Mind, Brain and Behavior Research Centre (CIMCYC), Spain.
Mind, Brain and Behavior Research Centre (CIMCYC), Spain; Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium.
J Neurosci Methods. 2018 Oct 1;308:248-260. doi: 10.1016/j.jneumeth.2018.06.017. Epub 2018 Jul 6.
The use of Multi-Voxel Pattern Analysis (MVPA) has increased considerably in recent functional magnetic resonance imaging (fMRI) studies. A crucial step consists in the choice of a method for the estimation of responses. However, a systematic comparison of the different estimation alternatives and their adequacy to predominant experimental design is missing. In the current study we compared three pattern estimation methods: Least-Squares Unitary (LSU), based on run-wise estimation, Least-Squares All (LSA) and Least-Squares Separate (LSS), which rely on trial-wise estimation. We compared the efficiency of these methods in an experiment where sustained activity needed to be isolated from zero-duration events as well as in a block-design approach and in a event-related design. We evaluated the sensitivity of the t-test in comparison with two non-parametric methods based on permutation testing: one proposed in Stelzer et al. (2013), equivalent to performing a permutation in each voxel separately and the Threshold-Free Cluster Enhancement. LSS resulted the most accurate approach to address the large overlap of signal among close events in the event-related designs. We found a larger sensitivity of Stelzer's method in all settings, especially in the event-related designs, where voxels close to surpass the statistical threshold with the other approaches were now marked as informative regions. Our results provide evidence that LSS is the most accurate approach for unmixing events with different duration and large overlap of signal. This is consistent with previous studies showing that LSS handles large collinearity better than other methods. Moreover, Stelzer's potentiates this better estimation with its large sensitivity.
多体素模式分析（MVPA）在最近的功能磁共振成像（fMRI）研究中得到了广泛应用。一个关键步骤包括选择一种用于估计响应的方法。然而，缺乏对不同估计方法的系统比较及其对主要实验设计的适应性的比较。在当前的研究中，我们比较了三种模式估计方法：基于运行的最小二乘单位（LSU）、基于试次的最小二乘全（LSA）和最小二乘分离（LSS）。我们在一个需要从零持续时间事件中分离持续活动的实验中，以及在块设计方法和事件相关设计中比较了这些方法的效率。我们评估了 t 检验的敏感性，并与基于置换检验的两种非参数方法进行了比较：一种是 Stelzer 等人提出的方法（2013 年），相当于在每个体素中分别进行置换，另一种是无阈值聚类增强方法。在事件相关设计中，LSS 是解决信号在接近事件中重叠较大的最准确方法。我们发现，Stelzer 方法在所有设置中的敏感性都更高，尤其是在事件相关设计中，对于其他方法接近超过统计阈值的体素，现在被标记为信息区域。我们的结果表明，LSS 是分离具有不同持续时间和信号重叠较大的事件的最准确方法。这与先前的研究一致，表明 LSS 比其他方法更好地处理大的共线性。此外，Stelzer 的方法具有较大的敏感性，进一步提高了这种更好的估计。
