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研究生:朱盈樺
研究生(外文):Ying-Hua Chu
論文名稱:人腦功能性核磁共振影像之有效性連結分析
論文名稱(外文):Effective connectivity analysis on functional magnetic resonance imaging of the human brain
指導教授:林發暄
指導教授(外文):Fa-Hsuan Lin
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:醫學工程學研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:54
中文關鍵詞:Granger causalitytransfer entropyfunctional magnetic resonance imaging (fMRI)effective connectivity
外文關鍵詞:Granger causalitytransfer entropyfunctional magnetic resonance imaging (fMRI)effective connectivity
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本研究在於發展功能性核磁共振影像 (functional magnetic resonance imaging, fMRI) 之有效性連結分析方法,以闡明人腦中訊息傳遞的因果關係。分析方法從參數模型的Granger causality (GCAR)至無模型的消息理論 (information theory),消息理論包含time delayed mutual information (MI)和transfer entropy (TE)。這些方法可以從雙變數推展至多變數分析,配合統計方法,提高辨別訊息傳遞方向的特異性 (specificity)。使用amplitude adjusted Fourier-transformed (AAFT) 演算方法,製造出統計所需的虛無分配 (null distribution),再進行消息方向可信度的虛無假設。在本研究的模擬針對雙變數的分析,雖然可以正確辨別方向,但在多變數時間序列的環境下,無論是GCAR、time delayed MI或TE,單單使用雙變數方法分析應無法避免間接傳遞的訊息,因此,配合適當的條件設定可避免上述情況發生。但綜合模擬結果,TE配合AAFT可以提供最好的特異性 (或最低的type I error)。這些分析方法也應用在真實的fMRI 實驗上,運用ultra-fast magnetic inverse imaging (InI) 方法取得高取樣的時間序列 (10 Hz),透過time delayed MI得到各活動腦區的時間延遲量。TE則估計出消息傳遞的方向。因此,於fMRI認知實驗上,配合使用time delayed MI和TE,將可以有效得到人腦活動區域中的因果關係。

The purpose of this thesis is to develop data-driven effective connectivity analysis tools to reveal causal relationships in the human brain using functional magnetic resonance imaging (fMRI) measurements. I study Granger causality (GCAR) and propose the information theory-based methods, including time delayed mutual information (MI) and transfer entropy (TE). These methods can process fMRI data using either a bivariate or a multivariate approach to respectively obtain efficient calculation or to improve the specificity of the causality detection. Also, to provide statistical inference, I propose to empirically estimate the null distribution of causality measures by the amplitude adjusted Fourier-transformed (AAFT) algorithm.
Provided with two coupled time series, My simulations show that the pair-wise GCAR, time delayed MI, and TE can distinguish the information flow, but can not avoid false causality estimation due to the potential indirect information flow. Therefore, appropriate conditioning is crucial to control the specificity of causality estimation. Simulation on the fMRI time series model suggests that compared to GCAR, TE combined with AAFT has a higher specificity (a lower type I error rate).
I also apply effective connectivity analysis to in vivo visuomotor fMRI experiments using ultra-fast magnetic inverse imaging (InI). Facilitated with a high volumetric sampling rate of 10 Hz, time delayed MI found the latency between activated brain areas in the lateralized conditions. TE further estimates potential bi-directional and uni-direction causal modulation in the lateralized visuomotor network. I conclude that the time delayed MI and TE can be useful tools to delineate causal interactions in spatiotemporal imaging of human brain during tasks and cognition.


論文口試委員審定書 i
中文摘要 ii
Abstract iii
Table of Contents v
List of Figures vii
List of Tables x
Introduction 1
Chapter 1 : Granger Causality (GCAR) 7
1.1 : Granger Causality in Bivariate Time Series 7
1.2 : Conditional Granger Causality 10
Chapter 2 : Information Theory 12
2.1: Bivariate Cases 12
2.2: Conditional Cases 14
Chapter 3 : Surrogates and Null Hypothesis 17
3.1: Linear Surrogate Algorithm 17
3.2: Statistical Inferences of the Estimated Causal Effect 21
Chapter 4 : Simulations 24
4.1: Vector Autoregressive Model 24
4.1.1: VAR(1) 26
4.1.2: Absolute Value of VAR(1) Convolution with HRF 27
4.2: Transfer Entropy (TE) and Granger Causality (GCAR) on fMRI Time Series Model. 29
4.2.1: Full fMRI Time Series 30
4.2.2: Deconvolution of fMRI Time Series (HRF) 33
Chapter 5 : Experimental InI Data Analysis 36
5.1: Subjects and Experiment 36
5.2: Result in vivo InI Data 37
5.2.1: Bivariate Time delayed Mutual Information (MI) 37
5.2.2: Bivariate Transfer entropy (TE) 40
5.2.3: Bivariate Granger Causality (GCAR) and Conditional GCAR 42
Chapter 6 : Discussion and Conclusion 47
Glossary 51
Reference 52


1.Friston, K.J., Statistical parametric mapping : the analysis of functional brain images. 2007, Burlington, M.A.: Academic.
2.McIntosh, A.R. and F. Gonzalez-Lima, Structural equation modeling and its application to network analysis in functional brain imaging. Human Brain Mapping, 1994. 2(1-2): p. 2-22.
3.Bullmore, E., et al., How good is good enough in path analysis of fMRI data? Neuroimage, 2000. 11(4): p. 289-301.
4.Friston, K.J., L. Harrison, and W. Penny, Dynamic causal modelling. Neuroimage, 2003. 19(4): p. 1273-1302.
5.Roebroeck, A., E. Formisano, and R. Goebel, The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution. Neuroimage, 2009. In Press, Corrected Proof.
6.Granger, C.W.J., Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 1969. 37(3): p. 414-&.
7.Kayser, A.S., F.T. Sun, and M. D''Esposito, A Comparison of Granger Causality and Coherency in fMRI-Based Analysis of the Motor System. Human Brain Mapping, 2009. 30(11): p. 3475-3494.
8.Deshpande, G., et al., Multivariate Granger causality analysis of fMRI data. Hum Brain Mapp, 2009. 30(4): p. 1361-73.
9.Roebroeck, A., E. Formisano, and R. Goebel, Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage, 2005. 25(1): p. 230-42.
10.Goebel, R., et al., Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magnetic Resonance Imaging, 2003. 21(10): p. 1251-1261.
11.Blinowska, K.J., R. Kus, and M. Kaminski, Granger causality and information flow in multivariate processes. Physical Review E, 2004. 70(5): p. 4.
12.Korzeniewska, A., et al., Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. Journal of Neuroscience Methods, 2003. 125(1-2): p. 195-207.
13.Lin, F.H., et al., Dynamic Granger-Geweke Causality Modeling With Application to Interictal Spike Propagation. Human Brain Mapping, 2009. 30(6): p. 1877-1886.
14.Zhou, Z., et al., Analyzing brain networks with PCA and conditional Granger causality. Hum Brain Mapp, 2009. 30(7): p. 2197-206.
15.Zhou, Z., et al., Detecting directional influence in fMRI connectivity analysis using PCA based Granger causality. Brain Res, 2009. 1289: p. 22-9.
16.Schreiber, T., Measuring information transfer. Physical Review Letters, 2000. 85(2): p. 461-464.
17.Shannon, C.E., A Mathematical Theory of Communication. Bell System Technical Journal, 1948. 27(4): p. 623-656.
18.Paluš, M., et al., Synchronization and information flow in EEGs of epileptic patients. Ieee Engineering in Medicine and Biology Magazine, 2001. 20(5): p. 65-71.
19.Jin, S.H., P. Lin, and M. Hallett, Linear and nonlinear information flow based on time-delayed mutual information method and its application to corticomuscular interaction. Clinical Neurophysiology, 2010. 121(3): p. 392-401.
20.Vakorin, V.A., N. Kovacevic, and A.R. McIntosh, Exploring transient transfer entropy based on a group-wise ICA decomposition of EEG data. Neuroimage, 2010. 49(2): p. 1593-1600.
21.Hinrichs, H., H.J. Heinze, and M.A. Schoenfeld, Causal visual interactions as revealed by an information theoretic measure and fMRI. Neuroimage, 2006. 31(3): p. 1051-1060.
22.Hinrichs, H., T. Noesselt, and H.J. Heinze, Directed information flow - A model free measure to analyze causal interactions in event related EEG-MEG-Experiments. Human Brain Mapping, 2008. 29(2): p. 193-206.
23.Chavez, M., J. Martinerie, and M. Le Van Quyen, Statistical assessment of nonlinear causality: application to epileptic EEG signals. Journal of Neuroscience Methods, 2003. 124(2): p. 113-128.
24.Theiler, J., et al., Testing for nonlinearity in time series: the method of surrogate data. Physica D: Nonlinear Phenomena, 1992. 58(1-4): p. 77-94.
25.Lin, F.H., et al., Dynamic magnetic resonance inverse imaging of human brain function. Magnetic Resonance in Medicine, 2006. 56(4): p. 787-802.
26.Geweke, J., Measurement of Linear Dependence and Feedback Between Multiple Time Series. Journal of the American Statistical Association, 1982. 77(378): p. 304-313.
27.Kirchgassner, G. and J. Wolters, Introduction to modern time series analysis. 2007, Berlin ; New York: Springer. ix, 274 p.
28.Schwarz, G., Estimating the dimension of a model. The annals of statistics, 1978. 6: p. 461-464.
29.Geweke, J.F., Measures of Conditional Linear Dependence and Feedback Between Time Series. Journal of the American Statistical Association, 1984. 79(388): p. 907-915.
30.Wikipedia. Nat (information) 30 September 2009 03:17 UTC 19 June 2010 15:04 UTC; Available from: http://en.wikipedia.org/w/index.php?title=Nat_(information)&oldid=317018269.
31.Fraser, A.M. and H.L. Swinney, Independent coordinates for strange attractors from mutual information. Physical Review A, 1986. 33(2): p. 1134-1140.
32.Paluš, M. and M. Vejmelka, Directionality of coupling from bivariate time series: How to avoid false causalities and missed connections. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 2007. 75(5): p. 056211.
33.Barnett, L., A.B. Barrett, and A.K. Seth, Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables. Physical Review Letters, 2009. 103(23): p. 238701.
34.Small, M. and K. Judd, Detecting nonlinearity in experimental data. International Journal of Bifurcation and Chaos, 1998. 8(6): p. 1231-1244.
35.Schreiber, T. and A. Schmitz, Improved surrogate data for nonlinearity tests. Physical Review Letters, 1996. 77(4): p. 635-638.
36.Dolan, K.T. and M.L. Spano, Surrogate for nonlinear time series analysis. Physical Review E, 2001. 64(4): p. 6.
37.Logothetis, N.K., et al., Neurophysiological investigation of the basis of the fMRI signal. Nature, 2001. 412(6843): p. 150-157.
38.Pereda, E., R.Q. Quiroga, and J. Bhattacharya, Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology, 2005. 77(1-2): p. 1-37.
39.Vazquez, A.L. and D.C. Noll, Nonlinear Aspects of the BOLD Response in Functional MRI. Neuroimage, 1998. 7(2): p. 108-118.
40.Chen, Y.H., et al., Analyzing multiple nonlinear time series with extended Granger causality. Physics Letters A, 2004. 324(1): p. 26-35.
41.Kraskov, A., H. Stogbauer, and P. Grassberger, Estimating mutual information. Physical Review E, 2004. 69(6): p. 16.
42.Silverman, B.W., Density estimation for statistics and data analysis. Monographs on statistics and applied probability. 1986, London ; New York: Chapman and Hall. 175 p.



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