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題名 fMRI資料架構分析為主之分類研究
A Geometry analysis - based classification study of fMRI patterns
作者 章珅鎝
貢獻者 周珮婷
章珅鎝
關鍵詞 fMRI
DCG tree
機器學習
雙層距離
日期 2015
上傳時間 13-Jul-2015 11:06:30 (UTC+8)
摘要 此篇論文研究哪種幾何架構較適合fMRI資料，我們使用DCG tree做分析，使用的資料為POP課題的紅與綠實驗數據，此資料的表現形式由Beta-series相關係數矩陣所呈現。在分析幾何形式時為了考慮變數分組之情形，使用了雙層距離的方法計算了個體間的距離。為避免太多變數導致有多餘的雜訊，使用了獨立雙樣本t檢定、主成份分析、個別區域之預測結果篩選出部分變數。我們使用交叉驗證的方式去算出我們的準確率，由DCG tree得到的分群結果，再使用cos⁡θ值去預測測試集的分類，為了使結果更好，我們提高DCG tree中的門檻值與將資料標準化。為了確認DCG tree較適合拿來做這類型研究，也使用SVM、LDA、KNN、K-means和HC tree這些演算法來與其做比較。最後得出使用歐幾里德雙層距離與t檢定篩選變數並提高門檻值能有最好的分類結果，且與其它方法比較後，也得出確實DCG tree有較精確的分類預測。
參考文獻 Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 10(3), 186-198. doi: 10.1038/nrn2575
Chou, E. (2014). Computed Data-geometry based Supervised and Semi-supervised Learning in High Dimensional Data. ProQuest, UMI Dissertations Publishing.
Cordes, D., Haughton, V., Carew, J. D., Arfanakis, K., & Maravilla, K. (2002). Hierarchical clustering to measure connectivity in fMRI resting-state data. Magnetic Resonance Imaging, 20(4), 305-317. doi: 10.1016/s0730-725x(02)00503-9
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi: 10.1007/bf00994018
Filzmoser, P., Baumgartner, R., & Moser, E. (1999). A hierarchical clustering method for analyzing functional MR images. Magnetic Resonance Imaging, 17(6), 817-826. doi: 10.1016/s0730-725x(99)00014-4
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2), 179-188.
Fix, E., & Hodges Jr, J. L. (1951). Discriminatory analysis-nonparametric discrimination: consistency properties: DTIC Document.
Fushing, H., Wang, H., Vanderwaal, K., McCowan, B., & Koehl, P. (2013). Multi-scale clustering by building a robust and self correcting ultrametric topology on data points. PLoS One, 8(2), e56259. doi: 10.1371/journal.pone.0056259
Hastie, Tibshirani, & Friedman. (2009). The Elements of Statistical Learning (2 ed.). New York: Springer-Verlag.
Jenatton, R., Gramfort, A., Michel, V., Obozinski, G., Bach, F., & Thirion, B. (2011). Multi-scale mining of fMRI data with hierarchical structured sparsity. Paper presented at the Pattern Recognition in NeuroImaging (PRNI), 2011 International Workshop on.
Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254.
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., . . . Turner, R. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences, 89(12), 5675-5679.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Misaki, M., Kim, Y., Bandettini, P. A., & Kriegeskorte, N. (2010). Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage, 53(1), 103-118. doi: 10.1016/j.neuroimage.2010.05.051
Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial overview. Neuroimage, 45(1 Suppl), S199-209. doi: 10.1016/j.neuroimage.2008.11.007
Rissman, J., Gazzaley, A., & D`Esposito, M. (2004). Measuring functional connectivity during distinct stages of a cognitive task. Neuroimage, 23(2), 752-763. doi: 10.1016/j.neuroimage.2004.06.035
Solomon, M., Ozonoff, S. J., Cummings, N., & Carter, C. S. (2008). Cognitive control in autism spectrum disorders. International Journal of Developmental Neuroscience, 26(2), 239-247.
Steinhaus, H. (1956). Sur la division des corp materiels en parties. Bull. Acad. Polon. Sci, 1, 801-804.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., . . . Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273-289.
Wang, H., Chen, C., & Fushing, H. (2012). Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders. PLoS One, 7(10), e45502. doi: 10.1371/journal.pone.0045502
Wang, X., Hutchinson, R. A., & Mitchell, T. M. (2003). Training fMRI classifiers to discriminate cognitive states across multiple subjects. Paper presented at the Advances in neural information processing systems.
描述 碩士
國立政治大學
統計研究所
102354023
103
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102354023
資料類型 thesis
dc.contributor.advisor 周珮婷zh_TW
dc.contributor.author (Authors) 章珅鎝zh_TW
dc.creator (作者) 章珅鎝zh_TW
dc.date (日期) 2015en_US
dc.date.accessioned 13-Jul-2015 11:06:30 (UTC+8)-
dc.date.available 13-Jul-2015 11:06:30 (UTC+8)-
dc.date.issued (上傳時間) 13-Jul-2015 11:06:30 (UTC+8)-
dc.identifier (Other Identifiers) G0102354023en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/76419-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 102354023zh_TW
dc.description (描述) 103zh_TW
dc.description.abstract (摘要) 此篇論文研究哪種幾何架構較適合fMRI資料，我們使用DCG tree做分析，使用的資料為POP課題的紅與綠實驗數據，此資料的表現形式由Beta-series相關係數矩陣所呈現。在分析幾何形式時為了考慮變數分組之情形，使用了雙層距離的方法計算了個體間的距離。為避免太多變數導致有多餘的雜訊，使用了獨立雙樣本t檢定、主成份分析、個別區域之預測結果篩選出部分變數。我們使用交叉驗證的方式去算出我們的準確率，由DCG tree得到的分群結果，再使用cos⁡θ值去預測測試集的分類，為了使結果更好，我們提高DCG tree中的門檻值與將資料標準化。為了確認DCG tree較適合拿來做這類型研究，也使用SVM、LDA、KNN、K-means和HC tree這些演算法來與其做比較。最後得出使用歐幾里德雙層距離與t檢定篩選變數並提高門檻值能有最好的分類結果，且與其它方法比較後，也得出確實DCG tree有較精確的分類預測。zh_TW
dc.description.tableofcontents 第一章 緒論 1
第一節 研究動機與目的 1
第二節 演算法介紹 3
第二章 文獻探討 10
第三章 研究方法 12
第一節 研究資料 12
第二節 研究方法 13
第三節 雙層距離 14
第四節 實驗過程 16
第四章 研究結果 21
第一節 資料分析 21
第二節 與其它方法之比較 27
第五章 結論與探討 28
第六章 參考文獻 31		zh_TW
dc.format.extent 1222878 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102354023en_US
dc.subject (關鍵詞) fMRIzh_TW
dc.subject (關鍵詞) DCG treezh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 雙層距離zh_TW
dc.title (題名) fMRI資料架構分析為主之分類研究zh_TW
dc.title (題名) A Geometry analysis - based classification study of fMRI patternsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 10(3), 186-198. doi: 10.1038/nrn2575
Chou, E. (2014). Computed Data-geometry based Supervised and Semi-supervised Learning in High Dimensional Data. ProQuest, UMI Dissertations Publishing.
Cordes, D., Haughton, V., Carew, J. D., Arfanakis, K., & Maravilla, K. (2002). Hierarchical clustering to measure connectivity in fMRI resting-state data. Magnetic Resonance Imaging, 20(4), 305-317. doi: 10.1016/s0730-725x(02)00503-9
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi: 10.1007/bf00994018
Filzmoser, P., Baumgartner, R., & Moser, E. (1999). A hierarchical clustering method for analyzing functional MR images. Magnetic Resonance Imaging, 17(6), 817-826. doi: 10.1016/s0730-725x(99)00014-4
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2), 179-188.
Fix, E., & Hodges Jr, J. L. (1951). Discriminatory analysis-nonparametric discrimination: consistency properties: DTIC Document.
Fushing, H., Wang, H., Vanderwaal, K., McCowan, B., & Koehl, P. (2013). Multi-scale clustering by building a robust and self correcting ultrametric topology on data points. PLoS One, 8(2), e56259. doi: 10.1371/journal.pone.0056259
Hastie, Tibshirani, & Friedman. (2009). The Elements of Statistical Learning (2 ed.). New York: Springer-Verlag.
Jenatton, R., Gramfort, A., Michel, V., Obozinski, G., Bach, F., & Thirion, B. (2011). Multi-scale mining of fMRI data with hierarchical structured sparsity. Paper presented at the Pattern Recognition in NeuroImaging (PRNI), 2011 International Workshop on.
Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254.
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., . . . Turner, R. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences, 89(12), 5675-5679.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Misaki, M., Kim, Y., Bandettini, P. A., & Kriegeskorte, N. (2010). Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage, 53(1), 103-118. doi: 10.1016/j.neuroimage.2010.05.051
Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial overview. Neuroimage, 45(1 Suppl), S199-209. doi: 10.1016/j.neuroimage.2008.11.007
Rissman, J., Gazzaley, A., & D`Esposito, M. (2004). Measuring functional connectivity during distinct stages of a cognitive task. Neuroimage, 23(2), 752-763. doi: 10.1016/j.neuroimage.2004.06.035
Solomon, M., Ozonoff, S. J., Cummings, N., & Carter, C. S. (2008). Cognitive control in autism spectrum disorders. International Journal of Developmental Neuroscience, 26(2), 239-247.
Steinhaus, H. (1956). Sur la division des corp materiels en parties. Bull. Acad. Polon. Sci, 1, 801-804.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., . . . Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273-289.
Wang, H., Chen, C., & Fushing, H. (2012). Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders. PLoS One, 7(10), e45502. doi: 10.1371/journal.pone.0045502
Wang, X., Hutchinson, R. A., & Mitchell, T. M. (2003). Training fMRI classifiers to discriminate cognitive states across multiple subjects. Paper presented at the Advances in neural information processing systems.		zh_TW