A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches.
A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches.
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DOI：
10.1016/j.neuroimage.2012.03.068
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发表时间：
2012-07-16
期刊：
影响因子：
5.7
通讯作者：
Johnson, Sterling C.
中科院分区：
文献类型：
--
作者：
McLaren, Donald G.;Ries, Michele L.;Xu, Guofan;Johnson, Sterling C.
关键词：
功能磁共振成像（fMRI）允许人们使用心理生理相互作用（PPI）方法研究任务相关的区域反应和任务依赖的连通性分析。后者提供了额外的机会来了解大脑区域如何以任务依赖的方式相互作用。目前统计参数映射（SPM8）中PPI的实现主要用于评估两个任务条件之间的连通性差异，而在实践中，功能磁共振成像任务经常使用两个以上的条件。在这里，我们评估了一种广义形式的上下文相关PPI (gPPI; http://www.nitrc.org/projects/gppi)，它被配置为通过跨越整个实验空间，在同一PPI模型中自动适应两个以上的任务条件，与SPM8中的标准实现相比。这些比较是使用模拟和经验数据集进行的。在模拟数据集中，我们使用赤池信息准则（Akaike Information Criterion， AIC）将交互贝塔估计与其期望值和模型拟合进行比较。我们发现gPPI中的相互作用β估计对不同的模拟数据模型具有鲁棒性，与预期β值没有差异，并且比使用标准PPI （sPPI）方法具有更好的模型拟合。在实证数据集中，我们比较了gPPI方法与sPPI方法的模型拟合。我们发现，与sPPI相比，gPPI方法改善了模型拟合。有几个区域在gPPI中变得不显著。这些区域均表现出较好的模型拟合效果。此外，有几个区域的任务相关连接仅使用gPPI方法检测，也具有改进的模型拟合。用所有方法检测到的区域有更多相似的模型拟合。这些结果表明gPPI在SPM中可能比标准实施具有更高的敏感性和特异性。由于不存在金本位，这种观念略有缓和；然而，具有已知结果的数据模拟支持我们关于gPPI的结论。总之，上下文相关PPI方法的广义形式增加了统计建模的灵活性，并有可能提高模型拟合、对真阴性结果的特异性和对真阳性结果的敏感性。
Functional MRI (fMRI) allows one to study task-related regional responses and task-dependent connectivity analysis using psychophysiological interaction (PPI) methods. The latter affords the additional opportunity to understand how brain regions interact in a task-dependent manner. The current implementation of PPI in Statistical Parametric Mapping (SPM8) is configured primarily to assess connectivity differences between two task conditions, when in practice fMRI tasks frequently employ more than two conditions. Here we evaluate how a generalized form of context-dependent PPI (gPPI; http://www.nitrc.org/projects/gppi), which is configured to automatically accommodate more than two task conditions in the same PPI model by spanning the entire experimental space, compares to the standard implementation in SPM8. These comparisons are made using both simulations and an empirical dataset. In the simulated dataset, we compare the interaction beta estimates to their expected values and model fit using the Akaike Information Criterion (AIC). We found that interaction beta estimates in gPPI were robust to different simulated data models, were not different from the expected beta value, and had better model fits than when using standard PPI (sPPI) methods. In the empirical dataset, we compare the model fit of the gPPI approach to sPPI. We found that the gPPI approach improved model fit compared to sPPI. There were several regions that became non-significant with gPPI. These regions all showed significantly better model fits with gPPI. Also, there were several regions where task-dependent connectivity was only detected using gPPI methods, also with improved model fit. Regions that were detected with all methods had more similar model fits. These results suggest that gPPI may have greater sensitivity and specificity than standard implementation in SPM. This notion is tempered slightly as there is no gold standard; however, data simulations with a known outcome support our conclusions about gPPI. In sum, the generalized form of context-dependent PPI approach has increased flexibility of statistical modeling, and potentially improves model fit, specificity to true negative findings, and sensitivity to true positive findings.
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