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基于功能磁共振的静息状态功能分割及在网络分析中的应用
Alternative TitleResting-State Functional Segmentation and Its Application in Functional Network Analysis with fMRI
孔维丹
Subtype工学硕士
Thesis Advisor臧玉峰
2008-05-28
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword功能磁共振 局部一致性 功能分割 脑功能网络 Functional Magnetic Resonance Imaging Regional Homogeneity Functional Segmentation Brain Functional Network
Abstract静息状态下,血氧依赖水平的低频(0.01-0.08Hz)涨落被认为反应了脑内自发的神经活动。研究静息状态下血氧依赖水平信号,有助于理解人脑静息状态下的活动。研究正常人静息状态脑功能,对于研究运动状态及病人的静息状态脑功能都有积极的作用。 选择感兴趣区域是功能磁共振成像(fMRI)研究中的重要步骤。一个好的感兴趣区域,对功能磁共振成像分析有巨大的帮助;而一个选择不当的感兴趣区域,则会使研究结果产生偏差。本研究旨在利用fMRI成像技术,从静息状态这一角度研究中如何更好地选择有利于进一步分析的感兴趣区域,并利用这些获得的区域进行脑功能网络的分析,从而进一步认识静息状态脑功能。 局部一致性(ReHo)是一种fMRI数据分析的新方法。在这项工作的基础之上,本文首先利用ReHo获得人脑静息状态下功能活动图。其次,我们将ReHo图进行了统计分析,得到了组级别之上的统计图,从而获得了适合组分析的功能图。在此结果之上,我们开辟了对功能图分割进行的新思路,利用分水岭算法将相连的功能区域分离,从而获得了基于功能分割的功能块。与传统的基于结构模板分块方法相比,模板中选择的区域往往具有先验的对称性,有些区域过大又往往无法区别对待区域内部的功能异性。我们利用ReHo分析得到的功能分块,很好地解决了这些问题。 脑功能具有两个基本的组织原则:功能分化与功能整合。基于ReHo方法的功能分块着重于研究功能分化,而基于这些分块的网络研究则着重于功能整合方面的研究。我们进一步通过从功能分割的结果中选取感兴趣区域,利用相关分析进行了几个常见脑功能网络的研究。我们在重现已有研究结果的中,证实了功能分割的有效性;进一步地,我们还发现,多数脑区都组织于一个正相关和负相关动态平衡的网络之中。
Other AbstractIn resting-state, the low frequency (0.01 - 0.08 Hz) fluctuation (LFF) of blood oxygenation level dependent (BOLD) signal has been assumed to reflect spontaneous neuronal activities. Studying the resting-state BOLD signal helps understand the activities in the brain. Studying the resting-state brain function of normal adult subject will be helpful in studying the task-state and resting-state brain function of patients. Selecting regions of interest (ROI) is an important step in functional magnetic resonance imaging (fMRI). A correctly selected ROI will do great help in fMRI analysis, while an incorrectly selected ROI will lead to wrong results. The aims of this study are to invest how to appropriately select ROIs in resting-state for further use by functional magnetic resonance imaging, and to perform a brain network analysis with these results to enlarge our understanding of resting-state brain function. Regional homogeneity (ReHo) is a new fMRI data analysis method. Based on the work, we first used the ReHo approach to obtain a functional brain in resting-state human brain. Then, a statistical analysis was performed on the ReHo maps and a group-level functional map was obtained. This group-level functional map was more suitable for functional analysis of the corresponding group. Based on this result, we proposed to perform watershed segmentation on this functional map to isolate the connected functional clusters. Compare to traditional ROI selection based on the structural template, ROI selected by template always tends to be symmetrical, and larger regions won’t be able to deal with the differences in the region. Functional clusters we obtained based on the ReHo approach resolved these problems. Brain function is organized under two basic major organizational priciples: functional segregation and functional integration. ReHo based functional segmentation concerned on the functional segregation while network analysis based on these functional regions concerned on the functional integration. We selected four ROIs in the segmentation result and performed a correlation analysis which found four frequently reported networks. This also validated the functional segmentation method. We also found that most of the brain regions were organized into a correlated and its anti-correlated network.
shelfnumXWLW1198
Other Identifier200528014628034
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7436
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
孔维丹. 基于功能磁共振的静息状态功能分割及在网络分析中的应用[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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