Bayesian deconvolution fMRI data using bilinear dynamical systems
Bayesian deconvolution fMRI data using bilinear dynamical systems
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DOI：
10.1016/j.neuroimage.2008.05.052
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发表时间：
2008-10-01
期刊：
影响因子：
5.7
通讯作者：
Woolrich, Mark
中科院分区：
文献类型：
--
作者：
Makni, Salima;Beckmann, Christian;Woolrich, Mark
Penny等人[Penny，W.，加赫拉马尼，Z.，Friston，K. J. 2005年。双线性动力系统哲学翻译长索克B生物科学360（1457）983-993]，线性动态系统（LDS）的一个特殊情况被用于对功能性MRI中BOLD响应的动态行为建模。这种状态空间模型称为双线性动力系统（BDS），用于对fMRI时间序列进行去卷积，以估计由实验范式的不同刺激引起的神经元反应。使用由Ghahramani和欣顿提出的期望最大化（EM）算法来估计BIDS模型参数[Ghahramani，Z.，欣顿，通用电气公司1996.线性动态系统的参数估计。多伦多大学计算机科学系技术报告]。在本文中，我们介绍了修改的BDS模型，以明确建模的空间变化的血液动力学反应功能（HRF）在大脑中使用的非参数方法。虽然在Penny等人（Penny，W.，加赫拉马尼，Z.，Friston，K. J. 2005年。双线性动力系统哲学翻译Soc. Lond. B生物科学360（1457）983-9931中，神经元激活和fMRI信号之间的关系被公式化为使用基函数（通常为两个或三个）的具有核展开的一阶卷积，在本文中，我们主张支持空间自适应GLM，其中执行HRF的局部非参数估计。此外，为了克服通常与简单EM估计相关的过拟合问题，我们提出了一个完整的变分贝叶斯（VB）解决方案来推断BDS模型参数。我们证明了我们的模型，这是能够估计的神经元活动和血液动力学反应功能，在每个体素的大脑的有用性。我们首先研究这种方法的行为时，适用于不同的时间和噪声特征的模拟数据。作为一个例子，我们将展示如何使用这种方法来提高从独立成分分析（伊卡）分析fMRI数据的估计的可解释性。我们最后证明了它的使用石油真实的功能磁共振成像数据在一个切片的大脑。(C)2008年爱思唯尔公司All rights reserved.
Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993], a particular case of the Linear Dynamical Systems (LDSs) was used to model the dynamic behavior of the BOLD response in functional MRI. This state-space model called bilinear, dynamical system (BDS), is used to deconvolve the fMRI time series in order to estimate the neuronal response induced by the different stimuli Of the experimental paradigm. The BIDS model parameters are estimated using an expectation-maximization (EM) algorithm proposed by Ghahramani and Hinton [Ghahramani, Z., Hinton, G.E. 1996. Parameter Estimation for Linear Dynamical Systems. Technical Report, Department of Computer Science, University of Toronto]. In this paper we introduce modifications to the BDS model in order to explicitly model the spatial variations of the haemodynamic response function (HRF) in the brain using a non-parametric approach. While in Penny et al. (Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-9931 the relationship between neuronal activation and fMRI signals is formulated as a first-order convolution with a kernel expansion using basis functions (typically two or three), in this paper, we argue in favor of a spatially adaptive GLM in which a local non-parametric estimation of the HRF is performed. Furthermore, in order to overcome the overfitting problem typically associated with simple EM estimates, we propose a full variational Bayes (VB) solution to infer the BDS model parameters. We demonstrate the usefulness of our model which is able to estimate both the neuronal activity and the haemodynamic response function in every voxel of the brain. We first examine the behavior of this approach when applied to simulated data with different temporal and noise features. As an example we will show how this method can be used to improve interpretability of estimates from an independent component analysis (ICA) analysis of fMRI data. We finally demonstrate its use oil real fMRI data in one slice of the brain. (C) 2008 Elsevier Inc. All rights reserved.