Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.
Rotman Research Institute, Baycrest, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Ontario, Canada.
Neuroimage. 2014 Feb 15;87:363-82. doi: 10.1016/j.neuroimage.2013.10.062. Epub 2013 Nov 5.
The existing functional connectivity assessment techniques rely on different mathematical and neuro-physiological models. They may consequently provide different sets of spatial connectivity maps and associated temporal responses within their significant spatiotemporal sets of components. Note that the word component is used to generically refer to spatio-temporal pairs of maps and associated time courses. Such differences may confound the application of functional connectivity measurements in neuroscientific and clinical applications. Using several performance metrics we evaluated six fMRI resting-state connectivity measurement techniques including three fully exploratory techniques: 1) Melodic-Independent Component Analysis (ICA), 2) agnostic Canonical Variates Analysis (aCVA), and 3) generalized Canonical Correlation Analysis (gCCA); and three seed-based techniques: 1) seed gCCA (sgCCA) and 2, 3) seed Partial Least Squares (sPLS) with a posterior cingulate seed and two different time-series normalizations. We separately assessed the temporal and spatial domains for: 1) technique stability as a function of sample size using RV coefficients, and 2) subspace component similarity between pairs of techniques using CCA. Overall gCCA was the only technique that displayed high temporal and spatial stabilities, together with high spatial and temporal subspace similarities with multiple other techniques. ICA, aCVA and sgCCA tended to be the most stable spatially and produced similar spatial subspaces. All techniques produced relatively unstable and dissimilar temporal subspaces, except sPLS that produced relatively high temporal and lower spatial subspace stabilities, but with unique power-spectral Hurst coefficients ≪ 1. Our results indicate that spatial maps from resting state data sets are much less dependent on the analysis technique used than are the associated time series. Such temporal variability is coupled with individual spatial component maps, which may be quite dissimilar across techniques even with similar spatial subspaces. Therefore, we suggest that consensus estimation approaches, i.e. a 2nd-level gCCA, would have great utility to produce and aid interpretation of stable results from BOLD fMRI resting state data analysis.
现有的功能连接评估技术依赖于不同的数学和神经生理模型。因此，它们可能会在其显著的时空成分集中提供不同的空间连接图和相关的时间响应。请注意，“组件”一词用于泛指时空对映射和相关时间序列。这些差异可能会混淆功能连接测量在神经科学和临床应用中的应用。我们使用了几种性能指标来评估六种 fMRI 静息状态连接测量技术，包括三种完全探索性技术：1）旋律独立成分分析（ICA），2）无偏协变量分析（aCVA）和 3）广义协方差分析（gCCA）；和三种基于种子的技术：1）种子 gCCA（sgCCA）和 2、3）基于后扣带回种子的种子偏最小二乘法（sPLS）和两种不同的时间序列归一化。我们分别评估了以下方面的时间和空间域：1）技术稳定性作为样本大小的函数，使用 RV 系数，以及 2）成对技术之间子空间组件相似性，使用 CCA。总体而言，gCCA 是唯一一种显示出高时空稳定性的技术，同时与多种其他技术具有高空间和时间子空间相似性。ICA、aCVA 和 sgCCA 倾向于具有最高的空间稳定性，并产生相似的空间子空间。除了 sPLS 产生相对较高的时间和较低的空间子空间稳定性外，所有技术产生的时间子空间都相对不稳定且不相似，但具有独特的幂律 Hurst 系数 ≪1。我们的结果表明，与相关时间序列相比，静息状态数据集的空间图对所使用的分析技术的依赖性要小得多。这种时间可变性与个体空间组件图相关，即使具有相似的空间子空间，不同技术之间的空间组件图也可能非常不同。因此，我们建议共识估计方法，即 2 级 gCCA，将极大地有助于从 BOLD fMRI 静息状态数据分析中产生和辅助解释稳定的结果。
