Department of Neurology, University of Minnesota, Minneapolis, MN 55455, USA
Costantino Iadecola, Department of Neurology, University of Minnesota, 516 Delaware Street SE, Minneapolis, MN 55455, USA.Email: iadec001{at}tc.umn.edu.
It has long been thought that there is a close relationship between brain activity and cerebral blood flow [reviewed by Raichle (Raichle, 1998)]. While in 1890 Roy and Sherrington proposed the concept of an intrinsic mechanisms responsible for coupling neural activity to blood flow (Roy and Sherrington, 1890
), more than a decade earlier the Italian physiologist Angelo Mosso was already undertaking a detailed study of the changes in cerebral hemodynamics associated with various psychological states in humans (Mosso, 1881
). Following up on these pioneering efforts, our understanding of the localization of brain function has advanced considerably thanks to techniques that detect the changes in brain blood flow associated with brain activity (Table 1
). One of the most recent approaches is based on monitoring activity-induced increases in cerebral oxygenation. During functional activation cerebral blood flow increases more than the oxygen demands of the brain (Fox and Raichle, 1986
), and such mismatch results in an increase in oxyhemoglobin and a decrease in deoxyhemoglobin (Raichle, 1998
). activity-induced increases in brain oxygenation can be detected either by magnetic resonance imaging (mri), as blood oxygenation level dependent (bold) contrast (Ogawa et al., 1992
), or, by optical imaging, as intrinsic signals (Grinvald et al., 1986
). Because of the importance and widespread use of these approaches in functional brain mapping, there is a great interest in elucidating the precise mechanisms by which these signals are generated and how accurately they reflect brain activity (Raichle, 1998
).
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These findings have notable implications for functional brain mapping using hemodynamic changes as a proxy for neural activity. On the one hand, the finding that intrinsic signals identify reasonably well the area of activation, assessed by electrophysiological recordings, supports the validity of using vascular-based methods to localize brain function. On the other hand, the observation that the topography of the intrinsic signals overlaps with the distribution of cerebral blood vessels within the activated region suggests that the vascular geometry of a brain region dictates the spatial distribution of these signals. Therefore, depending on the spatial relationships between vascular network and active neurons, there might be instances in which the area of activation will not match the area from which the signals are generated. This point is demonstrated by the data of Harrison et al. (Fig. 2) showing that intrinsic signals can be detected from areas in which there is no evoked neural activity. Although in some areas vascular and neural architecture are well matched, e.g. in the rodent whiskerbarrel cortex (Woolsey et al., 1996), this correspondence cannot be assumed to occur in all brain regions and for all activation paradigms. Another factor that may prevent complete overlap between vascular and activity maps is that the vascular dilatation responsible for the increase in blood flow evoked by neural activity is propagated in a retrograde fashion to upstream arterioles located outside the activated area (Iadecola et al., 1997
). This phenomenon, termed retrograde vasodilation, serves the purpose of dilating upstream arterioles that are critical for regional flow control. Therefore, during functional activation, an increase in flow is observed also in remote regions in which no neural activity can be detected.
Another implication of the data of Harrison et al. is that the intensity of the intrinsic signals generated from a brain region depends on local vascular density. Therefore, given an equal degree of neural activation, the area in which vascular density is more sparse will generate a less robust intrinsic signal. Consequently, the hot spots on a functional map may not necessarily indicate the areas that are most active in the execution of a specific task. While Harrison et al. focused on the relationship between intrinsic signals and vascular networks, Logothetis and colleagues investigated the specific components of the neural activity linked to the vascular signal (Logothetis et al., 2001). These authors recorded simultaneously electrophysiological parameters and BOLD fMRI in the monkey visual cortex and found that field potentials, rather than spiking activity, best predicted the vascular signal. This finding suggests that neural input and local processing, rather than neural output, are the most important determinants of the vascular change, a conclusion reached also by others on the basis of direct flow measurements in cerebellum (Mathiesen et al., 1998
). Logothetis and colleagues also point out that, due to its higher signal-to-noise ratio, neural activity can be present in areas in which no BOLD contrast is detected, resulting in underestimation of activated areas assessed by fMRI. This conclusion also suggests caution in the functional interpretation of activity maps based exclusively on the presence or absence of the BOLD signal.
The finding that there might be sphincter-like structures capable of controlling the distribution of flow within the cerebral microvascular network is also of interest, and its significance has to be discussed in the context of a large body of work suggesting that cerebral blood vessels receive neural projections from intrinsic neurons (Eckenstein and Baughman, 1984; Vaucher and Hamel, 1995
; Krimer et al., 1998
). It is tempting to speculate that these sphincter-like structures are controlled by neural projections originating from neurons dedicated to flow regulation. Such neurovascular units would be well suited to control the timing and spatial distribution of the increases in flow evoked by neural activity, and could regulate flow for purposes other than to support the changing energetic needs of the tissue. For example, these neural networks could mediate anticipatory increases in blood flow in preparation for, rather than in response to, an increase in energy demands. Such anticipatory neurovascular control could, perhaps, explain why during activation the increase in cerebral blood flow greatly exceeds the oxygen needs of the tissue. This hypothesis, however, awaits experimental confirmation.
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