氧血红蛋白、脑血流量和容量触发的血流诱导信号等。最后，径向基函数被引入作为参数模型来表示血流动力学方法中的任意时间输入序列，这对于理解与刺激间接相关的大脑区域至关重要。因此，第三，通过推断径向基参数，我们能够执行盲反卷积，这既允许重建最可能的血流动力学状态的动力学，也允许隐式重建实验诱导的导致这些状态变化的潜在突触动力学。从这项研究中，我们得出的结论是，尽管统计参数图 (SPM) 中使用了标准离散卷积方法，但 fMRI 分析中必须包含非线性 BOLD 现象和非特定输入时间序列 (C) 2003 Elsevier Inc. 保留所有权利。
In this paper, a new procedure is presented which allows the estimation of the states and parameters of the hemodynamic approach from blood oxygenation level dependent (BOLD) responses. The proposed method constitutes an alternative to the recently proposed Friston [Neuroimage 16 (2002) 513] method and has some advantages over it. The procedure is based on recent groundbreaking time series analysis techniques that have been, in this case, adopted to characterize hemodynamic responses in functional magnetic resonance imaging (fMRI). This work represents a fundamental improvement over existing approaches to system identification using nonlinear hemodynamic models and is important for three reasons. First, our model includes physiological noise. Previous models have been based upon ordinary differential equations that only allow for noise or error to enter at the level of observation. Secondly, by using the innovation method and the local linearization filter, not only the parameters, but also the underlying states of the system generating responses can be estimated. These states can include things like a flow-inducing signal triggered by neuronal activation, de-oxyhemoglobine, cerebral blood flow and volume. Finally, radial basis functions have been introduced as a parametric model to represent arbitrary temporal input sequences in the hemodynamic approach, which could be essential to understanding those brain areas indirectly related to the stimulus. Hence, thirdly, by inferring about the radial basis parameters, we are able to perform a blind deconvolution, which permits both the reconstruction of the dynamics of the most likely hemodynamic states and also, to implicitly reconstruct the underlying synaptic dynamics, induced experimentally, which caused these states variations. From this study, we conclude that in spite of the utility of the standard discrete convolution approach used in statistical parametric maps (SPM), nonlinear BOLD phenomena and unspecific input temporal sequences must be included in the fMRI analysis (C) 2003 Elsevier Inc. All rights reserved.