中文摘要：

针对传统静态功能连接分析技术不能准确反映大脑动态功能状态的问题,提出了一种基于全脑动态功能连接（DFC）分析对大脑的状态变化进行表达的方法。首先,利用个体的弥散张量成像（DTI）数据构建高精确度全脑网络,将运动任务下功能磁共振成像（fMRI）数据映射到相应DTI空间后,提取各节点fMRI信号;然后,采用滑动时间窗口方法计算随时间变化的全脑功能连接强度矩阵,并提取动态功能连接向量（DFCV）样本;最后,将所有个体的DFCV样本通过基于Fisher准则的字典学习（FDDL）算法进行稀疏表达和分类。共得到8个该运动任务下全脑功能连接状态模式,各模式的功能连接强度空间分布具有明显差异,模式1、模式2和模式3占据了大部分样本分布（77.6%）,且与平均静态功能连接强度矩阵之间的相似度明显高于其他5个模式。此外,大脑在各模式之间的状态迁移遵循一定的规律。实验结果表明,采用全脑DFC和FDDL学习相结合的方法,能够有效地对任务态下大脑的功能状态变化进行表达,为研究脑动态信息处理机制提供基础。

英文摘要：

Focusing on the limitation of conventional static Functional Connectivity （FC） techniques in investigating the dynamic functional brain states, an effective method based on whole-brain Dynamic Functional Connectivity （DFC） was proposed to characterize the time-varying brain states. First, the Diffusion Tensor Imaging （DTI） data were used to construct individual whole-brain networks with high accuracy and the functional Magnetic Resonance Imaging （fMRI） data of motor- related task was projected to the corresponding DTI space to extract the fMRI signals of each node for each subject. Then, one kind of sliding time window approach was applied to calculate the time-varying whole-brain functional connectivity strength matrix, and the corresponding Dynamic Functional Connectivity Vector （DFCV） samples were further extracted and collected. Finally, the DFCV samples were learned and classified by one sparse representation based method called Fisher Discriminative Dictionary Learning （ FDDL）. Total eight different whole-brain functional conneetome patterns representing the dynamic brain states were obtained from this motor-related task experiment. The spatial distributions of functional connectivity strength showed obvious variance within different patterns. The pattern #1, pattern #2 and pattern #3 covered most of the samples （ 77.6% ） and the similarities between each of them and the average static whole-brain functional connectivity strength matrix were obviously higher than other five patterns. Furthermore, the brain states were found to transfer from one pattern to another according to certain rules. The experimental results show that the proposed analysis method combining whole-brain DFC and FDDL learning is effective for describing and characterizing the dynamic brain states during task brain activity. It provides a foundation for exploring the dynamic information processing mechanism of the brain.