A simple permutation-based test of intermodal correspondence.
A simple permutation-based test of intermodal correspondence.
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一个简单的基于排列的多式联运对应测试。
DOI：
10.1002/hbm.25577
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发表时间：
2021-11
影响因子：
4.8
通讯作者：
Shinohara RT
中科院分区：
文献类型：
--
作者：
Weinstein SM;Vandekar SN;Adebimpe A;Tapera TM;Robert-Fitzgerald T;Gur RC;Gur RE;Raznahan A;Satterthwaite TD;Alexander-Bloch AF;Shinohara RT
神经成像研究中的许多关键发现涉及脑图之间的相似性，但用于测量这些发现的统计方法各不相同。目前最先进的方法包括将观察到的组级脑图（在多个受试者的每个图像位置平均强度后）与这些组级脑图的空间零模型进行比较。然而，这些方法通常会做出强烈的、可能不切实际的统计假设，比如协方差平稳性。为了解决这些问题，在本文中，我们建议使用受试者水平的数据和经典的排列测试框架来测试和评估脑图之间的相似性。我们的方法与传统的排列检验相当，因为它涉及随机排列受试者以产生多式联运对应统计量的零分布，我们将其与观察到的统计量进行比较以估计p值。我们在费城神经发育队列的模拟和真实神经成像数据中应用并比较了我们的方法。我们表明，我们的方法在检测已知的强相关模式之间的关系（皮质厚度和脑沟深度）方面表现良好，并且在不期望关联（皮质厚度和反向工作记忆任务的激活）时是保守的。值得注意的是，我们的方法对于定位大脑子区域内的多式联运关系是最灵活和可靠的，并且允许可推广的统计推断。我们建议使用经典的排列检验框架来研究多式联运对应，使用受试者水平的数据，同时需要最小的统计假设。我们将我们的方法与先前的方法进行了比较，这些方法涉及组级脑图的空间零建模，并说明和讨论了我们的方法在大脑子区域内定位多式联运关系的灵活性。
Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state‐of‐the‐art methods involve comparing observed group‐level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group‐level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject‐level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p‐value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n‐back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference. We propose using a classical permutation testing framework to study intermodal correspondence using subject‐level data while requiring minimal statistical assumptions. We compare our method to previous approaches involving spatial null modeling of group‐level brain maps and illustrate and discuss the flexibility of our method for localizing intermodal relationships within subregions of the brain.
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影响因子：
5.7
作者：
Alexander-Bloch AF;Shou H;Liu S;Satterthwaite TD;Glahn DC;Shinohara RT;Vandekar SN;Raznahan A
通讯作者：
Raznahan A
DOI：
10.1523/jneurosci.3554-12.2013
发表时间：
2013-02-13
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Alexander-Bloch A;Raznahan A;Bullmore E;Giedd J
通讯作者：
Giedd J
影响因子：
16.2
作者：
Cui, Zaixu;Li, Hongming;Satterthwaite, Theodore D.
通讯作者：
Satterthwaite, Theodore D.
影响因子：
7.7
作者：
Paquola, Casey;Bethlehem, Richard Ai;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
5.7
作者：
Rosen AFG;Roalf DR;Ruparel K;Blake J;Seelaus K;Villa LP;Ciric R;Cook PA;Davatzikos C;Elliott MA;Garcia de La Garza A;Gennatas ED;Quarmley M;Schmitt JE;Shinohara RT;Tisdall MD;Craddock RC;Gur RE;Gur RC;Satterthwaite TD
通讯作者：
Satterthwaite TD