臺灣博碩士論文加值系統

研究目的:
許多研究指出偏頭痛(Migraine)患者比一般人容易產生腦白質病灶，已知疼痛的感覺是由腦皮質稱為疼痛基質(Pain matrix)的相關腦區域來執行，因此偏頭痛可能與這些區域附近的腦白質有相關。近年有許多研究已顯示擴散峰度造影(DKI)能夠補足擴散張量造影(DTI)的缺陷而得到更完整的組織結構資訊，對於觀察像白質纖維如此細微的解剖構造時，較能貼近實際結構變化之情況而得到較有價值的訊息。
此外，體素型態分析方法(VBM)是利用估算體素內的變化量，來推估體積的變化，因此可用來估算腦皮質的變化。
因此本研究利用DKI來觀察偏頭痛患者的腦白質在疼痛基質相關腦區域的變化，並利用體素分析方法，觀察灰質部分之變化，希望能更完整地探討偏頭痛患者腦部結構的變化。

材料與方法:
本次研究使用Siemens 3.0T Skyra System擷取19位無預兆偏頭痛患者之影像(男/女:5/14；年齡:42±10歲)以及13位健康控制組(男/女:5/8；年齡:32±9歲)之磁振影像，包括全腦高解析T1影像及DKI影像，DKI掃描參數為b值=0、1000及2000s/mm2；擴散梯度方向20個方向，並重複擷取三次。
白質DKI影像處理的部分使用DKE tool(Diffusion Kurtosis Estimator)估算得到軸向擴散度(AD)、徑向擴散度(RD)、平均擴散度(MD)、擴散不等向性(FA)、軸向擴散峰度(AK)、徑向擴散峰度(RK)與平均擴散峰度(MK)，經過移動等修正並與標準頭校準。灰質影像部分則使用DARTEL(Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra)體素型態分析方法進行標準頭校準，兩種影像均利用搭載於MATLAB (Mathworks, Natick, MA, USA)內的軟體SPM8 (Statistical Parametric Mapping version 8)進行統計分析比較。

結果與討論:
由分析的結果得知偏頭痛患者的腦白質相較於健康對照組，在疼痛基質相關腦區域其擴散度增加、擴散受限程度減少，且在徑向參數(RD及RK)所顯示的差異較多，並發現顯著差異的部位有偏左側的情況，而在影像上能明顯觀察到DKI的影像相較於DTI有更顯著之差異。
另外偏頭痛患者之灰質在疼痛基質相關腦區域也有變化，其差異部位與白質相同有偏左側且有減少之情況，然而在小腦處有增加的現象。

結論:
目前偏頭痛的發生機制還尚未明瞭，由結果可知偏頭痛的與白質的變異的確是有相關性的，利用DKI的技術亦能較靈敏地偵測偏頭痛腦白質之變異，並也發現白質與灰質的變異部位與疼痛基質相關腦區是有關連的，雖然無法直接斷定參數變化與病理變化之關係，但相信我們的結果能提供未來偏頭痛相關研究的方向並期許能幫助臨床診斷之參考。

Purpose:
Specific white matter alterations in migraine have been shown in many studies. Because the sensations of pain are performed by pain matrix, the integrity of white matter near these regions might be altered in migraine patients.
Diffusion tensor imaging (DTI) has been used in a lot of studies to detect white matter alterations. However, because the Gaussian assumption in DTI is not a suitable model in the intracellular microenvironment, diffusion kurtosis imaging (DKI) which considers not only the Gaussian distribution but also the non-Gaussian distribution were demonstrated to better characterize white matter microstructural alterations.
In addition, voxel-based morphometry (VBM) has been used in many studies to estimate the volume or density changes of gray matter in the cerebral cortex.
Therefore, the aim of our study was to use DKI and VBM to comprehensively observe white matter and gray matter alterations near the regions of pain matrix in migraine.

Materials and Method:
Our study acquired whole brain T1 and DKI datasets from 19 Migraine without aura (MWoA) subjects (M/F=5/14, age=42±10 y/o) and13 healthy controls (M/F=5/8, age=32±9 y/o) on a Siemens 3.0T Skyra System. The DKI dataset were acquired using three b values(0, 1000 and 2000) in 20 non-collinear diffusion directions and repeated three times to improve data quality.
For white matter analysis, all DKI images were post-processed with DKE tool (Diffusion Kurtosis Estimator) to obtain axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), Fractional Anisotropy (FA), axial kurtosis (AK), radial kurtosis (RK), and mean kurtosis (MK).
For gray matter analysis, we used DARTEL VBM analysis for a series of automatic image processing, such as segmentation, modulation, and normalization. After spatial normalization, SPM8 (Statistical Parametric Mapping version 8) was performed for statistical analysis.

Results:
The results showed that there were more white matter alterations found by DKI than DTI.
We found significantly increased diffusivity and decreased diffusion anisotropy in multiple white matter regions, especially at left side of brain near the pain matrix in migraine subjects, and also found there were more alterations in the parameters of radial direction (RD and RK).
Moreover, the gray matter density were significantly decreased at the left side of brain near pain matrix in migraine subjects, but were significantly increased in cerebellum.

Conclusion:
The mechanism of migraine is still to be investigated. Our results demonstrated that DKI technique is helpful and more sensitive for detection of subtle white matter microstructural alterations in migraine subjects and can provide more in-depth insight into the distribution of water diffusion related to tissue microstructural alterations. Besides, DARTEL VMB is also helpful for detection of gray matter alterations in migraine subjects.
Although the significance of these changes in migraine is still to be elucidated, our study preliminarily evaluated the alterations of both white matter and gray matter structures in migraine and the results may provide the direction for future research and clinical diagnosis.

論文合格通過證明
學位論文授權書
致謝
中文摘要……………………………………………………………….....i
ABSTRACT……………………………………………………………...iv
目錄………………………………………………………………….......vi
圖目錄…………………………………………………………………...ix
表目錄…………………………………………………………………...xi
第一章 緒論…………………………………………………………....1
1.1 文獻回顧………………………………………………………….....1
1.2 研究動機………………………………………………………….....5
第二章 研究理論……………………………………………………....7
2.1 疼痛基質………………………………………………………….....7
2.2 擴散權重影像…………………………………………………….....9
2.2.1擴散權重之量化指標……………………………………….....11
2.3 擴散之等向性與不等向性……………………………………..….13
2.4 擴散張量影像……………………………………………………...15
2.4.1擴散張量之量化指標……………………………………….....17
2.5 擴散峰度影像…………………………………………………….19
2.5.1 擴散峰度之量化指標………………………………………..21
2.6 體素形態學測量分析………………………………………….....23
2.6.1 DARTEL影像校準…………………………………………..25
第三章 研究材料與方法…………………………………………....28
3.1 受試者…………………………………………………………….28
3.2白質擴散峰度分析之研究架構…………………………………..30
3.2.1 儀器及參數設定……………………………………………..30
3.2.2 移動及渦電流扭曲修正……………………………………..31
3.2.3白質擴散峰度量化指標分析………………………………...32
3.2.4 空間標準化對位……………………………………………..32
3.2.5 統計分析與VOI圈選……………………………………….35
3.3灰質體素基礎分析之研究架構…………………………………..37
3.3.1儀器及參數設定……………………………………………...37
3.3.2 DARTEL體素分析…………………………………………..38
3.3.3 統計分析……………………………………………………..39
第四章 研究結果……………………………………………………40
4.1 擴散張量及擴散峰度參數之統計分析……………………….....40
4.2 徑向擴散度……………………………………………………….46
4.3平均擴散度………………………………………………………..50
4.4擴散不等向性指標………………………………………………..53
4.5 軸向擴散峰度…………………………………………………….58
4.6 徑向擴散峰度…………………………………………………….66
4.7平均擴散峰度…………………………………………………......75
4.8灰質變化…………………………………………………………..86
第五章 討論………………………………………………………..90
5.1 擴散峰度影像與擴散張量影像之比較……………………….....90
5.2 擴散張量影像與擴散峰度參數值之變化…………………….....94
5.3偏頭痛之腦部白質變異…………………………………………..96
5.4 偏頭痛之腦部灰質變異………………………………………...104
5.5 實驗限制與誤差………………………………………………...107
5.5.1實驗分析誤差…………………………………………….…107
5.5.2受試者因素之誤差及限制………………………………….109
5.5.3掃描時間限制……………………………………………….110
第六章 結論………………………………………………………..111
參考文獻……………………………………………………………..112
附表一………………………………………………………………..116

1.Jones, D.K. and A. Leemans, Diffusion tensor imaging. Magnetic Resonance Neuroimaging: Methods and Protocols, 2011: p. 127-144.
2.Makris, N., et al., Diffusion tensor imaging. Neuropsychopharmacology: the fifth generation of progress. New York: Lippincott, Williams, and Wilkins, 2002: p. 357-71.
3.Lu, H., et al., Three‐dimensional characterization of non‐gaussian water diffusion in humans using diffusion kurtosis imaging. NMR in Biomedicine, 2006. 19(2): p. 236-247.
4.Society, H.C.C.o.t.I.H., The international classification of headache disorders. cephalalgia, 2004. 24: p. 1-160.
5.Society, H.C.C.o.t.I.H., The international classification of headache disorders, (beta version). Cephalalgia, 2013.
6.Bashir, A., et al., Migraine and structural changes in the brain A systematic review and meta-analysis. Neurology, 2013. 81(14): p. 1260-1268.
7.Szabó, N., et al., White matter microstructural alterations in migraine: a diffusion-weighted MRI study. Pain, 2012. 153(3): p. 651-656.
8.May, A., Chronic pain may change the structure of the brain. PAIN®, 2008. 137(1): p. 7-15.
9.Valfrè, W., et al., Voxel‐based morphometry reveals gray matter abnormalities in migraine. Headache: The Journal of Head and Face Pain, 2008. 48(1): p. 109-117.
10.DaSilva, A.F., et al., Interictal alterations of the trigeminal somatosensory pathway and PAG in migraine. Neuroreport, 2007. 18(4): p. 301.
11.Wang, S.-J., Epidemiology of migraine and other types of headache in Asia. Current neurology and neuroscience reports, 2003. 3(2): p. 104-108.
12.Wang, S.J., et al., Prevalence of migraine in Taipei, Taiwan: a population‐based survey. Cephalalgia, 2000. 20(6): p. 566-572.
13.Iannetti, G.D. and A. Mouraux, From the neuromatrix to the pain matrix (and back). Experimental brain research, 2010. 205(1): p. 1-12.
14.Brooks, J. and I. Tracey, From nociception to pain perception: imaging the spinal and supraspinal pathways. Journal of anatomy, 2005. 207(1): p. 19-33.
15.Price, D.D., Psychological and neural mechanisms of the affective dimension of pain. Science, 2000. 288(5472): p. 1769-1772.
16.Hagmann, P., et al., Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics, 2006. 26(suppl_1): p. S205-S223.
17.Mukherji, S.K., T.L. Chenevert, and M. Castillo, Diffusion-weighted magnetic resonance imaging. Journal of Neuro-Ophthalmology, 2002. 22(2): p. 118-122.
18.Lutsep, H., et al., Clinical utility of diffusion‐weighted magnetic resonance imaging in the assessment of ischemic stroke. Annals of neurology, 1997. 41(5): p. 574-580.
19.Beaulieu, C., The basis of anisotropic water diffusion in the nervous system–a technical review. NMR in Biomedicine, 2002. 15(7‐8): p. 435-455.
20.O’Donnell, L.J. and C.-F. Westin, An introduction to diffusion tensor image analysis. Neurosurgery clinics of North America, 2011. 22(2): p. 185-196.
21.Mori, S. and J. Zhang, Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron, 2006. 51(5): p. 527-539.
22.Jensen, J.H., Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic resonance in medicine, 2005. 53(6): p. 1432-1440.
23.Jensen, J.H., et al., Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging. Magnetic resonance in medicine, 2005. 53(6): p. 1432-1440.
24.Mechelli, A., et al., Voxel-based morphometry of the human brain: methods and applications. Current medical Imaging reviews, 2005. 1(2): p. 105-113.
25.Ashburner, J. and K.J. Friston, Voxel-based morphometry—the methods. Neuroimage, 2000. 11(6): p. 805-821.
26.Ashburner, J., Computational anatomy with the SPM software. Magnetic resonance imaging, 2009. 27(8): p. 1163-1174.
27.Jones, D.K., et al., The effect of filter size on VBM analyses of DT-MRI data. Neuroimage, 2005. 26(2): p. 546-554.
28.Ashburner, J., J.L. Andersson, and K.J. Friston, Image registration using a symmetric prior—in three dimensions. Human brain mapping, 2000. 9(4): p. 212-225.
29.Good, C.D., et al., A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 2001. 14(1): p. 21-36.
30.Ashburner, J., A fast diffeomorphic image registration algorithm. Neuroimage, 2007. 38(1): p. 95-113.
31.Goto, M., et al., Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects. Neuroradiology, 2013. 55(7): p. 869-875.
32.Jenkinson, M., et al., Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 2002. 17(2): p. 825-841.
33.Tabesh, A., et al., Estimation of tensors and tensor‐derived measures in diffusional kurtosis imaging. Magnetic resonance in medicine, 2011. 65(3): p. 823-836.
34.Evans, A.C., et al. 3D statistical neuroanatomical models from 305 MRI volumes. in Nuclear Science Symposium and Medical Imaging Conference, 1993., 1993 IEEE Conference Record. 1993. IEEE.
35.Lancaster, J.L., et al., Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template. Human brain mapping, 2007. 28(11): p. 1194-1205.
36.Mazziotta, J., et al., A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philosophical Transactions of the Royal Society B: Biological Sciences, 2001. 356(1412): p. 1293-1322.
37.Mori, S., et al., MRI atlas of human white matter. 2005: Elsevier.
38.Veraart, J., et al., More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging. Magnetic resonance in medicine, 2011. 65(1): p. 138-145.
39.Glenn, G.R., et al., Mapping the orientation of white matter fiber bundles: a comparative study of diffusion tensor imaging, diffusional kurtosis imaging, and diffusion spectrum imaging. American Journal of Neuroradiology, 2016. 37(7): p. 1216-1222.
40.Behbehani, M.M., Functional characteristics of the midbrain periaqueductal gray. Progress in neurobiology, 1995. 46(6): p. 575-605.
41.Kuhlenbeck, H. The human diencephalon. in A summary of development, structure, function and pathology. Conf. Neurol. Suppl. 1954.
42.Bjaalie, J.G., Functions of the pontine nuclei in cerebro-cerebellar communication. Trends in neurosciences (Regular ed.), 2000. 23(4).
43.Vaccarino, A.L. and R. Melzack, Temporal processes of formalin pain: differential role of the cingulum bundle, fornix pathway and medial bulboreticular formation. Pain, 1992. 49(2): p. 257-271.
44.Wu, Y., et al., Segmentation of the cingulum bundle in the human brain: a new perspective based on dsi tractography and fiber dissection study. Frontiers in neuroanatomy, 2016. 10: p. 84.
45.Kamali, A., et al., Tracing superior longitudinal fasciculus connectivity in the human brain using high resolution diffusion tensor tractography. Brain Structure and Function, 2014. 219(1): p. 269-281.
1.Jones, D.K. and A. Leemans, Diffusion tensor imaging. Magnetic Resonance Neuroimaging: Methods and Protocols, 2011: p. 127-144.
2.Makris, N., et al., Diffusion tensor imaging. Neuropsychopharmacology: the fifth generation of progress. New York: Lippincott, Williams, and Wilkins, 2002: p. 357-71.
3.Lu, H., et al., Three‐dimensional characterization of non‐gaussian water diffusion in humans using diffusion kurtosis imaging. NMR in Biomedicine, 2006. 19(2): p. 236-247.
4.Society, H.C.C.o.t.I.H., The international classification of headache disorders. cephalalgia, 2004. 24: p. 1-160.
5.Society, H.C.C.o.t.I.H., The international classification of headache disorders, (beta version). Cephalalgia, 2013.
6.Bashir, A., et al., Migraine and structural changes in the brain A systematic review and meta-analysis. Neurology, 2013. 81(14): p. 1260-1268.
7.Szabó, N., et al., White matter microstructural alterations in migraine: a diffusion-weighted MRI study. Pain, 2012. 153(3): p. 651-656.
8.May, A., Chronic pain may change the structure of the brain. PAIN®, 2008. 137(1): p. 7-15.
9.Valfrè, W., et al., Voxel‐based morphometry reveals gray matter abnormalities in migraine. Headache: The Journal of Head and Face Pain, 2008. 48(1): p. 109-117.
10.DaSilva, A.F., et al., Interictal alterations of the trigeminal somatosensory pathway and PAG in migraine. Neuroreport, 2007. 18(4): p. 301.
11.Wang, S.-J., Epidemiology of migraine and other types of headache in Asia. Current neurology and neuroscience reports, 2003. 3(2): p. 104-108.
12.Wang, S.J., et al., Prevalence of migraine in Taipei, Taiwan: a population‐based survey. Cephalalgia, 2000. 20(6): p. 566-572.
13.Iannetti, G.D. and A. Mouraux, From the neuromatrix to the pain matrix (and back). Experimental brain research, 2010. 205(1): p. 1-12.
14.Brooks, J. and I. Tracey, From nociception to pain perception: imaging the spinal and supraspinal pathways. Journal of anatomy, 2005. 207(1): p. 19-33.
15.Price, D.D., Psychological and neural mechanisms of the affective dimension of pain. Science, 2000. 288(5472): p. 1769-1772.
16.Hagmann, P., et al., Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics, 2006. 26(suppl_1): p. S205-S223.
17.Mukherji, S.K., T.L. Chenevert, and M. Castillo, Diffusion-weighted magnetic resonance imaging. Journal of Neuro-Ophthalmology, 2002. 22(2): p. 118-122.
18.Lutsep, H., et al., Clinical utility of diffusion‐weighted magnetic resonance imaging in the assessment of ischemic stroke. Annals of neurology, 1997. 41(5): p. 574-580.
19.Beaulieu, C., The basis of anisotropic water diffusion in the nervous system–a technical review. NMR in Biomedicine, 2002. 15(7‐8): p. 435-455.
20.O’Donnell, L.J. and C.-F. Westin, An introduction to diffusion tensor image analysis. Neurosurgery clinics of North America, 2011. 22(2): p. 185-196.
21.Mori, S. and J. Zhang, Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron, 2006. 51(5): p. 527-539.
22.Jensen, J.H., Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic resonance in medicine, 2005. 53(6): p. 1432-1440.
23.Jensen, J.H., et al., Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging. Magnetic resonance in medicine, 2005. 53(6): p. 1432-1440.
24.Mechelli, A., et al., Voxel-based morphometry of the human brain: methods and applications. Current medical Imaging reviews, 2005. 1(2): p. 105-113.
25.Ashburner, J. and K.J. Friston, Voxel-based morphometry—the methods. Neuroimage, 2000. 11(6): p. 805-821.
26.Ashburner, J., Computational anatomy with the SPM software. Magnetic resonance imaging, 2009. 27(8): p. 1163-1174.
27.Jones, D.K., et al., The effect of filter size on VBM analyses of DT-MRI data. Neuroimage, 2005. 26(2): p. 546-554.
28.Ashburner, J., J.L. Andersson, and K.J. Friston, Image registration using a symmetric prior—in three dimensions. Human brain mapping, 2000. 9(4): p. 212-225.
29.Good, C.D., et al., A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 2001. 14(1): p. 21-36.
30.Ashburner, J., A fast diffeomorphic image registration algorithm. Neuroimage, 2007. 38(1): p. 95-113.
31.Goto, M., et al., Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects. Neuroradiology, 2013. 55(7): p. 869-875.
32.Jenkinson, M., et al., Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 2002. 17(2): p. 825-841.
33.Tabesh, A., et al., Estimation of tensors and tensor‐derived measures in diffusional kurtosis imaging. Magnetic resonance in medicine, 2011. 65(3): p. 823-836.
34.Evans, A.C., et al. 3D statistical neuroanatomical models from 305 MRI volumes. in Nuclear Science Symposium and Medical Imaging Conference, 1993., 1993 IEEE Conference Record. 1993. IEEE.
35.Lancaster, J.L., et al., Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template. Human brain mapping, 2007. 28(11): p. 1194-1205.
36.Mazziotta, J., et al., A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philosophical Transactions of the Royal Society B: Biological Sciences, 2001. 356(1412): p. 1293-1322.
37.Mori, S., et al., MRI atlas of human white matter. 2005: Elsevier.
38.Veraart, J., et al., More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging. Magnetic resonance in medicine, 2011. 65(1): p. 138-145.
39.Glenn, G.R., et al., Mapping the orientation of white matter fiber bundles: a comparative study of diffusion tensor imaging, diffusional kurtosis imaging, and diffusion spectrum imaging. American Journal of Neuroradiology, 2016. 37(7): p. 1216-1222.
40.Behbehani, M.M., Functional characteristics of the midbrain periaqueductal gray. Progress in neurobiology, 1995. 46(6): p. 575-605.
41.Kuhlenbeck, H. The human diencephalon. in A summary of development, structure, function and pathology. Conf. Neurol. Suppl. 1954.
42.Bjaalie, J.G., Functions of the pontine nuclei in cerebro-cerebellar communication. Trends in neurosciences (Regular ed.), 2000. 23(4).
43.Vaccarino, A.L. and R. Melzack, Temporal processes of formalin pain: differential role of the cingulum bundle, fornix pathway and medial bulboreticular formation. Pain, 1992. 49(2): p. 257-271.
44.Wu, Y., et al., Segmentation of the cingulum bundle in the human brain: a new perspective based on dsi tractography and fiber dissection study. Frontiers in neuroanatomy, 2016. 10: p. 84.
45.Kamali, A., et al., Tracing superior longitudinal fasciculus connectivity in the human brain using high resolution diffusion tensor tractography. Brain Structure and Function, 2014. 219(1): p. 269-281.
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