臺灣博碩士論文加值系統

研究背景:
自閉症的核心症狀為社交活動與溝通能力缺損，以及侷限、重複的行為和興趣。但自閉患者也可能合併動作功能障礙，包括肢體平衡、動作模仿、動作計畫與動作執行，其能力比同年齡的孩童弱。但卻缺乏研究，綜合探討自閉症，與動作功能相關的神經白質束，以及局部腦灰質和白質結構之變異。
研究目的:
分析患自閉症兒童及青少年，其動作功能與正常發展者之差別；並探討自閉患者，與動作功能相關的神經白質束以及局部灰質、白質體積之變異。
研究方法:
研究組為55名自閉症患者，對照組為68名年紀及性別配對的正常發展之孩童及青少年，皆為男性，年齡範圍為6至18歲。兩組皆進行智力測驗，以及由家長及受測者填寫與動作功能相關的問卷。施測「劍橋自動化神經心理測驗, CANTAB」，以其中的三個分測驗-「反應時間, RT」、「劍橋河內塔測驗, SOC」，以及「內外向度轉換測驗, ID/ED」，去評估受試者之動作計畫及動作執行的能力，並作組間比較。進行「腦部核磁共振擴散頻譜造影, Diffusing Spectrum Image, DSI」，分析兩組受試者在與動作相關的白質神經纖維束的「擴散非等向性, Generalized Fractional Anisotropy, GFA」數值之大小是否有差異。應用「體素為基礎的形態計量學, Voxel-based Morphometry, VBM」，分析兩族群，在與動作功能相關的腦部灰質及白質區域體積是否有差異。最後，分析CANTAB中的三個分測驗的商數，與「白質神經纖維束GFA」，以及與「腦部灰質及白質區域結構」是否有相關性。
研究結果:
研究組的平均年齡為13.39±2.48歲，對照組的平均年齡為12.71±2.59歲 (p值=0.14)。CANTAB的RT分測驗中，自閉症組在5個選擇的RT中，使用比較短的移動時間，(自閉症組 vs 正常發展組: 355.62±103.97毫秒 vs 398.04±96.23毫秒, p值=0.023) ；於SOC中，自閉症於5個移動的題目，用比較較短的初始思考時間，(自閉症組 vs 正常發展組: 5302.85±3620.40毫秒 vs 6930.83±4975.65毫秒, p值=0.046)，但須依靠較多次的移動才能完成任務(自閉症組 vs 正常發展組: 7.60±1.65次 vs 6.85±1.40次,p值=0.008)，而且自閉症組在最少移動中可完成的題目較少(自閉症組 vs 正常發展組: 7.52±1.98個 vs 8.47±1.88個,p值=0.008)；在ID/ED中，自閉症組在ID以及ED轉換的測驗中，相較於正常發展者，皆須比較多次的嘗試才能完成任務(p值=0.027)，而且犯比較多次的錯誤(p值=0.22)。
藉由DSI分析，自閉症組在左側「頂葉-橋腦-小腦神經纖維束」有比較小的GFA，(交叉至對側小腦的神經纖維束，自閉症患者 vs 正常發展者: 0.348±0.010 vs 0.350±0.009,p值=0.019；至同側小腦的神經纖維束，自閉症患者 vs 正常發展者: 0.343±0.012 vs 0.344±0.011,p值=0.021)。若分段分析，則自閉症組在左側「頂葉-橋腦神經纖維束」有比較大的GFA。(自閉症患者 vs 正常發展者: 0.342±0.013 vs 0.342±0.012,p值=0.022)。
應用VBM分析，自閉症組相較於正常發展者，在左側「體感覺區」有比較大的白質體積。(自閉症患者 vs 正常發展者: 7.149±0.883 vs 6.936±0.594,p值=0.010)
在關聯性分析部分，自閉症患者，其在RT分測驗的「移動時間」，與左側頂葉交叉至右側小腦的「頂葉-橋腦-小腦神經纖維束」之GFA有正相關性(相關係數r:0.302,p值=0.027)。
結論:
自閉症患者相較於正常發展者，在CATAB中，有比較短的反應時間，但需要較多次的移動，犯比較多次的錯誤，才能完成試驗。自閉症患者，在DSI中，於左側「頂葉-橋腦-小腦神經纖維束」有比較小的GFA，若分段分析，在左側「頂葉-橋腦神經纖維束」有比較大的GFA。而在VBM分析，也發現，自閉症患者相較於正常發展者，在相應的左側「體感覺區」有比較大的白質體積。同時，自閉症患者的左側「頂葉-橋腦-小腦神經纖維束」之GFA，與動作功能具相關性。

關鍵詞: 自閉症 動作功能 擴散頻譜影像 體素為基礎的形態計量學

Background:
Motor impairment is frequently observed in individuals with autism spectrum disorder (ASD). However, there has been no research investigating the motor function related white matter fiber tracts integrity and regional white matter (WM) and grey (GM) matter structure alterations in individuals with ASD.
Objective:
To investigate the motor function related WM fiber tracts integrity and regional WM and GM volumes in individuals with ASD.
Method:
We recruited 55 boys with ASD and 68 age-matched typically developing control boys (TD), aged 6-18 years old. They received intelligence test, and three subsets of the Cambridge Neuropsychological Test Automated Battery (CANTAB): reaction time (RT), Stockings of Cambridge (SOC), intra-dimensional/extra-dimensional (ID/ED) to assess the motor performance. White matter (WM) fiber tracts integrity was analyzed by using diffusion spectrum imaging (DSI) controlling the age and FIQ, and the generalized fractional anisotropy (GFA) of the following tracts was computed: cortico-spinal tracts, cortico-ponto-cerebellar tracts, and dento-rubro-thalamo-cortical tracts. Voxel-based morphometry (VBM) was utilized to investigate the WM and GM volume in primary motor area, supplementary motor area, somatosensory area, and cerebellum controlling the age and FIQ. We then analyzed the correlation between the CANTAB score and GFA value of those WM tracts, and regional WM and GM volume in brain regions involving these tracts.
Result:
The final sample of analysis consisted of 55 ASD (13.39±2.48 years old) and 68 TD (12.71±2.59 years old) male subjects. The CANTAB score disclosed (1) the ASD group had shorter movement time (5-choices) in the RT subtest, (2) in the SOC subtest, the ASD group had shorter initial thinking time, required more moves to complete the task while solving the 5 movement problems, and solved fewer problems in minimal moves, (3) in the ID/ED set-shifting performance, the ASD group required more trials to reach criterion and made more errors.
The ASD group had decreased GFA value in the left parieto-ponto-cerebellar tract, crossed tract (ASD vs TD, 0.348±0.010 vs 0.350±0.009, p=0.01) and uncrossed tract (ASD vs TD, 0.343±0.012 vs 0.344±0.011, p=0.02), but increased GFA value in the left parieto-ponto tract (ASD vs TD, 0.342±0.013 vs 0.342±0.012, p=0.02) through DSI study. The VBM analysis revealed that the ASD group had increased local white matter volume in the left somatosensory area (ASD vs TD, 7.149±0.883 vs 6.936±0.594, p=0.01).
For ASD group, significant correlations were found between motor score and the GFA value of the left parieto-ponto-cerebellar tract (r=0.302, p=0.027).
Conclusion:
Individuals with ASD had altered WM volume in the left somatosensory area and altered GFA value in the left parieto-ponto-cerebellar tract. The altered integrity of the left parieto-ponto-cerebellar tract may predict the motor performance in individuals with ASD.

口試委員審定書……………………………………………………………………… i
謝辭…………………………………………………………………………………… ii
中文摘要……………………………………………………………………………… iii
英文摘要……………………………………………………………………………… vi
碩士論文內容
一、緒論：…………………………………………………………………………… 1
1.2 自閉症的動作功能障礙………………………………………………………… 1
1.2.1 自閉症的動作功能障礙之臨床症狀………………………………………… 1
1.2.2 自閉症的動作功能障礙之臨床研究………………………………………… 2
1.3 自閉症的動作功能之神經影像研究…………………………………………… 3
1.4 研究目的………………………………………………………………………… 4
二、研究方法與材料………………………………………………………………… 5
2.1 研究架構………………………………………………………………………… 5
2.1.1 受試者條件…………………………………………………………………… 5
2.1.2 受試者之基本資料…………………………………………………………… 5
2.1.3 研究設計……………………………………………………………………… 6
2.2 神經心理測驗與動作功能評估………………………………………………… 7
2.3 腦部核磁共振擴散頻譜造影(DIFFUSION SPECTRUM IMAGING, DSI) … 9
2.3.1 擴散磁振造影原理…………………………………………………………… 9
2.3.2 腦部核磁共振張量造影(Diffusion Tensor Imaging, DTI)………………… 10
2.3.3 腦部核磁共振擴散頻譜造影(Diffusion Spectrum Imaging, DSI)………… 11
2.3.4 擴散頻譜造影神經纖維束成像(Diffusion Spectrum Imaging Tractography) ………………………………………………………………………………………… 12
2.4 腦部體素為基礎的形態計量學(VOXEL BASED MORPHOMETRY, VBM) ………………………………………………………………………………………… 14
2.5 統計分析………………………………………………………………………… 15
三、結果:…………………………………………………………………………… 17
3.1 基本資料比較…………………………………………………………………… 17
3.2 神經心理測驗與動作功能結果………………………………………………… 17
3.3 腦部核磁共振擴散頻譜造影分析之結果(DSI RESULT)……………………… 18
3.4 腦部體素為基礎的形態計量學分析之結果(VBM RESULT)………………… 18
3.5 動作功能與與腦部影像之相關性分析………………………………………… 19
3.5.1 動作功能與神經纖維束之非等向性之相關性……………………………… 19
3.5.2 動作功能與局部腦灰質與白質容量之相關性……………………………… 19
四、討論：…………………………………………………………………………… 21
4.1 自閉症之動作功能障礙………………………………………………………… 21
4.2 自閉症與動作相關之神經纖維束與腦灰白質之變化………………………… 22
4.3 自閉症之動作功能與白質及神經纖維束之相關性分析……………………… 24
4.4 研究結果之重要性探討………………………………………………………… 25
五、展望……………………………………………………………………………… 26
六、論文英文簡述：………………………………………………………………… 27
七、參考文獻：……………………………………………………………………… 37
圖 一 「劍橋自動化神經心理測驗」之「反應時間測驗」……………………… 41
圖 二 「劍橋自動化神經心理測驗」之「劍橋河內塔測驗」…………………… 41
圖 三 「劍橋自動化神經心理測驗」之「內外向度轉換測驗」………………… 41
圖 四 「擴散頻譜造影」所分析與動作功能相關之神經纖維束………………… 41
圖 五 「腦部體素為基礎的形態計量學」所分析之腦區………………………… 43
圖 六 「反應時間-5個選擇之移動時間」與「左側頂葉-橋腦-小腦神經纖維束
(交叉的)之非等向性數值(GFA value)」之散布圖………………………………… 43
表 一 與動作功能相關的神經纖維束……………………………………………… 44
表 二 基本資料……………………………………………………………………… 44
表 三 自閉症組與正常發展組-神經纖維束之非等向性數值(GFA value)之比較……………………………………………………………………………………… 45
表 四 自閉症組與正常發展--神經纖維之非等向性數值(GFA value)之比較—
分段分析……………………………………………………………………………… 46
表 五 自閉症組與正常發展之腦部灰質與白質體積比較………………………… 47
表 六 運動功能商數與神經纖維束的非等向性數值(GFA value)之相關性分析… 48
表 七 運動功能商數與左側體感覺區白質容積(mm3)之相關性分析…………… 48
附錄 ………………………………………………………………………………… 49

Allen, G., Muller, R. A., & Courchesne, E. (2004). Cerebellar function in autism: functional magnetic resonance image activation during a simple motor task. Biol Psychiatry, 56(4), 269-278. doi: 10.1016/j.biopsych.2004.06.005
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry--the methods. Neuroimage, 11(6 Pt 1), 805-821. doi: 10.1006/nimg.2000.0582
Basser, P. J., Mattiello, J., & LeBihan, D. (1994a). Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B, 103(3), 247-254.
Basser, P. J., Mattiello, J., & LeBihan, D. (1994b). MR diffusion tensor spectroscopy and imaging. Biophys J, 66(1), 259-267. doi: 10.1016/S0006-3495(94)80775-1
Carper, R. A., Solders, S., Treiber, J. M., Fishman, I., & Muller, R. A. (2015). Corticospinal tract anatomy and functional connectivity of primary motor cortex in autism. J Am Acad Child Adolesc Psychiatry, 54(10), 859-867. doi: 10.1016/j.jaac.2015.07.007
Chen, Y. J., Lo, Y. C., Hsu, Y. C., Fan, C. C., Hwang, T. J., Liu, C. M., . . . Tseng, W. Y. (2015). Automatic whole brain tract-based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy. Hum Brain Mapp, 36(9), 3441-3458. doi: 10.1002/hbm.22854
Cheslack-Postava, K., & Jordan-Young, R. M. (2012). Autism spectrum disorders: toward a gendered embodiment model. Soc Sci Med, 74(11), 1667-1674. doi: 10.1016/j.socscimed.2011.06.013
Courchesne, E., Karns, C. M., Davis, H. R., Ziccardi, R., Carper, R. A., Tigue, Z. D., . . . Courchesne, R. Y. (2001). Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology, 57(2), 245-254.
David, F. J., Baranek, G. T., Giuliani, C. A., Mercer, V. S., Poe, M. D., & Thorpe, D. E. (2009). A pilot study: coordination of precision grip in children and adolescents with high functioning autism. Pediatr Phys Ther, 21(2), 205-211. doi: 10.1097/PEP.0b013e3181a3afc2
Fabbri-Destro, M., Cattaneo, L., Boria, S., & Rizzolatti, G. (2009). Planning actions in autism. Exp Brain Res, 192(3), 521-525. doi: 10.1007/s00221-008-1578-3
Farmer, C., Thurm, A., & Grant, P. (2013). Pharmacotherapy for the core symptoms in autistic disorder: current status of the research. Drugs, 73(4), 303-314. doi: 10.1007/s40265-013-0021-7
Gidley Larson, J. C., Bastian, A. J., Donchin, O., Shadmehr, R., & Mostofsky, S. H. (2008). Acquisition of internal models of motor tasks in children with autism. Brain, 131(Pt 11), 2894-2903. doi: 10.1093/brain/awn226
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 14(1 Pt 1), 21-36. doi: 10.1006/nimg.2001.0786
Gowen, E., & Hamilton, A. (2013). Motor abilities in autism: a review using a computational context. J Autism Dev Disord, 43(2), 323-344. doi: 10.1007/s10803-012-1574-0
Harrison, J., & Hare, D. J. (2004). Brief report: assessment of sensory abnormalities in people with autistic spectrum disorders. J Autism Dev Disord, 34(6), 727-730.
Haswell, C. C., Izawa, J., Dowell, L. R., Mostofsky, S. H., & Shadmehr, R. (2009). Representation of internal models of action in the autistic brain. Nat Neurosci, 12(8), 970-972. doi: 10.1038/nn.2356
Hughes, C., Russell, J., & Robbins, T. W. (1994). Evidence for executive dysfunction in autism. Neuropsychologia, 32(4), 477-492.
Idring, S., Rai, D., Dal, H., Dalman, C., Sturm, H., Zander, E., . . . Magnusson, C. (2012). Autism spectrum disorders in the Stockholm Youth Cohort: design, prevalence and validity. PLoS One, 7(7), e41280. doi: 10.1371/journal.pone.0041280
Jansiewicz, E. M., Goldberg, M. C., Newschaffer, C. J., Denckla, M. B., Landa, R., & Mostofsky, S. H. (2006). Motor signs distinguish children with high functioning autism and Asperger''s syndrome from controls. J Autism Dev Disord, 36(5), 613-621. doi: 10.1007/s10803-006-0109-y
Kamali, A., Kramer, L. A., Frye, R. E., Butler, I. J., & Hasan, K. M. (2010). Diffusion tensor tractography of the human brain cortico-ponto-cerebellar pathways: a quantitative preliminary study. J Magn Reson Imaging, 32(4), 809-817. doi: 10.1002/jmri.22330
Kern, J. K., Trivedi, M. H., Garver, C. R., Grannemann, B. D., Andrews, A. A., Savla, J. S., . . . Schroeder, J. L. (2006). The pattern of sensory processing abnormalities in autism. Autism, 10(5), 480-494. doi: 10.1177/1362361306066564
Keser, Z., Hasan, K. M., Mwangi, B. I., Kamali, A., Ucisik-Keser, F. E., Riascos, R. F., . . . Narayana, P. A. (2015). Diffusion tensor imaging of the human cerebellar pathways and their interplay with cerebral macrostructure. Front Neuroanat, 9, 41. doi: 10.3389/fnana.2015.00041
Lai, M. C., Lombardo, M. V., & Baron-Cohen, S. (2014). Autism. Lancet, 383(9920), 896-910. doi: 10.1016/S0140-6736(13)61539-1
Le Bihan, D., Mangin, J. F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging, 13(4), 534-546.
Lin, H. Y., Ni, H. C., Lai, M. C., Tseng, W. Y., & Gau, S. S. (2015). Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Mol Autism, 6, 29. doi: 10.1186/s13229-015-0022-3
Maglione, M. A., Gans, D., Das, L., Timbie, J., Kasari, C., Technical Expert, P., & Network, H. A. I. R.-B. (2012). Nonmedical interventions for children with ASD: recommended guidelines and further research needs. Pediatrics, 130 Suppl 2, S169-178. doi: 10.1542/peds.2012-0900O
Mostofsky, S. H., Bunoski, R., Morton, S. M., Goldberg, M. C., & Bastian, A. J. (2004). Children with autism adapt normally during a catching task requiring the cerebellum. Neurocase, 10(1), 60-64. doi: 10.1080/13554790490960503
Mostofsky, S. H., Burgess, M. P., & Gidley Larson, J. C. (2007). Increased motor cortex white matter volume predicts motor impairment in autism. Brain, 130(Pt 8), 2117-2122. doi: 10.1093/brain/awm129
Mostofsky, S. H., Powell, S. K., Simmonds, D. J., Goldberg, M. C., Caffo, B., & Pekar, J. J. (2009). Decreased connectivity and cerebellar activity in autism during motor task performance. Brain, 132(Pt 9), 2413-2425. doi: 10.1093/brain/awp088
Muller, R. A., Cauich, C., Rubio, M. A., Mizuno, A., & Courchesne, E. (2004). Abnormal activity patterns in premotor cortex during sequence learning in autistic patients. Biol Psychiatry, 56(5), 323-332. doi: 10.1016/j.biopsych.2004.06.007
Nebel, M. B., Joel, S. E., Muschelli, J., Barber, A. D., Caffo, B. S., Pekar, J. J., & Mostofsky, S. H. (2014). Disruption of functional organization within the primary motor cortex in children with autism. Hum Brain Mapp, 35(2), 567-580. doi: 10.1002/hbm.22188
Okuda, B., Tanaka, H., Tomino, Y., Kawabata, K., Tachibana, H., & Sugita, M. (1995). The role of the left somatosensory cortex in human hand movement. Exp Brain Res, 106(3), 493-498.
Ozonoff, S., Cook, I., Coon, H., Dawson, G., Joseph, R. M., Klin, A., . . . Wrathall, D. (2004). Performance on Cambridge Neuropsychological Test Automated Battery subtests sensitive to frontal lobe function in people with autistic disorder: evidence from the Collaborative Programs of Excellence in Autism network. J Autism Dev Disord, 34(2), 139-150.
Ozonoff, S., & Jensen, J. (1999). Brief report: specific executive function profiles in three neurodevelopmental disorders. J Autism Dev Disord, 29(2), 171-177.
Papadakis, N. G., Xing, D., Houston, G. C., Smith, J. M., Smith, M. I., James, M. F., . . . Carpenter, T. A. (1999). A study of rotationally invariant and symmetric indices of diffusion anisotropy. Magn Reson Imaging, 17(6), 881-892.
Pierpaoli, C., & Basser, P. J. (1996). Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med, 36(6), 893-906.
Robbins, T. W., James, M., Owen, A. M., Sahakian, B. J., McInnes, L., & Rabbitt, P. (1994). Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia, 5(5), 266-281.
Salamon, N., Sicotte, N., Drain, A., Frew, A., Alger, J. R., Jen, J., . . . Salamon, G. (2007). White matter fiber tractography and color mapping of the normal human cerebellum with diffusion tensor imaging. J Neuroradiol, 34(2), 115-128. doi: 10.1016/j.neurad.2007.03.002
Section On, C., Integrative, M., Council on Children with, D., American Academy of, P., Zimmer, M., & Desch, L. (2012). Sensory integration therapies for children with developmental and behavioral disorders. Pediatrics, 129(6), 1186-1189. doi: 10.1542/peds.2012-0876
Shadmehr, R., Smith, M. A., & Krakauer, J. W. (2010). Error correction, sensory prediction, and adaptation in motor control. Annu Rev Neurosci, 33, 89-108. doi: 10.1146/annurev-neuro-060909-153135
Toga, A. W., & Thompson, P. M. (2003). Mapping brain asymmetry. Nat Rev Neurosci, 4(1), 37-48. doi: 10.1038/nrn1009
Tuch, D. S. (2004). Q-ball imaging. Magn Reson Med, 52(6), 1358-1372. doi: 10.1002/mrm.20279
Tuch, D. S., Reese, T. G., Wiegell, M. R., & Wedeen, V. J. (2003). Diffusion MRI of complex neural architecture. Neuron, 40(5), 885-895.
Tuch, D. S., Wisco, J. J., Khachaturian, M. H., Ekstrom, L. B., Kotter, R., & Vanduffel, W. (2005). Q-ball imaging of macaque white matter architecture. Philos Trans R Soc Lond B Biol Sci, 360(1457), 869-879. doi: 10.1098/rstb.2005.1651
Williams, J. H., Whiten, A., & Singh, T. (2004). A systematic review of action imitation in autistic spectrum disorder. J Autism Dev Disord, 34(3), 285-299.
Young, R. P., & Zelaznik, H. N. (1992). The visual control of aimed hand movements to stationary and moving targets. Acta Psychol (Amst), 79(1), 59-78.
Zwaigenbaum, L., Bryson, S., Lord, C., Rogers, S., Carter, A., Carver, L., . . . Yirmiya, N. (2009). Clinical assessment and management of toddlers with suspected autism spectrum disorder: insights from studies of high-risk infants. Pediatrics, 123(5), 1383-1391. doi: 10.1542/peds.2008-1606