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Physiology and Physics of the fMRI Signal

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Part of the book series: Biological Magnetic Resonance ((BIMR,volume 30))

Abstract

The functional magnetic resonance imaging (fMRI) signal results from a complex interplay of basic physiological processes, namely cerebral metabolic rate of oxygen (CMRO2), cerebral blood flow (CBF), and cerebral blood volume (CBV). The ensuing change in the concentration of deoxygenated hemoglobin (dHb) affects the susceptibility of the tissue and gives rise to the so-called blood oxygenation level-dependent (BOLD) signal. The BOLD effect, together with the CBV change, which varies the amount of water protons in the extra- and intravascular space, is picked up as the fMRI signal by gradient- and spin-echo MRI sequences. In this chapter, we describe in detail the spatiotemporal properties of CMRO2, CBV, and CBF and how to model them mathematically. A special focus is on the resulting hemodynamic transients, such as the initial dip and the poststimulus undershoot. In the second half of the chapter, we review the fMRI physical mechanisms using the endogenous dHb-based contrast. In particular, we describe the relaxation rate as a function of susceptibility, diameter of blood vessels, MRI sequences, and magnetic field strength. With these parameters at hand, the fMRI signal and its intra- and extravascular contributions can be calculated from dHb and CBV changes. In summary, underlying the fMRI signal are numerous physiological and physical processes. The spatiotemporal information content of the fMRI signal, thus, depends on the dynamic properties of brain vascular and metabolic processes and the MRI parameters used (e.g., field strength, echo time, MRI sequence) to detect them.

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Notes

  1. 1.

    Hematocrit also has a strong influence on the relaxation rate and, hence, amplitude of the fMRI signal (Silvennoinen et al. 2003). However, the change in hematocrit during activation is low. Thus, it is an important variable for intersubject comparison of the fMRI signal amplitude but not for assessing the dynamics of the fMRI signal and the activation patterns within a subject.

  2. 2.

    We use the term ‘unmatching’ instead of the more common term ‘uncoupling’ for the transient mismatch in the relative amplitudes of the physiological variables as the latter term suggests a breakdown of the casual relationship between them, which does not have to necessarily be the case (see below).

  3. 3.

    A promising new dynamical approach directly capable of measuring oxygen metabolism in the mitochondria is to use flavoprotein fluorescence (reviewed in Vazquez et al. 2010).

  4. 4.

    These equations were developed in collaboration with Richard B. Buxton.

  5. 5.

    Note, however, that this method cannot distinguish between different vascular compartments within the volume element and attributes also blood flow in the venules/veins to CBF, in contrast to standard CBF definition utilized in PET, ASL, etc. excluding venular flow.

  6. 6.

    In contrast, Hillman et al. found that flow and volume changes occur first in the capillaries and later in the arteries (Hillman et al. 2007). Some of the contradictory findings in the literature might be explained that most of these results were obtained using intrinsic optical imaging, which is only sensitive to the first few hundred micrometers on the surface of the brain. That is, for example, an arteriolar changes occurring in a deeper layer before the capillary changes in an upper layer cannot be observed.

  7. 7.

    However, this method assumes that the blood oxygenation in the arteries does not change, which is in contradiction to the findings of the same group using oxygen-sensitive microelectrode measurements, summarized above (Vazquez et al. 2010).

  8. 8.

    Again, the following equations have been derived in collaboration with Richard B. Buxton.

  9. 9.

    CBV is the parameter measured in optical imaging of intrinsic signals but is often taken as reflecting CBF as well.

  10. 10.

    The third theoretical possibility of an initial CBF decrease has, to the best of our knowledge, not been suggested by anybody as a possible mechanism for the initial dip.

  11. 11.

    Of course, applying many refocusing pulses as in a Carr–Purcell pulse train with very short delay periods in between the pulses or applying a large B 1 field (relative to the magnitude of the magnetic field inhomogeneity) for spin locking during this delay will reduce or even eliminate this signal loss due to dynamic averaging. Such an approach, however, would be detrimental to functional imaging signals.

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Acknowledgments

Work reported here from the Center for Magnetic Resonance University of Minnesota was supported by National Institutes of Health (NIH) Biotechnology Research Center (BTRC) grants P41 RR08079 and P41 EB015894.

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Uludağ, K., Uğurbil, K. (2015). Physiology and Physics of the fMRI Signal. In: Uludag, K., Ugurbil, K., Berliner, L. (eds) fMRI: From Nuclear Spins to Brain Functions. Biological Magnetic Resonance, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7591-1_8

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