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In 2014, McDonough and Nashiro [1] derived multiscale entropy -a marker of signal complexity-from resting state functional MRI data (rsfMRI), and found that functional brain networks displayed unique multiscale entropy fingerprints. This is a finding with potential impact as an imaging-based marker of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of this study was that rsfMRI data from only 20 healthy individuals was used for analysis. To overcome this limitation, we aimed to replicate McDonough and Nashiro's finding in a large cohort of healthy subjects. We used rsfMRI from the Human Connectome Project (HCP) comprising 936 gender-matched healthy young adults aged 22-35, each with 4 × 14.4-minute rsfMRI data from 100 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We also performed a test-retest analysis on the data of four recording sessions in 10 previously reported resting state networks (RSNs). Given that the morphology of multiscale entropy patterns is affected by the choice of the tolerance parameter (r ), we performed the analyses at two r values: 0.5, similar to the original study and 0.15, a commonly used option in the literature. Our results were similar to previous findings by McDonough and Nashiro emphasising high temporal complexity in the default mode network and fronto-parietal networks, and low temporal complexity in the cerebellum. We also investigated the effect of temporal resolution (determined by fMRI repetition time) by downsampling rsfMRI time series. At a low temporal resolution, we observed increased entropy and variance across datasets likely due to fewer data points in the multiscale entropy analysis. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially for r = 0.5. We also showed a positive relationship between temporal complexity of RSNs and fluid intelligence (people's capacity to reason and think flexibly), suggesting that complex dynamics is an important attribute of optimized brain function.

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