fMRI中的时序信号比对分析+文献综述
关键词： 功能磁共振成像,功能连接性,层次聚类,小波变换,时频相干性
毕业设计说明书（论文）外文摘要
In this study, I explored the resting-state functional connectivity using fMRI signals, and gave a novel method in analyzing the functional connectivity through computing the coherence between two groups of fMRI signals. Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, Hierarchical Clustering Algorithm was used to group all the fMRI signals into several clusters. And then the typical signals are selected and then performed a time-frequency coherence analysis based on the wavelet transform, in order to investigate the dynamic behavior of resting-state connectivity across the course of a single scan. It has been proved that the clustering results are good and the selected signals are representative, and in this way we provide an effective and convenient method in analyzing the resting-state functional connectivity using fMRI signals.
Keywords: fMRI, functional connectivity, hierarchical clustering, wavelet transform, time-frequency coherence
目录
1 绪论 1
1.1 研究背景及意义 1
1.2 本论文的研究思路及所作主要工作 3
2 fMRI信号聚类的设计与实现 3
2.1 聚类的概念 3
2.2 聚类分析算法的分类 3
2.3 聚类算法的比较 5
2.4 层次聚类 5
2.5 距离和相似度度量 9
3 fMRI信号间的相干性 12
3.1 经典相干性的概念 12
3.2 小波相干性 12
3.3 显著性检测 15
4 实验结果 16
4.1 fMRI信号模拟，显示模块 16
4.2 fMRI信号聚类模块 20
4.3 小波相干性分析模块 23
结论 26
致谢 27
参考文献 28
1 绪论
1.1 研究背景及意义
1.1.1 功能磁共振成像(functional Magnetic Resonance Image, fMRI)
20世纪90年代以来，在传统磁共振成像（Magnetic Resonance Image, MRI）技术的基础上发展的功能磁共振成像技术已广泛应用于脑功能的临床和基础研究。fMRI结合了功能、解剖和影像三方面的因素，为临床磁共振诊断从单一形态学研究到与功能相结合的系统研究提供了强有力的技术支持。该技术具有无创伤性、无放射性、可重复性、较高的时间和空间分辨率、可准确定位脑功能区等特点，为脑神经科学提供了广阔的应用前景。