Resting-state Functional Connectivity Analysis Based On EEG And FMRI

With the development of brain imaging technology, electroencephalogram(EEG) and functional Magnetic Resonance Imaging(fMRI) have been used widely since its non-invasive. Activity of different brain regions correlated even in the absence of an externally prompted task, this absence of task is called resting state, and the correlation between activity signal time courses of distinct brain regions is called functional connectivity(FC). Interest changed from locating specific brain regions to study FC. FC is meaningful to explore the brain?s functional organization and to examine if it is altered in neurological or psychiatric diseases in the resting state.This paper aims to study FC of EEG in the resting state and FC of fMRI in the resting state to tackle the challenge of baseline choosing in cognitive neuroscience and depression diagnosing in psychiatric diseases. The main contents of this paper as follows.1) In order to tackle the challenge of the unstability of FC at resting state and the lack of understanding of baseline state, effective strategies of high time resolution EEG was applied. Here we investigated the dynamic FC based on the 64 electrodes EEG of 25 healthy subjects in eyes closed(EC) and eyes open(EO) resting-state. A data-driven approach based on independent component analysis, standardized low-resolution tomography analysis, sliding time window and graph theory were employed. Dynamic changes of FC over time with EC and EO in the visual network, the default mode network etc. were discovered. And principal component analysis was used to the concatenated dynamic FC matrix for finding meaningful FC patterns. These patterns are similar with fMRI resting state network, we demonstrate the feasibility of this new approach by analysising the EEG data in dynamic FC. Our results complement traditional stationary analysis, and reveal novel insights into choosing the type of resting condition in experimental design and EEG clinical research.2) In order to solve the problem that the lack of depression diagnosis effectively, effective strategies of high spatial resolution fMRI was applied. 22 healthy subjects and 19 patients with depressive fMRI data were used. Resting-state FC network was constructed by using graph theory. Analysis of the resulting network shows statistical characteristic of complex network: the distribution of degree is close to the scale-free network. The clustering coefficient is orders of magnitude larger than those of equivalent random networks, which is typical of small-world network. To the patients with depressive disorder, the characteristic index small-world of their FC network is obviously different from the control group. By applying principal component analysis to the dynamic FC in the resting state, meaningful FC patterns which were different between patients and control group were discovered. These patterns revealed the main differences located at emotional and cognitive control network. Finally, dynamic change of FC in several local area was shown by using sliding window. Those results, which provides a new idea and approach to investigate the effect and regulation mechanism of depression.