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Test-retest reliability of dynamic functional connectivity in resting state fMRI

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&NA; While static functional connectivity (sFC) of resting state fMRI (rfMRI) measures the average functional connectivity (FC) over the entire rfMRI scan, dynamic FC (dFC) captures the temporal variations of… Click to show full abstract

&NA; While static functional connectivity (sFC) of resting state fMRI (rfMRI) measures the average functional connectivity (FC) over the entire rfMRI scan, dynamic FC (dFC) captures the temporal variations of FC at shorter time windows. Although numerous studies have implemented dFC analyses, only a few studies have investigated the reliability of dFC and this limits the biological interpretation of dFC. Here, we used a large cohort (N = 820) of subjects and four rfMRI scans from the Human Connectome Project to systematically explore the relationship between sFC, dFC and their test‐retest reliabilities through intra‐class correlation (ICC). dFC ICC was explored through the sliding window approach with three dFC statistics (standard deviation, ALFF, and excursion). Excursion demonstrated the highest dFC ICC and the highest age prediction accuracy. dFC ICC was generally higher at window sizes less than 40 s. sFC and dFC were negatively correlated. Compared to sFC, dFC was less reliable. While sFC and sFC ICC were positively correlated, dFC and dFC ICC were negatively correlated, indicating that FC that was more dynamic was less reliable. Intra‐network FCs in the frontal‐parietal, default mode, sensorimotor and visual networks demonstrated high sFC and low dFC. Moreover, ICCs of both sFC and dFC in these regions were higher. The above results were consistent across two brain atlases and independent component analysis‐based networks, multiple window sizes and all three dFC statistics. In summary, dFC is less reliable than sFC and additional experiments are required to better understand the neurophysiological relevance of dFC.

Keywords: dfc; sfc; icc; resting state; functional connectivity

Journal Title: NeuroImage
Year Published: 2018

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