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      Modular co-organization of functional connectivity and scale-free dynamics in the human brain

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          Abstract

          Scale-free neuronal dynamics and interareal correlations are emergent characteristics of spontaneous brain activity. How such dynamics and the anatomical patterns of neuronal connectivity are mutually related in brain networks has, however, remained unclear. We addressed this relationship by quantifying the network colocalization of scale-free neuronal activity—both neuronal avalanches and long-range temporal correlations (LRTCs)—and functional connectivity (FC) by means of intracranial and noninvasive human resting-state electrophysiological recordings. We found frequency-specific colocalization of scale-free dynamics and FC so that the interareal couplings of LRTCs and the propagation of neuronal avalanches were most pronounced in the predominant pathways of FC. Several control analyses and the frequency specificity of network colocalization showed that the results were not trivial by-products of either brain dynamics or our analysis approach. Crucially, scale-free neuronal dynamics and connectivity also had colocalized modular structures at multiple levels of network organization, suggesting that modules of FC would be endowed with partially independent dynamic states. These findings thus suggest that FC and scale-free dynamics—hence, putatively, neuronal criticality as well—coemerge in a hierarchically modular structure in which the modules are characterized by dense connectivity, avalanche propagation, and shared dynamic states.

          Author Summary

          The framework of criticality has been suggested to explain the scale-free dynamics of neuronal activity in complex interaction networks. However, the in vivo relationship between scale-free dynamics and functional connectivity (FC) has remained unclear. We used human intracranial and noninvasive electrophysiological measurements to map scale-free dynamics and connectivity. We found that the propagation of fast activity avalanches and the interareal coupling of slow, long-range temporal correlations—two key forms of scale-free neuronal dynamics—were nontrivially colocalized with the strongest functional connections. Most importantly, scale-free dynamics and FC exhibited similar modular network structures. FC and scale-free dynamics, and possibly also neuronal criticality, appear to co-emerge in a modular architecture in which the modules are characterized internally by shared dynamic states, avalanche propagation, and dense functional connectivity.

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          Most cited references56

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          Power-law distributions in empirical data

          Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
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            Large-scale cortical correlation structure of spontaneous oscillatory activity.

            Little is known about the brain-wide correlation of electrophysiological signals. We found that spontaneous oscillatory neuronal activity exhibited frequency-specific spatial correlation structure in the human brain. We developed an analysis approach that discounts spurious correlation of signal power caused by the limited spatial resolution of electrophysiological measures. We applied this approach to source estimates of spontaneous neuronal activity reconstructed from magnetoencephalography. Overall, correlation of power across cortical regions was strongest in the alpha to beta frequency range (8–32 Hz) and correlation patterns depended on the underlying oscillation frequency. Global hubs resided in the medial temporal lobe in the theta frequency range (4–6 Hz), in lateral parietal areas in the alpha to beta frequency range (8–23 Hz) and in sensorimotor areas for higher frequencies (32–45 Hz). Our data suggest that interactions in various large-scale cortical networks may be reflected in frequency-specific power envelope correlations.
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              Investigating the electrophysiological basis of resting state networks using magnetoencephalography.

              In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation-level-dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.
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                Author and article information

                Journal
                Netw Neurosci
                Netw Neurosci
                netn
                Network Neuroscience (Cambridge, Mass.)
                MIT Press (One Rogers Street, Cambridge, MA 02142-1209USAjournals-info@mit.edu )
                2472-1751
                June 01 2017
                Spring 2017
                : 1
                : 2
                : 143-165
                Affiliations
                [1 ]Neuroscience Center, University of Helsinki, Finland
                [2 ]BioMag laboratory, HUS Medical Imaging Center, Helsinki University Central Hospital, Finland
                [3 ]Department of Computer Science, University of Helsinki, Finland
                [4 ]Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Italy
                [5 ]Claudio Munari Epilepsy Surgery Centre, Niguarda Hospital, Italy
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [* ]Corresponding author: alexander.zhigalov@ 123456helsinki.fi .
                Article
                NETN_a_00008
                10.1162/NETN_a_00008
                5988393
                29911674
                184d0daa-c634-4964-85db-6d54be9c6538
                © 2017 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 November 2016
                : 19 February 2017
                Funding
                Funded by: Vetenskapsrådet, FundRef http://dx.doi.org/10.13039/501100004359;
                Award ID: 621-2012-4911
                Funded by: Vetenskapsrådet, FundRef http://dx.doi.org/10.13039/501100004359;
                Award ID: 013-61X-08276-26-4
                Categories
                Research
                Custom metadata
                Zhigalov, A., Arnulfo, G., Nobili, L., Palva, S., & Palva, J. M. (2017). Modular co-organization of functional connectivity and scale-free dynamics in the human brain. Network Neuroscience, 1(2), 143–165. https://doi.org/10.1162/netn_a_00008

                scale-free dynamics,functional connectome,modular networks,neuronal avalanches,long-range temporal correlations

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