Brain Criticality Predicts Individual Levels of Inter-areal Synchronization in Human Electrophysiological Data

Overview

Journal Nat Commun

Specialty Biology

Date 2023 Aug 7

PMID 37550300

Authors

Marco Fusca

Felix Siebenhuhner

Sheng H Wang

Vladislav Myrov

Gabriele Arnulfo

Lino Nobili

J Matias Palva

Satu Palva

Affiliations

Soon will be listed here.

Abstract

Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.

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References

Siegel M, Donner T, Engel A . Spectral fingerprints of large-scale neuronal interactions. Nat Rev Neurosci. 2012; 13(2):121-34. DOI: 10.1038/nrn3137. View

Haldeman C, Beggs J . Critical branching captures activity in living neural networks and maximizes the number of metastable States. Phys Rev Lett. 2005; 94(5):058101. DOI: 10.1103/PhysRevLett.94.058101. View

Toker D, Pappas I, Lendner J, Frohlich J, Mateos D, Muthukumaraswamy S . Consciousness is supported by near-critical slow cortical electrodynamics. Proc Natl Acad Sci U S A. 2022; 119(7). PMC: 8851554. DOI: 10.1073/pnas.2024455119. View

Smith R, Tournier J, Calamante F, Connelly A . Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage. 2012; 62(3):1924-38. DOI: 10.1016/j.neuroimage.2012.06.005. View

Shew W, Yang H, Petermann T, Roy R, Plenz D . Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J Neurosci. 2009; 29(49):15595-600. PMC: 3862241. DOI: 10.1523/JNEUROSCI.3864-09.2009. View

Arnulfo G, Wang S, Myrov V, Toselli B, Hirvonen J, Fato M . Long-range phase synchronization of high-frequency oscillations in human cortex. Nat Commun. 2020; 11(1):5363. PMC: 7584610. DOI: 10.1038/s41467-020-18975-8. View

Palva S, Palva J . Roles of Brain Criticality and Multiscale Oscillations in Temporal Predictions for Sensorimotor Processing. Trends Neurosci. 2018; 41(10):729-743. DOI: 10.1016/j.tins.2018.08.008. View

Honkanen R, Rouhinen S, Wang S, Palva J, Palva S . Gamma Oscillations Underlie the Maintenance of Feature-Specific Information and the Contents of Visual Working Memory. Cereb Cortex. 2014; 25(10):3788-801. DOI: 10.1093/cercor/bhu263. View

Deco G, Kringelbach M, Jirsa V, Ritter P . The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Sci Rep. 2017; 7(1):3095. PMC: 5465179. DOI: 10.1038/s41598-017-03073-5. View

10.

Candelaria-Cook F, Solis I, Schendel M, Wang Y, Wilson T, Calhoun V . Developmental trajectory of MEG resting-state oscillatory activity in children and adolescents: a longitudinal reliability study. Cereb Cortex. 2022; 32(23):5404-5419. PMC: 9712698. DOI: 10.1093/cercor/bhac023. View

11.

Bajo R, Castellanos N, Cuesta P, Aurtenetxe S, Garcia-Prieto J, Gil-Gregorio P . Differential patterns of connectivity in progressive mild cognitive impairment. Brain Connect. 2012; 2(1):21-4. DOI: 10.1089/brain.2011.0069. View

12.

Vinck M, Perrenoud Q . Layers of Rhythms - from Cortical Anatomy to Dynamics. Neuron. 2019; 101(3):358-360. DOI: 10.1016/j.neuron.2019.01.028. View

13.

Fisher R, Scharfman H, deCurtis M . How can we identify ictal and interictal abnormal activity?. Adv Exp Med Biol. 2014; 813:3-23. PMC: 4375749. DOI: 10.1007/978-94-017-8914-1_1. View

14.

Muller P, Meisel C . Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load. PLoS Comput Biol. 2023; 19(3):e1010919. PMC: 10027224. DOI: 10.1371/journal.pcbi.1010919. View

15.

Palva S, Palva J . Functional roles of alpha-band phase synchronization in local and large-scale cortical networks. Front Psychol. 2011; 2:204. PMC: 3166799. DOI: 10.3389/fpsyg.2011.00204. View

16.

Nikulin V, Brismar T . Long-range temporal correlations in alpha and beta oscillations: effect of arousal level and test-retest reliability. Clin Neurophysiol. 2004; 115(8):1896-908. DOI: 10.1016/j.clinph.2004.03.019. View

17.

Di Santo S, Villegas P, Burioni R, Munoz M . Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization. Proc Natl Acad Sci U S A. 2018; 115(7):E1356-E1365. PMC: 5816155. DOI: 10.1073/pnas.1712989115. View

18.

Muthukumaraswamy S . Functional properties of human primary motor cortex gamma oscillations. J Neurophysiol. 2010; 104(5):2873-85. DOI: 10.1152/jn.00607.2010. View

19.

Arnulfo G, Hirvonen J, Nobili L, Palva S, Palva J . Phase and amplitude correlations in resting-state activity in human stereotactical EEG recordings. Neuroimage. 2015; 112:114-127. DOI: 10.1016/j.neuroimage.2015.02.031. View

20.

Bruining H, Hardstone R, Juarez-Martinez E, Sprengers J, Avramiea A, Simpraga S . Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics. Sci Rep. 2020; 10(1):9195. PMC: 7280527. DOI: 10.1038/s41598-020-65500-4. View