Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level.
Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level.
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DOI：
10.1016/j.neuroimage.2016.05.023
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发表时间：
2016-11-15
期刊：
影响因子：
5.7
通讯作者：
Cox RW
中科院分区：
文献类型：
--
作者：
Chen G;Shin YW;Taylor PA;Glen DR;Reynolds RC;Israel RB;Cox RW
自然和连续刺激下的FMRI数据采集（例如，观看视频或听音乐）最近变得流行，这是由于与传统的基于任务的实验设计相比，它需要较少的操作和任务中涉及的更现实/复杂的背景。受试者之间的同步或响应相似性通常通过任何一对受试者之间的受试者间相关性（ISC）来测量。在组水平上，总结ISC值的收集是复杂的，它们的相互关系，这必然导致违反独立性假设的典型参数方法，如学生的t检验。非参数的方法，如自举和排列测试，以前被采用的测试目的，通过重新排列的时间序列的每个主题，但这些具体的方法在控制假阳性率（FPR）的定量有效性从来没有被探索过。在这里，我们调查的ISC组分析的方法，已在文献中，并讨论这些方法中涉及的问题。然后，我们提出了计算量较小的非参数方法，可以在组水平上进行（单样本和双样本分析），相比流行的方法循环移动的EPI时间序列在个人层面上。作为新方法的一部分，采用主题方式（SW）的RESISTANCE，而不是元素方式（EW）的RESISTANCE，使交换性和独立性的假设得到满足，ISC值之间的模式化的相关结构可以更准确地捕捉。我们通过仿真研究了所有方法的FPR可控性和功率实现，以及它们在应用于真实的实验数据集时的性能。新的方法被证明是有效的和强大的，他们已经实施到一个开放源码程序，3dNPT，在AFNI（http：afni.nimh.nih.gov）。
FMRI data acquisition under naturalistic and continuous stimuli (e.g., watching a video or listening to music) has become popular recently due to the fact that it entails less manipulation and more realistic/complex contexts involved in the task, compared to the conventional task-based experimental designs. The synchronization or response similarities among subjects are typically measured through inter-subject correlation (ISC) between any pair of subjects. At the group level, summarizing the collection of ISC values is complicated by their intercorrelations, which necessarily lead to the violation of independence assumed in typical parametric approaches such as Student’s t-test. Nonparametric methods, such as bootstrapping and permutation testing, have previously been adopted for testing purposes by resampling the time series of each subject, but the quantitative validity of these specific approaches in terms of controllability of false positive rate (FPR) has never been explored before. Here we survey the methods of ISC group analysis that have been employed in the literature, and discuss the issues involved in those methods. We then propose less computationally intensive nonparametric methods that can be performed at the group level (for both one- and two-sample analyses), as compared to the popular method of circularly shifting the EPI time series at the individual level. As part of the new approaches, subject-wise (SW) resampling is adopted instead of element-wise (EW) resampling, so that exchangeability and independence assumptions are satisfied, and the patterned correlation structure among the ISC values can be more accurately captured. We examine the FPR controllability and power achievement of all the methods through simulations, as well as their performance when applied to a real experimental dataset. The new methodologies are shown to be both efficient and robust, and they have been implemented into an open source program, 3dNPT, in AFNI (http://afni.nimh.nih.gov).
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影响因子：
4.3
作者：
Chen G;Saad ZS;Adleman NE;Leibenluft E;Cox RW
通讯作者：
Cox RW
影响因子：
5.7
作者：
Arbabshirani MR;Damaraju E;Phlypo R;Plis S;Allen E;Ma S;Mathalon D;Preda A;Vaidya JG;Adali T;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
5.7
作者：
Cohen, MS
通讯作者：
Cohen, MS
DOI：
10.1111/ejn.12173
发表时间：
2013-05
期刊：
The European journal of neuroscience
影响因子：
--
作者：
Abrams DA;Ryali S;Chen T;Chordia P;Khouzam A;Levitin DJ;Menon V
通讯作者：
Menon V
影响因子：
56.9
作者：
Hasson, U;Nir, Y;Malach, R
通讯作者：
Malach, R