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 language||English (US)|
|Number of pages||5|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Jan 10 2006|
- Neural networks
- Time-series analysis