FMRI group analysis combining effect estimates and their variances
FMRI group analysis combining effect estimates and their variances
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DOI：
10.1016/j.neuroimage.2011.12.060
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发表时间：
2012-03-01
期刊：
影响因子：
5.7
通讯作者：
Cox, Robert W.
中科院分区：
文献类型：
--
作者：
Chen, Gang;Saad, Ziad S.;Cox, Robert W.
传统的功能磁共振成像（FMRI）组分析有两个关键的假设，并不总是合理的。首先，来自每个受试者的数据被压缩成每个体素的单个数字，假设受试者内的差异对于所有受试者的影响是相同的，或者相对于跨受试者的差异可以忽略不计。其次，假设所有数据值均来自相同的高斯分布，没有离群值。我们提出了一种方法，不做这样强的假设，并提出了一个计算效率高的频率论方法的功能磁共振成像组分析，我们长期的混合效应多层次分析（MEMA），它结合了跨学科的变异性和精度估计每个感兴趣的影响，从个别受试者的分析。平均而言，更准确的测试会导致更高的统计功效，特别是当传统的方差假设不成立或存在离群值时。此外，各种异质性措施可与MEMA，可以帮助研究人员在进一步改善建模。我们的方法允许组效应t检验和条件之间和组之间的比较。此外，它还能够纳入特定受试者的协变量，如年龄，智商或行为数据。仿真结果表明，本文采用的检验统计量在功率增益和I类差错控制之间取得了较好的平衡。我们的方法是在一个开源的，免费分发的程序，可用于存储在通用神经影像文件传输（NIfTI）格式的任何数据集实例化。到目前为止，更准确的测试，包括内和跨学科的变异性的主要障碍是高计算成本。我们的高效实现使这种方法变得实用。我们建议使用它来代替传统组分析中不太准确的方法。爱思唯尔公司出版
Conventional functional magnetic resonance imaging (FMRI) group analysis makes two key assumptions that are not always justified. First, the data from each subject is condensed into a single number per voxel, under the assumption that within-subject variance for the effect of interest is the same across all subjects or is negligible relative to the cross-subject variance. Second, it is assumed that all data values are drawn from the same Gaussian distribution with no outliers. We propose an approach that does not make such strong assumptions, and present a computationally efficient frequentist approach to FMRI group analysis, which we term mixed-effects multilevel analysis (MEMA), that incorporates both the variability across subjects and the precision estimate of each effect of interest from individual subject analyses. On average, the more accurate tests result in higher statistical power, especially when conventional variance assumptions do not hold, or in the presence of outliers. In addition, various heterogeneity measures are available with MEMA that may assist the investigator in further improving the modeling. Our method allows group effect t-tests and comparisons among conditions and among groups. In addition, it has the capability to incorporate subject-specific covariates such as age, IQ or behavioral data. Simulations were performed to illustrate power comparisons and the capability of controlling type I errors among various significance testing methods, and the results indicated that the testing statistic we adopted struck a good balance between power gain and type I error control. Our approach is instantiated in an open-source, freely distributed program that may be used on any dataset stored in the universal neuroimaging file transfer (NIfTI) format. To date, the main impediment for more accurate testing that incorporates both within- and cross-subject variability has been the high computational cost. Our efficient implementation makes this approach practical. We recommend its use in lieu of the less accurate approach in the conventional group analysis. Published by Elsevier Inc.