YI SiWei, GUO ShuiXia. Study on the Topological Properties of Whole Brain Dynamic Functional Connectivity Network Based on fMRI Data[J]. Chinese Journal of Applied Probability and Statistics, 2018, 34(2): 177-190. DOI: 10.3969/j.issn.1001-4268.2018.02.006
Citation: YI SiWei, GUO ShuiXia. Study on the Topological Properties of Whole Brain Dynamic Functional Connectivity Network Based on fMRI Data[J]. Chinese Journal of Applied Probability and Statistics, 2018, 34(2): 177-190. DOI: 10.3969/j.issn.1001-4268.2018.02.006

Study on the Topological Properties of Whole Brain Dynamic Functional Connectivity Network Based on fMRI Data

  • There're about 10^11 neurons in the human brain.Through the synaptic junction, neurons have formed a highly complex network.And it is really important to figure out the information expressed in the network, which will contribute to the resolution of the prevention and diagnosis of cognitive disorder of human beings. This paper uses the schizophrenia and healthy controlled subjects' fMRI data to construct the brain network model, in order to explores abnormal topological properties of schizophrenics' brain network based on graph theory. When studying the human brain network information traditionally by the basement of graph theory, it's all assure that the human brain network model has invariance, so it takes the whole period of time series data in constructing human network model, which is a kind static network. However, it's hard to ensure this because of the nonstationarity of fMRI functional time series data. Thus, when constructing human brain network model, we should take its time-variation into consideration, then construct a dynamic brain network. We can explore the brain network information better. In this research, we segment the time series data, using time windows, to constructing dynamic brain network model, then analyze it combined with the knowledge of graph theory, thereby reducing effects that the nonstationarity of fMRI functional time series data will have. Comparing dynamic brain network of the schizophrenic patients with normal controls subjects' in different level, the results show that there are difference in single node property, group network property of schizophrenic patients and normal control subjects' whole brain dynamic functional connectivity network. The discovery of these difference in network topological properties has provide new clues for the further study on the pathological mechanism of schizophrenia.
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