Dynamic Bayesian network modeling of fMRI: A comparison of group-analysis methods
Dynamic Bayesian network modeling of fMRI: A comparison of group-analysis methods
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DOI：
10.1016/j.neuroimage.2008.01.068
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发表时间：
2008-06-01
期刊：
影响因子：
5.7
通讯作者：
McKeown, Martin J.
中科院分区：
文献类型：
--
作者：
Li, Junning;Wang, Z. Jane;McKeown, Martin J.
贝叶斯网络（BN）建模最近被引入作为一种工具，用于确定脑区域之间的依赖性，从功能磁共振成像（fMRI）数据。然而，迄今为止的研究还没有探索最佳的方式，有意义地结合个别确定的BN模型，使群体推理。我们对比了三种主要方法的结果：“虚拟典型主题”（VTS）方法，该方法将组数据集中或平均，就好像它们是从单个假设的虚拟典型主题中采样的一样;“个体结构”（IS）方法，为每个主题学习单独的BN，然后在各个结构中找到共性，以及“共同结构”（CS）方法，其在每个主题的BN上施加相同的网络结构，但允许参数在主题之间不同。为了探索这三种方法的效果，我们将它们应用于fMRI研究，探索左旋多巴药物对10名帕金森病（PD）患者的运动效果，因为这种药物的深刻临床效果表明，PD受试者在药物治疗后的fMRI激活应该开始接近年龄匹配的对照组。我们发现，根据贝叶斯信息标准（BIC）评分，这些方法中没有一种通常上级其他方法，并且它们导致了相当不同的组水平结果。IS方法对左旋多巴药物对大脑连接的正常化作用更敏感。然而，对于更同质的对照人群，VTS方法更上级。群体分析方法应仔细选择，同时考虑统计和生物医学证据。(c)2008年爱思唯尔公司All rights reserved.
Bayesian network (BN) modeling has recently been introduced as a tool for determining the dependencies between brain regions from functional-magnetic-resonance-imaging (fMRI) data. However, studies to date have yet to explore the optimum way for meaningfully combining individually determined BN models to make group inferences. We contrasted the results from three broad approaches: the "virtual-typical-subject" (VTS) approach which pools or averages group data as if they are sampled from a single, hypothetical virtual typical subject; the "individual-structure" (IS) approach that learns a separate BN for each subject, and then finds commonality across the individual structures, and the "common-structure" (CS) approach that imposes the same network structure on the BN of every subject, but allows the parameters to differ across subjects. To explore the effects of these three approaches, we applied them to an fMRI study exploring the motor effect of L-dopa medication on ten subjects with Parkinson's disease (PD), as the profound clinical effects of this medication suggest that fMRI activation in PD subjects after medication should start approaching that of age-matched controls. We found that none of these approaches is generally superior over the others, according to Bayesian-information-criterion (BIC) scores, and that they led to considerably different group-level results. The IS approach was more sensitive to the normalization effect of the L-dopa medication on brain connectivity. However, for the more homogeneous control population, the VTS approach was superior. Group-analysis approaches should be selected carefully with consideration of both statistical and biomedical evidence. (c) 2008 Elsevier Inc. All rights reserved.