Bayesian spatiotemporal inference in functional magnetic resonance imaging
Bayesian spatiotemporal inference in functional magnetic resonance imaging
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DOI：
10.1111/j.0006-341x.2001.00554.x
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发表时间：
2001-06-01
期刊：
影响因子：
1.9
通讯作者：
Fahrmeir, L
中科院分区：
文献类型：
--
作者：
Gössl, C;Auer, DP;Fahrmeir, L
利用功能磁共振成像（fMRI）对人脑进行定位是认知和临床神经科学领域的一个新兴领域。目前检测大脑激活区域的技术主要分为两个步骤。第一，传统的相关方法。回归和时间序列分析用于通过fMRI信号时间过程与所呈现刺激的参考函数的单独的逐像素比较来评估激活。如果有的话，在单独的第二步骤中考虑由相邻像素之间的相关性引起的空间方面。本文的目的是提出分层贝叶斯方法，允许同时将像素之间的时间和空间依赖关系直接在模型制定。出于计算可行性的原因，模型必须相对简约，而不能过度简化。我们介绍了参数和半参数的空间和时空模型，证明适当的，并说明其适用于视觉功能磁共振成像数据的性能。
Mapping of the human brain by means of functional magnetic resonance imaging (fMRI) is an emerging held in cognitive and clinical neuroscience. Current techniques to detect activated areas of the brain mostly proceed in two steps. First, conventional methods of correlation. regression, and time series analysis are used to assess activation by a separate, pixelwise comparison of the fMRI signal time courses to the reference function of a presented stimulus. Spatial aspects caused by correlations between neighboring pixels are considered in a separate second step, if at all. The aim of this article is to present hierarchical Bayesian approaches that allow one to simultaneously incorporate temporal and spatial dependencies between pixels directly in the model formulation. For reasons of computational feasibility, models have to be comparatively parsimonious, without oversimplifying. We introduce parametric and semiparametric spatial and spatiotemporal models that proved appropriate and illustrate their performance applied to visual fMRI data.