臺灣博碩士論文加值系統

傳統功能性核磁共振影像在研究人腦靜態的訊號時，多專注在低頻率（低於0.1Hz）的人腦訊號振動。在我們的實驗中，我們為了提高時間上的解析度，犧牲了影像的涵蓋範圍，利用單張快速迴訊平面造影(TR 為0.1秒)的技術，以專注於研究人腦左右視覺區域之間的功能性連接。快速掃描技術提供給我們很高的取樣頻率，有助於我們在分析訊號時排除生理雜訊的干擾，同時又能讓我們研究更高頻率上的振動。在實驗中，我們使用了時域上的相關性分析和訊號包絡的相關性分析來衡量在不同頻率下人腦左右視覺區的功能性連接。我們的結果證明，一如傳統靜態人腦功能性核磁共振影像實驗的結果，人腦的左右視覺區靜態的訊號，在0.08Hz以下是高度相關的。同時，在0.1Hz到0.2Hz的頻率帶內，這兩個視覺區的訊號也高度相關。

Conventional resting-state fMRI often looks for spontaneous BOLD signals at frequencies below 0.1Hz. In this study, we used high-speed single-slice EPI with TR = 100 ms to investigate the functional connectivity between hemispheric visual cortex. We sacrificed the coverage of the brain without compromising the spatial resolution in order to achieve a high sampling rate (10 Hz). With the high sampling rate, we were able to prevent the physiological noise from aliasing with the BOLD signals of interest and investigate the spontaneous BOLD signal at frequencies above 0.1Hz. We used time series correlation and oscillatory envelope correlation to assess the functional connectivity. Our results show that left visual cortex and right visual cortex are highly correlated at the frequency band lower than 0.08Hz and at the frequency band between 0.1Hz and 0.2Hz.

口試委員會審定書 #
誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
Chapter 1 Introduction 1
Chapter 2 Method 5
2.1 Participants 5
2.2 Data acquisition 6
2.3 Pre-processing 7
2.3.1 Filtering of physiological noises 7
2.4 Selection of the region-of-interest 8
2.5 Functional connectivity analysis by correlation 9
2.5.1 Frequency selection by band-pass filtering 9
2.5.2 Functional connectivity assessed by time series correlation 10
2.5.3 Functional connectivity assessed by oscillatory envelope correlation 10
Chapter 3 Results 13
3.1 Functional connectivity assessed by time series correlation 13
3.2 Functional connectivity assessed by oscillatory envelope correlation 17
3.3 Time series correlation between left and right visual cortex 20
3.4 Oscillatory envelope correlations between left and right visual cortex 21
Chapter 4 Discussion 23
REFERENCE 26

[1]Ances, B. M. (2004). "Coupling of changes in cerebral blood flow with neural activity: what must initially dip must come back up." J Cereb Blood Flow Metab 24(1): 1-6.
[2]Ances, B. M., et al. (1998). "Transcranial laser doppler mapping of activation flow coupling of the rat somatosensory cortex." Neurosci Lett 257(1): 25-28.
[3]Ogawa, S., Lee, T. M., Nayak, A. &; Glynn, P. (1990) Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields.. Magn. Reson. Med. 14,68-78.
[4]Fox, P. T., Raichle, M. E. (1986) Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. Proc. Natl. Acad. Sci. USA 83, 1140-1144.
[5]Fox, P. T., Raichle, M. E., Mintun, M. A. &; Dence, C. (1988) Nonoxidaive glucose consumption during focal physiological nerural activity. Science 241, 462-464.
[6]Ogawa, S., Lee, T. M., Kay, A. R. &; Tank, D. W. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl Acad. Sci. USA 87, 9868–9872 (1990)
[7]Boxerman JL, Bandettini PA, Kwong KK, Baker JR, Davis TL, Rosen BR, Weisskoff RM (1995) The intravascular contribution to fMRI signal change: Monte Carlo modeling and diffusion-weighted studies in vivo. Magn Reson Med 34: 4–10.
[8]Kim SG, Ugurbil K (1997) Comparison of blood oxygenation and cerebral blood flow effects in fMRI: estimation of relative oxygen consumption change. Magn Reson Med 38: 59–65.
[9]Fox, M. D. and M. E. Raichle (2007). "Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging." Nat Rev Neurosci 8(9): 700-711.
[10]Cordes, D., Haughton, V.M., Arfanakis, K., Wendt, G.J., Turski, P.A., Moritz, C.H., Quigley, M.A., Meyerand, M.E., 2000. Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR Am. J. Neuroradiol. 21 (9), 1636–1644.
[11]Aertsen, A.M., Gerstein, G.L., Habib, M.K., Palm, G., 1989. Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J. Neurophysiol. 61 (5), 900–917.
[12]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.
[13]Biswal, B. B., VanKylen, J., &; Hyde, J. S. (1997). Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. Nmr in Biomedicine, 10(4-5), 165-170.
[14]Broyd, Samantha J.; Demanuele, Charmaine; Debener, Stefan; Helps, Suzannah K.; James, Christopher J.; Sonuga-Barke, Edmund J.S. (2009). "Default-mode brain dysfunction in mental disorders: A systematic review". Neuroscience &; Biobehavioral Reviews 33 (3): 279–96.
[15]Birn, R.M., Diamond, J.B., Smith, M.A., Bandettini, P.A., 2006. Separating respiratory-variation-related fluctuations from neu- ronal-activity-related fluctuations in fMRI. Neuroimage 31 (4), 1536–1548.
[16]Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008. The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40 (2), 644–654.
[17]Shmueli, K., van Gelderen, P., de Zwart, J.A., Horovitz, S.G., Fukunaga, M., Jansma, J.M., Duyn, J.H., 2007. Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. Neuroimage 38 (2), 306–320.
[18]Lin, F. H., et al. (2006). "Dynamic magnetic resonance inverse imaging of human brain function." Magn Reson Med 56(4): 787-802.
[19]Lin, F. H., et al. (2008). "Event-related single-shot volumetric functional magnetic resonance inverse imaging of visual processing." Neuroimage 42(1):
[20]Lin, F. H., et al. (2008). "Linear constraint minimum variance beamformer functional magnetic resonance inverse imaging." Neuroimage 43(2): 297-311.
[21]Zahneisen, B., et al. (2011). "Three-dimensional MR-encephalography: fast volumetric brain imaging using rosette trajectories." Magn Reson Med 65(5): 1260-1268.
[22]Feinberg, D. A., et al. (2010). "Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging." PLoS One 5(12): e15710.
[23]Baria, A. T., et al. (2011). "Anatomical and functional assemblies of brain BOLD oscillations." J Neurosci 31(21): 7910-7919.
[24]Boubela, R. N., et al. (2013). "Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest." Front Hum Neurosci 7: 168.
[25]Lee, H. L., et al. (2013). "Tracking dynamic resting-state networks at higher frequencies using MR-encephalography." Neuroimage 65: 216-222.
[26]Mingoia, G., et al. (2013). "Frequency domains of resting state default mode network activity in schizophrenia." Psychiatry Res 214(1): 80-82.
[27]Nir, Y., et al. (2006). "Widespread functional connectivity and fMRI fluctuations in human visual cortex in the absence of visual stimulation." Neuroimage 30(4): 1313-1324.
[28]Cordes, D., et al. (2000). "Mapping functionally related regions of brain with functional connectivity MR imaging." AJNR Am J Neuroradiol 21(9): 1636-1644.
[29]Lowe, M. J., et al. (1998). "Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations." Neuroimage 7(2): 119-132.
[30]Kleiner M, Brainard D, Pelli D, 2007, "What''s new in Psychtoolbox-3?" Perception 36 ECVP Abstract Supplement.
[31]Brainard, D. H. (1997) The Psychophysics Toolbox, Spatial Vision 10:433-436.
[32]Pelli, D. G. (1997) The VideoToolbox software for visual psychophysics: Transforming numbers into movies, Spatial Vision 10:437-442.
[33]Sarkka, S., et al. (2012). "Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER." Neuroimage 60(2): 1517-1527.
[34]Rogers, B. P., et al. (2007). "Assessing functional connectivity in the human brain by fMRI." Magn Reson Imaging 25(10): 1347-1357.
[35]Bedrosian, E. (December 1962), "A Product Theorem for Hilbert Transforms", Rand Corporation Memorandum (RM-3439-PR)
[36]Cordes, D., Haughton, V. M., Arfanakis, K., Carew, J. D., Turski, P. A., Moritz, C. H., et al. (2001). Frequencies con- tributing to functional connectivity in the cerebral cortex in “resting- state” data. AJNR Am. J. Neuroradiol. 22, 1326–1333.
[37]I. Daubechies, “The wavelet transform, time-frequency localization and signal analysis,” IEEE Transactions on Information Theory, vol. 36, no. 5, pp. 961–1005, 1990.
[38]M. Unser and A. Aldroubi, “A review of wavelets in biomedical applications,” Proceedings of the IEEE, vol. 84, pp. 626–638, 1996.