易思维, 郭水霞. 基于fMRI数据的全脑动态功能连接网络拓扑属性的研究[J]. 应用概率统计, 2018, 34(2): 177-190. DOI: 10.3969/j.issn.1001-4268.2018.02.006
引用本文: 易思维, 郭水霞. 基于fMRI数据的全脑动态功能连接网络拓扑属性的研究[J]. 应用概率统计, 2018, 34(2): 177-190. DOI: 10.3969/j.issn.1001-4268.2018.02.006
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

基于fMRI数据的全脑动态功能连接网络拓扑属性的研究

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

  • 摘要: 人的大脑有约10^11个神经元,神经元之间通过其突触相互连接而组成一个高度复杂的网络,挖掘该网络的信息意义十分重大, 将有助于解决人类认知性障碍疾病的预防和诊断.本文利用精神分裂症病人和正常对照受试者的功能性磁共振成像~(functionalmagnetic resonance imaging, fMRI)~数据来构造人脑网络模型, 再基于图论方法对精神分裂症病人的脑网络的异常拓扑属性进行探索.在传统的基于图论方法对人脑网络信息进行挖掘时,都是假设人脑网络模型具有时不变性, 因而在构造人脑网络模型时是取整个时间段的时间序列数据进行构造的,构造出的是一种静态不变的网络, 然而~fMRI~功能像时间序列数据具有不平稳性, 难以保证时不变这一前提. 因此, 在构造人脑网络模型时, 应该考虑其时变性的特点,构造一个动态的脑网络, 这样才能更好地挖掘人脑网络的信息. 本文利用取时间窗口,对时间序列数据进行分段计算, 构造动态的脑网络模型, 再结合图论知识进行分析,从而降低了~fMRI~功能像时间序列数据不平稳性对结果的影响. 通过对精神分裂症病人和正常对照受试者不同水平的动态脑网络进行对比,结果发现精神分裂症病人和正常对照受试者的全脑动态功能连接网络的单个 节点的属性、组网络的属性出现差异,这些网络属性差异的发现为进一步研究精神分裂症的病理机制提供了新的线索.

     

    Abstract: 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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