EEG-based Functional Network Constructed Using Nonlinear Method And Analysis

The functional architecture of the human brain has been extensively described interms of functional networks. In recent years, there has been an explosion of studies onbrain functional network, based largely on graph theory. These studies have consistentlyshown many important statistical properties of the human brain, includingsmall-worldness, modularity, and the existence of highly connected network hubs.Importantly, some quantifiable network properties were related to cognitive function,aging, and various neuropsychiatric diseases.In this study, likelihood synchronization method definition and principles weredescribed in detail. By programming and simulating, the validity of the method has beenverified that can better capture the synchronization relationship between thenon-stationary signals. Then some parameters commonly used in the network statisticalmethods and network analysis were introduced in this papers. The final part of thisstudy, including the practical application of the above-described method:⑴Concernedabout major depression patients and healthy subjects, EEG data of the emotionalpictures stimulation tasks were used to construct functional brain networks. Thestatistics showed that some network parameters are significantly different between twogroups, and the HAMD scores of patients has a significant positive correlation with thenormal clustering coefficient. In summary, this study indicated that cognitiveimpairment with depression may be due to the lack of long distance connection infunctional brain networks.⑵Using the visual search experimental paradigm to studythe impact of age, attention mechanisms to functional brain network. By statisticalanalysising and network module detecting, the result support that fronto-parietalnetwork is more involved in top-down search processes and temporoparietal regionmore involved in bottom-up search processes. Also found that the elder need not onlythe fronto-parietal network compensation cognitive, also need more active in the righthemisphere of the brain network to compete the visual search task.