Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Neuroimage. 2011 Jul 15;57(2):331-3. doi: 10.1016/j.neuroimage.2010.11.012. Epub 2010 Nov 12.
In this paper, we address the critical assessment of Ramsey et al. of our method for learning partially directed graphs from meta-analysis imaging data (Neumann et al., 2010). We argue that our method provides valid and interpretable results when applied to data representing a single experimental paradigm. Simulations further suggest that, despite theoretical limitations, the application of our method to mixed probability distributions yields reliable results with error rates at acceptable levels. Finally, we discuss the nature of meta-analysis data and the notion of causality in the context of functional neuroimaging.
在本文中，我们对 Ramsey 等人对我们从荟萃分析成像数据中学习部分有向图的方法（Neumann 等人，2010 年）的批判性评估进行了讨论。我们认为，当应用于代表单个实验范式的数据时，我们的方法提供了有效且可解释的结果。模拟进一步表明，尽管存在理论限制，但将我们的方法应用于混合概率分布可以产生可靠的结果，误差率处于可接受的水平。最后，我们讨论了荟萃分析数据的性质以及功能神经影像学中因果关系的概念。
