报告人：蔡世民 合作者：禚钊，乔赫元，傅忠谦，周佩玲 电子科学与技术系

1. Cluster structure and localization of brain functional networks based on the ERP signals of auditory task 报告人：蔡世民 合作者：禚钊，乔赫元，傅忠谦，周佩玲 电子科学与技术系

2. Outline • Introduction • Data Acquisition • Phase Synchronization • Results

3. Introduction • What is brain functional networks? A brain functional network can be derived from the physiological signals such as EEG,MEG, ECoG, and fMRI. Nodes: ROIs (fMRI) or channels (EEG,MEG,ECoG). Edges : correlation (interaction) between ROIs or channels.

4. Introduction (cont.) • Construction of large-scale brain functional networks --Pearson correlation coefficient --Correlation coefficient based on Wavelet transform --Mutual information --Nonlinear interdependence --Phase synchronization based on Hilbert transform

5. Introduction (cont.)

6. Introduction (cont.) • Brain functional networks posses some common structures of complex networks --small-world property (D. S. Bassett, Neuroscientist 12, 512, 2006) --scale-free property (V. M. Eguiluz, et al. PRL 94, 018102, 2005) --Hierarchical organization (C. S. Zhou, et al. PRL 97, 238103, 2006)

7. Data Acquisition • Five persons were asked to distinguish between synonymous and non-synonymous word pairs (the second word presented 1 second after the first) they heard. • Data epochs were extracted from 2 sec before the second word onset to 2 sec after the second word onset. • Sampling rate (Hz) 200.

8. Data Acquisition (cont.) • 61-channel ERP signal. Letters refer to the main areas of the cortex: F: the frontal (额叶), T: left and right temporal (颞叶), P: the parietal (顶叶), O: the occipital (枕叶), C : central, FP: frontopolar(额极), AF: anterior frontal(前额叶).

9. Data Acquisition (cont.) • The testee was cued to move a particular figure by displaying the corresponding word, such as “thumb”; • Each cue lasted two seconds following an another two seconds resting period; • Band pass filtered between 0.15 and 200 Hz, and sampled at 1000 Hz; • The experiment lasted 400 seconds for echa testee.

10. Data Acquisition (cont.) Sketch of ECoG recording

11. Phase Synchronization • Phase of real value time series • bivariate phase synchronization index If two time series are complete phase synchronized, this value will be the maximum.

12. Generating Networks • Divide data into four parts:1st ,2nd ,3rd and 4th second. resting state: 1st and 4th seconds; auditory task state: 2nd and 3rd seconds • Fixed mean degree for four parts mean degree: 4-30,increased by 2. The thresholds as a function of mean degree ⟨k⟩.

13. Generating Networks (cont.) • Divide data into two parts: -- task state: 1st 2 seconds -- resting: 2nd 2 seconds • Fixed mean degree for two parts --mean degree: 4-30,increased by 2. ECoG

14. Results • Networks show different property during rest and task state for EEG

15. Results (cont.) • Networks show different property during • rest and task state for ECoG

16. Results (cont.)

17. Results (cont.) ECoG EEG Networks show small-world property

18. 6.Conclusion and outlook • The diversity of topology between the resting and task states suggests the variance of correlations among the functional modules. • The larger cluster coefficients during task mean that the correlations of cortex regions are more localized in the large-scale brain functional networks • The connectivity of networks under task state presents a better performance than that under resting state via the estimation of giant components’ sizes. • Moreover, the mean path lengths of brain functional networks confirm the small world property. • Future work will focus on the location of community during the cognitive process and the relationship between the large-scale functional networks and micro-scale neural dynamics via diffusion tensor imaging