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
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.
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
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)
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.
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 ).
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.
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.
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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 partsmean degree: 4-30,increasedby 2.The thresholds as a function of mean degree k .⟨ ⟩
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
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
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