Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, 3000 Leuven, Flemish Brabant, Belgium.
Department of Cognitive Science, Johns Hopkins University, Baltimore, USA.
Neuroimage. 2021 Dec 15;245:118686. doi: 10.1016/j.neuroimage.2021.118686. Epub 2021 Oct 31.
Representational similarity analysis (RSA) is a key element in the multivariate pattern analysis toolkit. The central construct of the method is the representational dissimilarity matrix (RDM), which can be generated for datasets from different modalities (neuroimaging, behavior, and computational models) and directly correlated in order to evaluate their second-order similarity. Given the inherent noisiness of neuroimaging signals it is important to evaluate the reliability of neuroimaging RDMs in order to determine whether these comparisons are meaningful. Recently, multivariate noise normalization (NN) has been proposed as a widely applicable method for boosting signal estimates for RSA, regardless of choice of dissimilarity metrics, based on evidence that the analysis improves the within-subject reliability of RDMs (Guggenmos et al. 2018; Walther et al. 2016). We revisited this issue with three fMRI datasets and evaluated the impact of NN on within- and between-subject reliability and RSA effect sizes using multiple dissimilarity metrics. We also assessed its impact across regions of interest from the same dataset, its interaction with spatial smoothing, and compared it to GLMdenoise, which has also been proposed as a method that improves signal estimates for RSA (Charest et al. 2018). We found that across these tests the impact of NN was highly variable, as also seems to be the case for other analysis choices. Overall, we suggest being conservative before adding steps and complexities to the (pre)processing pipeline for RSA.
代表性相似性分析（RSA）是多元模式分析工具包中的一个关键元素。该方法的核心构建是表示差异矩阵（RDM），它可以为来自不同模态（神经影像学、行为和计算模型）的数据集生成，并直接相关联，以评估它们的二阶相似性。鉴于神经影像学信号固有的噪声，评估神经影像学 RDM 的可靠性以确定这些比较是否有意义非常重要。最近，多元噪声归一化（NN）已被提出作为一种广泛适用的方法，用于提高 RSA 的信号估计，无论选择何种相似度度量，这是基于分析可以提高 RDM 内在个体可靠性的证据（Guggenmos 等人，2018 年；Walther 等人，2016 年）。我们用三个 fMRI 数据集重新研究了这个问题，并使用多种相似度度量来评估 NN 对个体内和个体间可靠性以及 RSA 效应量的影响。我们还评估了它对同一数据集中感兴趣区域的影响，评估了它与空间平滑的相互作用，并将其与 GLMdenoise 进行了比较，后者也被提出作为一种可以提高 RSA 信号估计的方法（Charest 等人，2018 年）。我们发现，在这些测试中，NN 的影响高度可变，这似乎与其他分析选择的情况一样。总体而言，我们建议在 RSA 的（预处理）处理管道中添加步骤和复杂性之前要保持谨慎。
