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Review
. 2009 Mar;29(3):441-63.
doi: 10.1038/jcbfm.2008.134. Epub 2008 Nov 12.

Metabolic and hemodynamic events after changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings

Silvia Mangia  1 , Federico Giove, Ivan Tkác, Nikos K Logothetis, Pierre-Gilles Henry, Cheryl A Olman, Bruno Maraviglia, Francesco Di Salle, Kâmil Uğurbil
Affiliations
Review

Metabolic and hemodynamic events after changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings

Silvia Mangia et al. J Cereb Blood Flow Metab. 2009 Mar.

Abstract

Unraveling the energy metabolism and the hemodynamic outcomes of excitatory and inhibitory neuronal activity is critical not only for our basic understanding of overall brain function, but also for the understanding of many brain disorders. Methodologies of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) are powerful tools for the noninvasive investigation of brain metabolism and physiology. However, the temporal and spatial resolution of in vivo MRS and MRI is not suitable to provide direct evidence for hypotheses that involve metabolic compartmentalization between different cell types, or to untangle the complex neuronal microcircuitry, which results in changes of electrical activity. This review aims at describing how the current models of brain metabolism, mainly built on the basis of in vitro evidence, relate to experimental findings recently obtained in vivo by (1)H MRS, (13)C MRS, and MRI. The hypotheses related to the role of different metabolic substrates, the metabolic neuron-glia interactions, along with the available theoretical predictions of the energy budget of neurotransmission will be discussed. In addition, the cellular and network mechanisms that characterize different types of increased and suppressed neuronal activity will be considered within the sensitivity-constraints of MRS and MRI.

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Figures

Figure 1
Figure 1
Schematics of the metabolic neuron-glia coupling hypothesis as suggested by Pellerin and Magistretti (1994). During depolarization, glutamate (Glu) is released in the intersynaptic space, and subsequently recycled by the Glu-Gln cycle. The transport of glutamate into astrocytes occurs with a concomitant inflow of Na+, leading to activation of Na+/K+-ATPase. The pump activates astrocytic glycolysis, which results in lactate (Lac) production. Sibson et al (1998) further emphasized that astrocytic glycolysis is needed to quickly satisfy the energy demands of the neurotransmitter-recycling. Lactate, once released, can be taken up by neurons and then converted to pyruvate (Pyr) before entering into the TCA cycle. Even if direct neuronal uptake of glucose is not excluded by the ANLSH, lactate is considered to be the major fuel of the neuronal oxidative metabolism linked to neurotransmission.
Figure 2
Figure 2
Schematics of the redox-switch/redox-coupling hypothesis as suggested by Cerdan et al (2006). The model emphasizes the bi-directional shuttling of lactate and pyruvate between neurons and astrocytes. Glucose enters in both neurons and astrocytes. Monocarboxylate transporters regulate the flux of lactate and pyruvate from neurons and astrocytes into the extra-cellular space, and vice-versa from the extra-cellular space into neurons and astrocytes. Monocarboxylate concentrations are related to the cytosolic cellular redox state (NAD+/NADH ratio), and their release into the extracellular space act as trans-cellular couplers of the cellular cytosolic redox states of neurons and astrocytes. Whenever the cellular redox state is reduced in one cell type, glycolysis of that cell type is inhibited favoring the oxidation of extracellular lactate. The cellular cytosolic redox state is also regulated by the malate-aspartate shuttle. A description of the malate-aspartate shuttle is provided in Figure 6.
Figure 3
Figure 3
Schematics of the hypothesis about lactate as suggested by Schurr (2006). Cytosolic glycolysis leads to lactate formation, which regenerates NAD+. Lactate is then transported into mitochondria where it is converted to pyruvate to enter the TCA cycle.
Figure 4
Figure 4
Changes of metabolite concentrations during sustained visual stimulation, as revealed by the analysis of the difference (C and D) between spectra acquired during rest (A) and stimulation (B). D same as C, but the spectrum acquired during stimulation was line-broadened by 0.4 Hz in order to match the linewidth of the spectrum acquired at rest (elimination of the BOLD effect on metabolites). (E-L): LCModel fit (Provencher, 1993) of the difference spectrum D. Spectra were summed from different subjects (N = 12). Minute but significant changes in metabolite concentrations (∼0.2 μmol/g) were observed for lactate, aspartate and glutamate. In particular, [Lac] increased by 23% ± 5% (p < 0.0005), [Glu] increased by 3% ± 1% (p < 0.01), whereas [Asp] decreased by 15% ± 6% (p < 0.05). Finally, [Glc] showed a tendency to decrease during activation periods. From Mangia et al (2007a).
Figure 5
Figure 5
Schematics of metabolic events following increased neuronal activity as suggested by Mangia et al (2007a). Increased CMRglc during activation leads to slight decreased [Glc]. The small augment of the steady-state level of [Lac] results from an increased rate of glycolysis and TCA cycle that is accompanied by an increased steady-state [Pyr]. Lactate efflux from the brain to the plasma occurs since plasma and brain [Lac] are supposed to be different. An increased rate of the malate-aspartate shuttle is expected for increase oxidative metabolism (details about the malate-aspartate shuttle are provided in Figure 6).
Figure 6
Figure 6
Schematics of the malate-aspartate shuttle (MAS). The MAS transfers reducing equivalents from NADH in the cytosol to the mitochondria by relying on the following steps: electron transfer from NADH to oxaloacetate in the cytosol forming malate (replenishing NAD+); transport of malate into mitochondria in exchange for α-ketoglutarate; conversion of malate to oxaloacetate, with formation of NADH; transamination of oxaloacetate to aspartate in conjunction with the conversion of glutamate to α-ketoglutarate; transport of aspartate from the mitochondria to the cytosol in cotransport of cytosolic glutamate to the mitochondria. Grey arrows indicate the changes in [Glu] and [Asp] observed during stimulation (Mangia et al, 2007a); such changes were interpreted in agreement with an increased flux through the MAS expected for increased oxidative metabolism.
Figure 7
Figure 7
Two estimations of energy consumption of the human brain cortex. For uniformity, the housekeeping component (intracellular transport and signaling, vegetative metabolism) has been set to 45% (in black). Postsynaptic: postsynaptic ion fluxes induced by glutamate. Spiking: pumping out of the Na+ influx during action potentials. Neurotransmitters recycling: actions (mainly astrocytic) related to glutamate recycling. Presynaptic: extrusion of presynaptic Ca2+ influx related to Glu extrusion, exocytosis of neurotransmitter vescicles. The total values of the components related to neurotransmission (light gray) and to resting potential (dark gray) are shown. Values are rounded. Above: adapted from the estimation by Attwell and Iadecola (2002). Below: adapted from the estimation by Lennie (2003).
Figure 8
Figure 8
Schematics representing different pathways which result in neuronal firing suppression (Tepper et al, 2004; Timofeev et al, 2001). Triangles represent pyramidal cells, blue squares represent glutamatergic interneurons, pink squares represent GABAergic interneurons; size corresponds to proportion of the cortical population. Green connecting lines represent excitatory connections, red lines indicate inhibitory inputs; line thickness indicates either firing rates (connecting lines) or strength of excitatory postsynaptic potentials (EPSPs) in cells. A) Direct inhibition: IPSPs from inhibitory interneurons compete on pyramidal cells with the EPSPs coming from excitatory interneurons and from the thalamus. B) Disfacilitation: inhibitory interneurons primarily act on the excitatory interneurons, which reduce their firing on pyramidal cells. C) Feedback inhibition: collaterals of pyramidal cells excite inhibitory interneurons, which conversely inhibit excitatory interneurons. D) Inhibition on inhibition: inhibitory neurons inhibit other inhibitory neurons, overall reducing inhibition on pyramidal cells (Tsodyks et al, 1997).
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