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姓名 柯煜庭(Yu-ting Ko)  查詢紙本館藏   畢業系所 生物醫學工程研究所
論文名稱 探討同步功能性磁振造影與腦電圖於夜間睡眠研究
(Investigating Dynamic Brain Oscillation and Synchronization in Nocturnal Sleep using Simultaneous fMRI and EEG)
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摘要(中) 睡眠對人類非常的重要,其功能除了恢復身體的疲憊外,睡眠中的大腦還進行了多種認知功能像是訊息的傳輸、記憶鞏固…等。這些功能皆源自於大腦本身的自發反應,但是自發活化反應的產生與機制仍然無法清楚解釋。以往在自發腦活化的研究中,大多探討在低頻振盪下的功能性連結,像是在預設模式網路(Default Mode Network)中產生與前額葉連結中斷,與睡眠中意識的消退有關聯。但是對於低頻振盪甚至是較高的頻帶其頻率的關聯性並沒有太深入的了解,因此我們利用了同步腦電圖與靜息態功能性磁振造影來進行研究,觀察夜間睡眠期間的大腦自發活化反應的情形。藉由腦電圖在睡眠當中具有分辨睡眠階段的能力,以及其不同頻帶的波形在生理和心理上的意義,探討靜息態功能性磁振造影訊號的頻率關係是否也具有相同的判別功效。本實驗從睡前經過睡眠到清醒之間這樣的差異性來進行觀察,並研究四個主要的關於睡眠調節和影響的大腦網路: 1. 主要運動區(Primary Motor Cortex) 2. 預設模式網路(Default Mode Network) 3. 海馬迴網路(Hippocampal Network) 和4. 丘腦網路(Thalamic Network),除觀察各大腦腦區活化和連結性變化外,也對照腦電圖上頻帶改變的相關性。我們的結果發現功能性連結在睡眠中對側大腦會有下降的情形產生,但是在DMN下則是前後腦的功能性連結下降,將ALFF與功能性連結相比發現兩者在睡眠不同階段下會有正好相反的趨勢產生。其中ALFF分頻結果顯示出睡眠中的大腦改變最劇烈的頻帶會落在低頻的部份(0.01-0.1Hz),而在頻率的對應關係上,我們發現在不同大腦區域下隨著睡眠階段不一樣,腦電圖與功能性磁振造影的頻率對應也會有所不同。
摘要(英) Sleep recovers our tired body and refreshes our brain. Multiple types of cognitive function occurs during sleep, including information processing, memory consolidation…etc. These functions are originated from the spontaneous activities in the brain and the mechanism remains to be solved in sleep. Previous study found the default mode network had loss of longitudinal connectivity, related with the drop of consciousness level in the deep sleep. However, unlike EEG waves, the low-frequency fMRI fluctuations are lack of its spectral meanings, and the connectivity changes only provide the empirical observations. Therefore, we used the simultaneous EEG-fMRI recordings during sleep to map the spectral associations of thelow-frequency activities between two modalities in this study. EEG can help us to identify the sleep stages. It also provided the different frequency bands which have particular meaning of the physiological and psychological. So, we can compare the frequency corresponding between EEG and fMRI in the different statements. Furthermore, by the EEG frequency bands, we can more understand the different frequency on the fMRI signal spontaneous fluctuation in resting-state.
關鍵字(中) ★ 功能性磁振造影
★ 腦電圖
★ 睡眠
★ 希爾伯特黃轉換
關鍵字(英)
論文目次 Acknowledgements ................................................................................................................................ VI
中文摘要 ............................................................................................................................................... VII
Abstract ................................................................................................................................................ VIII
Contents.................................................................................................................................................. IX
List of Figures ........................................................................................................................................ XI
List of Table .......................................................................................................................................... XII
Chapter 1. Introduction ........................................................................................................................... 1
1.1 Research Purpose ................................................................................................................... 1
1.2 Hypothesis ............................................................................................................................. 2
1.3 Thesis structure ...................................................................................................................... 3
Chapter 2. Background ............................................................................................................................ 4
2.1 Sleep Physiology ........................................................................................................................... 4
2.2 Sleep Scoring by Electroencephalography .................................................................................... 5
2.3 Resting-state fMRI in Sleep .......................................................................................................... 8
2.4 Hilbert-Huang Transform ............................................................................................................ 11
2.4.1 Empirical Mode Decomposition .......................................................................................... 13
2.4.2 Ensemble Empirical Mode Decomposition .......................................................................... 16
2.4.3Hilbert Spectrum Analysis .................................................................................................... 16
Chapter 3. Materials and Methods ........................................................................................................ 18
3.1 Participants .................................................................................................................................. 18
3.2 Experiment Design ...................................................................................................................... 18
3.3 EEG ............................................................................................................................................. 21
3.3.1 EEG Protocol........................................................................................................................ 22
X
3.3.2 EEG Preprocessing ............................................................................................................... 22
3.4 Functional MRI ........................................................................................................................... 23
3.4.1 fMRI Protocol ...................................................................................................................... 24
3.4.2 fMRI Preprocessing .............................................................................................................. 25
3.4.3 Functional Connectivity analysis ......................................................................................... 28
3.4.4 ALFF analysis ...................................................................................................................... 29
3.5 Neurovascular coupling (EEG-fMRI).................................................................................. 30
3.5.1 HHT analysis ................................................................................................................... 31
3.5.2 Fusion .............................................................................................................................. 32
3.6 Group Statistics Analysis ..................................................................................................... 32
Chapter 4. Sleep scoring results ............................................................................................................ 33
Chapter 5. Functional Connectivity and ALFF ..................................................................................... 34
Chapter 6. ALFF frequency bands ........................................................................................................ 40
Chapter 7. Fusion .................................................................................................................................. 44
Chapter 8. Limitation ............................................................................................................................ 56
Chapter 9. Conclusions ......................................................................................................................... 57
Reference ............................................................................................................................................... 58
參考文獻 1. Andrade, K. C., Spoormaker, V. I., Dresler, M., Wehrle, R., Holsboer, F., Samann, P. G., & Czisch, M. (2011). Sleep spindles and hippocampal functional connectivity in human NREM sleep. J Neurosci, 31(28), 10331-10339. doi: 10.1523/JNEUROSCI.5660-10.2011
2. Aserinsky, E., & Kleitman, N. (1953). Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science, 118(3062), 273-274.
3. Baria, A. T., Baliki, M. N., Parrish, T., & Apkarian, A. V. (2011). Anatomical and functional assemblies of brain BOLD oscillations. J Neurosci, 31(21), 7910-7919. doi: 10.1523/JNEUROSCI.1296-11.2011
4. Berry, K. J., & Mielke, P. W., Jr. (2000). A Monte Carlo investigation of the Fisher Z transformation for normal and nonnormal distributions. Psychol Rep, 87(3 Pt 2), 1101-1114.
5. Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med, 34(4), 537-541.
6. Biswal, B. B. (2012). Resting state fMRI: a personal history. Neuroimage, 62(2), 938-944. doi: 10.1016/j.neuroimage.2012.01.090
7. Bronzino, J. D., Stisser, P., Forbes, W. B., Tracy, C., Resnick, O., & Morgane, P. J. (1980). Power spectral analysis of the EEG following protein malnutrition. Brain Res Bull, 5(1), 51-60.
8. Buckner, R. L. (2012). The serendipitous discovery of the brain′s default network. Neuroimage, 62(2), 1137-1145. doi: 10.1016/j.neuroimage.2011.10.035
9. Buysse, D. J., Reynolds, C. F., 3rd, Monk, T. H., Hoch, C. C., Yeager, A. L., & Kupfer, D. J. (1991). Quantification of subjective sleep quality in healthy elderly men and women using the Pittsburgh Sleep Quality Index (PSQI). Sleep, 14(4), 331-338.
10. Carno, M. A., & Connolly, H. V. (2005). Sleep and sedation in the pediatric intensive care unit. Crit Care Nurs Clin North Am, 17(3), 239-244. doi: 10.1016/j.ccell.2005.04.005
11. Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res, 29(3), 162-173.
12. Davis, H., Davis, P. A., Loomis, A. L., Harvey, E. N., & Hobart, G. (1937). Changes in Human Brain Potentials during the Onset of Sleep. Science, 86(2237), 448-450. doi: 10.1126/science.86.2237.448
13. Goldman, R. I., Stern, J. M., Engel, J., Jr., & Cohen, M. S. (2002). Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport, 13(18), 2487-2492. doi: 10.1097/01.wnr.0000047685.08940.d0
14. Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A, 100(1), 253-258. doi: 10.1073/pnas.0135058100
15. Han, Y., Wang, J., Zhao, Z., Min, B., Lu, J., Li, K., . . . Jia, J. (2011). Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study. Neuroimage, 55(1), 287-295. doi: 10.1016/j.neuroimage.2010.11.059
16. Heine, L., Soddu, A., Gomez, F., Vanhaudenhuyse, A., Tshibanda, L., Thonnard, M., . . . Demertzi, A. (2012). Resting state networks and consciousness: alterations of multiple resting state network connectivity in physiological, pharmacological, and pathological consciousness States. Front Psychol, 3, 295. doi: 10.3389/fpsyg.2012.00295
17. Hori, T., Sugita, Y., Koga, E., Shirakawa, S., Inoue, K., Uchida, S., . . . Sleep Computing Committee of the Japanese Society of Sleep Research, S. (2001). Proposed supplements and amendments to ′A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects′, the Rechtschaffen & Kales (1968) standard. Psychiatry Clin Neurosci, 55(3), 305-310. doi: 10.1046/j.1440-1819.2001.00810.x
18. Horovitz, S. G., Braun, A. R., Carr, W. S., Picchioni, D., Balkin, T. J., Fukunaga, M., & Duyn, J. H. (2009). Decoupling of the brain′s default mode network during deep sleep. Proc Natl Acad Sci U S A, 106(27), 11376-11381. doi: 10.1073/pnas.0901435106
19. Horovitz, S. G., Fukunaga, M., de Zwart, J. A., van Gelderen, P., Fulton, S. C., Balkin, T. J., & Duyn, J. H. (2008). Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study. Hum Brain Mapp, 29(6), 671-682. doi: 10.1002/hbm.20428
20. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., . . . Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. 454, 903-955.
21. Huang, N. E., Shen, Z., Long, S. R., Wu, M. L. C., Shih, H. H., Zheng, Q. N., . . . Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences, 454(1971), 903-995.
22. Huang, N. E., Wu, M. L., Qu, W. D., Long, S. R., & Shen, S. S. P. (2003). Applications of Hilbert-Huang transform to non-stationary financial time series analysis. Applied Stochastic Models in Business and Industry, 19(3), 245-268. doi: Doi 10.1002/Asmb.501
23. Huang, N. E., & Wu, Z. H. (2008). A review on Hilbert-Huang transform: method and its applications to geophysical studies. Reviews of Geophysics, 46(2). doi: Artn Rg2006
24. Doi 10.1029/2007rg000228
25. Jonathan R.L. Schwartz, a. T. R. (2008). Neuropathology of Sleep and Wakefulness: Basic Science and Clinical Implications. Current Neuropharmacology, 6, 367-378.
26. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev, 29(2-3), 169-195.
27. Klimesch, W., Doppelmayr, M., Schwaiger, J., Auinger, P., & Winkler, T. (1999). ′Paradoxical′ alpha synchronization in a memory task. Brain Res Cogn Brain Res, 7(4), 493-501.
28. Lin, C. F., & Zhu, J. D. (2012). Hilbert-Huang transformation-based time-frequency analysis methods in biomedical signal applications. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 226(3), 208-216. doi: 10.1177/0954411911434246
29. Liu, X., Wang, S., Zhang, X., Wang, Z., Tian, X., & He, Y. (2014). Abnormal amplitude of low-frequency fluctuations of intrinsic brain activity in Alzheimer′s disease. J Alzheimers Dis, 40(2), 387-397. doi: 10.3233/JAD-131322
30. Llinas, R. R., & Steriade, M. (2006). Bursting of thalamic neurons and states of vigilance. J Neurophysiol, 95(6), 3297-3308. doi: 10.1152/jn.00166.2006
31. McGinty, D., & Szymusiak, R. (2000). The sleep-wake switch: A neuronal alarm clock. Nat Med, 6(5), 510-511. doi: 10.1038/74988
32. Merica, H., & Blois, R. (1997). Relationship between the time courses of power in the frequency bands of human sleep EEG. Neurophysiologie Clinique-Clinical Neurophysiology, 27(2), 116-128. doi: Doi 10.1016/S0987-7053(97)85664-X
33. Peruzzi, W. T. (2005). Sleep in the intensive care unit. Pharmacotherapy, 25(5 Pt 2), 34S-39S.
34. Picchioni, D., Duyn, J. H., & Horovitz, S. G. (2013). Sleep and the functional connectome. Neuroimage, 80, 387-396. doi: 10.1016/j.neuroimage.2013.05.067
35. Smith, E. E., & Jonides, J. (1999). Storage and executive processes in the frontal lobes. Science, 283(5408), 1657-1661.
36. Stickgold, R. (1998). Sleep: off-line memory reprocessing. Trends Cogn Sci, 2(12), 484-492.
37. Van Dijk, K. R., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol, 103(1), 297-321. doi: 10.1152/jn.00783.2009
38. Walker, M. P., & Stickgold, R. (2004). Sleep-dependent learning and memory consolidation. Neuron, 44(1), 121-133. doi: 10.1016/j.neuron.2004.08.031
39. Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., . . . Smith, S. M. (2009). Bayesian analysis of neuroimaging data in FSL. Neuroimage, 45(1 Suppl), S173-186. doi: 10.1016/j.neuroimage.2008.10.055
40. Wu, C. W., Liu, P. Y., Tsai, P. J., Wu, Y. C., Hung, C. S., Tsai, Y. C., . . . Lin, C. P. (2012). Variations in connectivity in the sensorimotor and default-mode networks during the first nocturnal sleep cycle. Brain Connect, 2(4), 177-190. doi: 10.1089/brain.2012.0075
41. Wu, Z. H., & Huang, N. E. (2004). A study of the characteristics of white noise using the empirical mode decomposition method. Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences, 460(2046), 1597-1611. doi: DOI 10.1098/rspa.2003.1221
42. Zang, Y. F., He, Y., Zhu, C. Z., Cao, Q. J., Sui, M. Q., Liang, M., . . . Wang, Y. F. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev, 29(2), 83-91. doi: 10.1016/j.braindev.2006.07.002
43. Zietsch, B. P., Hansen, J. L., Hansell, N. K., Geffen, G. M., Martin, N. G., & Wright, M. J. (2007). Common and specific genetic influences on EEG power bands delta, theta, alpha, and beta. Biol Psychol, 75(2), 154-164. doi: 10.1016/j.biopsycho.2007.01.004
44. Zou, Q. H., Zhu, C. Z., Yang, Y., Zuo, X. N., Long, X. Y., Cao, Q. J., . . . Zang, Y. F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods, 172(1), 137-141. doi: 10.1016/j.jneumeth.2008.04.012
45. Zuo, X. N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., . . . Milham, M. P. (2010). The oscillating brain: complex and reliable. Neuroimage, 49(2), 1432-1445. doi: 10.1016/j.neuroimage.2009.09.037
指導教授 吳昌衛(Chang-wei Wu) 審核日期 2014-8-29
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