Combining voxel intensity and cluster extent with permutation test framework
Combining voxel intensity and cluster extent with permutation test framework
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DOI：
10.1016/j.neuroimage.2004.04.035
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发表时间：
2004-09-01
期刊：
影响因子：
5.7
通讯作者：
Nichols, TE
中科院分区：
文献类型：
--
作者：
Hayasaka, S;Nichols, TE
在脑图像数据的大规模单变量分析中，统计推断通常基于信号的强度或空间范围。基于体素强度的测试对高强度信号具有很高的灵敏度，而基于聚类范围的测试对空间扩展信号敏感。为了从两者的优势中获益，需要将强度和程度信息结合起来。结合体素强度和聚类程度的方法多种多样，目前已经提出了几种组合方法。Poline et al. [neuroimage] 16(1997) 831最小P值方法对强度或程度显著的信号敏感。Bullmore et al. s [IEEE译]。Med. image . 18(1999) 321聚类质量法可以检测到强度和范围足够大的信号，即使仅从强度或范围来看它们并不显著。在这项工作中，我们研究了使用组合函数的这种组合推理方法（Pesarin， F., 2001）。多元置换检验。Wiley, New York)和排列框架[j] holmes等人。血流Metab. 16(1996) 71，它允许我们在不知道其分布的情况下检查组合体素强度和聚类范围信息的不同方法。我们还尝试通过使用加权组合函数来校准组合推理，加权组合函数根据感兴趣的信号调整测试。此外，我们提出了组合函数的组合函数元组合，它将多个组合函数的优势整合到一个统计量中。我们发现组合测试能够检测到单独通过体素或簇大小测试无法检测到的信号。我们还发现，加权组合函数可以根据感兴趣的信号校准组合测试，适当地强调强度或程度。虽然不一定比单个组合函数更敏感；元组合函数对所有类型的信号都很敏感，因此可以作为汇总所有组合函数的单一测试。(C) 2004爱思唯尔公司版权所有。
In a massively univariate analysis of brain image data, statistical inference is typically based on intensity or spatial extent of signals. Voxel intensity-based tests provide great sensitivity for high intensity signals, whereas cluster extent-based tests are sensitive to spatially extended signals. To benefit from the strength of both, the intensity and extent information needs to be combined. Various ways of combining voxel intensity and cluster extent are possible, and a few such combining methods have been proposed. Poline et al.'s [Neurolmage 16 (1997) 831 minimum P value approach is sensitive to signals whose either intensity or extent is significant. Bullmore et al.'s [IEEE Trans. Med. Imag. 18 (1999) 321 cluster mass method can detect signals whose intensity and extent are sufficiently large, even when they are not significant by intensity or extent alone. In this work, we study such combined inference methods using combining functions (Pesarin, F., 2001. Multivariate Permutation Tests. Wiley, New York) and permutation framework lHolmes et al., J. Cereb. Blood Flow Metab. 16 (1996) 71, which allow us to examine different ways of combining voxel intensity and cluster extent information without knowing their distribution. We also attempt to calibrate combined inference by using weighted combining functions, which adjust the test according to signals of interest. Furthermore, we propose meta-combining, a combining function of combining functions, which integrates strengths of multiple combining functions into a single statistic. We found that combined tests are able to detect signals that are not detected by voxel or cluster size test alone. We also found that the weighted combining functions can calibrate the combined test according to the signals of interest, emphasizing either intensity or extent as appropriate. Though not necessarily more sensitive than individual combining functions; the meta-combining function is sensitive to all types of signals and thus can be used as a single test summarizing all the combining functions. (C) 2004 Elsevier Inc. All rights reserved.