精神分裂症是一種慢性、嚴重的精神疾病。近年來，精神分裂症中白質神經的連結性更成為了研究的重要議題，而擴散張量造影(DTI)則提供了在研究白質變化上的有效技術。在分析方法上，全腦分析方法相較於傳統ROI方法雖然提供了一種較客觀的分析方式，但在某些步驟如影像對位與平滑卻不免喪失資訊，為了改善全腦分析的缺點，我們發展出一套基於經驗模態分解(EMD)與濾波反投影的特徵擷取系統。在本篇研究我們探討了經驗模態分解於應用時所遇到的問題，如三次樣條、邊界效應與混模問題，並提出改善的方法。藉由這套系統，我們可以取得帶有不同生理意義的本質模態函數(IMF)影像，然後結合Tract-based spatial statistics 的全腦分析方法，探討經過不同本質模態函數增強後的影像，會對分析結果有何影響。從結果發現，較高頻的本質模態函數如IMF1與IMF2能增強較細小的神經管束，並協助我們找出原本方法學難以發現的白質區域。在本研究中，我們發現在經由我們方法於大腦中的穹窿、左側下縱束、左側上縱束等區域，能夠找出原始方法所無法發現之區域。而低頻成分如IMF3與IMF4，則是增強白質主要神經束，但統計結果並未與原本方法學有太大不同。總結來說，本方法能提取不同特徵並加強特定資訊於全腦分析方法上，提供另一種有別於傳統分析精神分裂症白質異常的方法。

Schizophrenia is a chronic, severe brain illness. In recent years, connectivity of nerve in white matter becomes an important issue in schizophrenia and DTI have been proved as a powerful technique on white matter. Although, the voxelwise analysis method can provide a more objective method comparing to the ROI method, it would lost the information when do the steps, such like registration and smoothing. To improve the shortcomings in voxelwise analysis, we developed a feature characterization system for image based on empirical mode decomposition (EMD) and filtered back projection. In this study, we discussed the problems in EMD such like cubic spline, boundary effect and mode mixing and proposed the modified method. With the proposed system, we can obtained the intrinsic mode function (IMF) images respectively, which is thought to contain different physical meanings in different IMFs. Then, we combined with TBSS and observed the IMF enhanced image would result in any effect on this method. From results, we found that the high frequency components, such like IMF1 and IMF2 can enhance the minor tract information and help us find the abnormal region in schizophrenia which is hard to find in traditional method. In this study, we found that the abnormal regions in fornix, left inferior longitudinal fasciculus, left superior longitudinal fasciculus which were hard to find in traditional method. The low frequency components, such like IMF3 and IMF4 enhance the major white matter tract, but the statistic results were almost same as traditional method. In summary, our method can extract the feature and enhance the specific information on the voxelwise analysis which can provide another way to analyze the abnormality area in schizophrenia different from traditional way.

[1]. A. C. Kak and M. Slaney, Principle of Computerized Tomographic Imaging, IEEE Press, 1999.
[2]. B. P. J. Parnas and G. M. Innocenti, “Schizophrenia trait features, binding and cortico-cortical connectivity: a neurodevelopmental pathogenetic hypothesis,” Neurol Psychiatry Brain Res, vol. 4, pp.185-196, 1996.
[3]. D. Le Bihan, “Molecular diffusion, tissue microdynamics and microstructure,” NMR Biomed, vol. 8, pp. 375-386, 1995.
[4]. F. N. Fritsch and R. E. Carlson, “Monotone piecewise cubic interpolation,” SIAM J. Numer. Anal, vol. 17, no. 2, pp. 238-246, 1980.
[5]. I. Feinberg, “Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence?” J Psychiatry Res, vol. 17, no.4, pp.319-334, 1982.
[6]. J. G. Beaumont and S. J. Dimond, “Brain disconnection and schizophrenia,” Br J Psychiatry, vol. 123, no. 577, pp. 661-662, 1973.
[7]. J. Foong, M. Maier, C. A. Clark, G. J. Barker, D. H. Miller and M. A. Ron, “Neuropathological abnormalities of the corpus callosum in schizophrenia: a diffusion tensor imaging study,” Journal of Neurology Neurosugery and Psychiatry, vol. 68, no. 2, pp. 242-244, 2000.
[8]. J. Ashburner and K. J. Friston, “Voxel-based morphometry-the methods,” Neuroimage, vol. 11, pp. 805-821, 2000.
[9]. J. C. Nunes, S. Guyot and E. Deléchelle, “Texture analysis based on local analysis of the bidimensional empirical mode decomposition,” Machine Vision and Applications, vol. 16, no. 3, pp 177-188, May 2005.
[10]. J. Fitzsimmons, M. Kubicki, K. Smith, G. Bushell, R. San Jose Estepar, C.-F. Westin, P. G. Nestor, M.A. Niznikiewicz, R. Kikinis, R.W. McCarley and M.E. Shenton, “Diffusion tractography of the fornix in schizophrenia,” Schizophrenia Research, vol. 107, no. 1, pp. 39-46, January 2009.
[11]. K. Zeng and M. He, “A simple boundary process technique for empirical mode decomposition,” Proc. IEEE International Geoscience and Remote Sensing Symposium IGARSS '04, vol. 6, pp. 4258-4261, 2004.
[12]. K. H. Karlsgodt, T. G. van Erp, R. A. Poldrack, C. E. Bearden, K. H. Nuechterlein and T. D. Cannon, “Diffusion tensor imaging of the superior longitudinal fasciculus and working memory in recent-onset schizophrenia,” Biological Psychiatry, vol. 63, no. 5, pp.512-518, 2008.
[13]. M. A. Horsfield, “Mapping eddy current induced fields for the correction of diffusion-weighted echo planar images,” Magnetic Resonance Imaging, vol. 17, no. 9, pp. 1335-1345, 1999.
[14]. M. Jenkinson and S. M. Smith. “A global optimisation method for robust affine registration of brain images,” Medical Image Analysis, vol. 5, no. 2, pp.143-156, 2001.
[15]. M. Jenkinson, P. R. Bannister, J. M. Brady, and S.M. Smith. “Improved optimisation for the robust and accurate linear registration and motion correction of brain images,” NeuroImage, vol. 17, no. 2, pp. 825-841, 2002.
[16]. M. Kubicki, R. McCarley, C. F. Westin, H. J. Park, S. Maier, R. Kikinis, F. A. Jolesz, and M. E.Shenton, “A review of diffusion tensor imaging studies in schizophrenia,” Journal of Psychiatirc Research, vol. 41, pp. 15-30, 2007.
[17]. M. Kubicki, C. F. Westin, P. G. Nestor, C. G. Wible, M. Frumin, S. E. Maier, R. Kikinis, F. A. Jolesz, R. W. McCarley and M. E. Shenton, “Cingulate fasciculus integrity disruption in schizophrenia: a magnetic resonance diffusion tensor imaging study,” Biological Psychiatry, vol. 54, no. 11, pp.1171-1180, 2003.
[18]. M. Ashtari, J. Cottone, B. A. Ardekani, K. Cervellione, P. R. Szeszko, J. Wu, S. Chen and S. Kumra, “Disruption of white matter integrity in the inferior longitudinal fasciculus in adolescents with schizophrenia as revealed by fiber tractography,” Archives of General Psychiatry, vol. 64, no. 11, pp. 1270-1280, 2007.
[19]. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non- stationary time series analysis,” Proc. R. Soc. Lond. A, vol. 454, pp. 903- 995, 1998.
[20]. O. Monchi, M. Petrides, V. Petre, K. Worsley and A. Dagher, “Wisconsin card sorting revisited: distinct neural circuits participating in different stages of the task indentified by event-related functional magnetic resonance imaging,” Neuroscience, vol. 21, no. 19, pp. 7733-7741, 2001.
[21]. O. Guillin, A. Abi-Dargham and M. Laruelle, “Neurobiology of dopamine in schizophrenia,” Int Rev Neurobiol, vol. 78, pp. 1-39, 2007.
[22]. P. J. Basser, J. Mattiello and D. LeBihan, “Estimation of the effective self-diffusion tensor from the NMR spin echo,” JMagnReson B, vol. 103, no. 3, pp.247-254, 1994.
[23]. P. J. Basser, “New histological and physiological stains derived from diffusion-tensor MR images,” Ann NY AcadSci, vol. 820, pp.123-138, 1997.
[24]. R. W. McCarley, G. Wible, M. Frumin, Y. Hirayasu, J. J. Levitt, I. A. Fischer and M. E. Shenton, “MRI anatomy of schizophrenia,” Biol Psychiatry, vol. 45, no. 9, pp. 1099-1119, 1999.
[25]. R. A. A. Kanaan, J. S. Kim, W. E. Kaufmann, G. D. Pearlson, G. J. Barker and P. K. McGuire,“Diffusion tensor imaging in schizophrenia,” Biological Psychiatry, vol. 58, no. 12, pp. 921-929, 2005.
[26]. S. R. Kay, A. Fiszbein and L. A. Opier, “The positive and negative syndrome scale (PANSS) for schizophrenia,” Schizophr Bull, vol. 13, no. 2, pp.263-276, 1987.
[27]. S.M. Smith, M. Jenkinson, H. Johansen-Berg, D. Rueckert, T.E. Nichols, C.E. Mackay, K.E. Watkins, O. Ciccarelli, M. Z. Cader, P. M. Matthews and T. E. J. Behrens, “Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data,” NeuroImage, vol. 31, pp. 1487-1505, 2006.
[28]. S. M. Smith and T. E. Nichols, “Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference,” NeuroImage, vol. 44, no. 1, pp. 83-98, 2009.
[29]. T. J. Crow, “Molecular pathology of schizophrenia: more than one disease process?” British Medical Journal, vol. 280, no. 6207, pp. 66–68, 1980.
[30]. T. E. Nichols and A. P. Holmes. “Nonparametric permutation tests for functional neuroimaging: a primer with examples,” Human Brain Mapping, vol. 15, pp. 1-25, 2002.
[31]. V. Cheung, C. P. Y. Chiu, C. W. Law, C. Cheung, C. L. M Hui., K. K. S. Chan, P. C. Sham, M. Y. Deng, K. S. Tai, P.-L. Khong, G. M. McAlonan, S.-E Chua and E. Chen, “Positive symptoms and white matter microstructure in never-medicated first episode schizophrenia,” Psychological Medicine, vol. 41, no. 8, pp. 1709-1719, 2011.
[32]. Y. Xu, B. Liu, J. Liu and S. Riemenschneider, “Two-dimensional empirical mode decomposition by finite elements,” Proc. R. Soc. A, vol. 462, no. 2074, pp.3081-3096, 2006.
[33]. Y. Deng, W. Wang, C. Qian, Z. Wang and D. Dai, “Boundary-processing technique in EMD method and Hilbert transform, ” Chinese Science Bulletin, vol. 46, no. 1, pp.954-960, 2001
[34]. Z. Zhao and Y. Wang, “A new method for processing end effect in empirical mode decomposition,” Proc. IEEE Inter- national Conference on Circuits and Systems for Communications ICCSC, pp. 841-845, 2007.
[35]. Z. Wu, and N. E Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol.1, no. 1, pp. 1-41, 2009.