EEG Functional Connectivity Analysis In The Resting State

With the development of brain imaging technology, the study of brain functionbecomes more and more deep. It changed from locating specific brain regions toexplaining the relationship between different brain regions dynamically. Resting stateis a special state of brain. Under this state, people do not perform specific cognitivetasks and keep quiet, relaxed and awake. Resting state functional connectivity reflectsthe nature and inherent connectivity modes of brain. In recent years, brain resting statefunctional connectivity has become a popular research field.At present, researchers mainly use functional Magnetic Resonance Imaging(fMRI) to study brain resting state functional connectivity. fMRI with a high spatialresolution can get close relationship between resting state functional connectivity andbrain anatomy structure. But the expensive fMRI device needs special experimentalenvironment to sample brain data. What’s more, temporal resolution of fMRI is low.So using fMRI to study brain functional connectivity has disadvantages. Whileelectroencephalography (EEG) as a common technology to monitor brain activity hasa high temporal resolution and the relatively inexpensive EEG device does not needspecial experimental environment to sample brain data. So it is one of the traditionalmeans of brain research. Using EEG to study brain resting state functionalconnectivity can make up the shortage of temporal resolution of fMRI to study brainfunctional connectivity. Besides, for those patients with brain diseases being sensitiveto small space of fMRI device, EEG can also monitor their brain activity. All in all,using EEG to analyze functional connectivity in the resting state has importantsignificance in scientific and clinical fields.We combined ICA, sLORETA, sliding time windows, graph theory, hierarchicalcluster analysis and t-test with EEG data to study brain resting state functionalconnectivity. High resolution EEG signals were collected by using Brain VisionAnalyzer64channels for25healthy participants under both eyes-closed andeyes-open resting states. We analyzed the functional connectivity of alpha (8-12Hz)and beta (13-25Hz) rhythm in the resting state respectively. We also explorednon-stationarity functional connectivity of alpha (8-12Hz) rhythm in the resting state.Our works revealed dynamic changes of brain resting state networks in temporal andspatial domains. The work mainly included three aspects as follows. For functional connectivity of alpha rhythm in the resting state, we analyzed theEEG alpha rhythm of64channels for25healthy participants under both eyes-closedand eyes-open resting states. The results demonstrate functional connectivity of alpharhythm in eyes-open state is more obvious than eyes-closed state. Default modenetwork and dorsal attention network of resting state networks in the eyes-open stateare more significant. Besides, visual association cortices are prominent in theeyes-open state. Our results make up the defect use of a limited number of electrodes(19channels) to analyze functional connectivity in the existing research and improvethe reliability of related conclusions.For functional connectivity of beta rhythm in the resting state, we analyzed theEEG beta rhythm of64channels for25healthy participants under both eyes-closedand eyes-open resting states. The results demonstrate functional connectivity of betarhythm in eyes-closed state is more obvious than eyes-open state. During the coursefrom eyes-closed to eyes-open states, the functional connectivity of beta rhythmdecreases in parietal, occipital and temporal regions of right hemisphere dominantlyand increases in bilateral frontal regions. Premotor cortex, primary somatosensorycortex, secondary somatosensory cortex, visual association cortices, default modenetwork are more significant in eyes-closed state. To the best of our knowledge, this isthe first study to achieve results of functional connectivity of beta rhythm in theresting state.For non-stationarity functional connectivity of alpha rhythm in the resting state,we applied sliding time windows to analyze the EEG alpha rhythm of64channels for25healthy participants under both eyes-closed and eyes-open resting states. Weexplored multiple resting state networks of alpha rhythm under both eyes-closed andeyes-open resting states changed dynamically over time. These resting state networksinclude default mode network, visual association cortices, somatosensory cortex,visual speech area, semantic processing cortex and so on. These results greatlyenriched the conclusions of functional connectivity of alpha rhythm in resting state.