Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, USA.
Neuroimage. 2012 Jul 16;61(4):1277-86. doi: 10.1016/j.neuroimage.2012.03.068. Epub 2012 Mar 30.
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.
功能磁共振成像 (fMRI) 允许人们使用心理生理交互 (PPI) 方法研究与任务相关的区域反应和任务相关的连接分析。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。目前在统计参数映射 (SPM8) 中实现的 PPI 主要配置用于评估两种任务条件之间的连接差异，而实际上 fMRI 任务通常采用超过两种条件。在这里，我们评估了一种广义的上下文相关 PPI (gPPI; http://www.nitrc.org/projects/gppi) 的形式，它通过跨越整个实验空间，配置为自动适应同一 PPI 模型中的多个任务条件，与 SPM8 中的标准实现相比如何。这些比较是使用模拟和经验数据集进行的。在模拟数据集上，我们比较了交互 beta 估计值与其预期值和使用赤池信息量准则 (AIC) 的模型拟合。我们发现，gPPI 中的交互 beta 估计值对不同的模拟数据模型具有鲁棒性，与预期的 beta 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。在经验数据集上，我们比较了 gPPI 方法和 sPPI 方法的模型拟合。我们发现 gPPI 方法的模型拟合优于 sPPI。有几个区域在 gPPI 中变得没有统计学意义。这些区域的模型拟合都明显优于 gPPI。此外，还有一些区域仅使用 gPPI 方法检测到任务相关的连接，也具有更好的模型拟合。使用所有方法检测到的区域具有更相似的模型拟合。这些结果表明，gPPI 可能比 SPM 中的标准实现具有更高的灵敏度和特异性。由于没有黄金标准，这种说法稍微有些缓和；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，广义的上下文相关 PPI 方法具有更强的统计建模灵活性，并有可能提高模型拟合度、对真阴性发现的特异性以及对真阳性发现的敏感性。
