臺灣博碩士論文加值系統

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

1.Ogawa, S., et al., Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences, 1990. 87(24): p. 9868-9872.
2.Abler, B., et al., Investigating directed influences between activated brain areas in a motor-response task using fMRI. Magnetic resonance imaging, 2006. 24(2): p. 181-185.
3.Uddin, L.Q., et al., Functional connectivity of default mode network components: correlation, anticorrelation, and causality. Human brain mapping, 2009. 30(2): p. 625-637.
4.Liao, W., et al., Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PloS one, 2010. 5(1): p. e8525.
5.Lee, J.-H., J. Kim, and S.-S. Yoo, Real-time fMRI-based neurofeedback reinforces causality of attention networks. Neuroscience Research, 2012. 72(4): p. 347-354.
6.Friston, K.J., Functional and effective connectivity: a review. Brain Connectivity, 2011. 1(1): p. 13-36.
7.Sporns, O., Brain connectivity. Scholarpedia, 2007. 2(10): p. 4695.
8.Mclntosh, A. 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.
9.Friston, K.J., L. Harrison, and W. Penny, Dynamic causal modelling. Neuroimage, 2003. 19(4): p. 1273-1302.
10.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.
11.Granger, C.W., Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 1969: p. 424-438.
12.Baccalá, L.A. and K. Sameshima, Partial directed coherence: a new concept in neural structure determination. Biological cybernetics, 2001. 84(6): p. 463-474.
13.Kamiński, M., et al., Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biological cybernetics, 2001. 85(2): p. 145-157.
14.Havlicek, M., et al., Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data. Neuroimage, 2010. 53(1): p. 65-77.
15.Seth, A., Granger causality. Scholarpedia, 2007. 2(7): p. 1667.
16.Hesse, W., et al., The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. Journal of Neuroscience Methods, 2003. 124(1): p. 27-44.
17.Sato, J.R., et al., A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality. Neuroimage, 2006. 31(1): p. 187-196.
18.McFarlin, D.R., D.L. Kerr, and J.B. Nitschke, Upsampling to 400-ms Resolution for Assessing Effective Connectivity in Functional Magnetic Resonance Imaging Data with Granger Causality. Brain connectivity, 2013. 3(1): p. 61-71.
19.Penny, W.D., A. Holmes, and K. Friston, Random effects analysis. Human brain function, 2003: p. 843-850.
20.Zatorre, R.J., J.L. Chen, and V.B. Penhune, When the brain plays music: auditory–motor interactions in music perception and production. Nature Reviews Neuroscience, 2007. 8(7): p. 547-558.
21.Seth, A.K., A MATLAB toolbox for Granger causal connectivity analysis. Journal of neuroscience methods, 2010. 186(2): p. 262-273.
22.Zhou, Z., et al., Detecting directional influence in fMRI connectivity analysis using PCA based Granger causality. Brain research, 2009. 1289: p. 22-29.
23.Buckner, R.L., F.M. Krienen, and B.T. Yeo, Opportunities and limitations of intrinsic functional connectivity MRI. Nature neuroscience, 2013. 16(7): p. 832-837.
24.Ding, M., et al., Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biological cybernetics, 2000. 83(1): p. 35-45.
25.Zhao, Y., et al., Tracking time-varying causality and directionality of information flow using an error reduction ratio test with applications to electroencephalography data. Physical Review E, 2012. 86(5): p. 051919.
26.Schwarz, G., Estimating the dimension of a model. The annals of statistics, 1978. 6(2): p. 461-464.
27.Kwiatkowski, D., et al., Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of econometrics, 1992. 54(1): p. 159-178.
28.Jia, H., X. Hu, and G. Deshpande, Finite Number of Brain Network Configurations Revealed from Time-varying Connectivity Assessment of Resting State fMRI. ISMRM, 2013.
29.Liu, X., et al., Contributions of the thalamocortical system towards sound-specific auditory plasticity. Neuroscience ＆ Biobehavioral Reviews, 2011. 35(10): p. 2155-2161.
30.Kasess, C.H., et al., The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling. Neuroimage, 2008. 40(2): p. 828-837.
31.Chen, H., et al., Evaluation of the effective connectivity of supplementary motor areas during motor imagery using Granger causality mapping. Neuroimage, 2009. 47(4): p. 1844-1853.
32.Wikipedia contributors. Thalamus. 27 October 2013 18:07 UTC 10 November 2013 08:58 UTC]; Available from: http://en.wikipedia.org/w/index.php?title=Thalamus＆oldid=578983415.
33.Lee Rodgers, J. and W.A. Nicewander, Thirteen ways to look at the correlation coefficient. The American Statistician, 1988. 42(1): p. 59-66.
34.Bianchi, A., et al., Frequency-based approach to the study of semantic brain networks connectivity. Journal of neuroscience methods, 2012.
35.Friston, K., R. Moran, and A.K. Seth, Analysing connectivity with Granger causality and dynamic causal modelling. Current opinion in neurobiology, 2012.