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Decomposing neural synchrony: toward an explanation for near-zero phase-lag in cortical oscillatory networks.

Rajagovindan R, Ding M - PLoS ONE (2008)

Bottom Line: Recent theories have considered the importance of this phenomenon in establishing an effective communication framework among neuronal ensembles.Two factors, among possibly others, can be hypothesized to contribute to the near-zero phase-lag relationship: (1) positively correlated common input with no significant relative time delay and (2) bidirectional interaction.A rigorous mathematical theorem is used for the first time to empirically examine the factors that contribute to this phenomenon.

View Article: PubMed Central - PubMed

Affiliation: The J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.

ABSTRACT

Background: Synchronized oscillation in cortical networks has been suggested as a mechanism for diverse functions ranging from perceptual binding to memory formation to sensorimotor integration. Concomitant with synchronization is the occurrence of near-zero phase-lag often observed between network components. Recent theories have considered the importance of this phenomenon in establishing an effective communication framework among neuronal ensembles.

Methodology/principal findings: Two factors, among possibly others, can be hypothesized to contribute to the near-zero phase-lag relationship: (1) positively correlated common input with no significant relative time delay and (2) bidirectional interaction. Thus far, no empirical test of these hypotheses has been possible for lack of means to tease apart the specific causes underlying the observed synchrony. In this work simulation examples were first used to illustrate the ideas. A quantitative method that decomposes the statistical interdependence between two cortical areas into a feed-forward, a feed-back and a common-input component was then introduced and applied to test the hypotheses on multichannel local field potential recordings from two behaving monkeys.

Conclusion/significance: The near-zero phase-lag phenomenon is important in the study of large-scale oscillatory networks. A rigorous mathematical theorem is used for the first time to empirically examine the factors that contribute to this phenomenon. Given the critical role that oscillatory activity is likely to play in the regulation of biological processes at all levels, the significance of the proposed method may extend beyond systems neuroscience, the level at which the present analysis is conceived and performed.

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Coherence and phase spectra for site pair (S1,7b) in monkey GE.(A) Coherence spectra for site pair (S1,7b) in monkey GE indicating strong beta band synchrony. (B) The relative phase spectra for the same site pair. The near-zero phase-lag in the beta band is the result of both positively correlated common input and reciprocal interaction.
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pone-0003649-g005: Coherence and phase spectra for site pair (S1,7b) in monkey GE.(A) Coherence spectra for site pair (S1,7b) in monkey GE indicating strong beta band synchrony. (B) The relative phase spectra for the same site pair. The near-zero phase-lag in the beta band is the result of both positively correlated common input and reciprocal interaction.

Mentions: Inspection of the causality spectrum between the site pair (S1,7b) in monkey GE revealed the presence of bidirectional interaction in the beta band (Fig. 1(B) and Fig. 2(A)). The coherence function and the associated phase spectrum are shown in Figure 5. This channel pair was further analyzed to identify the effect of bidirectional interaction on phase-lag. The strength of directional interaction is expressed as the sum of feedforward and feedback influences. After partialing out the influence of the common input, the magnitude of phase-lag was found to be negatively correlated with the strength of reciprocal interaction (r = −0.455, p = 0.0045). This result supports our early assertion that, in addition to instantaneous causality, bidirectional interaction may also contribute to near-zero phase-lag.


Decomposing neural synchrony: toward an explanation for near-zero phase-lag in cortical oscillatory networks.

Rajagovindan R, Ding M - PLoS ONE (2008)

Coherence and phase spectra for site pair (S1,7b) in monkey GE.(A) Coherence spectra for site pair (S1,7b) in monkey GE indicating strong beta band synchrony. (B) The relative phase spectra for the same site pair. The near-zero phase-lag in the beta band is the result of both positively correlated common input and reciprocal interaction.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2573956&req=5

pone-0003649-g005: Coherence and phase spectra for site pair (S1,7b) in monkey GE.(A) Coherence spectra for site pair (S1,7b) in monkey GE indicating strong beta band synchrony. (B) The relative phase spectra for the same site pair. The near-zero phase-lag in the beta band is the result of both positively correlated common input and reciprocal interaction.
Mentions: Inspection of the causality spectrum between the site pair (S1,7b) in monkey GE revealed the presence of bidirectional interaction in the beta band (Fig. 1(B) and Fig. 2(A)). The coherence function and the associated phase spectrum are shown in Figure 5. This channel pair was further analyzed to identify the effect of bidirectional interaction on phase-lag. The strength of directional interaction is expressed as the sum of feedforward and feedback influences. After partialing out the influence of the common input, the magnitude of phase-lag was found to be negatively correlated with the strength of reciprocal interaction (r = −0.455, p = 0.0045). This result supports our early assertion that, in addition to instantaneous causality, bidirectional interaction may also contribute to near-zero phase-lag.

Bottom Line: Recent theories have considered the importance of this phenomenon in establishing an effective communication framework among neuronal ensembles.Two factors, among possibly others, can be hypothesized to contribute to the near-zero phase-lag relationship: (1) positively correlated common input with no significant relative time delay and (2) bidirectional interaction.A rigorous mathematical theorem is used for the first time to empirically examine the factors that contribute to this phenomenon.

View Article: PubMed Central - PubMed

Affiliation: The J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.

ABSTRACT

Background: Synchronized oscillation in cortical networks has been suggested as a mechanism for diverse functions ranging from perceptual binding to memory formation to sensorimotor integration. Concomitant with synchronization is the occurrence of near-zero phase-lag often observed between network components. Recent theories have considered the importance of this phenomenon in establishing an effective communication framework among neuronal ensembles.

Methodology/principal findings: Two factors, among possibly others, can be hypothesized to contribute to the near-zero phase-lag relationship: (1) positively correlated common input with no significant relative time delay and (2) bidirectional interaction. Thus far, no empirical test of these hypotheses has been possible for lack of means to tease apart the specific causes underlying the observed synchrony. In this work simulation examples were first used to illustrate the ideas. A quantitative method that decomposes the statistical interdependence between two cortical areas into a feed-forward, a feed-back and a common-input component was then introduced and applied to test the hypotheses on multichannel local field potential recordings from two behaving monkeys.

Conclusion/significance: The near-zero phase-lag phenomenon is important in the study of large-scale oscillatory networks. A rigorous mathematical theorem is used for the first time to empirically examine the factors that contribute to this phenomenon. Given the critical role that oscillatory activity is likely to play in the regulation of biological processes at all levels, the significance of the proposed method may extend beyond systems neuroscience, the level at which the present analysis is conceived and performed.

Show MeSH