Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Abstract It has been hypothesized that resting state networks (RSNs) likely display unique temporal complexity fingerprints, quantified by their multi-scale entropy patterns McDonough and Nashiro (2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that resting state functional magnetic resonance imaging (rsfMRI) data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 1000 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-minute rsfMRI recordings and parcellated into 379 brain regions. We quantified multi-scale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multi-scale entropy is affected by the choice of its tolerance parameter ( r ) and embedding dimension ( m ), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014) . Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the sub-cortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time T R ) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a significant relationship was observed between temporal complexity of RSNs and fluid intelligence (people’s capacity to reason and think flexibly) suggesting that complex dynamics of the human brain is an important attribute of high-level brain function..