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研究生:鄭彥翔
研究生(外文):Yen-HsiangCheng
論文名稱:利用動態格蘭傑因果分析功能性核磁共振影像之有效性連結
論文名稱(外文):Evaluating Effective Connectivity in fMRI using Dynamic Granger Causality
指導教授:吳明龍
指導教授(外文):Ming-Long Wu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:醫學資訊研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:38
中文關鍵詞:功能性核磁共振影像格蘭傑因果有效性連結
外文關鍵詞:fMRIGranger CausalityEffective Connectivity
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  • 被引用被引用:0
  • 點閱點閱:147
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  • 下載下載:6
  • 收藏至我的研究室書目清單書目收藏:0
功能性磁振造影(Functional magnetic resonance imaging, fMRI)及血氧濃度相依對比(Blood oxygen-level dependent, BOLD)的發明使我們能夠以非侵入性的方法研究人類大腦的運作。其中格蘭傑因果關係分析法(Granger causality analysis, GCA)在近十年中已經被神經科學家廣泛地應用在分析有效性連結(Effective connectivity)上,即大腦中各個結構之間是如何互動以完成工作的。
要計算格蘭傑因果關係,時間序列必須滿足共變異恆定(Covariance stationary)這個前提假設。然而神經系統天生的特性卻是動態以及時變的,因此我們提出了動態格蘭傑因果關係分析法(Dynamic Granger causality analysis, DGCA):一種基於視窗法(Windowing-based)的格蘭傑因果來分析大腦網路的動態變化,並且透過提昇採樣(Upsampling)來增進計算因果關係的時間解析度。我們將這個方法應用在聽覺-運動的實驗上,結果顯示出動態格蘭傑因果關係不只能滿足計算的前提假設,還能提供更加豐富的有效性連結資訊。除此之外,本篇研究展示了一個結合視窗法,提昇採樣以及聚類分析的有效性連結分析流程,並且得出大腦在執行聽覺-運動實驗時可以被歸類成少數幾個有效性連結特徵圖形。

The invention of functional magnetic resonance imaging (fMRI) and blood oxygen level dependent contrast (BOLD) enable us to study how the brain works by non-invasive methods. Granger causality analysis (GCA) is a popular method to analyze effective connectivity that has been widely used in neurosciences in the last decade. We can use GCA to explore the interactions between the brain structures to find how the brain performs tasks and identify the hidden functional architecture.
However, the application of GCA assumes that the analyzed time series must be covariance stationary (CS), it’s unlike the nature of nervous system that is dyanamic and time-varying. We proposed a windowing-based Granger causality analysis to deal with upsampled non-CS time series called dynamic Granger causality analysis (DGCA), and verify the dynamic causal relationships in a simple auditory-motor task experiment. The results show that the dynamic Granger causality analysis perform much more effective connectivity information than non-dynamic Granger causality analysis. Our study demonstrate a new workflow to evaluate effective connectivity with upsampled data and windowing-based Granger causality analysis. Accroding to the results of group analysis by clustering method, we find out the patterns of the brain states while performing auditory-motor task.

摘要...III
Abstract...IV
誌謝...V
Index...VI
Chapter 1:Introduction...1
1-1:Functional MRI and BOLD Signal...1
1-2:Functional Connectivity and Effective Connectivity...2
1-3:Granger Causality Analysis...2
Chapter 2:Materials and Methods...4
2-1:Image Preprocessing...4
2-2:ROI Selection and Time Series Preprocessing...5
2-3:Conditional Granger Causality Analysis...9
2-4:Dynamic Granger Causality Analysis...10
2-5:K-Means Clustering Methods...12
2-6:Subjects and Experiment Parameters...13
Chapter 3:Results...16
3-1:fMRI Time Series and Conditional Granger Causality Analysis...16
3-2:Dynamic Granger Causality Analysis...20
3-3:Results of K-Means Clustering Method...22
Chapter 4:Discussion and Conclusion...30
4-1:Data Preprocessing with Upsampling...30
4-2:Granger Causality Parameters...30
4-3:Conditional and Dynamic Granger Causality Results...32
4-4:K-Means Clustering Parameters...33
4-5:The Pattern Sequences and Task Paradigm...33
4-6:Conclusion and Future Work...34
References...36

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