Evaluation of spatio-temporal decomposition techniques for group analysis of fMRI resting state data sets

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.