Background

The analysis of connectivity has become a fundamental tool in human neuroscience. Granger Causality Mapping is a data-driven method that uses Granger Causality (GC) to assess the existence and direction of influence between signals, based on temporal precedence of information. More recently, a theory of Granger causality has been developed for state-space (SS-GC) processes, but little is known about its statistical validation and application on functional magnetic resonance imaging (fMRI) data.

New method

We explored different multivariate computational frameworks to define the optimal combination for GC estimation. We hypothesized a new heuristic, combining SS-GC with a distinct statistical validation technique, Time Reversed Testing, validating it on synthetic data. We test its performance with a number of experimental parameters, including block structure, sampling frequency, noise and system mean pairwise correlation, using a statistical framework of binary classification.

Results

We found that SS-GC with time reversed testing outperforms other frameworks. The results validate the application of SS-GC to generative models. When estimating reliable causal relations, SS-GC returns promising results, especially when considering synthetic data with a high impact of noise and sampling rate.

Conclusions

In this study, we empirically explored the boundaries of SS-GC with time reversed testing, a data-driven causality analysis framework with potential applicability to fMRI data.

中文翻译：

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适用于BOLD模拟数据的状态空间Granger因果关系分析的边界：一种比较建模和模拟方法。

背景

连接性分析已成为人类神经科学的基本工具。Granger因果关系映射是一种数据驱动的方法，它使用Granger因果关系（GC）根据信息的时间优先级来评估信号之间影响的存在和方向。最近，已经针对状态空间（SS-GC）过程开发了格兰杰因果关系理论，但对其统计验证和在功能磁共振成像（fMRI）数据上的应用知之甚少。

新方法

我们探索了不同的多元计算框架来定义GC估计的最佳组合。我们假设了一种新的启发式方法，将SS-GC与独特的统计验证技术（时间反向测试）结合起来，可以对合成数据进行验证。我们使用二进制分类的统计框架，通过许多实验参数（包括块结构，采样频率，噪声和系统平均成对相关性）测试其性能。

结果

我们发现带有时间反向测试的SS-GC优于其他框架。结果验证了SS-GC在生成模型中的应用。当估计可靠的因果关系时，SS-GC会返回有希望的结果，尤其是在考虑对噪声和采样率具有较高影响的合成数据时。

结论

在这项研究中，我们通过时间逆向测试经验性地探索了SS-GC的边界，它是一种数据驱动的因果关系分析框架，对fMRI数据具有潜在的适用性。