Application Of Minimum Spanning Tree In Schizophrenia EEG Brain Network

With the increasing social pressure,mental illness has become one of the main factors leading to sub-health,and schizophrenia is the most common mental illness.The early diagnosis of the disease is also particularly important.Most of the current diagnostic methods were based on the Demographic and behavioral information,and the doctor’s subjective judgments based on these.However,there are a lot of subjective factors for the diagnosis of schizophrenia which has a unpredictable impact.With the development of complex network theory and brain function imaging,the study of schizophrenia disease based on EEG brain network has made some progress,which provides a new way for pathology cognition and early diagnosis of the disease.However,recent studies have pointed out that the traditional analysis method of complex network has some drawbacks.Traditional and complex research methods of brain network include the unweighted network and the weighted network.The choice of the threshold T is essentially random in relation to the problem that the threshold is not related to the threshold selection.The choice of thresholds may cause false or noisy connections in the network(T’s selection is smaller)or may discard some weak connections including important information in the network(T’s selection is larger).The number of edges has an impact on the measurement of the attributes of brain network.And the weighted network is also affected by the noise connection and the average functional connection strength.Therefore,the analysis method of the unweighted network and the weighted network may lead to unacceptable results due to the problem of methodology.Therefore,this study introduces the minimum spanning tree(MST)into the study of complex brain network,hoping to solve the problems existing in the traditional network analysis method,and makes valuable exploration in the EEG the analysis method of brain network.In the EEG brain network of schizophrenia and normal subjects,the study analyzed and discussed the three methods among unweighted network,the weighted network and the minimum spanning tree network.The EEG data used in this study includes 40 patients with schizophrenia and 40 normal subjects from the Beijing Huilongguan Hospital.All the data were preprocessed.The unweighted network and the weighted brain network were constructed by the phase lag index(PLI).Subsequently,the study achieved the aim that made the different attributes of the different groups of network attributes as a classification of classification characteristics of the study.Finally,from the theoretical analysis,correlation analysis and classification of the results proved that MST into the EEG brain network research is effective and feasible and a useful complement to the traditional method of complex brain network analysis.The main contents of this paper are as follows:(1)Different thresholds used to construct normal and schizophrenia control at different densities.Calculate the unweight brain network attributes of schizophrenia and normal subjects and extract the unweight network attributes with significant differences.(2)Calculate the weighted brain network attributes of schizophrenia and normal subjects and extract the weighted network attributes with significant differences.(3)Using Kruskal algorithm to construct the MST of schizophrenia and normal control.Calculate the MST brain network attributes of schizophrenia and normal subjects and extract the weighted network attributes with significant differences.(4)From the theoretical analysis,correlation analysis and classification of the results proved that MST into the EEG brain network research is effective and feasible and a useful complement to the traditional method of complex brain network analysis.In conclusion,MST has a better manifestation than traditional analysis method of complex network in EEG brain network research.And using it as a supplement to traditional complex network can provide better ideas and methods for EEG brain network research.