Although little more that a decade old, fMRI already has an established pantheon of "giants in the field." Seiji Ogawa, one of these giants, was the first to observe that when rodents breathe, 100 percent oxygen-diminished contrast is observed in brain images acquired with T2*-weighted signal. Based on prior work, most notably by Thulborn, Ogawa was correct in assuming that the change in signal contrast was due to a decrease in deoxyhemoglobin levels coursing through the vasculature. It is this observation that sparked the wildfire spread of fMRI throughout the field of cognitive neuroscience. The elegant paper by Thompson et al. resolves both a stylistic imprecision and a conceptual error made by these early pioneers.

Although Ogawa coined the term BOLD—Blood Oxygen Level Dependent—to account for the observed increase in blood vessel signal, MRI is essentially blind to blood oxygen levels, per se; deoxyhemoglobin, the true source of signal change, did not, apparently, lend itself to a snappy eponym. Since those early experiments 13 years ago, essentially all studies...  Read more

Although little more that a decade old, fMRI already has an established pantheon of "giants in the field." Seiji Ogawa, one of these giants, was the first to observe that when rodents breathe, 100 percent oxygen-diminished contrast is observed in brain images acquired with T2*-weighted signal. Based on prior work, most notably by Thulborn, Ogawa was correct in assuming that the change in signal contrast was due to a decrease in deoxyhemoglobin levels coursing through the vasculature. It is this observation that sparked the wildfire spread of fMRI throughout the field of cognitive neuroscience. The elegant paper by Thompson et al. resolves both a stylistic imprecision and a conceptual error made by these early pioneers.

Although Ogawa coined the term BOLD—Blood Oxygen Level Dependent—to account for the observed increase in blood vessel signal, MRI is essentially blind to blood oxygen levels, per se; deoxyhemoglobin, the true source of signal change, did not, apparently, lend itself to a snappy eponym. Since those early experiments 13 years ago, essentially all studies have focused on the relationship between neuronal metabolism and deoxyhemoglobin (or oxyhemoglobin), using various flavors of MRI, spectroscopy and optic imaging. Coupling an oxygen-sensitive microelectrode with signal unit recording in the cat visual cortex, the current study is noteworthy for establishing a relationship between a true blood oxygen level-dependent signal and neuronal metabolism.

Although Ogawa was correct in attributing the BOLD response to a shift in deoxyhemoglobin, he initially erred in predicting how this response will map onto changes in metabolism. Justifiably, he predicted that an increase in neuronal metabolism will lead to an increased consumption of oxygen, an increase in deoxyhemoglobin, and, therefore, a decrease in T2*-weighted signal intensity (Ogawa et al., 1990). It was only after he and his colleagues systematically shifted the chronic metabolic state of neurons, using various drug infusions, that they reformulated their prediction: that an increase in metabolism will result in a decrease in deoxyhemoglobin, perhaps through an overflow of fresh blood, leading to an increase in signal intensity, while a decrease in metabolism will result in a reverse response (Ogawa et al., 1990). By and large, this has turned out to be true when one measures the BOLD signal a few seconds after a shift in metabolism has been initiated. Why the hemodynamic response is designed this way, and what are the biochemical mediators of this delayed response are still under investigation. In any case, since brain cells are thought to be aerobic energy-producers, it was fair to assume that at least for an instant in time the increased metabolic demands of neurons (and maybe glia) would be associated with a decrease in local oxygen levels. This assumption has been indirectly corroborated by MRI studies describing the early dip in T2*-weighted signal, and by optic imaging. Finally, thanks to the current study, a direct proof is this assumption is established.

Does this definitive finding have direct implications for fMRI’s clinical use? Any advance that increases the precision of the technology is welcomed; however, the precision we gain from the current study does not fill the void that currently limits fMRI’s clinical utility. The "early dip" in oxygen and T2*-weighted signal is spatially more precise, possessing higher anatomical fidelity than the more conventional "delayed rise" in signal. This is so because the early dip is capillary-based, while the delayed rise is predominantly venule-based, and the early dip is, therefore, more focal and closer to cell bodies. Although this gain in spatial precision might aid in detecting brain dysfunction in relatively small brain areas like the hippocampal subregions or the nuclei of the basal ganglia, the gain will likely be most helpful in addressing basic questions regarding submillimeter circuits—such as the ocular dominance columns. The trade-off is that the amplitude of the early dip is much smaller than the amplitude of the delayed rise in signal, and, of course, signal-to-noise is critical for diagnostic purposes.

This leads into what many consider the more burning fMRI imprecision that hampers its clinical utility. As typically practiced, a patient is asked to perform an activation task and the acute change in the BOLD response above some reference baseline is used as the metric of brain function. The problem is that we now know that the baseline state—sometimes called the resting state, but more appropriately termed the chronic state—is different across brain areas, and, more disturbingly, different between patients and controls. So, for example, we know from PET studies that the chronic metabolic state of the occipital cortex is higher than that of the frontal cortex (Raichle et al., 2001), and we know from MRI studies that the occipital cortex has a higher level of deoxyhemoglobin in the chronic state (Davis et al., 1998), and has shorter T2* relaxation times (Wansapura et al., 1999). There is now a rising chorus of studies showing that the chronic state will influence the amplitude of the acute BOLD response, and so it is difficult to make comparisons between groups regarding differences in the amplitude of the acute response. Thus, although many papers, including work from my own lab, have shown that AD patients have a diminished acute response in BOLD signal, we should be cautious in our interpretation of these findings. Certainly, these unresolved sources of noise diminish the potential of using fMRI for diagnostic purposes. Instead of increasing spatial fidelity of the MRI signal—the main gain of the early dip—what we really need are ways to increase signal validity, particularly in the context of a diseased brain. Issues of signal validity are currently being explored, either by attempts at calibrating the acute BOLD response or by attempting to understand and exploit the chronic metabolic state in order to "localize the lesion.

References:
Ogawa S et al. Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn Reson Med. 1990;14(1):68-78. (No abstract available)

Ogawa S et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A. 1990;87(24):9868-72. (No abstract available)

Raichle ME et al. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98(2):676-82. Abstract

Davis TL et al. Calibrated functional MRI: mapping the dynamics of oxidative metabolism. Proc Natl Acad Sci U S A. 1998;95(4):1834-9. Abstract

Wansapura JP et al. NMR relaxation times in the human brain at 3.0 tesla. J Magn Reson Imaging. 1999;9(4):531-8. Abstract

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