Revisiting multi-subject random effects in fMRI: Advocating prevalence estimation
Revisiting multi-subject random effects in fMRI: Advocating prevalence estimation
复制标题
重新审视功能磁共振成像中的多受试者随机效应：提倡患病率估计
DOI：
10.1016/j.neuroimage.2013.08.025
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发表时间：
2012
期刊：
影响因子：
5.7
通讯作者：
Y. Benjamini
中科院分区：
文献类型：
--
作者：
Jonathan D. Rosenblatt;M. Vink;Y. Benjamini
随机效应分析被引入到功能磁共振研究中，以便将研究组的结果推广到整个人群。概括研究结果显然比检测研究组内的激活更难，因为为了显著，激活必须大于受试者之间的变异性。事实上，在使用随机效果分析时，检测到的区域比使用固定效果时要小。经典随机效应模型背后的统计假设是，每个位置的效应在受试者身上呈正态分布，而激活指的是非零均值效应。我们认为，与真实的群体变异性相比，该模型是不现实的。在真实的群体变异性中，由于功能解剖不一致和注册异常，一些受试者是活跃的，一些受试者并不在每个大脑位置。我们提出了一种高斯混合随机效应，它摊销了受试者之间的空间差异，并使用每个位置的激活流行率来量化它。我们给出了这一流行率的正式定义和估计程序。所提出的分析的最终结果是显著激活的位置的患病率的地图，突出在许多大脑中常见的激活区域。(B)与通常在激活区域显示p值不同--在大样本量情况下，p值很容易收敛到0--流行率估计值与真实流行率相反。
Random effect analysis has been introduced into fMRI research in order to generalize findings from the study group to the whole population. Generalizing findings is obviously harder than detecting activation within the study group since in order to be significant, an activation has to be larger than the inter-subject variability. Indeed, detected regions are smaller when using random effect analysis versus fixed effects. The statistical assumptions behind the classic random effect model are that the effect in each location is normally distributed over subjects, and “activation” refers to a non-null mean effect. We argue that this model is unrealistic compared to the true population variability, where due to function–anatomy inconsistencies and registration anomalies, some of the subjects are active and some are not at each brain location.We propose a Gaussian-mixture-random-effect that amortizes between-subject spatial disagreement and quantifies it using the prevalence of activation at each location. We present a formal definition and an estimation procedure of this prevalence. The end result of the proposed analysis is a map of the prevalence at locations with significant activation, highlighting activation regions that are common over many brains.Prevalence estimation has several desirable properties: (a) It is more informative than the typical active/inactive paradigm. (b) In contrast to the usual display of p-values in activated regions – which trivially converge to 0 for large sample sizes – prevalence estimates converge to the true prevalence.
影响因子：
2.5
作者：
Fedorenko, Evelina;Hsieh, Po-Jang;Kanwisher, Nancy
通讯作者：
Kanwisher, Nancy