臺灣博碩士論文加值系統

先前有許多研究指出阿茲海默症患者 (Alzheimer’s disease, AD) 相較於正常人具有不同默認網路模式 (Default mode network, DMN) 的特徵。然而，這些研究卻鮮少考慮了生理雜訊影響的因素。因此，本實驗將針對自發性生理活動對於描述阿茲海默症患者之默認網路模式的影響進行研究，其目的在於檢驗當由自發性心跳以及呼吸所造成的生理雜訊從靜息態功能性核磁共振影像 (functional magnetic resonance imaging, fMRI) 中去除後，其默認網路模式的特徵將會造成多少的改變。
靜息態功能性磁振造影經由T2*加權之平面回波成像 (Echo planer imaging, EPI) 擷取400秒的影像。心跳及呼吸的信號將分別由脈波血氧儀以及呼吸帶監測以及紀錄。我們利用RETROICOR (Gary H. Glover, 2000) 的方式去除與相位相關的生理假影。其餘相較低頻率之生理影響，例如：相同相位卻不同振幅之呼吸變異以及相同相位卻不同時間間隔的心跳速率變異，將由RVHRCOR (Chang and Glover, 2009) 的方式做校正。
校正生理訊號以前，阿茲海默症患者與正常對照組相比具有不同的默認網路模式之特徵，然而，這個差異在生理訊號影響被去除後將不能再被顯著地觀察到。此外，我們也發現阿茲海默症患者與正常對照組在由呼吸體積與心率變率有不同的功能性核磁共振影像反應。我們的研究結果顯示，控制與自發性生理活動相關之能性核磁共振影像信號在取得更加敏感及特異性的阿茲海默症患者之默認網路模式特徵上非常重要。

Studies have reported that the Alzheimer''s disease (AD) patients have different default mode network (DMN) characteristics from normal subjects. However, few studies considered the contamination of physiological noise in DMN characterization. Here, we study the impact of spontaneous physiology on characterizing the DMN of AD patients.
Cardiac and respiratory cycles were respectively recorded using a pulse oximeter and a respiration belt concurrently with resting-state fMRI measurements. We used RETROICOR to remove phase-locked physiological artifacts. Other low frequency physiological effects of the same phase but different amplitudes (for respiration variations) or intervals (for heart rate) were corrected by RVHRCOR.
Without correcting physiological noise, AD patients show significantly different DMN characteristics from the healthy subjects. However, this difference became less significant physiological noise correction. We also found that the physiological response functions were different between AD patients and healthy subjects. Our results suggest the importance of controlling spontaneous physiology in using hemodynamic responses to achieve more sensitive and specific characterization of the DMN in AD patients and healthy subjects.

口試委員會審定書 i
誌謝 ii
中文摘要 iii
ABSTRACT iv
CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES x
Chapter 1 Introduction 1
Chapter 2 Methods 5
2.1 Materials 5
2.1.1 Subjects 5
2.1.2 MRI Data Acquisition 5
2.1.3 Physiological monitoring 6
2.2 Data Analysis 6
2.2.1 Preprocessing 6
2.2.2 Physiological Noise Correction 7
2.2.3 Resting-State Functional Connectivity Analysis 8
2.2.4 Image Quality Analysis 9
2.2.5 Group-Specific Physiological Response Analysis 10
Chapter 3 Results 13
3.1 Subjects 13
3.2 Cardiac and respiratory fluctuation measurements 13
3.3 fMRI time series analyses 14
3.3.1 Temporal signal-to-noise ratio (tSNR) 14
3.3.2 Effects of RV and HR 16
3.4 Functional connectivity analysis 17
3.4.1 DMN characterization 17
3.4.2 Comparing DMN’s between AD patients and healthy controls 20
3.4.3 DMN’s before and after physiological noise correction 22
3.4.4 Interaction between physiological noise correction and subject groups 24
3.5 Physiological response functions 25
Chapter 4 Discussion 26
4.1 ICA vs. Seed-based analysis 27
4.2 Effects of Global Signal Regression (GSR) 27
4.3 Physiological Response Function 28
4.4 Sampling Rate Affects PRF Feature Detection and Correcting Physiological Noise Methods 32
4.5 Other diseases 33
Chapter 5 Conclusion 34
REFERENCES 35
Appendix 38

Bailey DL, Townsend DW, Valk PE, Maisey MN. 2005. Positron emission tomography: Springer.
Beckmann CF, DeLuca M, Devlin JT, Smith SM. (2005): Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London B: Biological Sciences 360(1457):1001-1013.
Behzadi Y, Restom K, Liau J, Liu TT. (2007): A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37(1):90-101.
Bharat Biswal, F. Zerrin Yetkin, Victor M. Haughton, Hyde JS. (1995): Functional Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar MRI. Magnetic Resonance in Medicine 34(4):537-541.
Birn RM, Smith MA, Jones TB, Bandettini PA. (2008): The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. NeuroImage 40(2):644-54.
Brownell GL, Sweet WH. (1953): Localization of brain tumors with positron emitters. Nucleonics 11(11):40-45.
Buckner RL, Andrews‐Hanna JR, Schacter DL. (2008): The brain''s default network. Annals of the New York Academy of Sciences 1124(1):1-38.
Buckner RL, Carroll DC. (2007): Self-projection and the brain. Trends in Cognitive Sciences 11(2):49-57.
Carusone LM, Srinivasan J, Gitelman DR, Mesulam MM, Parrish TB. (2002): Hemodynamic Response Changes in Cerebrovascular Disease: Implications for Functional MR Imaging. American Journal of Neuroradiology 23(7):1222-1228.
Chang C, Cunningham JP, Glover GH. (2009): Influence of heart rate on the BOLD signal: the cardiac response function. NeuroImage 44(3):857-69.
Chang C, Glover GH. (2009): Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. Neuroimage 47(4):1448-59.
Cole DM, Smith SM, Beckmann CF. (2010): Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in systems neuroscience 4:8.
Fox MD, Zhang D, Snyder AZ, Raichle ME. (2009): The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101(6):3270-83.
Gary H. Glover T-QLaR. (2000): Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine 44(1):162-167.
Ghatan PH, Hsieh JC, Wirsén-Meurling A, Wredling R, Eriksson L, Stone-Elander S, Levander S, Ingvar M. (1995): Brain Activation Induced by the Perceptual Maze Test: A PET Study of Cognitive Performance. NeuroImage 2(2, Part A):112-124.
Girouard H, Iadecola C. (2005): Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. Journal of Applied Physiology 100(1):328-335.
Girouard H, Iadecola C. (2006): Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. Journal of Applied Physiology 100(1):328-335.
Gorges M, Müller H-P, Lulé D, Ludolph AC, Pinkhardt EH, Kassubek J. (2013): Functional Connectivity Within the Default Mode Network Is Associated With Saccadic Accuracy in Parkinson''s Disease: A Resting-State fMRI and Videooculographic Study. Brain Connectivity 3(3):265-272.
Greg Allen HB, Roderick McColl, Andrea L. Hester, Julie A. Fields, Myron F. Weiner, Wendy K. Ringe, Anne M. Lipton, Matthew Brooker, Elizabeth McDonald, Craig D. Rubin, C. Munro Cullum. (2007): Reduced Hippocampal Functional Connectivity in Alzheimer Disease. Archives of Neurology 64(10):1482-1487.
Gunnar Krüger, Glover GH. (2001): Physiological Noise in Oxygenation-Sensitive Magnetic Resonance Imaging. Magnetic Resonance in Medicine 46(4):631-637.
Gusnard DA, Raichle ME. (2001): Searching for a baseline: Functional imaging and the resting human brain. Nat Rev Neurosci 2(10):685-694.
Handwerker DA, Ollinger JM, D''Esposito M. (2004): Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 21(4):1639-1651.
Hutchinson M, Schiffer W, Joseffer S, Liu A, Schlosser R, Dikshit S, Goldberg E, Brodie JD. (1999): Task-specific deactivation patterns in functional magnetic resonance imaging. Magnetic Resonance Imaging 17(10):1427-1436.
Hutton C, Josephs O, Stadler J, Featherstone E, Reid A, Speck O, Bernarding J, Weiskopf N. (2011): The impact of physiological noise correction on fMRI at 7 T. NeuroImage 57(1):101-12.
Krüger G, Glover GH. (2001): Physiological noise in oxygenation‐sensitive magnetic resonance imaging. Magnetic resonance in medicine 46(4):631-637.
Lindquist MA, Loh JM, Atlas LY, Wager TD. (2009): Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling. Neuroimage 45(1):S187-S198.
Logothetis NK. (2003): The Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal. The Journal of Neuroscience 23(10):3963-3971.
Margulies DS, Kelly AC, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. (2007): Mapping the functional connectivity of anterior cingulate cortex. Neuroimage 37(2):579-588.
Marumo K, Takizawa R, Kawakubo Y, Onitsuka T, Kasai K. (2009): Gender difference in right lateral prefrontal hemodynamic response while viewing fearful faces: a multi-channel near-infrared spectroscopy study. Neuroscience research 63(2):89-94.
Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA. (2009): The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? NeuroImage 44(3):893-905.
Ogawa S, Lee T-M, Kay AR, Tank DW. (1990): Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences 87(24):9868-9872.
Raichle ME, Mintun MA. (2006): BRAIN WORK AND BRAIN IMAGING. Annual Review of Neuroscience 29(1):449-476.
Rombouts SA, Barkhof F, Goekoop R, Stam CJ, Scheltens P. (2005a): Altered resting state networks in mild cognitive impairment and mild Alzheimer''s disease: an fMRI study. Hum Brain Mapp 26(4):231-9.
Rombouts SARB, Goekoop R, Stam CJ, Barkhof F, Scheltens P. (2005b): Delayed rather than decreased BOLD response as a marker for early Alzheimer''s disease. NeuroImage 26(4):1078-1085.
Rosazza C, Minati L. (2011): Resting-state brain networks: literature review and clinical applications. Neurological Sciences 32(5):773-785.
Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE and others. (2013): An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage 64:240-56.
Schroeter ML, Zysset S, Kruggel F, Von Cramon DY. (2003): Age dependency of the hemodynamic response as measured by functional near-infrared spectroscopy. Neuroimage 19(3):555-564.
Sheline YI, Raichle ME, Snyder AZ, Morris JC, Head D, Wang S, Mintun MA. (2010): Amyloid Plaques Disrupt Resting State Default Mode Network Connectivity in Cognitively Normal Elderly. Biological Psychiatry 67(6):584-587.
Smith SM, Miller KL, Moeller S, Xu J, Auerbach EJ, Woolrich MW, Beckmann CF, Jenkinson M, Andersson J, Glasser MF and others. (2012): Temporally-independent functional modes of spontaneous brain activity. Proc Natl Acad Sci U S A 109(8):3131-6.
Starck T, Remes J, Nikkinen J, Tervonen O, Kiviniemi V. (2010): Correction of low-frequency physiological noise from the resting state BOLD fMRI—Effect on ICA default mode analysis at 1.5 T. Journal of neuroscience methods 186(2):179-185.
Tohka J, Foerde K, Aron AR, Tom SM, Toga AW, Poldrack RA. (2008): Automatic independent component labeling for artifact removal in fMRI. Neuroimage 39(3):1227-1245.
Triantafyllou C, Hoge R, Krueger G, Wiggins C, Potthast A, Wiggins G, Wald L. (2005): Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. Neuroimage 26(1):243-250.
Wang K, Liang M, Wang L, Tian L, Zhang X, Li K, Jiang T. (2007): Altered functional connectivity in early Alzheimer''s disease: a resting-state fMRI study. Hum Brain Mapp 28(10):967-78.
Wang L, Zang Y, He Y, Liang M, Zhang X, Tian L, Wu T, Jiang T, Li K. (2006): Changes in hippocampal connectivity in the early stages of Alzheimer''s disease: Evidence from resting state fMRI. NeuroImage 31(2):496-504.
Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, Li Q, Zuo XN, Castellanos FX, Milham MP. (2013): A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76:183-201.
Yee S-H, Liu H-L, Hou J, Pu Y, Fox PT, Gao J-H. (2000): Detection of the brain response during a cognitive task using perfusion‐based event‐related functional MRI. NeuroReport 11(11):2533-2536.
Zhang D, Raichle ME. (2010): Disease and the brain''s dark energy. Nat Rev Neurol 6(1):15-28.
Zhang N, Rane P, Huang W, Liang Z, Kennedy D, Frazier JA, King J. (2010): Mapping resting-state brain networks in conscious animals. J Neurosci Methods 189(2):186-96.
Zlokovic BV. (2011): Neurovascular pathways to neurodegeneration in Alzheimer''s disease and other disorders. Nat Rev Neurosci 12(12):723-38.