Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

Abstract

Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia

“…The rightful question arises of how the performance of entropy-related measures could be affected by the presence of long-range correlations. In an earlier study we found that regions with stronger autocorrelation expressed lower PE in their local FC dynamics and vice versa (Racz et al, 2019). However, according to Xiong et al (2017) this cannot be simply a consequence of long-range autocorrelation, as it only introduces a constant bias that is independent of the degree of autocorrelation.…”

“…Interestingly, in a subsequent study using the same dataset, the authors reported higher global SE in the SZ group that was later revealed to be the consequence of connections with higher SE in the visual recognition and auditory networks (Jia and Gu, 2019). These results may seem contradictory at first, nevertheless, in light of previous findings, they rather highlight the fact that FC dynamics vary greatly among brain regions (Racz et al, 2019) and that various regions could be affected in different ways in SZ. Our current results indicate a lower dynamical complexity of delta-band DFC in SZ.…”

“…Information-theoretical entropy-related measures (such as PE or SE) refer on the temporal complexity of the process with higher values implying more unpredictable behavior. Regional differences in PE has been shown to reflect the functional organization of the brain (Racz et al, 2019). It has also been reported recently that several dynamic connections of the amygdala show a decrease with aging in its complexity as measured by SE, however, this decrease was absent in patients suffering from SZ (Jia et al, 2017).…”

“…Specifically, it has been demonstrated by previous studies that regional alterations of DFC could play a relevant role in SZ (Damaraju et al, 2014;Jia et al, 2017;Jia and Gu, 2019), which may will be overlooked when investigating network characteristics on the global level only. Considering in addition, that multiscale and entropy-related properties of DFC were shown to express significant regional variability over the cortex (Racz et al, 2019), an extension of the current framework to the analysis of local connectivity dynamics appears as an important future research direction. In this study, only datasets of 14-14 HC individuals and SZ patients were analyzed, that limits the applicability and power of machine learning classifiers.…”

“…Temporal complexity of brain network dynamics can also be efficiently captured in entropy-related measures -which capture the information production rate of processes -such as sample entropy (SE) (Richman and Moorman, 2000) or permutation entropy (PE) (Bandt and Pompe, 2002). Indeed, temporal complexity of DFC has been shown to express characteristic regional patterns that reflect well the underlying functional organization of the brain (Racz et al, 2019). Similar studies revealed that patients with SZ express higher SE in their FC dynamics than healthy control (HC) individuals (Jia et al, 2017;Jia and Gu, 2019).…”