论文标题
重新审查聚类范围推断：大脑活动的定量和定位
Cluster extent inference revisited: quantification and localization of brain activity
论文作者
论文摘要
基于空间范围阈值的群集推断是在神经影像中找到激活的大脑区域的最流行分析方法。但是，该方法有几个众所周知的问题。虽然可以通过某种激活来找到大脑区域的功能，但是当前定义的方法不允许任何信号的进一步定量或定位。在本文中，我们修复了此差距。我们表明，可以使用群集扩展推理（1.）来推断感兴趣的解剖区域中信号的存在，以及（2.）来量化在任何群集或目标群中的活动体素的百分比。这些额外的推论是免费的，即它们不需要进一步调整α级测试，同时保留全家家庭错误控制。我们通过将方法嵌入封闭的测试过程中，并解决了由于嵌入而导致的图形理论K分离器问题，从而实现了群集推断的可能性的扩展。新方法可以与随机场理论或排列结合使用。我们证明了该方法在大规模应用中对神经模仿数据库的神经成像数据的有用性。
Cluster inference based on spatial extent thresholding is the most popular analysis method for finding activated brain areas in neuroimaging. However, the method has several well-known issues. While powerful for finding brain regions with some activation, the method as currently defined does not allow any further quantification or localization of signal. In this paper we repair this gap. We show that cluster-extent inference can be used (1.) to infer the presence of signal in anatomical regions of interest and (2.) to quantify the percentage of active voxels in any cluster or region of interest. These additional inferences come for free, i.e. they do not require any further adjustment of the alpha-level of tests, while retaining full familywise error control. We achieve this extension of the possibilities of cluster inference by an embedding of the method into a closed testing procedure, and solving the graph-theoretic k-separator problem that results from this embedding. The new method can be used in combination with random field theory or permutations. We demonstrate the usefulness of the method in a large-scale application to neuroimaging data from the Neurovault database.
