基于同步多维数据流的脑网络动态特征辨识方法研究

发布时间：2018-02-02 03:50

本文关键词： 动态特征辨识多维同步数据流脑功能网络磁共振成像　出处：《计算机应用研究》2017年11期 　论文类型：期刊论文

【摘要】：针对人脑实时变化的特性,为了更好地观测和描述人脑网络的动态特征,在基于功能磁共振成像的脑功能网络重构技术基础上,给出了一种人脑网络动态特征辨识方法。首先利用同步多维数据流的即时更新能力,将在静息态功能磁共振成像数据采集区间上的血氧水平依赖信号由大时间序列分解重构为每个采样点上的小时间窗口序列,构建连续时间点上的状态观测窗口,从而实现对人脑功能共振信号的特定时间状态辨识;然后运用相关分析对状态观测窗口信号进行分析,得到单状态观测矩阵,最终构建全脑在整个数据采集区间上的动态特征矩阵。实验结果显示该方法可以为人脑网络的动态特征观测和描述提供一种有效手段,也为进一步研究人脑网络的动态特征演变奠定了基础。
[Abstract]:In order to better observe and describe the dynamic characteristics of human brain network, the functional network reconstruction technology based on functional magnetic resonance imaging (fMRI) is proposed. A method of dynamic feature identification of human brain network is presented. Firstly, the real-time updating ability of synchronous multidimensional data stream is used. The oxygen level dependent signals in the resting functional magnetic resonance imaging data acquisition region are decomposed from large time series into small time window sequences at each sampling point to construct a state observation window at a continuous time point. In order to realize the special time state identification of the human brain functional resonance signal; Then the state observation window signal is analyzed by correlation analysis, and the single state observation matrix is obtained. Finally, the dynamic feature matrix of the whole brain in the whole data acquisition region is constructed. The experimental results show that this method can provide an effective means for the observation and description of the dynamic characteristics of the human brain network. It also lays a foundation for further research on the evolution of dynamic characteristics of human brain network.
【作者单位】：昆明理工大学信息工程与自动化学院;云南警官学院信息网络安全学院;
【基金】：国家自然科学基金资助项目(61263017) 昆明理工大学人才培养基金资助项目(201303120)
【分类号】：R445.2;TN911.6
【正文快照】： 0引言随着核磁共振成像(magnetic resonance imaging,MRI)技术的快速发展和成熟,基于功能磁共振成像(functional magneticresonance imaging,f MRI)技术的脑网络重构方法已成为脑科学研究中的重要研究领域,该方法基于射频脉冲和血氧动力学响应原理,即神经元兴奋所引起的血氧水

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