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Synchronous dynamic brain networks revealed by magnetoencephalography.

Abstract：

:We visualized synchronous dynamic brain networks by using prewhitened (stationary) magnetoencephalography signals. Data were acquired from 248 axial gradiometers while 10 subjects fixated on a spot of light for 45 s. After fitting an autoregressive integrative moving average model and taking the residuals, all pairwise, zero-lag, partial cross-correlations (PCC(ij)(0)) between the i and j sensors were calculated, providing estimates of the strength and sign (positive and negative) of direct synchronous coupling between neuronal populations at a 1-ms temporal resolution. Overall, 51.4% of PCC(ij)(0) were positive, and 48.6% were negative. Positive PCC(ij)(0) occurred more frequently at shorter intersensor distances and were 72% stronger than negative ones, on the average. On the basis of the estimated PCC(ij)(0), dynamic neural networks were constructed (one per subject) that showed distinct features, including several local interactions. These features were robust across subjects and could serve as a blueprint for evaluating dynamic brain function.

journal_name

Proc Natl Acad Sci U S A

journal_title

Proceedings of the National Academy of Sciences of the United States of America

authors

Langheim FJ,Leuthold AC,Georgopoulos AP

doi

10.1073/pnas.0509623102

keywords:

subject

Has Abstract

pub_date

2006-01-10 00:00:00

pages

455-9

issue

2

eissn

0027-8424

issn

1091-6490

pii

0509623102

journal_volume

103

pub_type

杂志文章

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