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首页> 外文期刊>NeuroImage >Dynamic Bayesian network modeling of fMRI: a comparison of group-analysis methods.
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Dynamic Bayesian network modeling of fMRI: a comparison of group-analysis methods.

机译：功能磁共振成像的动态贝叶斯网络建模：组分析方法的比较。

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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.
机译：最近引入了贝叶斯网络（BN）建模作为一种从功能磁共振成像（fMRI）数据确定大脑区域之间的依存关系的工具。但是，迄今为止的研究尚未探索将有意义的组合单独确定的BN模型进行小组推理的最佳方法。我们对三种主要方法的结果进行了对比：“虚拟典型对象”（VTS）方法，将组数据汇总或平均，就好像它们是从单个假设的虚拟典型对象中采样的; “个体结构”（IS）方法为每个主题学习一个单独的BN，然后在各个结构之间找到共性，而“公共结构”（CS）方法将相同的网络结构强加给每个人的BN主题，但允许参数在主题之间有所不同。为了探索这三种方法的效果，我们将它们应用于功能磁共振成像研究，探讨左旋多巴药物对十名帕金森病（PD）受试者的运动作用，因为这种药物的深远临床效果表明功能磁共振成像在PD受试者中的激活用药后应开始接近与年龄匹配的对照者。我们发现，根据贝叶斯信息准则（BIC）分数，这些方法通常都不比其他方法优越，并且它们导致了不同的小组级结果。 IS方法对左旋多巴药物对大脑连接的正常化作用更为敏感。但是，对于更均一的对照人群，VTS方法更好。应同时考虑统计和生物医学证据，仔细选择组分析方法。

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