Research Of Construction And Analysis Of Brain Network Based On EEG Data

The brain is an extremely complex network in nature.Its complexity is not only reflected in the hundreds of billions of neurons,but also expressed in complex connection between these neurons to direct the cognitive expression.In order to fully understand the function of the brain,the analysis based on event-related brain network is proposed.It provides a new way to present the information interaction patterns associated with cognitive activities between multiple regions,such as the introduction of complex network statistics.At the same time,it plays an important role in the exploration of schizophrenia pathology mechanism.According to the above analysis,this research aims to deepen the study of brain network node selection and construction,besides,and explore the differences in network measurements and causal connections between the two groups,as well as the neural origin of auditory P300 aberration in schizophrenic patients.First of all,the thesis introduces the method of brain network node selection,which mainly involves the necessity of brain source localization,the selection of brain model,the solution of the forward and inverse problem.When solving the inverse problem by using classical minimum norm estimation,the solution is unstable and not unique.Thus,in order to overcome these two problems,the algorithm of the weighted Tikhnoov regularization minimum norm estimation is proposed.Secondly,the method of the renormalized partial directed coherence based on Granger causality is described.It is used to build the brain network and avoids the priori hypothesis of non-linearity,unstability and randomness of EEG data.Then,EEG signals are recorded in 16 schizophrenic patients and 17 healthy controls during an auditory oddball paradigm.By means of the method described above,the corresponding brain networks are constructed.Finally,the statistical analysis of brain network between the two groups is completed.The results indicated: 1)the schizophrenic patients had reduced clustering coefficient,local and global efficiency and increased characteristic path length near the time P300 appears;2)P300 amplitude of normal controls was significantly correlated with network measurements while no such relationship existed in patients.As for the relationship between network topology characteristics and P300 latency,the two groups showed no correlation between them;3)the patients’ brain network had a wide range of disconnections between temporal and frontal/parietal/control motor cortices;4)the patients showed more effective connections negatively correlated with P300 amplitude.The findings suggest that the abnormal P300 amplitude of schizophrenic patients may result from directed connections between specific brain areas mainly associated with attention allocation and executive function as well as the disruption of perfect balance between functional integration and separation because of slower information transfer and less efficient information processing...