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多主体搜索正确识别了 Smith 等人模拟研究的 DCM 模型中的因果关系和大多数因果方向。

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study.

机构信息

Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

出版信息

Neuroimage. 2011 Oct 1;58(3):838-48. doi: 10.1016/j.neuroimage.2011.06.068. Epub 2011 Jul 1.

Abstract

Smith et al. report a large study of the accuracy of 38 search procedures for recovering effective connections in simulations of DCM models under 28 different conditions. Their results are disappointing: no method reliably finds and directs connections without large false negatives, large false positives, or both. Using multiple subject inputs, we apply a previously published search algorithm, IMaGES, and novel orientation algorithms, LOFS, in tandem to all of the simulations of DCM models described by Smith et al. (2011). We find that the procedures accurately identify effective connections in almost all of the conditions that Smith et al. simulated and, in most conditions, direct causal connections with precision greater than 90% and recall greater than 80%.

摘要

史密斯等人报告了一项针对 38 种搜索程序在 28 种不同条件下对 DCM 模型中有效连接恢复准确性的大型研究。他们的结果令人失望:没有一种方法可以可靠地找到并引导连接,而不会出现大量的假阴性、假阳性或两者兼而有之。我们使用多个主题输入,将先前发表的搜索算法 IMaGES 和新的方向算法 LOFS 结合起来,应用于史密斯等人描述的所有 DCM 模型的模拟。我们发现,这些程序几乎可以在史密斯等人模拟的所有条件下准确识别有效连接,并且在大多数条件下,定向因果连接的精度大于 90%,召回率大于 80%。

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