Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

Abstract

An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity.

Charting moment-to-moment brain signal variability from early to late childhood

“…A potentially related finding is the observation of generally increased EEG coherence within the right compared to the left hemisphere of adults (Tucker et al, 1986), which was originally attributed to the greater white-to-gray matter ratio of the right hemisphere (Gur et al, 1980). Given evidence that increased functional connectivity is positively correlated with the variability of signals recorded using multiple imaging modalities (McDonough & Nashiro, 2014; Misic et al, 2011), this may provide a partial explanation for our findings. We remain skeptical of this interpretation, however, given that Barry and colleagues (2012) discovered enhanced left hemisphere EEG coherence in a sample of children that was similar in age to our community sample, and we are unaware of any systematic factors that would explain the apparent discrepancy.…”

“…Given that the complexity of neural signals is positively related to the degree of functional connectivity (McDonough & Nashiro, 2014; Misic et al, 2011), specifically between distal brain areas (Vakorin et al, 2011), we expected to observe a parametric increase in MSE values of EEG signals from early to late childhood at multiple time scales, perhaps with a stronger magnitude of differences at the coarser scales. Moreover, we hypothesized that these developmental trends would honor well-known spatial gradients of electrocortical maturation, with posterior cortical areas developing prior to more anterior zones (Matousek & Petersen, 1973).…”

“…Although largely overlooked in the past, the amount of information integrated across cortical networks ( i.e. , greater information transmission) increases the unpredictability of signals recorded at the population level (McDonough & Nishira, 2014; Misic et al, 2011; Vakorin et al, 2011). The irregularity of large-scale neuronal signals, in this case, simultaneously reflects and promotes the exploration of a diversified network repertoire, thereby extending the brain’s representational capacity (Deco & Kringelbach, 2016; Ghosh et al, 2008).…”

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

“…The amount of data points was sufficient for SE calculation, as recommended in [34,78] (at least 10 m and preferable 30 m , that is, 100 to 900). Moreover, a minimum of 50 data points was also reported in EEG/MEG [52,79] and fMRI [28,60] studies. Figure 1 illustrates examples of time series data ranging from fine to coarse scales.…”

Hierarchical dynamics of informational patterns and decision-making

“…In this paper, we consider the hierarchical dynamics of cognitive networks that represent different mental activities not in a physical brain space, as in [11][12][13], but in a cognitive space, i.e. in the phase space of the corresponding dynamical model that describes such a process.…”