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.
Highlights
Research over the past decade has provided evidence that the temporal fluctuations of brain activity are crucial and fundamental properties of the human brain (e.g., Biswal et al, 2010; Deco et al, 2011; Wig et al, 2011)
The current study examined the relationship between neural complexity and functional connectivity in each of the resting-state www.frontiersin.org networks (RSNs) to test our hypothesis that they would be related in all networks, but the complexity-connectivity relationship would depend on time scale
RESTING-STATE NETWORKS To verify that the dual regression analyses appropriately captured the four RSNs of interest, we averaged the subject-specific spatial maps
Summary
Research over the past decade has provided evidence that the temporal fluctuations of brain activity are crucial and fundamental properties of the human brain (e.g., Biswal et al, 2010; Deco et al, 2011; Wig et al, 2011). The synchrony, or correlation, of these temporal fluctuations between regions (i.e., functional connectivity) is one way to assess the communication of information in the brain and give rise to separate functional networks. These networks have been associated with specific cognitive processes (Seeley et al, 2007; Dosenbach et al, 2008; Vincent et al, 2008; Smith et al, 2009; Laird et al, 2011) and disruptions of these networks are associated with corresponding clinical disorders (e.g., Seeley et al, 2009; Posner et al, 2014). The more complex the pattern of brain activity, the more rich the information (e.g., Tononi et al, 1994, 1998; Garrett et al, 2013; Nakagawa et al, 2013) or more integrated the information (e.g., Vakorin et al, 2011; McIntosh et al, 2013)
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