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

Abstract

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.

Spatiotemporal complexity patterns of resting‐state bioelectrical activity explain fluid intelligence: Sex matters

“…The present study revealed that better gf task performance was associated with greater left frontal AUC and AvgEnt , higher parietal AvgEnt , and larger left frontal relative to right frontal AvgEnt (Table 2, Figure 9). These results are partially congruent with previous evidence from resting‐state fMRI studies (McDonough & Nashiro, 2014; Omidvarnia et al, 2019; Saxe, Calderone, & Morales, 2018) demonstrating increased brain entropy in the prefrontal cortex, inferior temporal lobes and cerebellum associated with higher gf (Saxe et al, 2018), a positive relationship of fluid intelligence with the complexity of resting‐state networks including the FPN (Omidvarnia et al, 2019) or with the temporal variability of the middle frontal, inferior parietal and visual cortices (Yang et al, 2019). A higher intelligence level also turned out to coexist with more efficient organization of the whole‐brain network (van den Heuvel et al, 2009) or mainly the FPN (Duncan, 2010; Langer et al, 2012).…”

“…In all of these cases, the mMSE vectors were stable and characterized by a skewed inverted‐U shape across time scales, which is typical for EEG and MEG signals (Costa et al, 2005; Courtiol et al, 2016; Grandy, Garrett, Schmiedek, & Werkle‐Bergner, 2016; Kosciessa et al, 2019; Kuntzelman, Jack Rhodes, Harrington, & Miskovic, 2018; see Figure 8). Indeed, this pattern also persists in other modalities, such as fMRI (e.g., Grandy et al, 2016; McDonough & Nashiro, 2014; McDonough & Siegel, 2018; Omidvarnia, Zalesky, Ville, Jackson, & Pedersen, 2019) or in simulation studies (e.g., Courtiol et al, 2016; Grandy et al, 2016; Kuntzelman et al, 2018). For our dataset the mMSE values stabilized at the coarse‐grained time series for scale ε = 12.…”

“…This concept has recently met some criticism (e.g., Kosciessa et al, 2019) and, therefore, should be treated with caution. Some methodological constraints of distinguishing fine and coarse timescales have also been pointed out (Omidvarnia et al, 2019). Specifically, fine entropy was thought to be more repeatable than coarse complexity.…”

“…Therefore, the stability of mMSE vectors should be checked with the use of internal consistency or test–retest methods, especially longitudinal stability. To date, attempts to do this have been made in a few EEG (Kuntzelman et al, 2018) and fMRI studies (McDonough & Siegel, 2018; Omidvarnia et al, 2019), and only for selected entropy algorithms.…”

Avalanche criticality in individuals, fluid intelligence and working memory

“…This left us with 295 subjects for further analysis, and 162 of them were females. All the participants were between the ages of 22-36, 58 were between the ages of [22][23][24][25]130 were between the ages of [26][27][28][29][30]104 were between the ages of 31-35, and 3 were 36 years old.…”