簡易檢索 / 詳目顯示

回結果列表
研究生： 吳倚伶
Wu, Yi-Ling
論文名稱： 獨立成分分析應用在注意力缺陷過動症 之靜息態腦功能影像: 小動物模式之初步研究
Default mode network activity of ADHD using independent component analysis: preliminary results on a small animal model
指導教授： 王福年
Wang, Fu-Nien
口試委員: 黃騰毅
Huang, Teng-Yi
林益如
Lin, Yi-Ru
蔡尚岳
Tsai, Shang-Yueh
學位類別： 碩士
Master
系所名稱： 原子科學院 - 生醫工程與環境科學系
Department of Biomedical Engineering and Environmental Sciences
論文出版年： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 53
中文關鍵詞： 注意力缺陷過動症 、自發性高血壓大鼠 、功能性磁振造影 、靜息態網絡 、獨立成分分析
外文關鍵詞： Attention Deficit Hyperactivity Disorder, Spontaneously Hypertensive rat, fMRI, Resting state network, Independent Component Analysis
相關次數： 點閱：2  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 獨立成分分析乃基於數據驅動概念進行訊號分析。本實驗主要利用獨立成分分析該特性，欲建立大鼠腦組織中可能存在之靜息態網絡。材料使用自發性高血壓大鼠 (Spontaneously Hypertensive rat, SHR)與該同品系控制組Wistar Kyoto Rat (WKY)，兩者在過去文獻常作為注意力缺陷過動症的基因與行為動物模型。配合低頻功率譜之權重分析以及條件性種子功能連結分析 (Seed-based Functional connectivity Analysis)，我們發現了實驗組與控制組腦區存在不同腦訊號連結模式 具注意力缺陷過動症之實驗組在腦皮質區有過高的活化現象。
    本篇重點主要分為四項: 1.）ICA 應用在兩實驗組於不同取樣頻率之低頻訊號成分影像比較2.）低頻功率譜權重分析(ALFF/ fALFF) 3.）靜息態腦功能交叉連結影像（RSFC matrix）於不同取樣頻率之組間比較。我們的研究顯示了小動物模擬疾病腦網絡的潛在可行性，以實現小動物在高磁場磁振造影中關於腦網絡的先驅研究。

    In current study, we adapted independent component analysis as a data-driven based method to establish the possible Resting State Network (RSN) in rat brain. Early work has shown the feasibility of Spontaneously Hypertension Rats in modeling the behavior, gene expression, and pharmacological research of Attention Deficit Hyperactivity Disorder. Therefore, coupling ICA with ALFF analysis and Seed-based Functional Connectivity Analysis, we intended to establish the feasibility of having SHR as a brain function model of ADHD. In this report, we determined the distinguishable connectivity pattern in both groups and SHR exhibits generally intense activation in the cortex areas. Four findings have been revealed from the approaches: 1.) Comparison of ICA maps with different sampling rate. 2.) ALFF/ fALFF analysis. 3.) Cross correlation values in Resting state functional connectivity matrix (RSFC matrix) with different sampling rate. Our study reveals potential feasibility to model the dysfunction network in ADHD, and further, to achieve pioneer investigation in brain network under high field MRI scanner in small animal.

    CHAPTER 1 INTRODUCTION 1 1.1. functional MRI 1 1.1.1. Theory: what is bold signal? 1 1.1.2. Blood oxygen level-dependent (BOLD)& CBV, CBF, CMRO2 … 1 1.1.3. fMRI: applications 2 1.2. Resting state 3 1.2.1. Default network: Spontaneous low frequency 3 1.2.2. Default network: Amplitude of low-frequency fluctuation ( ALFF analysis) 3 1.2.3. Default network: fractional Amplitude of low-frequency fluctuation 4 1.2.4. Resting state network: make sense? 4 1.2.5. Resting state network: Application 4 1.3. Attention Deficit and Hyperactivity Disorder(ADHD) 5 1.3.1. Characteristic/ Gene deficit 5 1.3.2. Human Patients fMRI results 5 1.3.3. ADHD Animal model: WKY, SHR 6 1.4. Aim of the study 7 CHAPTER 2 THEORY 8 2.1. Preface: analysis 8 2.2. Mathematical preliminaries 8 2.2.1. Multivariate probability density function 9 2.2.2. Definition of Uncorrelatedness and whiteness 10 2.2.3. Pre-processing ICA: PCA 11 2.2.4. Post-processing ICA: 13 CHAPTER 3 MATERIALS AND METHODS 16 3.1. Animal Preparation 16 3.2. Data Acquisition 16 3.3. Processing 17 3.3.1. Data pre-processing 17 3.3.2. Independent Component Analysis: 18 3.3.3. Seed methods: 19 CHAPTER 4 RESULTS 20 4.1. ALFF/mALFF 20 4.1.1. Power spectrum of Low frequency 20 4.1.2. ALFFand fALFF t-test 21 4.2. Independent component analysis 21 4.2.1. TR1000ms multiple slice 21 4.2.2. TR300ms single slice 22 4.3. Seed methods 23 4.3.1. fMRI: activation maps: correlation maps 23 4.3.2. Cross correlation maps: TR1000 ms multiple slice and TR 300 single slice 23 4.3.3. Resting state functional connectivity: cross correlation matrix 25 CHAPTER 5 DISCUSSION 32 5.1 Power spectrum of low frequency fluctuation 32 5.2 ICA 34 5.3 Seed methods 35 5.4 Concerns 36 5.5 Conclusion. 37 5.6 Future work 37 REFERENCE I APPENDIX VII

    Reference
    1. Huettel, S.A., G. McCarthy, and A.W. Song, Functional magnetic resonance imaging. 2008, New York: W.H. Freeman.
    2. Vazquez, Cerebral oxygen delivery and consumption during evoked neural activity. Frontiers in Neuroenergetics, 2010.
    3. Magnuson, M., W. Majeed, and S.D. Keilholz, Functional connectivity in blood oxygenation level-dependent and cerebral blood volume-weighted resting state functional magnetic resonance imaging in the rat brain. Journal of Magnetic Resonance Imaging, 2010. 32(3): p. 584-592.
    4. Jafri, M.J., et al., A method for functional network connectivity among spatially independent resting-state components in schizophrenia. NeuroImage, 2008. 39(4): p. 1666-1681.
    5. Tinaz, S., H.E. Schendan, and C.E. Stern, Fronto-striatal deficit in Parkinson's disease during semantic event sequencing. Neurobiology of Aging, 2008. 29(3): p. 397-407.
    6. Helmich, R.C., et al., Cerebral compensation during motor imagery in Parkinson's disease. Neuropsychologia, 2007. 45(10): p. 2201-2215.
    7. Agosta, F., et al., Language networks in semantic dementia. Brain, 2009. 133(1): p. 286-299.
    8. Castellanos, F.X., et al., Cingulate-Precuneus Interactions: A New Locus of Dysfunction in Adult Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 2008. 63(3): p. 332-337.
    9. Ford, J.M., et al., Delayed hemodynamic responses in schizophrenia. NeuroImage, 2005. 26(3): p. 922-931.
    10. Sirotin, Y.B. and A. Das, Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity. Nature, 2009. 457(7228): p. 475-479.
    11. Schwarz, A.J., et al., Functional connectivity in the pharmacologically activated brain: Resolving networks of correlated responses tod-amphetamine. Magnetic Resonance in Medicine, 2007. 57(4): p. 704-713.
    12. Schwarz, A.J., et al., In vivo mapping of functional connectivity in neurotransmitter systems using pharmacological MRI. NeuroImage, 2007. 34(4): p. 1627-1636.
    13. Friston, K.J., Functional and Effective Connectivity in Neuroimaging: A Synthesis Human Brain Mapping, 1994. 2: p. 56-78.
    14. Bharat Biswal, F.Z.Y., Victor M. Haughton, James S. Hyde Functional Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar MRI. Magnetic Resonance in Medicine, 1995. 34: p. 537-541.
    15. Cordes, D., Mapping Functionally Related Regions of Brain with Functional Connectivity MR Imaging. AJNR Am J Neuroradiol, 2000. 21.
    16. Buckner, R.L., J.R. Andrews-Hanna, and D.L. Schacter, The Brain's Default Network: Anatomy, Function, and Relevance to Disease. Annals of the New York Academy of Sciences, 2008. 1124(1): p. 1-38.
    17. Marcus E. Raichle* ² , A.M.M., Abraham Z. Snyder*, William J. Powers ³ , Debra A. Gusnard* § , and a.G.L.S. ³, A default mode of brain function. PNAS, 2001. 98: p. 676-682.
    18. Greicius, M.D., Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 2002. 100(1): p. 253-258.
    19. Yu-Feng, Z., et al., Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development, 2007. 29(2): p. 83-91.
    20. Zou, Q.-H., et al., An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 2008. 172(1): p. 137-141.
    21. M. J. Lowe, B.J.M., and J. A. Sorenson, Functional Connectivity in Single and Multislice Echoplanar Imaging Using Resting-State Fluctuations. NEUROIMAGE, 1998. 7: p. 119-132.
    22. Yang, H., et al., Abnormal spontaneous brain activity in medication-naïve ADHD children: A resting state fMRI study. Neuroscience Letters, 2011. 502(2): p. 89-93.
    23. Smit, D.J.A., et al., Scale-Free Modulation of Resting-State Neuronal Oscillations Reflects Prolonged Brain Maturation in Humans. Journal of Neuroscience, 2011. 31(37): p. 13128-13136.
    24. Greicius, M.D., Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI. Proceedings of the National Academy of Sciences, 2004. 101(13): p. 4637-4642.
    25. Li Liu, L.-L.Z., Yaming Li*, Qiongmin Ma, Baojuan Li , Hui Shen , Dewen Hu, Altered Cerebellar Functional Connectivity with Intrinsic Connectivity Networks in Adults with Major Depressive Disorder. 2012. 7(6): p. e39516-.
    26. Sagvolden, T., Validation of Animal Models of ADHD. The Centre for Advanced Study (CAS), 2004: p. 74-78.
    27. Deborah E. Lynn, G.L., May Yang, M.S.,James T. McCracken,James J. McGough,Janeen Ishii,Sandra K. Loo,Stanley F. Nelson, Susan L. Smalley, Temperament and Character Profiles and the Dopamine D4 Receptor Gene in ADHD. Am J Psychiatry, 2005. 162: p. 906-914.
    28. del Campo, N., U. Müller, and B.J. Sahakian, Neural and Behavioral Endophenotypes in ADHD. 2012.
    29. Troels Bjerre, J.H.C.H.N., Peter Mondrup Rasmussen and K.H.M. Lars Kai Hansen, Unified Ica-Spm Analysis Of Fmri Experiments : Implementation of an ICA graphical user interface for the SPM pipeline. Proceedings of BIOSTEC - BIOSIGNALS 2009 Conference, 2009.
    30. Castellanos, F.X. and E. Proal, Large-scale brain systems in ADHD: beyond the prefrontal–striatal model. Trends in Cognitive Sciences, 2012. 16(1): p. 17-26.
    31. Sebastian, A., et al., Neural correlates of interference inhibition, action withholding and action cancelation in adult ADHD. Psychiatry Research: Neuroimaging, 2012.
    32. Bush, G., Cingulate, Frontal, and Parietal Cortical Dysfunction in Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 2011. 69(12): p. 1160-1167.
    33. Qiu, M.-g., et al., Changes of Brain Structure and Function in ADHD Children. Brain Topography, 2010. 24(3-4): p. 243-252.
    34. Seidman, L.J., E.M. Valera, and N. Makris, Structural Brain Imaging of Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 2005. 57(11): p. 1263-1272.
    35. Seidman, L.J., et al., Dorsolateral Prefrontal and Anterior Cingulate Cortex Volumetric Abnormalities in Adults with Attention-Deficit/Hyperactivity Disorder Identified by Magnetic Resonance Imaging. Biological Psychiatry, 2006. 60(10): p. 1071-1080.
    36. Rommelse, N.N.J., et al., A review on cognitive and brain endophenotypes that may be common in autism spectrum disorder and attention-deficit /hyperactivity disorder and facilitate the search for pleiotropic genes. Neuroscience & Biobehavioral Reviews, 2011. 35(6): p. 1363-1396.
    37. Liston, C., et al., Atypical Prefrontal Connectivity in Attention-Deficit/Hyperactivity Disorder: Pathway to Disease or Pathological End Point? Biological Psychiatry, 2011. 69(12): p. 1168-1177.
    38. Fair, D.A., et al., Atypical Default Network Connectivity in Youth with Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 2010. 68(12): p. 1084-1091.
    39. Srinivas Rachakonda 1 , E.E., Nicolle Correa 3 and Vince Calhoun 14 Group ICA of fMRI Toolbox (GIFT) Manual 2011.
    40. Russell, V.A., T. Sagvolden, and E. Johansen, Behavioral and Brain Functions, 2005. 1(1): p. 9.
    41. Hill, J.C., K. Herbst, and F. Sanabria, Characterizing operant hyperactivity in the Spontaneously Hypertensive Rat. Behavioral and Brain Functions, 2012. 8(1): p. 5.
    42. Chess, A.C., et al., Set shifting in a rodent model of attention-deficit/hyperactivity disorder. Behavioral Neuroscience, 2011. 125(3): p. 372-382.
    43. Srinivas Rachakonda, E.E.a.V.C., GIFT v1.3c Functions. 2007.
    44. Durston, S., et al., Differential effects of DRD4 and DAT1 genotype on fronto-striatal gray matter volumes in a sample of subjects with attention deficit hyperactivity disorder, their unaffected siblings, and controls. Molecular Psychiatry, 2005. 10(7): p. 678-685.
    45. Mill, J., T. Sagvolden, and P. Asherson, Behavioral and Brain Functions, 2005. 1(1): p. 24.
    46. Zhou, D., W.K. Thompson, and G. Siegle, MATLAB toolbox for functional connectivity. NeuroImage, 2009. 47(4): p. 1590-1607.
    47. Aapo Hyvärinen, E.O., Independent Component Analysis: Algorithms and Applications. Neural Networks, 2000. 13(4-5): p. 411-430.
    48. Hyvärinen, A., J. Karhunen, and E. Oja, Independent component analysis. 2001, New York, NY [u.a.]: Wiley.
    49. Nakao, Y., Effects of anesthesia on functional activation of cerebral blood flow and metabolism. Proceedings of the National Academy of Sciences, 2001. 98(13): p. 7593-7598.
    50. Williams, K.A., et al., Comparison of α-chloralose, medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat. Magnetic Resonance Imaging, 2010. 28(7): p. 995-1003.
    51. Bruce F. Cullen, M., * and Malcolm G. Miller, MD, Drug Interactions and Anesthesia: A Review Anesthesia And Analgesia, 1979. 58(5): p. 413-423.
    52. Yan, DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in System Neuroscience, 2010.
    53. Harrison, B.J., et al., REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing. PLoS ONE, 2011. 6(9): p. e25031.
    54. Majeed, W., M. Magnuson, and S.D. Keilholz, Spatiotemporal dynamics of low frequency fluctuations in BOLD fMRI of the rat. Journal of Magnetic Resonance Imaging, 2009. 30(2): p. 384-393.
    55. Majeed, W., et al., Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. NeuroImage, 2011. 54(2): p. 1140-1150.
    56. Srinivas Rachakonda 1 , E.E., Nicolle Correa 3 and Vince Calhoun 14 Group ICA of fMRI Toolbox (GIFT) Manual 2006: p. 138.
    57. Schöpf, V., et al., Group ICA of resting-state data: a comparison. Magnetic Resonance Materials in Physics, Biology and Medicine, 2010. 23(5-6): p. 317-325.
    58. Li, Y.-O., T. Adalı, and V.D. Calhoun, Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 2007. 28(11): p. 1251-1266.
    59. John Hershberger, N.S., Subhash Suri∗, Cluster Hull:A Technique for Summarizing Spatial Data Streams. Proceeding ICDE '06 Proceedings of the 22nd International Conference on Data Engineering, 2006.
    60. Erhardt, E.B., et al., Comparison of multi-subject ICA methods for analysis of fMRI data. Human Brain Mapping, 2011. 32(12): p. 2075-2095.
    61. Johan Himberg, A.H.a., Icasso: Software For Investigating The Reliability Of Ica Estimates By Clustering And Visualization. Neural Networks for Signal Processing, 2003: p. 259- 268
    62. Pawela, C.P., et al., Resting-state functional connectivity of the rat brain. Magnetic Resonance in Medicine, 2008. 59(5): p. 1021-1029.
    63. Hutchison, R.M., et al., Resting-State Connectivity Identifies Distinct Functional Networks in Macaque Cingulate Cortex. Cerebral Cortex, 2011. 22(6): p. 1294-1308.
    64. Paxinos, G. The rat nervous system. 2004; Available from: http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=199296.
    65. Van Groen, T. and J.M. Wyss, Connections of the retrosplenial granular b cortex in the rat. The Journal of Comparative Neurology, 2003. 463(3): p. 249-263.
    66. Tian, L., et al., Enhanced resting-state brain activities in ADHD patients: A fMRI study. Brain and Development, 2008. 30(5): p. 342-348.
    67. Tian, L., et al., Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder. Neuroscience Letters, 2006. 400(1-2): p. 39-43.
    68. D. Cordes’, J.C., I-I. Eghbalnid, E. Meyerand’, M. Quigley’, K Arfanakis’, A. Assad?, P. k Turski3, V. Haugthon3 Resting-State Functional Connectivity Study using Independent Component Analysis ISMRM, 1999.
    69. He, Y., et al., Robust Reproducible Resting State Networks in the Awake Rodent Brain. PLoS ONE, 2011. 6(10): p. e25701.
    70. De Luca, M., et al., fMRI resting state networks define distinct modes of long-distance interactions in the human brain. NeuroImage, 2006. 29(4): p. 1359-1367.
    71. Rubinov, M. and O. Sporns, Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 2010. 52(3): p. 1059-1069.
    72. Joel, S.E., et al., On the relationship between seed-based and ICA-based measures of functional connectivity. Magnetic Resonance in Medicine, 2011. 66(3): p. 644-657.
    73. Naveau, M., et al., A Novel Group ICA Approach Based on Multi-scale Individual Component Clustering. Application to a Large Sample of fMRI Data. Neuroinformatics, 2012.
    74. V.D. Calhoun, 2 * T. Adali, 2 G.D. Pearlson, 1 and J.J. Pekar 3,4, A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis. Human Brain Mapping, 2001. 14: p. 140-151.

    無法下載圖示 全文公開日期 本全文未授權公開 (校內網路)
    全文公開日期 本全文未授權公開 (校外網路)

    QR CODE