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Functional Magnetic Resonance Imaging

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Abstract

Functional magnetic resonance imaging has become a primary tool for psychological and cognitive studies or preclinical brain research. As a technique to map brain function, fMRI measures the blood oxygenation level–dependent signal as a collective effect of changes in cerebral blood flow, cerebral blood volume, and cerebral metabolic rate of oxygen following changes in neural activity. The use of fMRI in combination with carefully designed task paradigms has enabled scientists to map perceptual, cognitive, or behavioral functions onto brain regions and networks. Spontaneous activity observed with fMRI in task-free resting states has been used to reveal intrinsic functional networks that collectively depict the brain’s functional architecture or connectome. Naturalistic paradigms for fMRI are increasingly used to map brain activation, address neural representation and coding, and characterize brain networks while humans are engaged in a realistic and dynamic environment similar to daily life experiences. In this chapter, we discuss the principles, methods, and applications of fMRI, with emphasis on its biophysical and physiological basis, experimental designs and analysis methods, and applications to human and animal studies. Example data or results from empirical studies are presented to help illustrate methods or support scientific views.

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References

  1. B. He, Z. Liu, Multimodal functional neuroimaging: Integrating functional MRI and EEG/MEG. IEEE Rev. Biomed. Eng. 1, 23–40 (2008)

    Google Scholar 

  2. R.W. Brown, Y.-C. Cheng, M.E. Haack, M.R. Thompson, R. Venkatesan, Magnetic Resonance Imaging: Physical Principles and Sequence Design, 2nd edn. (Wiley, Hoboken, New Jersey, 2014)

    Google Scholar 

  3. S.A. Huettel, A.W. Song, G. McCarthy, Functional Magnetic Resonance Imaging, 3rd edn. (Oxford University Press, 2014)

    Google Scholar 

  4. Z.-P. Liang, P.C. Lauterbur, Principles of Magnetic Resonance Imaging: A Signal Processing Perspective (IEEE Press, 1999)

    Google Scholar 

  5. S. Ogawa, T.M. Lee, A.R. Kay, D.W. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation. PNAS 87(24), 9868–9872 (1990)

    CAS  Google Scholar 

  6. L. Pauling, C.D. Coryell, The magnetic properties and structure of hemoglobin, oxyhemoglobin and carbonmonoxyhemoglobin. PNAS 22(4), 210–216 (1936)

    CAS  Google Scholar 

  7. K.R. Thulborn, J.C. Waterton, P.M. Matthews, G.K. Radda, Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. Biochim. Biophys. Acta 714(2), 265–270 (1982)

    CAS  Google Scholar 

  8. R. Turner, D. Le Bihan, C.T.W. Moonen, D. Despres, J. Frank, Echo-planar time course MRI of cat brain oxygenation changes. Magn. Reson. Med. 22(1), 159–166 (1991)

    CAS  Google Scholar 

  9. P.A. Bandettini, E.C. Wong, S.R. Hinks, R.S. Tikofsky, J.S. Hyde, Time course EPI of human brain function during task activation. Magn. Reson. Med. 25(2), 390–397 (1992)

    CAS  Google Scholar 

  10. K.K. Kwong, J.W. Belliveau, D.A. Chesler, I.E. Goldberg, R.M. Weisskoff, B.P. Poncelet, D.N. Kennedy, B.E. Hoppel, M.S. Cohen, R. Turner, Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. U S A 89(12), 5675–5679 (1992)

    CAS  Google Scholar 

  11. S. Ogawa, D.W. Tank, R. Menon, J.M. Ellermann, S.-G. Kim, H. Merkle, K. Ugurbil, Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. PNAS 89(13), 5951–5955 (1992)

    CAS  Google Scholar 

  12. R.B. Buxton, The physics of functional magnetic resonance imaging. Rep. Prog. Phys. 76(9), 096601 (2013)

    Google Scholar 

  13. S.-G. Kim, S. Ogawa, Biophysical and physiological origins of blood oxygenation level dependent fMRI signals. J. Cereb. Blood Flow Metab. 32(7), 1188–1206 (2012)

    CAS  Google Scholar 

  14. R.B. Buxton, E.C. Wong, L.R. Frank, Dynamics of blood flow and oxygenation changes during brain activation: The balloon model. Magn. Reson. Med. 39(6), 855–864 (1998)

    CAS  Google Scholar 

  15. P.T. Fox, M.E. Raichle, Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. PNAS 83(4), 1140–1144 (1986)

    CAS  Google Scholar 

  16. M.K. Stehling, R. Turner, P. Mansfield, Echo-planar imaging: Magnetic resonance imaging in a fraction of a second. Science 254(5028), 43–50 (1991)

    CAS  Google Scholar 

  17. G.H. Glover, Spiral imaging in fMRI. NeuroImage 62(2), 706–712 (2012)

    Google Scholar 

  18. D.C. Noll, J.D. Cohen, C.H. Meyer, W. Schneider, Spiral k-space MR imaging of cortical activation. J. Magn. Reson. Imaging 5(1), 49–56 (1995)

    CAS  Google Scholar 

  19. S. Moeller, E. Yacoub, C.A. Olman, E. Auerbach, J. Strupp, N. Harel, K. Ugurbil, Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 63(5), 1144–1153 (2010)

    Google Scholar 

  20. K. Setsompop, B.A. Gagoski, J.R. Polimeni, T. Witzel, V.J. Wedeen, L.L. Wald, Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn. Reson. Med. 67(5), 1210–1224 (2012)

    Google Scholar 

  21. N.K. Logothetis, What we can do and what we cannot do with fMRI. Nature 453(12), 869–883 (2008)

    CAS  Google Scholar 

  22. G.C. Petzold, V.N. Murthy, Role of astrocytes in neurovascular coupling. Neuron 71(5), 782–797 (2011)

    CAS  Google Scholar 

  23. N.K. Logothetis, J. Pauls, M. Augath, T. Trinath, A. Oeltermann, Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001)

    CAS  Google Scholar 

  24. Z. Ding, Y. Huang, S.K. Bailey, Y. Gao, L.E. Cutting, B.P. Roger, A.T. Newton, J.C. Gore, Detection of synchronous brain activity in white matter tracts at rest and under functional loading. PNAS 115(3), 595–600 (2018)

    CAS  Google Scholar 

  25. J.R. Gawryluk, E.L. Mazerolle, R.C. D’Arcy, Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions. Front. Neurosci. 8, 239 (2014)

    Google Scholar 

  26. L. Marussich, K.-H. Lu, H. Wen, Z. Liu, Mapping white-matter functional organization at rest and during naturalistic visual perception. NeuroImage 146, 1128–1141 (2017)

    Google Scholar 

  27. G.M. Boynton, S.A. Engel, G.H. Glover, D.J. Heeger, Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 16(13), 4207–4221 (1996)

    CAS  Google Scholar 

  28. G.H. Glover, Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage 9(4), 416–429 (1999)

    CAS  Google Scholar 

  29. J. Cao, K.-H. Lu, S.T. Oleson, R.J. Phillips, D. Jaffey, C.L. Hendren, T.L. Powley, Z. Liu, Gastric stimulation drives fast BOLD responses of neural origin. NeuroImage 197, 200–211 (2019)

    Google Scholar 

  30. L.D. Lewis, K. Setsompop, B.R. Rosen, J.R. Polimeni, Fast fMRI can detect oscillatory neural activity in humans. PNAS 113(43), E6679–E6685 (2016)

    CAS  Google Scholar 

  31. K.L. Friston, A.P. Holmes, K.J. Worsley, J.-P. Poline, C.D. Frith, R.S.J. Frackowiak, Statistical parametric maps in functional imaging: A general linear approach. Hum. Brain Mapp. 2(4), 189–210 (1994)

    Google Scholar 

  32. T.T. Liu, The development of event-related fMRI designs. NeuroImage 62(2), 1157–1162 (2012)

    Google Scholar 

  33. G.T. Buracas, G.M. Boynton, Efficient design of event-related fMRI experiments using M-sequences. NeuroImage 16, 801–813 (2002)

    Google Scholar 

  34. A.M. Derrington, P. Lennie, Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of macaque. J. Physiol. 357, 219–240 (1984)

    CAS  Google Scholar 

  35. E. Duff, J. Xiong, B. Wang, R. Cunnington, P.T. Fox, G. Egan, Complex spatio-temporal dynamics of fMRI BOLD: A study of motor learning. NeuroImage 34(1), 156–168 (2007)

    Google Scholar 

  36. N. Kriegeskorte, R. Goebel, P.A. Bandettini, Information-based functional brain mapping. PNAS 103(10), 3863–3868 (2006)

    CAS  Google Scholar 

  37. M.D. Fox, M.E. Raichle, Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007)

    CAS  Google Scholar 

  38. B. Biswal, F.Z. Yetkin, V.M. Haughton, J.S. Hyde, Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (1995)

    CAS  Google Scholar 

  39. C.F. Bechmann, M. DeLuca, J. Devlin, S.M. Smith, Investigation into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B 360(1457), 1001–1013 (2005)

    Google Scholar 

  40. J. Cao, K.-H. Lu, T.L. Powley, Z. Liu, Vagal nerve stimulation triggers widespread responses and alters large-scale functional connectivity in the rat brain. PLoS One 12(12), e0189518 (2017)

    Google Scholar 

  41. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7(6), 1129–1159 (1995)

    CAS  Google Scholar 

  42. V.D. Calhoun, J. Liu, T. Adali, A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage 45, S163–S172 (2009)

    Google Scholar 

  43. K.R.A. Van Dijk, T. Hedden, A. Venkataraman, K.C. Evans, S.W. Lazar, R.L. Buckner, Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. J. Neurophysiol. 103(1), 297–321 (2010)

    Google Scholar 

  44. D.C. Van Essen, S.M. Smith, D.M. Barch, T.E.J. Behren, E. Yacoub, K. Ugurbil, The WU-Minn human connectome project: An overview. NeuroImage 80, 62–79 (2013)

    Google Scholar 

  45. S.M. Smith, P.T. Fox, K.L. Miller, D.C. Glahn, M.P. Fox, C.E. Mackay, N. Filippini, K.E. Watkins, R. Toro, A.R. Laird, C.F. Beckmann, Correspondence of the brain's functional architecture during activation and rest. PNAS 106(31), 13040–13045 (2009)

    CAS  Google Scholar 

  46. M.E. Raichle, The Brain’s default mode network. Annu. Rev. Neurosci. 38, 433–447 (2015)

    CAS  Google Scholar 

  47. Y. Zhang, G. Chen, H. Wen, K.-H. Lu, Z. Liu, Musical imagery involves Wernicke’s area in bilateral and anti-correlated network interactions in musicians. Sci. Rep. 7(17066), 2017 (2017)

    Google Scholar 

  48. Y. Zhang, K. Han, R.M. Worth, Z. Liu, Connecting concepts in the brain by mapping cortical representations of semantic relations. biorxiv, https://doi.org/10.1101/649939 (2019)

  49. U. Hasson, Y. Nir, I. Levy, G. Fuhrmann, R. Malach, Intersubject synchronization of cortical activity during natural vision. Science 303(5664), 1634–1640 (2004)

    CAS  Google Scholar 

  50. K.-H. Lu, S. Hung, H. Wen, L. Marussich, Z. Liu, Mapping white-matter functional organization at rest and during naturalistic visual perception. PLoS One 11(8), e0161797 (2016)

    Google Scholar 

  51. E. Simony, C.J. Joney, J. Chen, O. Losiksky, Y. Yeshurun, A. Wiesel, U. Hasson, Dynamic reconfiguration of the default mode network during narrative comprehension. Nat. Commun. 7, 12141 (2016)

    CAS  Google Scholar 

  52. L.K. Lynch, K.-H. Lu, H. Wen, Y. Zhang, A.J. Saykin, Z. Liu, Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions. Hum. Brain Mapp. 39(12), 4939–4948 (2018)

    Google Scholar 

  53. T. Naselaris, K.N. Kay, S. Nishimoto, J.L. Gallant, Encoding and decoding in fMRI. NeuroImage 56(2), 400–410 (2011)

    Google Scholar 

  54. M. Eickenberg, V.G. Gramfort, B. Thirion, Seeing it all: Convolutional network layers map the function of the human visual system. NeuroImage 152, 184–194 (2017)

    Google Scholar 

  55. U. Güçlü, M.A.J. van Gerven, Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35(27), 100005–110014 (2015)

    Google Scholar 

  56. H. Wen, J. Shi, Y. Zhang, K.-H. Lu, J. Cao, Z. Liu, Neural encoding and decoding with deep learning for dynamic natural vision. Cereb. Cortex 28(12), 4136–4160 (2018)

    Google Scholar 

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Correspondence to Zhongming Liu .

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Homework

Homework

Please mark all the correct answers for each of the following questions. Note that each question may have one or more than one correct answer.

  1. 1.

    Which of the following nuclei is the most abundant for functional magnetic resonance imaging?

    1. (A)

      1H

    2. (B)

      13C

    3. (C)

      31P

    4. (D)

      19F

  2. 2.

    Hydrogen protons spin at about 300 MHz in a 7 Tesla MRI system. Which of the following is close to the gyromagnetic ratio (MHz T−1) of 1H spins?

    1. (A)

      42.6

    2. (B)

      6.53

    3. (C)

      40.1

    4. (D)

      11.3

  3. 3.

    Which of the following are true about on-resonance RF excitation?

    1. (A)

      It transmits an oscillating magnetic field along the longitudinal direction.

    2. (B)

      It transmits an oscillating magnetic field in the transverse plane.

    3. (C)

      It transmits an oscillating magnetic field with a frequency that matches the Larmor frequency of the target spin systems.

    4. (D)

      It transmits energy to be effectively absorbed by the target spin systems.

  4. 4.

    Which of the following contribute to the blood oxygenation level-dependent contrast?

    1. (A)

      Cerebral blood flow

    2. (B)

      Cerebral blood volume

    3. (C)

      Cerebral metabolic rate of oxygen

    4. (D)

      Myelin density

  5. 5.

    Which of the following regional changes occur accompanying local neural activation?

    1. (A)

      Arterioles dilate

    2. (B)

      Blood flow increases

    3. (C)

      Oxygen consumption increases

    4. (D)

      Blood oxygenation level increases

  6. 6.

    What happens when the concentration of deoxy-hemoglobin increases?

    1. (A)

      Transverse relaxation becomes faster

    2. (B)

      Transverse magnetization decays faster

    3. (C)

      Longitudinal relaxation becomes faster

    4. (D)

      Longitudinal magnetization recovers faster

  7. 7.

    Which of the following are TRUE about the hemodynamic response function (HRF)?

    1. (A)

      It indicates the hemodynamic response given an impulse input stimulus

    2. (B)

      It indicates the hemodynamic response given a sustained block of input stimulus

    3. (C)

      In HRF, the peak response delays from the time zero

    4. (D)

      In HRF, the peak response precedes the time zero

  8. 8.

    How fast is the fMRI signal typically sampled?

    1. (A)

      Every millisecond

    2. (B)

      Every second

    3. (C)

      Every minute

    4. (D)

      Every hour

  9. 9.

    To derive the response model (or design matrix) for the BOLD time series analysis, one needs to

    1. (A)

      Convolve the stimulus paradigm with the hemodynamic response function

    2. (B)

      Multiply the stimulus paradigm with the hemodynamic response function

    3. (C)

      Add the stimulus paradigm with the hemodynamic response function

    4. (D)

      None of the above

  10. 10.

    In the block design, what would be a typical block duration?

    1. (A)

      30 seconds ON vs. 30 seconds OFF

    2. (B)

      30 milliseconds ON vs. 30 milliseconds OFF

    3. (C)

      30 minutes ON vs. 30 minutes OFF

    4. (D)

      None of the above

  11. 11.

    Which of the following are TRUE about resting state fMRI?

    1. (A)

      It is used to report instrumental noises from the MRI scanner

    2. (B)

      It is used to measure spontaneous brain activity in the absence of overt tasks or stimuli

    3. (C)

      It is used to measure fluctuations in membrane potentials around −70 mV

    4. (D)

      None of the above

  12. 12.

    Functional connectivity as observed with resting state fMRI refers to?

    1. (A)

      Temporal correlations between the signals observed from different brain locations

    2. (B)

      Anatomical connections between different brain locations

    3. (C)

      The relationship between neural and vascular signals in the brain

    4. (D)

      None of the above

  13. 13.

    When applied to resting state fMRI data, independent component analysis

    1. (A)

      Separates brain networks without specifying a seed location

    2. (B)

      Assumes brain networks are spatially independent of one another

    3. (C)

      Shows where in the brain is at rest

    4. (D)

      None of the above

  14. 14.

    To map brain activations with a continuous period of naturalistic stimuli, one can

    1. (A)

      Calculate the voxel-wise correlation between the fMRI signals from a subject during two repeated sessions of the same stimuli

    2. (B)

      Calculate the voxel-wise correlation between the fMRI signals from two subjects during the same stimuli

    3. (C)

      Use a response model derived from convolving a boxcar function with the canonical HRF

    4. (D)

      Calculate the seed-based correlation based on the fMRI signals recorded from a single session

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Liu, Z., Cao, J. (2020). Functional Magnetic Resonance Imaging. In: He, B. (eds) Neural Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-43395-6_11

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