應用碎形維度及腦廻指數分析法量化小腦萎縮之研究:
以小腦型多重系統萎縮症為例

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饒啟文(Chii-Wen Jao) 查詢紙本館藏

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論文名稱

應用碎形維度及腦廻指數分析法量化小腦萎縮之研究: 以小腦型多重系統萎縮症為例
(A Study for Quantifying Cerebellar Atrophy Using Fractal Dimension and Gyrification Index Analysis: Exemplifying by Multiple System Atrophy of the Cerebellar Type (MSA-C) )

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摘要(中)

小腦型多重系統萎縮症(MSA-C)是一種中樞神經系統萎縮的疾病。本研究應用兩種方法，一為“立體碎形維度(3D-FD)”，另一為“立體腦廻指數（3D-GI）”以達量化MSA-C病人小腦（包括腦幹）萎縮程度及比較與正常人有無明顯差異的目的。我們嘗試評估此兩種方法是否能排除性別差異及抑制因隨年齡增加而造成小腦縮小的現象，並且是否能精確地量化小腦和腦幹的萎縮程度。總計有16位正常人和16 位MSA-C患者參加此研究。我們根據腦部T1核磁共振影像(MR image)重建小腦白質及小腦灰質的三維立體影像，進而探討MSA-C病人小腦各部區域的萎縮情形。正常人與MSA-C病人的小腦灰質與白質的立體碎形維度、立體腦廻指數及體積分別被仔細計算並加以比較。結果顯示正常人的小腦灰質及白質立體碎形維度與立體腦廻指數的數值均在穩定的範圍區間且具微小變異量，沒有明顯的性別差異，也沒有會隨年齡增加而下降的情形。MSA-C病人小腦白質的三種量測數據，皆比正常人明顯的偏低，且在發病初期就十分明顯，顯示MSA-C病人的小腦白質的萎縮為小腦萎縮的最初發病區域。在初期的發病階段，病人中的小腦灰、白質的腦廻指數皆比正常人明顯的偏低。本研究所應用的立體碎形維度及立體腦廻指數分析法皆能準確的呈現小腦萎縮情形及精確的量化小腦形態複雜度的變化。另一方面，使用立體腦廻指數為二維基底的K-means分類法，我們可以很容易且精確的將正常人與MSA-C病人分群及歸類。

摘要(英)

Multiple System Atrophy of the Cerebellar Type (MSA-C) is a degenerative neurological disease of the central nervous system. This study aimed to employ two methods named, “three-dimensional fractal dimension (3D-FD)” and “surface-based three-dimensional gyrification index (3D-GI)” to quantify morphological changes in normal cerebellum (including brainstem) and atrophic cerebellum in MSA-C patients. We assessed whether the 3D-FD and 3D-GI can alleviate the effects of gender and age in volumetric measure, so that cerebellum and brainstem atrophy can be quantified more accurately. Sixteen healthy subjects and 16 MSA-C patients participated in this study. We compared the images of 3D reconstructed cerebellum gray matter (3D-CBGM) and cerebellum white matter (3D-CBWM) to detect the atrophic region of cerebellum in MSA-C patients. Furthermore, we compared 3D-FD, 3D-GI and volumes of CBGM and CBWM between normal and MSA-C patient groups based on T1-weighted MR images. Both the 3D-FD and 3D-GI values of normal group were in a stable range with small variances, exhibited no gender effect and no age-related shrinkage. All the CBWM measures of MSA-C group revealed significantly lower values compared to the control group, even in the early stage of MSA-C. This result indicated that CBWM is supposed to be the onset region of cerebellar atrophy in MSA-C patients. Both the 3D-FD and 3D-GI methods are superior to conventional volumetric methods in quantifying the structural changes of CBGM and CBWM because they exhibit smaller variances, no gender effect and less age-related shrinkage. Among the three measures, both the CBGM 3D-GI and CBWM 3D-GI revealed significantly lower values in the MSA-C patients compared with healthy subjects, especially in the early phases of the disease. The 3D-FD and 3D-GI methods can precisely indicate the degeneration of the cerebellar folding structure and exactly reflect the morphological changes in cerebellum. Using the two-dimensional K-means classifier of CBWM and CBGM 3D-GI values, we can effectively discriminate the MSA-C patients from healthy subjects.

關鍵字(中)

★ 腦廻指數
★ 碎形維度
★ 萎縮
★ 小腦
★ 小腦型多重系統萎縮症

關鍵字(英)

★ MSA-C
★ Gyrification Index
★ Fractal Dimension
★ Atrophy
★ Cerebellum

論文目次

摘要 .....................................................i
Abstract ................................................ii
Acknowledgements ........................................iv
List of Contents ........................................vi
List of Figures .......................................viii
List of Tables ..........................................xi
Chapter 1 Introduction
1.1 Motivation and Background ...........................1
1.2 Limitations of Previous Works for Quantifying
Atrophy of MSA-C ....................................2
1.3 Gender Effect and Age-Related Shrinkage of Cerebellar
Volume ..............................................3
1.4 Fractal Dimension and Gyrification Index analysis ...4
1.5 Dissertation Purpose and Contribution ...............6
1.6 Dissertation Organization ...........................6
Chapter 2 FD and GI for Quantifying Morphological
Complexity of Cerebellum
2.1 Concept of “Fractal”...............................7
2.2 Fractal Dimension (FD) estimation ...................8
2.3 3-D Box-counting method for FD estimation ...........8
2.4 Assessing the Accuracy and Sensitivity of 3D FD
Estimation ..........................................12
2.5 Concept of Gyrification Index (GI) .................13
2.6 Surface-Based 3D Gyrification Index ................14
2.7 Surface Area Calculation for 3D-GI Estimation ......17
Chapter 3 Material and Methods
3.1 Demographic Analyses of Subjects ...................20
3.2 Data acquisition ...................................22
3.3 MR Image and Measure Process .......................22
3.4 Statistical Analyses ...............................25
3.5 Assessment for Discriminative Accuracy of each
Measure ............................................25
Chapter 4 Results and Discussion
4.1 Results ............................................27
4.1.1 MSA-C patients show localized atrophy in
cerebellum ......................................27
4.1.2 The measured data are appropriate for ANOVA
Analysis ........................................29
4.1.3 Significant gender difference in CBWM and CBGM
volumetric measure within normal group ..........34
4.1.4 Significant age-related shrinkage in volumetric
measure of CBGM and CBWM within normal group ....36
4.1.5 Significant smaller standard deviation and stable
range of 3D-FD and 3D-GI values for the normal
subjects ........................................39
4.1.6 Significantly decreased 3D-FD and 3D-GI values for
the MSA-C group .................................39
4.1.7 Significantly decreased 3D-FD and 3D-GI values in
early phase of MSA-C patients ...................42
4.1.8 Superior discriminative accuracy of 3D-FD and
3D-GI Measures ..................................42
4.1.9 Significant correlation between 3D-FD and 3D-GI
measures ........................................44
4.2 Discussion .........................................47
Chapter 5 Conclusion and Future Works
5.1 Conclusion .........................................53
5.2 Future Works .......................................54
References ..............................................55
List of Publications ....................................66
Author’’s Information ....................................67

參考文獻

[1]. Wenning, G.K., Colosimo, C., Geser, F., Werner Poewe, W., 2004. Multiple system atrophy. The Lancent Neurology 3, 93-103.
[2]. Wenning, G.K., Seppi, K., Tison, F., Jellinger,K., 2002. A novel grading scale for striatonigral degeneration (multiple system atrophy). J. Neural. Transm.109, 307–320.
[3]. Papp, M.I., Kahn, J.E., Lantos, P.L., 1989, Glial cytoplasmic inclusions in the CNS of patients with multiple system atrophy (striatonigral degeneration, olivopontocerebellar atrophy and Shy–Drager syndrome). J. Neurol. Sci. 94, 79-100.
[4]. Soma, H., Yabe, I., Takei, A., Fujiki, N., Yanagihara, T., Sasaki, H., 2006. Heredity in multiple system atrophy. J. Neuro. Sci. 240, 107-110.
[5]. Wenning, G.K, Ben-Shlomo, Y., Magalhaes, M., Daniel, S.E., Quinn, N.P., 1994. Clinical features and natural history of multiple system atrophy. An analysis of 100 cases. Brain 117, 835-845.
[6]. Berciano, J., 1982. Olivopontocerebellar Atrophy. A review of 117 cases, J. Neurol. Sci. 53, 253-272.
[7]. Gilman, S., Low, P.A., Quinn, N., Albanese, A., Ben-Shlomo, Y., Fowler, C.J., Kaufmann, H., Klockgether, T., Lang, A.E., Lantos, P.L., Litvan, I., Mathias, C.J., Oliver, E., Robertson, D., Schatz, I., Wenning, G.K., 1999. Consensus statement on the diagnosis of multiple system atrophy. J. Neurol. Sci. 163, 94-98.
[8]. Wu, Y.T., Shyu, K.K., Jao, C.W., Wang, Z.Y., Soong, B.W., Wu, H.M., Wang, P.S., 2010. Fractal dimension analysis for quantifying cerebellar morphological change of multiple system atrophy of the cerebellar type (MSA-C). Neuroimage 49, 539–551.
[9]. Pemde, H.K., Bakhshi, S., Kalra, V.,1995, Olivopontocerebellar atrophy: a case report, Brain & Development. 17, 130-132
[10]. Matsusue, E., Fujii, S., Kanasaki, Y., Sugihara, S., Miyata, H., Ohama, E., Ogawa, T., 2008. Putaminal lesion in multiple system atrophy: postmortem MR-pathological correlations. Neuroradiology 50, 559-567.
[11]. Paviour, D.C., Price, S.L., Jahanshahi, M., Lees, A.J., Fox, N.C., 2006. Longitudinal MRI in progressive supranuclear palsy and multiple system atrophy: rates and regions of atrophy. Brain 129, 1040-1049.
[12]. Miyatake, S., Mochizuki, H., Naka, T., Ugawa, Y., Tanabe, H., Kuzume, D., Suzuki, M., Ogata, K., Kawai, M., 2010. Brain volume analyses and somatosensory evoked potentials in multiple system atrophy. J Neurol 257, 419-425.
[13]. Kates WR, Ikuta I, Burnette CP. 2009. Gyrification Patterns in Monozygotic Twin Pairs Varying in Discordance for Autism. Autism Res 2: 267-278.
[14]. McIntosh AM, Moorhead TWJ, McKirdy J, Hall J, Sussmann JED, Stanfield AC, et al. Prefrontal gyral folding and its cognitive correlates in bipolar disorder and schizophrenia. Acta Psychiatr Scand 2009; 119: 192-1981.
[15]. Toro R., Perron M., Pike B., Richer L., Veillette S., Pausova Z., Paus T., 2008. Brain Size and Folding of the Human Cerebral Cortex. Cereb. Cortex 18, 2352-2357.
[16]. White, T., Su, S., Schmidt, M., Kao, C.Y., Sapiro, G., 2010. The development of gyrification in childhood and adolescence. Brain Cogn 72, 36-45.
[17]. Im, K., Lee, J.M., Lyttelton, O., Kim, S.H., Evans, A.C., Kim, S.I., 2008. Brain size and cortical structure in the adult human brain. Cerebral Cortex 18, 2181-2191.
[18]. Esteban, F.J., Sepulcre, J., de Miras, J.R., Navas, J., de Mendizábal, N.V., Goñi, J., Quesada, J.M., Bejarano, B.,Villoslada, P., 2009. Fractal dimension analysis of grey matter in multiple sclerosis. J. Neuro. Sci. 282, 67-71.
[19]. Bonnici, H.M., Moorhead, T.W.J., Stanfield, A.C., Harris, J.M., Owens, D.G., Johnstone, E.C., Lawrie, S.M., 2007. Pre-frontal lobe gyrification index in schizophrenia, mental retardation and comorbid groups: An automated study. Neuroimage 35, 648-654.
[20]. Bearden CE, van Erp TGM, Dutton RA, Lee AD, Simon TJ, Cannon TD, Emanuel BS, McDonald-McGinn D, Zackai EH, Thompson PM. 2009. Alterations in Midline Cortical Thickness and Gyrification Patterns Mapped in Children with 22q11.2 Deletions. Cereb Cortex 19:115-126.
[21]. Magnotta VA, Andreasen NC, Svhultz SK, Harris G,Gizadlo T, Heckel D, Nopoulos P,FlaumM. 1999. Quantitative in vivo measurement of gyrification in the human brain: changes associated with aging. Cereb Cortex 9: 151-160.
[22]. Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RSR, Busa E, Morris JC, Dale AM, Fischl B. 2004. Thinning of the Cerebral Cortex in Aging. Cereb Cortex 14:721-730.
[23]. Brenneis, C., Egger, K., Scherfler, C., Seppi, K., Schocke, M., 2007. Progression of brain atrophy in multiple system atrophy. J. Neurol. 254, 191–196.
[24]. Minnerop, M., Specht, K., Ruhlmann, J., Schimke, N., Abele, M., Weyer, A., Wüllner, U., T. Klockgether, T., 2007. Voxel-based morphometry and voxel-based relaxometry in multiple system atrophy-A comparison between clinical subtypes and correlations with clinical parameters. NeuroImage 36, 1086-1095.
[25]. Specht, K., Minnerop, M., Müller-Hübenthal, J., Klockgetherb,T., 2005. Voxel-based analysis of multiple-system atrophy of cerebellar type: complementary results by combining voxel-based morphometry and voxel-based relaxometry. NeuroImage 25, 287-293.
[26]. Bürk, K., Globas, C., Wahl, T., Bühring, U., Diety, K., Yühlke, C., Luft, A., Schuly, J.B., Voigt, K., Dichgans, J., 2004. MRI-based volumetric differentiation of sporadic cerebellar ataxia. Brain 127, 175-181.
[27]. Miyatake, S., Mochizuki, H., Naka, T., Ugawa, Y., Tanabe, H., Kuzume, D., Suzuki, M., Ogata, K., Kawai, M., 2010. Brain volume analyses and somatosensory evoked potentials in multiple system atrophy. J Neurol 257, 419-425.
[28]. Horimoto, Y., Aiba, I., Yasuda, T., Ohkawa, Y., Katayama, T., Yokokawa, Y., Goto, A., Ito, Y., 2000. Cerebral atrophy in multiple system atrophy by MRI. J. Neurol. Sci. 173, 109-112.
[29]. Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N.R. Friston, K.J., Frackowiak, R.S.J., 2001. A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains, NeuroImage, 14, 21–36.
[30]. Raz, N., Gunning-Dixon, F., Head, D., Rodrigue, K.M., Williamson, A., Acker, J.D. 2004. Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume. Neurobiol. Aging 25, 377-396.
[31]. Smith, C.D., Chebrolu, H., Wekstein, D.R., Schmitt, F.A., Markesbery, W.R., 2007. Age and gender effects on human brain anatomy: A pixel-based morphometric study in healthy elderly. Neurobiology of Aging 28, 1075-1087.
[32]. Pell, G.S., Briellmann, R.S., Chan, C.H., Pardoe, H., Abbott, D.F., Jackson, G.D., 2008. Selection of the control group for VBM analysis: Influence of covariates, matching and sample size. NeuroImage 41, 1324-1335.
[33]. Davatzikos, C. 2004. Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage 23 , 17-20.
[34]. Bearden, C.E., van Erp, T.G., Dutton, R,A,, Lee, A.D., Simon, T.J., Cannon, T.D, Emanuel, B.S., McDonald-McGinn, D., Zackai, E.H., Thompson, P.M., 2009. Alterations in midline cortical thickness and gyrification patterns mapped in children with 22q11.2 deletions. Cereb Cortex 19,115-126.
[35]. Zhang, L., Liu, J.Z., Dean, D., Sahgal, V., Yue, G.H., 2006. A three-dimensional fractal analysis method for quantifying white matter structure in human brain. J. Neurosci. Methods 150, 242-253.
[36]. Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N.R. Friston, K.J., Frackowiak, R.S.J., 2001. A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains, NeuroImage, 14, 21–36.
[37]. Chen, X., Sachdev, P.S., Wen, W., Anstey, K.J., 2007. Sex differences in regional gray matter in healthy individuals aged 44–48 years: A voxel-based morphometric study. NeuroImage 36, 691–699.
[38]. Raz, N., Gunning-Dixon, F., Head, D., Rodrigue, K.M., Williamson, A., Acker, J.D. 2004. Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume. Neurobiol. Aging 25, 377-396.
[39]. Torvik, A., Torp, S., Lindboe, C.F., 1986. Atrophy of the cerebellar vermis in ageing: a morphometric and histological study. J Neurol Sci 76, 283-294
[40]. Oguro, H., Okada, K., Yamaguchi, S., Kobayashi, S. 1998. Sex differences in morphology of the brain stem and cerebellum with normal ageing. Neuroradiology 40, 788-792.
[41]. Raz, N., Dupuis, J.H., Briggs, S.D., McGavran, C., Acker, J.D., 1998. Differential effects of age and sex on the cerebellar hemispheres and the vermis: A prospective MR study. AJNR Am J Neuroradiol 19, 65-71.
[42]. Raz, N., Gunning-Dixon, F., Head, D., Williamson, A., Acker, J.D., 2001. Age and sex Differences in the cerebellum and the ventral pons: A prospective MR study of healthy adults. AJNR Am J Neuroradiol 22, 1161-1167.
[43]. Luft, A.R., Skalej, M., Schulz, J.B, Welte, D., Kolb, R., Bürk, K., Kolckgether, T., Voigt, K., 1999. Patterns of age-related shrinkage in cerebellum and brainstem observed in vivo using three-dimensional MRI volumetry. Cereb Cortex 9, 712-721.
[44]. Xu, J., Kobayashi, S., Yamaguchi, S., Iijima, K.I., Okada, K., Yamashita, K., 2000. Gender effects on age-related changes in brain structure. AJNR Am J Neuroradiol 21, 112-118.
[45]. Chen, X., Sachdev, P.S., Wen, W., Anstey, K.J., 2007. Sex differences in regional gray matter in healthy individuals aged 44–48 years: A voxel-based morphometric study. NeuroImage 36, 691–699.
[46]. Mandelbrot B.B., 1983, The Fractal Geometry of Nature. New York ,W.H. Freeman.
[47]. Ha, T.H., Yoon, U., Lee, K.J., Shin, Y.W., Lee, J.M., Kim, I.Y., Ha, K.S., Kim, S.I., Kwon, J.S., 2005. Fractal dimension of cerebral cortical surface in schizophrenia and obsessive–compulsive disorder. Neuroscience Letters 384, 172-176.
[48]. Esteban FJ, Sepulcre J, de Miras JR, Navas J, de Mendizabal NV, Goni J, et al. Fractal dimensional analysis of grey matter in multiple sclerosis. Journal of the neurological sciences (2009) 282:67-71.
[49]. Fernández E. and Jelinek H.F., 2001. Use of Fractal Theory in Neuroscience: Methods, Advantages, and Potential Problems. Methods 24, 309-321.
[50]. Shan, Z.Y., Liu, J.Z., Glassa, J.O., Gajjarc, A., Lid, C.S., Reddicka, W.E., 2006. Quantitative morphologic evaluation of white matter in survivors of childhood medulloblastoma. Magnetic Resonance Imaging 24, 1015–1022.
[51]. Free, S.L., Sisodiya, S.M., Cook, M.J., Fish, D.R., Shorvon, S.D., 1996. Three dimension fractal analysis of the white matter surface from magnetic resonance images of the human brain. Cereb. Cortex 6, 830-836.Baloh, R.W., Ying, S.H., Jacobson K.M., 2003. A longitudinal study of gait and balance dysfunction in normal older people. Arch. Neurol. 60, 835-839.
[52]. Zhang, L., Dean, D., Liu, J.Z., Sahgal, V., Wang, X., Yue, G.H., 2007. Quantifying degeneration of white matter in normal aging using fractal dimension. Neurobiology of Aging 28, 1543-1555.
[53]. Sandu, A.L., Rasmussen Jr., I.A. b, Lundervold, A., Frank Kreuder, F., Neckelmann, G., Hugdahl, K., Specht, K., 2008.Fractal dimension analysis of MR images reveals grey matter structure irregularities in schizophrenia. Computerized Medical Imaging and Graphics 32 , 150-158.
[54]. Kiselev VG, Hahn, KR, Auer, DP, 2003. Is the brain cortex a fractal? NeuroImage 20, 1765-74.
[55]. Shan, Z.Y., Liu, J.Z., Glassa, J.O., Gajjarc, A., Lid, C.S., Reddicka, W.E., 2006. Quantitative morphologic evaluation of white matter in survivors of childhood medulloblastoma. Magnetic Resonance Imaging 24, 1015–1022.
[56]. Liu, J.Z., Zhang, L.D., Yue, G.H., 2003. Fractal Dimension in Human Cerebellum Measured by Magnetic Resonance Imaging. Bioph. J. 85, 4041-4046.
[57]. Esteban, F.J., Sepulcre, J., de Mendizábal, N.V., Goñi, J., Navas, J., de Miras, J.R., Bejarano, B., Masdeu, J.C., Villoslada, P., 2007. Fractal dimension and white matter changes in multiple sclerosis. NeuroImage 36, 543-549.
[58]. Zilles, K., Armstrong, E., Schleicher, A., Kretschmann, H.J. 1988. The human pattern of gyrification in the cerebral cortex. Anat Embryol 179, 173-179.
[59]. Harris, J.M., Yates, S., Miller, P., Best, J.J.K., Johnstone, E.C., Lawrie, S.M., 2004. Gyrification in first-episode schizophrenia: A morphometric study. Biol Psychiatry 55, 141-147.
[60]. Oyegbile, T., Hansen, R.,Magnotta, V., O, Leary, D., Bell, B., Seidenberg, M., Hermann,B.P., 2004. Quantitativemeasurement of cortical surface features in localization-related temporal lobe epilepsy. Neuropsychology 18, 729–737.
[61]. Moorhead, T.W., Harris, J.M., Stanfield A.C., Job, D.E., Best J.J.K., Johnstone E.C., Lawrie S.M., 2006. Automated computation of the yrification index in prefrontal lobes: method and comparison with manual implementation. NeuroImage 31, 1560-1566.
[62]. Zilles, K., Schleicher, A., Langemann, C., Amunts, K., Morosan, P., Palomero-Gallagher, N., Schormann, T., Mohlberg, H., Burgel, U., Steinmetz, H., Schlaug, G., Roland, P.E., 1997. Quantitative analysis of Sulci in the human cerebral cortex: development, regional heterogeneity, gender difference, asymmetry, intersubject variability and cortical architecture. Hum Brain Map 5, 218-221.
[63]. Wu,Y.T., Shyu,K.K., Jao,C.W., Liao, Y.L., Wang, T.Y., Wu, H.M., Wang, P.S., Soong,B.W., 2012. Quantifying cerebellar atrophy in multiple system atrophy of the cerebellar type (MSA-C) using three-dimensional gyrification index. NeuroImage 61, 1-9.
[64]. Kesler, S.R., Vohr, B., Schneider, K.C., Katz, K.H., Makuch, R.W., Reiss, A.L., Ment, L.R. 2006. Increased temporal lobe gyrification in preterm children. Neuropsychologia 44, 445-453.
[65]. Mirakhur, A., Moorhead T.W.J. Stanfield A.C., Mckirdy J., Sussmann, E.D., Hall, J., Lawrie, S.M., Johnstone, E.C., Mclntosh, A.M., 2009. Changes in gyrification over 4 years in bipolar disorder and their association with brain-derived neurotrophic factor valine66 methionine variant. Biol Psychiatry 66, 293-297.
[66]. Gaser, C., Luders, E., Thompson, P.M., Lee, A.D., Dutton, R.A., Geaga, J.A., Hayashi, K.M., Bellugi, U., Galaburda, A.M., Korenberg, J.R., Mills, D.L., Toga, A.W., Reiss, A.L., 2006. Increased local gyrification mapped in Williams syndrome. Neuroimage 33, 46-54.
[67]. Zhang, Y., Yu, C., Zhou, Y., Li, K., Li, C., Jiang, T., 2009. Decreased gyrification in major depressive disorder. Neuroreport 20, 378-380.
[68]. Zhang, Y., Zhou, Y., Yu, C., Lin, L., Li, C., Jiang, T., 2010. Reduced Cortical Folding in Mental Retardation. Am J Neuroradiol 31, 1063- 1067.
[69]. Peitgen, H.O., Jurgens, H., Saupe, D., Chaos and Fractals New Frontiers of Science. New York: Springer; 1992.
[70]. Buchnicek, M, Nezadal, M, Zmeskal, O., 2000. Numeric calculation of fractal dimension. Proceedings of the third conference on prediction, synergetic and more.
[71]. Nezadal, M., Zmeskal, O., Buchnicek, M., 2001.The box-counting: critical study. In: Proceedings of the fourth conference on prediction, synergetic and more. P.18 ISBN 80-7318-030-8
[72]. Rodriguez-Carranza, C.E., Mukherjee, P, Vigneron, D., Barkovich, J., Studholme, C., 2008. A framework for in vivo quantification of regional brain folding in premature neonates. NeuroImage 41, 462-478.
[73]. Lindblad, J., 2005. Surface area estimation of digitized 3D objects using weighted local configurations. Image and Vision Computing 23, 111-122 .
[74] Lorensen, W.E., Cline, H.E., 1987. Marching cubes: a high resolution 3D surface construction algorithm. Computer Graphics 21, 163-169.
[75]. Gilman, S., Wenning, G.K., Low, P.A., Brooks, D.J., Mathias, C.J., Trojanowski, J,Q,, Wood, N.W., Colosimo, C., Dürr, A., Fowler, C.J., Kaufmann, H., Klockgether, T., Lees, A., Poewe, W., Quinn, N., Revesz, T., Robertson, D., Sandroni, P., Seppi ,K., Vidailhet, M., 2008. Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71, 670–676
[76]. Bang, O.Y., Huh, K., Lee, P.H., Kim, H.J., 2003. Clinical and neuroradiological features of patients with spinocerebellar ataxias from Korean kindreds. Arch Neurol 60, 1566-1574.
[77]. Kim, S.E., Choi, J.Y., Choe, Y.S., Choi, Y., Lee, W.Y., 2003. Serotonin transporters in the midbrain of Parkinson’’s disease patients: a study with 123I-β-CIT SPECT. J Nucl Med 44, 870-876.
[78]. Jarque, C.M., Bera, A.K., 1980. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economic Letters 6, 255-259.
[79]. Mauchly, J.W., 1940. Significance test for sphericity of a normal n-variate distribution. The Annals of Mathematical Statistics 11, 204-209.
[80]. Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford, U.K.: Oxford University.
[81]. Specht, K., Minnerop, M., Abele, M., Reul, J., Wüllner, U., Klockgether, T., 2003. In vivo voxel-based morphometry in multiple system atrophy of the cerebellar type. Arch. Neurol. 60, 1431-1435.
[82]. Thach, W.T., Bastian, A.J., 2004. Role of the cerebellum in the control and adaptation of gait in health and disease. Prog Brain Res 143, 353-366.
[83]. King, R.D., Brown, B., Hwang, M., Jeon, T., George, A.T., 2010. Fractal dimension analysis of the cortical ribbon in mild Alzheimer’s disease. NeuroImage 53, 471-479.
[84]. Zook, J.M., Iftekharuddin, K.M., 2005. Statistical analysis of fractal-based brain tumor detection algorithms. Magnetic Resonance Imaging 23, 671-678.
[85]. Iftekharuddin, K.M., Jia, W., Marsh, R., 2000. A fractal analysis approach to identification of tumor in brain MR images. Eng. Med. Biol. Soc., Proc. of the 22nd Annu. Intl. Conference of the IEEE. 4, 3064-3066.
[86]. Pereira, D., Zambrano, C., Martin-Landrove, M., 2000. Evaluation of malignancy in tumors of the central nervous system using fractal dimension. Eng. Med. Biol. Soc. Proc. of the 22nd Annu. Intl. Conference of the IEEE. 3, 1775-1778.
[87]. Peng, C.K., Mietus, J.E., Liu, Y., Lee, C., Jeffrey, M., Hausdorff, J.M., Stanley, H.E.,Goldberger, A.L., Lipsitz, L.A., 2002. Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects. Annals. of Bio. Eng. 30, 683-692.
[88]. Stoodley CJ, Jeremy D. Schmahmann JD., 2009. Functional topography in the human cerebellum: A meta-analysis of neuroimaging studies. NeuroImage 44, 489-501.
[89]. Thach WT, Bastian AJ., 2004. Role of the cerebellum in the control and adaptation of gait in health and disease. Prog Brain Res 143, 353-366.
[90]. Fukuyama H, Ouchi Y, Matsuzaki S, Nagahama Y, Yamauchi H, Ogawa M., 1997. Brain functional activity during gait in normal subjects: a SPECT study. Neurosci Lett 228, 183-186.
[91]. Quattrone A, Cerasa A., 2008. Essential head tremor is associated with cerebellar vermis atrophy: A volumetric and voxel-based morphometry MR imaging study. AJNR 29, 1692-1697.

指導教授

徐國鎧、吳育德
(Kuo-Kai Shyu、Yu-Te Wu)

審核日期

2012-7-2

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