Synchronous dynamic brain networks revealed by magnetoencephalography

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

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 oij) between the 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 PCCoij were positive, and 48.6% were negative. Positive PCCoij occurred more frequently at shorter intersensor distances and were 72% stronger than negative ones, on the average. On the basis of the estimated PCCoij, 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.

Original languageEnglish (US)
Pages (from-to)455-459
Number of pages5
JournalProceedings of the National Academy of Sciences of the United States of America
Volume103
Issue number2
DOIs
StatePublished - Jan 10 2006

Keywords

  • Neural networks
  • Synchrony
  • Time-series analysis

Fingerprint

Dive into the research topics of 'Synchronous dynamic brain networks revealed by magnetoencephalography'. Together they form a unique fingerprint.

Cite this