Neurovascular coupling - Scholarpedia

Transcription

1 Neurovascular coupling From Scholarpedia Brian N. Pasley and Ralph D. Freeman (2008), Scholarpedia, 3(3):5340. revision #34739 [link to/cite this article] Curator: Dr. Brian N. Pasley, Helen Wills Neuroscience Institute, University of California, Berkeley Curator: Dr. Ralph D. Freeman, School of Optometry, University of California, Berkeley, CA Neurovascular coupling refers to the relationship between local neural activity and subsequent changes in cerebral blood flow (CBF). The magnitude and spatial location of blood flow changes are tightly linked to changes in neural activity through a complex sequence of coordinated events involving neurons, glia, and vascular cells. Many vascular-based functional brain imaging techniques, such as fmri, rely on this coupling to infer changes in neural activity. Contents 1 Physiological consequences of neural activation 2 Coupling to synaptic and spiking neural activity 3 Coupling in space, time, and amplitude 4 Coupling during abnormal brain states 5 References 6 Recommended reading 7 See also Figure 1: Summary of physiological changes linking neural and vascular responses. Physiological consequences of neural activation Brain activation is accompanied by a complex sequence of cellular, metabolic, and vascular processes. Although the mechanisms linking these events are still under investigation, the basic sequence can be summarized as follows (Fig. 1). Various cellular processes of neurons, such as the restoration of ionic gradients and neurotransmitter recycling, require energy in the form of adenosine triphosphate (ATP). ATP is synthesized first by glycolysis, which is anaerobic and

2 produces a small amount of ATP, and then by oxidative glucose metabolism, which requires oxygen and produces a large amount of ATP. In the brain, about 90% of glucose is metabolized by the latter mechanism, i.e., aerobically. Cerebral metabolism thus depends on a constant supply of both glucose and oxygen. A continuous supply of these two energy substrates is maintained by CBF, which delivers glucose and oxygen to neural tissue through the complex web of blood vessels in the brain s vascular system (Fig. 2). Accordingly, during neural activity, increases in oxygen and glucose consumption are followed by an increase in CBF. Whereas the fractional increases in CBF and glucose consumption are similar in magnitude, oxygen consumption increases much less than CBF, leading to a net increase in the amount of oxygen present in the blood and tissue. This oversupply of oxygen due to the mismatch between CBF and oxygen consumption is the basis of blood-oxygenation level dependent (BOLD) fmri, which detects alterations in levels of deoxygenated hemoglobin and cerebral blood volume. While it is clear that alterations in neural activity and metabolism are correlated with changes in CBF, the mechanisms linking these processes are a matter of debate. One possibility is that blood flow is controlled directly by energy demand. In fact, this idea was originally proposed over a century ago by Roy and Sherrington (1890): the brain possesses an intrinsic mechanism by which its vascular supply can be varied locally in correspondence with local variations of functional activity. In this view, regional blood flow is controlled by feedback mechanisms that are sensitive to variations in the concentrations of ionic and molecular metabolic by-products. These by-products, such as K+, nitric oxide (NO), adenosine, carbon dioxide (CO 2 ) and arachidonic acid metabolites, may directly or indirectly alter blood flow by depolarizing (or hyperpolarizing) the vascular smooth muscle cells which trigger vasodilation (or vasoconstriction). Although intuitive, the idea that energy supply (i.e., local blood flow) is controlled directly by energy Figure 2: The cerebral vascular system. A plastic emulsion was injected into the brain vessels, and brain parenchymal tissue was dissolved (Zlokovic & Apuzzo, 1998; provided by permission from Lippincott Williams & Wilkins) demand (i.e., metabolic activity) appears to be oversimplified. An alternative possibility is that local blood flow is controlled directly by a feedforward mechanism involving neuronal signaling via neurotransmitters (Attwell and Iadecola, 2002; Lauritzen, 2005). Evidence for this mechanism suggests that astrocytes may play an important role in linking neurotransmitter activity to vascular responses (Harder et al., 1998; Pellerin and Magistretti, 2004). Astrocytes are a critical component for glutamate recycling. A cascade of chemical events within the astrocyte may then link the rate of glutamate cycling to the production of vasoactive chemical agents (Raichle and Mintun, 2006). In this view, neurovascular coupling is mediated by neuronal signaling mechanisms via glial pathways, rather than by mechanisms that sense energy consumption. In addition, neurovascular coupling might be mediated by diffusion of products of neuronal activity without the involvement of glial cells (Attwell and Iadecola, 2002). Finally, there is evidence that direct neuronal innervation of smooth muscle cells can also control blood flow (Hamel, 2004; Iadecola, 2004). Ultimately, it is likely that multiple mechanisms, both feedforward and feedback, function to mediate neurovascular coupling (Attwell and Iadecola, 2002; Lauritzen, 2005; Uludag et al., 2004). It is important to note that neurovascular coupling must provide for continuous brain function which requires metabolic nutrients and the elimination of waste products such as CO 2 and excessive heat. The fact that this process occurs on a fine spatio-temporal scale provides the basis for many powerful neuroimaging techniques. Coupling to synaptic and spiking neural activity Although neural activity and blood flow are closely coupled, it is important to understand which aspects of neural activity

3 drive the vascular response. Synaptic transmission and action potentials represent two different aspects of neural activity, each with their own molecular basis and energy requirements. Synaptic activity is reflected in the local field potential (LFP) and can be considered as the input to the neuron, while action potentials (spikes) are the output signals that permit communication between neurons. In many cases, LFPs and spikes are highly correlated and will therefore both correlate with the vascular response. This is likely to be the case in most studies of bottom-up sensory processing where both efferent and afferent activity increase proportionally (Logothetis and Wandell, 2004). However, there are likely to be many situations where spikes and subthreshold responses are dissociated. For instance, higher level brain areas may be subject to feedback and neuromodulatory signals that alter subthreshold membrane potential but do not elicit spikes (Logothetis and Wandell, 2004). Several experimental investigations have noted such dissociations between LFPs and spikes, including: In cerebellar cortex, stimulation of parallel fibers inhibits Purkinje cell spiking, but increases overall synaptic activity. LFPs and CBF both increased, whereas spiking activity was eliminated (Mathiesen et al., 1998). Visual stimuli presented at high flicker rates elicit LFP but not spiking activity in primary visual cortex. Strong coupling between LFP and vascular responses was observed even in the absence of spikes (Viswanathan and Freeman, 2007). Attentional modulation of activity in V1 is observed with fmri (Gandhi et al., 1999), but not with neurophysiological recordings of spike activity. The vascular response observed with fmri may therefore reflect increased subthreshold depolarizations that do not elicit spikes, but do drive the vascular response (but see Yoshor et al., 2007). In summary, existing data suggest that when afferent and efferent activity are correlated, the vascular response can be considered to reflect both spike rate and synaptic activity. However, when the two are dissociated, the vascular response more closely reflects synaptic activity and may not serve as an indicator of spike rate in the local neural population. Coupling in space, time, and amplitude Figure 3: Schematic of a typical CBF response to brief neural activation. The use of vascular responses to infer neural activity requires an understanding of how these two signals are coupled in space, time, and amplitude. Space When a limited focal population of neurons is activated, how well-localized is the vascular response? This is an important question for the application of neuroimaging techniques and it is necessary for the derivation of functional maps of cortical columns and lamina. Spatial resolution of the vascular response depends in large part on what component of the vascular system is considered. The dense network of capillaries penetrating the gray matter affords the highest spatial resolution, with an

4 intervessel distance of ~25um, while the larger compartments of arteries and veins are much more sparse and less confined to individual functional modules (Pawlik et al., 1981). Thus, it has been shown, using different imaging modalities, that columnar maps can be obtained by enhancing signal from the capillary component while minimizing contributions from large vessels (Moon et al., 2007; Vanzetta et al., 2004; Yacoub et al., 2007). There are a few reports of a submillimeter vascular point spread function (PSF) (Duong et al., 2001; Harel et al., 2006), while other estimates are on the order of 1-2mm (Harel et al., 2006; Thompson et al., 2005), 2mm (Shmuel et al., 2007), or 3.5mm (Engel et al., 1997; Parkes et al., 2005). Overall, this estimate depends on the magnetic field (e.g., 1.5 Tesla vs. 7 Tesla), technique (spin echo vs. gradient echo BOLD fmri), species (e.g., cat vs. human), and brain region (e.g., thalamus vs. cortex). Thus, while the underlying vascular PSF appears to provide submillimeter spatial specificity in some instances, the PSF of signals obtained from various imaging methods can be larger than the size of a functional module (e.g., ~1mm for a human ocular dominance column). Therefore, most successful high resolution studies have relied on a differential mapping approach, whereby two orthogonal conditions are compared in order to suppress spatially non-specific components of the signal (Grinvald et al., 2000). Use of the early metabolic response (i.e., initial dip ) may provide improved localization and the ability to generate single condition functional maps (Frostig et al., 1990; Thompson et al., 2003), but practical use of the initial dip for functional mapping is severely limited by poor SNR and brief duration. Continued optimization of imaging techniques to selectively enhance capillary CBF responses may permit routine acquisition of single condition functional maps at high spatial resolution. Time Perhaps the most severe experimental limitation imposed by neurovascular coupling is the lack of temporal information in the vascular response. The CBF response to a brief period of neural activation is typically delayed by 1-2 seconds and peaks 4-6 seconds after the neural response (Fig. 3). Because of this temporal filter, fast modulation of neural activity is unlikely to be reflected in the vascular response. The delay of CBF relative to neuronal activity is probably not due to slow reaction of smooth muscle cells. Instead, the delay is probably related to the slow diffusion and uptake of neurovascular mediators. In spite of this slow process, several studies suggest that the temporal fidelity of the vascular response (i.e., how accurately it reflects neuronal timing) may be fine enough to encode temporal differences in neuronal activity on the order of milliseconds (Kellman et al., 2003; Ogawa et al., 2000). Amplitude If the relationship between neural activity and vascular response is linear, the interpretation of neuroimaging studies is greatly simplified. In general, amplitude coupling appears to be largely linear (Li and Freeman, 2007; Logothetis et al., 2001), at least for stimulus durations larger than 4s, although various nonlinearities have been noted. For instance, neural responses below a certain amplitude may not evoke a CBF response (Sheth et al., 2004). Furthermore, neural responses may saturate, while vascular responses continue to increase (Sheth et al., 2004). In most investigations, however, there is a large range over which neural and vascular responses maintain a linear relationship. Coupling during abnormal brain states Neurovascular coupling may be altered during abnormal physiological states such as those found in the diseased or aging brain. These abnormal states could potentially alter the cellular and molecular mechanisms underlying the link between neural activity and blood flow. This would complicate the use of vascular responses to infer neural function in such conditions. Several examples of neurovascular coupling during abnormal brain states are given below. Disease and aging The relationship between neural activity and CBF can be altered in the diseased brain. These alterations can include changes in both the chemical mediators of neurovascular coupling and the dynamics of the vascular system itself (D'Esposito et al., 2003; Iadecola, 2004). For example, ionic channels on vascular smooth muscle can be altered in hypertension, diabetes, and Alzheimer s disease (D'Esposito et al., 2003; Iadecola, 2004), which could lead to abnormal patterns of vasodilation after neural activation. In addition, neural injury of many kinds is known to cause gliosis, e.g., a proliferation of astrocytes in the damaged area. Given the presumed function of astrocytes in linking neurotransmitter activity to vascular changes, neurovascular coupling in damaged tissue may differ from that of intact neural tissue. Finally, aging and vascular pathology can also change the vascular system itself, primarily through increasing tortuosity,

5 reactivity, or reducing elasticity of the blood vessels (D'Esposito et al., 2003). In these cases, vasodilation or constriction will be limited and may therefore disrupt the relationship between vascular and neural responses. Pharmacology Many drugs, such as nicorandil or diazoxide, are vasoactive, and can therefore cause large vascular responses with little or no change in neural activity. Hypercapnia, in which the concentration of CO 2 in the blood is increased, also leads to vasodilation. The sensitivity of the vascular system to various medications and chemicals is a major consideration for interpreting neuroimaging results in clinical populations. The issue is further complicated by the fact that a number of agents (such as CO 2 and caffeine) could have opposing effects on metabolism and CBF. Electrical stimulation Electrical stimulation of the brain, for example through microelectrodes, deep brain stimulation, or transcranial magnetic stimulation (TMS), causes a variety of cellular alterations that could alter neurovascular coupling. These include swelling of the cell body, increases in extracellular potassium, and alteration of local metabolite concentrations. However, simultaneous measurements of neural and vascular responses to stimulation demonstrate that normal neurovascular coupling is maintained after TMS application (Allen et al., 2007). Furthermore, BOLD fmri measurements of electrical microstimulation reveal activation in expected projection sites of the stimulated region (Tolias et al., 2005). These results suggest that the cellular effects of electrical stimulation do not substantially alter neurovascular coupling. The examples noted above highlight the important observation that CBF responses reflect a mixture of neuronal and vascular effects. In some cases, these factors are unrelated. Importantly, there are quantitative biophysical models of neurovascular coupling which can be used to estimate the relative contributions of these physiological parameters from measured BOLD and CBF data (Davis et al., 1998; Stephan et al., 2004; van Zijl et al., 1998; Zheng et al., 2002). These models permit an approach whereby the distinct contributions from metabolic and vascular processes can be estimated and used to disambiguate neuronal and vascular effects when interpreting fmri responses (Smith et al., 2002; Stefanovic et al., 2004; Uludag et al., 2004; Pasley et al., 2007). References Allen, E.A., Pasley, B.N., Duong, T., and Freeman, R.D. (2007). Transcranial magnetic stimulation elicits coupled neural and hemodynamic consequences. Science (New York, N.Y 317, Attwell, D., and Iadecola, C. (2002). The neural basis of functional brain imaging signals. Trends in neurosciences 25, D'Esposito, M., Deouell, L.Y., and Gazzaley, A. (2003). Alterations in the BOLD fmri signal with ageing and disease: a challenge for neuroimaging. Nature reviews 4, Davis, T.L., Kwong, K.K., Weisskoff, R.M., and Rosen, B.R. (1998). Calibrated functional MRI: mapping the dynamics of oxidative metabolism. Proceedings of the National Academy of Sciences of the United States of America 95, Duong, T.Q., Kim, D.S., Ugurbil, K., and Kim, S.G. (2001). Localized cerebral blood flow response at submillimeter columnar resolution. Proceedings of the National Academy of Sciences of the United States of America 98, Engel, S.A., Glover, G.H., Wandell, B.A. (1997). Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cerebral Cortex 7, Frostig, R.D., Lieke, E.E., Ts'o, D.Y., and Grinvald, A. (1990). Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. Proceedings of the National Academy of Sciences of the United States of America 87,

6 Gandhi, S.P., Heeger, D.J., and Boynton, G.M. (1999). Spatial attention affects brain activity in human primary visual cortex. Proceedings of the National Academy of Sciences of the United States of America 96, Grinvald, A., Slovin, H., and Vanzetta, I. (2000). Non-invasive visualization of cortical columns by fmri. Nature neuroscience 3, Hamel, E. (2004). Cholinergic modulation of the cortical microvascular bed. Progress in brain research 145, Harder, D.R., Alkayed, N.J., Lange, A.R., Gebremedhin, D., and Roman, R.J. (1998). Functional hyperemia in the brain: hypothesis for astrocyte-derived vasodilator metabolites. Stroke; a journal of cerebral circulation 29, Harel, N., Ugurbil, K., Uludag, K., and Yacoub, E. (2006). Frontiers of brain mapping using MRI. J Magn Reson Imaging 23, Iadecola, C. (2004). Neurovascular regulation in the normal brain and in Alzheimer's disease. Nature reviews 5, Kellman, P., Gelderen, P., de Zwart, J.A., and Duyn, J.H. (2003). Method for functional MRI mapping of nonlinear response. NeuroImage 19, Lauritzen, M. (2005). Reading vascular changes in brain imaging: is dendritic calcium the key? Nature reviews 6, Li, B., and Freeman, R.D. (2007). High-resolution neurometabolic coupling in the lateral geniculate nucleus. J Neurosci 27, Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., and Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fmri signal. Nature 412, Logothetis, N.K., and Wandell, B.A. (2004). Interpreting the BOLD signal. Annual review of physiology 66, Mathiesen, C., Caesar, K., Akgoren, N., and Lauritzen, M. (1998). Modification of activity-dependent increases of cerebral blood flow by excitatory synaptic activity and spikes in rat cerebellar cortex. The Journal of physiology 512 ( Pt 2), Moon, C.H., Fukuda, M., Park, S.H., and Kim, S.G. (2007). Neural interpretation of blood oxygenation level-dependent fmri maps at submillimeter columnar resolution. J Neurosci 27, Ogawa, S., Lee, T.M., Stepnoski, R., Chen, W., Zhu, X.H., and Ugurbil, K. (2000). An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. Proceedings of the National Academy of Sciences of the United States of America 97, Parkes, L.M., Schwarzbach, J.V., Bouts, A.A., Deckers, R.H., Pullens, P., Kerskens, C.M., Norris, D.G. (2005). Quantifying the spatial resolution of the gradient echo and spin echo BOLD response at 3 tesla. Magnetic Resonance in Medicine 54, Pasley, B.N., Inglis, B.A., and Freeman, R.D. (2007). Analysis of oxygen metabolism implies a neural origin for the negative BOLD response in human visual cortex. NeuroImage 36, Pawlik, G., Rackl, A., and Bing, R.J. (1981). Quantitative capillary topography and blood flow in the cerebral cortex of cats: an in vivo microscopic study. Brain research 208, Pellerin, L., and Magistretti, P.J. (2004). Neuroenergetics: calling upon astrocytes to satisfy hungry neurons. Neuroscientist 10, Raichle, M.E., and Mintun, M.A. (2006). Brain work and brain imaging. Annual review of neuroscience 29, Roy, C.S., and Sherrington, C.S. (1890). On the Regulation of the Blood-supply of the Brain. The Journal of physiology 11,

7 Sheth, S.A., Nemoto, M., Guiou, M., Walker, M., Pouratian, N., and Toga, A.W. (2004). Linear and nonlinear relationships between neuronal activity, oxygen metabolism, and hemodynamic responses. Neuron 42, Shmuel, A., Yacoub, E., Chaimow, D., Logothetis, N.K., and Ugurbil, K. (2007). Spatio-temporal point-spread function of fmri signal in human gray matter at 7 Tesla. NeuroImage 35, Smith, A.J., Blumenfeld, H., Behar, K.L., Rothman, D.L., Shulman, R.G., and Hyder, F. (2002). Cerebral energetics and spiking frequency: the neurophysiological basis of fmri. Proceedings of the National Academy of Sciences of the United States of America 99, Stefanovic, B., Warnking, J.M., and Pike, G.B. (2004). Hemodynamic and metabolic responses to neuronal inhibition. NeuroImage 22, Stephan, K.E., Harrison, L.M., Penny, W.D., and Friston, K.J. (2004). Biophysical models of fmri responses. Current opinion in neurobiology 14, Thompson, J.K., Peterson, M.R., and Freeman, R.D. (2003). Single-neuron activity and tissue oxygenation in the cerebral cortex. Science (New York, N.Y 299, Thompson, J.K., Peterson, M.R., and Freeman, R.D. (2005). Separate spatial scales determine neural activity-dependent changes in tissue oxygen within central visual pathways. J Neurosci 25, Tolias, A.S., Sultan, F., Augath, M., Oeltermann, A., Tehovnik, E.J., Schiller, P.H., and Logothetis, N.K. (2005). Mapping cortical activity elicited with electrical microstimulation using FMRI in the macaque. Neuron 48, Uludag, K., Dubowitz, D.J., Yoder, E.J., Restom, K., Liu, T.T., and Buxton, R.B. (2004). Coupling of cerebral blood flow and oxygen consumption during physiological activation and deactivation measured with fmri. NeuroImage 23, van Zijl, P.C., Eleff, S.M., Ulatowski, J.A., Oja, J.M., Ulug, A.M., Traystman, R.J., and Kauppinen, R.A. (1998). Quantitative assessment of blood flow, blood volume and blood oxygenation effects in functional magnetic resonance imaging. Nature medicine 4, Vanzetta, I., Slovin, H., Omer, D.B., and Grinvald, A. (2004). Columnar resolution of blood volume and oximetry functional maps in the behaving monkey; implications for FMRI. Neuron 42, Viswanathan, A., and Freeman, R.D. (2007). Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nature neuroscience 10, Yacoub, E., Shmuel, A., Logothetis, N., and Ugurbil, K. (2007). Robust detection of ocular dominance columns in humans using Hahn Spin Echo BOLD functional MRI at 7 Tesla. NeuroImage 37, Yoshor, D., Ghose, G.M., Bosking, W.H., Sun, P., and Maunsell, J.H. (2007). Spatial attention does not strongly modulate neuronal responses in early human visual cortex. J Neurosci 27, Zheng, Y., Martindale, J., Johnston, D., Jones, M., Berwick, J., and Mayhew, J. (2002). A model of the hemodynamic response and oxygen delivery to brain. NeuroImage 16, Zlokovic, B. and Apuzzo, M. (1998). Strategies to Circumvent Vascular Barriers of the Central Nervous System. Neurosurgery 43, Recommended reading Shulman, R. G. and Rothman, D. L. (2005) Brain energetics and neuronal activity. John Wiley and Sons Limited. Buxton, R. B. (2001) Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques. Cambridge University Press.

8 See also Brain, Neuron, Synapse, Action Potential, fmri, Functional Imaging Brian N. Pasley, Ralph D. Freeman (2008) Neurovascular coupling. Scholarpedia, 3(3):5340, (go to the first approved version) Created: 13 October 2007, reviewed: 6 March 2008, accepted: 11 March 2008 Invited by: Dr. Eugene M. Izhikevich, Editor-in-Chief of Scholarpedia, the free peer reviewed encyclopedia Action editor: Dr. Eugene M. Izhikevich, Editor-in-Chief of Scholarpedia, the free peer reviewed encyclopedia Reviewer C: Dr. Kamil Uludag, Max Planck Institute for Biological Cybernetics, Tübingen, Germany Retrieved from "" Categories: Neuroscience This page was last modified 17:39, 12 March Copyright (C) ISSN Patent pending. Served in sec.