Phase-lags in large scale brain synchronization : Methodological considerations and in-silico analysis

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Petkoski , S , Palva , J M & Jirsa , V K 2018 , ' Phase-lags in large scale brain synchronization : Methodological considerations and in-silico analysis ' , PLoS Computational Biology , vol. 14 , no. 7 , 1006160 . https://doi.org/10.1371/journal.pcbi.1006160

Title: Phase-lags in large scale brain synchronization : Methodological considerations and in-silico analysis
Author: Petkoski, Spase; Palva, J. Matias; Jirsa, Viktor K.
Contributor: University of Helsinki, Doctoral Programme Brain & Mind
Date: 2018-07
Language: eng
Number of pages: 30
Belongs to series: PLoS Computational Biology
ISSN: 1553-7358
URI: http://hdl.handle.net/10138/239261
Abstract: Architecture of phase relationships among neural oscillations is central for their functional significance but has remained theoretically poorly understood. We use phenomenological model of delay-coupled oscillators with increasing degree of topological complexity to identify underlying principles by which the spatio-temporal structure of the brain governs the phase lags between oscillatory activity at distant regions. Phase relations and their regions of stability are derived and numerically confirmed for two oscillators and for networks with randomly distributed or clustered bimodal delays, as a first approximation for the brain structural connectivity. Besides in-phase, clustered delays can induce anti-phase synchronization for certain frequencies, while the sign of the lags is determined by the natural frequencies and by the inhomogeneous network interactions. For in-phase synchronization faster oscillators always phase lead, while stronger connected nodes lag behind the weaker during frequency depression, which consistently arises for in-silico results. If nodes are in antiphase regime, then a distance Pi is added to the in-phase trends. The statistics of the phases is calculated from the phase locking values (PLV), as in many empirical studies, and we scrutinize the method's impact. The choice of surrogates do not affects the mean of the observed phase lags, but higher significance levels that are generated by some surrogates, cause decreased variance and might fail to detect the generally weaker coherence of the interhemispheric links. These links are also affected by the non-stationary and intermittent synchronization, which causes multimodal phase lags that can be misleading if averaged. Taken together, the results describe quantitatively the impact of the spatio-temporal connectivity of the brain to the synchronization patterns between brain regions, and to uncover mechanisms through which the spatio-temporal structure of the brain renders phases to be distributed around 0 and Pi.
Subject: STATE FUNCTIONAL CONNECTIVITY
TIME-DELAY
COUPLED OSCILLATORS
NEURONAL OSCILLATIONS
CEREBRAL-CORTEX
KURAMOTO MODEL
DYNAMICS
NETWORKS
FLUCTUATIONS
MECHANISMS
3112 Neurosciences
1182 Biochemistry, cell and molecular biology
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