Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach

By Martin A. Lindquist, Yuting Xu, Mary Beth Nebel, Brian C. Caffo. NeuroImage, 2014

366 citations recorded by the source corpus.

To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-windows technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.

Subfields: Cognitive Neuroscience, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/684f27093d91cd5c95acdc07a500b37b6843ea66

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In Cognitive Neuroscience

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Citations in the corpus

Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, background): “These sharp state transitions were due to abrupt changes in the signal intensities at the state transition points (Lindquist et al., 2014) that acted as outliers, especially for smaller windows (25 TRs, 50 TRs) in which the averaging in the SWC includes a smaller number of points.”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, methodology): “This conclusion is in line with the findings reported in Lindquist et al. (2014).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “…the minimum of ∼ 60 s that has been previously recommended (Leonardi & Van De Ville, 2015; Zalesky & Breakspear, 2015), and one can consider the choice of window size to be a tunable ﬁlter that can be optimized based on the question of interest (Lindquist et al., 2014; V. Vergara et al., in press).”