Send to

Choose Destination
Neuroimage. 2017 Sep;158:155-175. doi: 10.1016/j.neuroimage.2017.07.005. Epub 2017 Jul 5.

Comparing test-retest reliability of dynamic functional connectivity methods.

Author information

Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA.
Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins University, USA.
Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, USA.
Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, USA.
Department of Biostatistics, Johns Hopkins University, USA.
Department of Biostatistics, Johns Hopkins University, USA. Electronic address:


Due to the dynamic, condition-dependent nature of brain activity, interest in estimating rapid functional connectivity (FC) changes that occur during resting-state functional magnetic resonance imaging (rs-fMRI) has recently soared. However, studying dynamic FC is methodologically challenging, due to the low signal-to-noise ratio of the blood oxygen level dependent (BOLD) signal in fMRI and the massive number of data points generated during the analysis. Thus, it is important to establish methods and summary measures that maximize reliability and the utility of dynamic FC to provide insight into brain function. In this study, we investigated the reliability of dynamic FC summary measures derived using three commonly used estimation methods - sliding window (SW), tapered sliding window (TSW), and dynamic conditional correlations (DCC) methods. We applied each of these techniques to two publicly available rs-fMRI test-retest data sets - the Multi-Modal MRI Reproducibility Resource (Kirby Data) and the Human Connectome Project (HCP Data). The reliability of two categories of dynamic FC summary measures were assessed, specifically basic summary statistics of the dynamic correlations and summary measures derived from recurring whole-brain patterns of FC ("brain states"). The results provide evidence that dynamic correlations are reliably detected in both test-retest data sets, and the DCC method outperforms SW methods in terms of the reliability of summary statistics. However, across all estimation methods, reliability of the brain state-derived measures was low. Notably, the results also show that the DCC-derived dynamic correlation variances are significantly more reliable than those derived using the non-parametric estimation methods. This is important, as the fluctuations of dynamic FC (i.e., its variance) has a strong potential to provide summary measures that can be used to find meaningful individual differences in dynamic FC. We therefore conclude that utilizing the variance of the dynamic connectivity is an important component in any dynamic FC-derived summary measure.

[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center