Phase Synchrony among Neuronal Oscillations in the Human Cortex

By J. Matias Palva, Satu Palva, Kai Kaila. Journal of Neuroscience, 2005

683 citations recorded by the source corpus.

Synchronization of neuronal activity, often associated with network oscillations, is thought to provide a means for integrating anatomically distributed processing in the brain. Neuronal processing, however, involves simultaneous oscillations in various frequency bands. The mechanisms involved in the integration of such spectrally distributed processing have remained enigmatic. We demonstrate, using magnetoencephalography, that robust cross-frequency phase synchrony is present in the human cortex among oscillations with frequencies from 3 to 80 Hz. Continuous mental arithmetic tasks demanding the retention and summation of items in the working memory enhanced the cross-frequency phase synchrony among α (∼10 Hz), β (∼20 Hz), and γ (∼30-40 Hz) oscillations. These tasks also enhanced the “classical” within-frequency synchrony in these frequency bands, but the spatial patterns of α, β, and γ synchronies were distinct and, furthermore, separate from the patterns of cross-frequency phase synchrony. Interestingly, an increase in task load resulted in an enhancement of phase synchrony that was most prominent between γ- and α-band oscillations. These data indicate that cross-frequency phase synchrony is a salient characteristic of ongoing activity in the human cortex and that it is modulated by cognitive task demands. The enhancement of cross-frequency phase synchrony among functionally and spatially distinct networks during mental arithmetic tasks posits it as a candidate mechanism for the integration of spectrally distributed processing.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d4ad75ae158028b45d595e6ba9bc4baa01b40f17

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Citations in the corpus

Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions (2014, methodology): “…C EP TE D M AN U SC R IP T invasively recorded neuronal signals, for instance among EEG sensors (Doesburg et al., 2008; Gruber and Müller, 2005), MEG sensors (Palva et al., 2005) and cortical regions as obtained using source reconstruction methods (Palva and Palva, 2012; S. Palva et al., 2010).”
Inter-Brain Synchronization during Social Interaction (2010, neutral): “have been proposed as a key mechanism for information integration [28], temporal binding [51], flexible neuronal communication [34,52] and unified cognitive processes [45,53].”
New vistas for α-frequency band oscillations (2007, neutral): “However, in studies reporting strengthened a-phase synchrony, some show a simultaneous amplitude increase [ 11 ,30,31], whereas others show an associated amplitude suppression [9,10,12].”
Transfer entropy—a model-free measure of effective connectivity for the neurosciences (2010, background): “Complicated types of coupling such as crossfrequency phase coupling (Palva et al. 2005) should be readily detectable without prior specification, e.”
The role of oscillations and synchrony in cortical networks and their putative relevance for the pathophysiology of schizophrenia. (2008, background): “For example, phase-to-phase interactions have been observed between several frequency bands by Palva et al.(100) The authors tested cross-frequency coupling (n:m phase synchrony) inMEGdata through examining phase synchronization between oscillations during amental arithmetic task.”
Nonlinear multivariate analysis of neurophysiological signals. (2005, background): “However, higher order PS is not unlikely to be present in these data (Bhattacharya and Petsche, 2005b; Palva et al., 2005; Tass et al., 1998) and actually, the existence of locking (or entrainment) between frequencies that are close to rational relation (nω1≈mω2) is one of the hallmarks of this…”
The role of phase synchronization in memory processes (2011, background): “Additionally, magnetoencephalography (MEG) data in an arithmetic task requiring maintenance and manipulation of information in working memory have suggested a load-dependent enhancement of alpha–gamma couplin”
Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition (2010, background): “On possibility is phase-to-phase coupling (Palva et al., 2005), e.g., the phase 2009).”