功能性核磁共振造影 (Functional magnetic resounce imaging ; fMRI)，已經被廣泛的應用在臨床檢測大腦血液流動的功能性變化狀況。因此對於一些腦部疾病能提供非侵入性功能診斷的有效支援。在功能性核磁共振造影臨床的診斷與分析上面，找出因刺激而造成反應的大腦反應區域，則是本篇論文所要探討的主要重點。
我們首先藉由獨立成分分析(Independent component analysis; ICA)將功能性核磁共振造影影像分解出許多符合統計上獨立非高斯訊號的小成分，進而找出符合於刺激參考函式的最佳成分。為了正確選出反應區域與訊號，我們提出一個選擇成分的方法，針對每一個成分，計算傅利葉轉換作為成分的排序標準，然後在時間和空間上的判斷上，以大腦血液流動模型的關係函數印證時間序的正向或者是反向於參考函式；最後，再配合人腦解剖學上的知識來判定空間上正負Z值的區域是否相符於腦神經反應區域模型。以上述規則找出最佳大腦活動區域成分，來增加選擇成分的可靠性。同時，我們也採用彈性對位的演算法建立標準腦，透過標準腦來分析提供較為準確之多對象分析結果。而且根據標準腦的比較驗證，我們發現所提出的系統可以成功地找出許多在臨床上Statistical Parametric Mapping (SPM)所無法找到的大腦活動區域。

Functional magnetic resounce imaging (fMRI) has been commonly used to measure the hemodynamic response of brain which is proven to be related with the neural activities in the brain. Therefore, it has become a non-invasive clinical tool for the diagnoses of various kinds of brain or neural system diseases. One of the most important tasks in analyzing the fMRI is to identify the neural activation areas from intra- or inter-subject functional images. This is also the major goal of the proposed image analysis system in this thesis.
Independent component analysis (ICA) is a technique that attempts to separate sensory image data into spatial independent non-Gaussian components which are then used to determine the component with time course best matched with the time course of stimulation. In this thesis, we have proposed a novel method which is a two-staged process for the selection of spatial independent component. In the first stage, fast Fourier transform is computed and used to rank the frequency response of the spatial independent component. These ranked components are then used to estimate the correlation coefficient with respect to the reference function of hemodynamic response in the second stage. Z statistics is then used to confirm the relationship between brain responses to the structure of human brain. We also applied elastic registration to build a standard brain for inter-subject analysis of neuronal activations. Comparing the results obtained by using the proposed standard brain and by using the Statistical Parametric Mapping (SPM), the proposed method can successfully indicate the regions of neuronal activity that were not correctly identified by SPM.

[1] http://www.news.wisc.edu/packages/emotion/media.html
[2] S. Ogawa, D. W. Tank, R, Menon, J. M. Ellermann, S. Kim, H. Merkle, K. Ugurbil, “ Intrinsic signal changes accompanying sensory stimulation brain mapping with magnetic resonance imaging” Proc Natl Sci U S A, vol. 89, pp. 5951-5955, 1992.
[3] K. K. Kwong, J. W. Belliveau, D. A. Chesler, I. E. Goldberg, R. M. Weisskoff, B. P. Poncelet, D. N. Kennedy, B. E. Hoppel, M. S. Cohen, R. Turner, H. Cheng, T. J. Brandy, B. R. Rosen, “Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation” Proc Natl Sci U S A, vol. 89, pp. 5675-5679, 1992.
[5] K. J. Friston, “Statistical Parametric Mapping and Other Analyses of Function Imaging Data” In: Toga AW, Mazziotta JC (eds): Mrain Mapping: The Methods. San Diego: Academic Press, pp. 363-396, 1996.
[6] A. J. Bell, T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution” Neural Comput, vol.7, pp. 1129-1159, 1995.
[7] P. Comon, “Independent component analysis: A new concept?” Signal Processing, vol. 36, pp. 287-314, 1994.
[8] C.H. Moritz, B. P. Rogers, M. E. Meyerand, “Power spectrum ranked independent component analysis of a periodic fMRI complex motor paradigm” Hum Brain Mapp, vol. 18, pp. 111-122, 2003.
[9] B. Biswal, F. Z. Yetkin, V. M. Haughton, J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI” Magn. Res. Med, vol.34, pp. 537-541, 1995.
[10] S. Periaswamy and H. Farid, “Elastic registration in the presence of intensity variations ” IEEE Transactions on Medical Imaging, vol. 22, pp. 865-874, 2003.
[11] D. L. Collins, P. Neelin, T. M. Peters, A. C. Evans, ”Automatic 3D inter-subject registration of mrvolumetric data in standardized talairach space” J. Comput Assist Tomoger, vol. 18, no. 2, pp. 192-204, 1994.
[12] O. Nestares, D. J. Heeger,” Robust multiresolution alignment of MRI brain volumes” Magn Reson Med, vol. 43, pp. 705-715, 2000.
[13] A. Roche, G. Malandain, N. Ayache, S. Prima, “Toward a better comprehension of similarity measures used in medical image registration” in Proc MICCAI, vol. 1679, pp. 555-566, 1999.
[14] SPM99, Statistical parametric mapping, http://www.fil.ion.ucl.ac.uk/spm/, 1999.
[15] A. V. Oppenheim, “Application of Digital Signal Processing” Prentice Hall, 1978.
[16] W. H. Press, S.A. Teukolsky, W. T. Vetterling, B. P. Flannery, “Numerical Recipes in C: The Art of Scientific Computing, 2nd ed” Cambridge University Press, 1992.
[17] M. J. McKeown, S. Makeig, G. C. Browm, T-P Jung, S. S. Kindermann, A. J. Bell, T. J. Sejnowski, “Analysis of fMRI data by blind separation into independent spatial component” Hum Brain Mapp, vol. 6, pp. 160-188, 1998.
[18] J. R. Moeller, S. C. Strother, “A regional covariance approach to the analysis of functional patterns in position emission tomographic data” Cereb Blood Flow Metlab, vol. 11, pp. A121-A135, 1991.
[19] C. Jutten, J. Herault, “Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture” Signal Process, vol. 24, pp. 1-10, 1991.
[20] R. Linsker, “Local synaptic learning rules suffice to maximize mutual information in a linear network” Neural Computation, vol. 4, pp. 691-702, 1992.
[21] H. Abdi, In N J Salkind(Ed.), “Encyclopedia of Measurement and Statistics” Thousand Oasks, CA: Sage, 2007.
[22] http://en.wikipedia.org/wiki/Main_Page
[23] http://140.122.143.37/learning/basic/sensation.htm