|
| |
|
|
|
|
||||
| Home Help Feedback Subscriptions Archive Search Table of Contents | ||||
First published online May 26, 2006
Journal of Experimental Biology 209, 2304-2311 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.02208
Review Article: Phenotypic Plasticity of the Brain |
Neuron–glia metabolic coupling and plasticity
Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland and Centre de Neurosciences Psychiatriques, CHUV, Departement de Psychiatrie, Site de Cery, CH1008 Prilly/Lausanne, Switzerland
e-mail: pierre.magistretti{at}unil.ch
Accepted 14 March 2006
Summary
The coupling between synaptic activity and glucose utilization (neurometabolic coupling) is a central physiological principle of brain function that has provided the basis for 2-deoxyglucose-based functional imaging with positron emission tomography (PET). Astrocytes play a central role in neurometabolic coupling, and the basic mechanism involves glutamate-stimulated aerobic glycolysis; the sodium-coupled reuptake of glutamate by astrocytes and the ensuing activation of the Na-K-ATPase triggers glucose uptake and processing via glycolysis, resulting in the release of lactate from astrocytes. Lactate can then contribute to the activity-dependent fuelling of the neuronal energy demands associated with synaptic transmission. An operational model, the `astrocyte–neuron lactate shuttle', is supported experimentally by a large body of evidence, which provides a molecular and cellular basis for interpreting data obtained from functional brain imaging studies. In addition, this neuron–glia metabolic coupling undergoes plastic adaptations in parallel with adaptive mechanisms that characterize synaptic plasticity. Thus, distinct subregions of the hippocampus are metabolically active at different time points during spatial learning tasks, suggesting that a type of metabolic plasticity, involving by definition neuron–glia coupling, occurs during learning. In addition, marked variations in the expression of genes involved in glial glycogen metabolism are observed during the sleep–wake cycle, with in particular a marked induction of expression of the gene encoding for protein targeting to glycogen (PTG) following sleep deprivation. These data suggest that glial metabolic plasticity is likely to be concomitant with synaptic plasticity.
Key words: neuro-metabolic coupling, plasticity, astrocyte, glia, sleep–wake cycle
Brain energy metabolism
Background
The energy requirements of the brain are amazingly high; indeed, while
representing only 2% of the body mass, its oxygen and glucose utilization
account for approximately 20% of those of the whole organism, almost ten times
more than those predicted on a mass basis
(Magistretti, 1999
). A similar
mismatch is observed for blood flow destined to the brain, which represents
over 10% of cardiac output. In addition to these quantitative aspects, brain
metabolism has other distinctive features, in particular its regional
variability and the nature of its cellular determinants. At the macroscopic
level, one regional variability is manifested by the difference in energy
metabolism between grey and white matter
(Clarke and Sokoloff, 1994).
But a much finer feature of brain metabolism is that its regional variability
is strongly determined by the ever-changing spatially and temporally specified
levels of synaptic activity. Thus one of the founding principles of brain
physiology is that metabolism and flow are tightly coupled with neuronal
activity; this fact has been appreciated since the turn of the 19th century
when Sherrington proposed that "...the brain possess an intrinsic
mechanism by which its vascular supply can be varied locally in correspondence
with local variations of functional activity"
(Roy and Sherrington,
1890).
The pioneering work of Louis Sokoloff and his colleagues in the 1970s and
1980s using the 2-deoxyglucose (2-DG) autoradiographic technique and its
in vivo extension to humans with 18-fluoro-DG imaging of glucose
utilization by positron emission tomography (PET)
(Sokoloff et al., 1977), has
clearly demonstrated a similar coupling between neuronal activity and glucose
metabolism. Indeed, this tight relationship between neuronal activity with
blood flow and metabolism has provided the basis for the functional brain
imaging techniques that are now widely in use by cognitive neuroscientists and
clinicians (Mazziotta et al.,
2000). Thus local changes in glucose utilization, blood flow and
oxygen utilization for PET and, mostly, variations in the level of hemoglobin
oxygenation for functional magnetic resonance imaging (fMRI) during
well-defined behavioral tasks or mental states, are taken as indicators of the
activity of specific neuronal pathways, allowing a novel and extremely fertile
appraisal of the neural substrates of brain functions, in particular those at
a higher level.
While being very appropriate and widely used for functional brain mapping,
the cellular mechanisms that are at the basis of the coupling between neuronal
activity and metabolism, and hence at the basis of the signals detected by
functional imaging techniques, have only recently begun to be unraveled. Our
group has contributed over the last ten years to the understanding of such
cellular and molecular mechanisms
(Pellerin and Magistretti,
1994).
Briefly we have identified a key role of astrocytes in coupling synaptic
activity to glucose utilization, through molecular mechanisms that involve the
sequential intervention of astrocyte-specific glutamate transporters and
sodium-potassium ATPase, activation of glycolysis in astrocytes and
monocarboxylate transporter-mediated exchange of lactate from astrocytes to
neurons (Magistretti and Pellerin,
1999; Magistretti et al.,
1999). The basic mechanism in neurometabolic coupling is astrocyte
glutamate-stimulated aerobic glycolysis, such that the sodium-coupled
re-uptake of glutamate by astrocytes and the ensuing activation of the
Na-K-ATPase triggers glucose uptake and its glycolytic processing, resulting
in the release of lactate from astrocytes. Lactate can then contribute to the
activity-dependent fuelling of the neuronal energy demands associated with
synaptic transmission (Magistretti and
Pellerin, 1999). A large body of experimental evidence (for recent
reviews, see Pellerin and Magistretti,
2003; Pellerin and
Magistretti, 2004) led to the proposal of an operational model,
the `astrocyte–neuron lactate shuttle'
(Bittar et al., 1996;
Pellerin et al., 1998).
Recently a series of results obtained by independent laboratories provided
further support for this model (Kasischke
et al., 2004; Loaiza et al.,
2003; Serres et al.,
2004). This body of evidence provides a molecular and cellular
basis for interpreting data obtained with functional brain imaging studies.
Thus the picture that emerges is that glucose metabolism is coupled to
glutamate-mediated neuronal activity via molecular mechanisms that
are largely, although possibly not exclusively, based on a coupling role of
astrocytes (Fig. 1). A similar
role of astrocytes in coupling neuronal activity to blood flow has also been
recently suggested (Mulligan and MacVicar,
2004; Takano et al.,
2006; Zonta et al.,
2003).
|
Activation vs baseline
The emphasis on glutamate as a determining coupling signal deserves some
discussion. First it should be remembered that over 90% of synapses, at least
in the cerebral cortex, use glutamate as their neurotransmitter
(Braitenberg and Schuz, 1998).
Second, the physiological studies concerned with neurometabolic coupling on
one hand, and the main focus of functional imaging studies on the other, are
centered around what we generally refer to as `activation'. The idea is that a
given behavioral task will involve the activation of a specific neuronal
pathway resulting in glutamate release and in the temporally and spatially
coupled metabolic and vascular responses that provide the signal detected with
functional imaging techniques. The question is: `How large is the increase
in the metabolic or vascular response during "activation", when
compared to "baseline" activity?'. The answer is:
`Surprisingly small!'. Indeed, there is a general consensus that
during a given behavioral task, blood flow and glucose utilization increase by
an average of 10% over baseline, at most by 20%, depending on the paradigm
used, and oxygen consumption even less, possibly remaining under 5% increase
(Raichle, 2003). It is worth
noting here that these data indicate a partial uncoupling of glucose
utilization and oxygen consumption, suggesting that during activation, the
brain resorts to a transient glycolytic processing of glucose, a consideration
consistent with the proposed cellular mechanisms of neurometabolic coupling
pointing at a central role of glutamate-stimulated glycolysis in astrocytes
(Fox et al., 1988;
Magistretti and Pellerin,
1999).
The next questions then are: `Why is basal brain activity so high and,
more importantly from a physiological point of view, what kind of neural
activity is occurring during baseline?' The question about the nature of
the mechanisms underlying baseline brain activity has attracted considerable
attention recently both from the physiological angle and from the neuroimaging
perspective, as the intuition is that neurobiologically important information
may have been overlooked by almost exclusively focusing on activation
(Gusnard and Raichle, 2001).
Indeed, baseline activity not only represents 90% of brain metabolism, but
also appears to be dynamically regulated, varying under a variety of
physiological and pathological conditions
(Raichle, 2003;
Reiman et al., 1996;
Shulman et al., 1997).
Before briefly reviewing the instances during which baseline activity
varies, it is important to address the issue of how baseline is defined. This
issue has been tackled most lucidly by Marc Raichle in a series of
illuminating articles (Raichle,
2003; Raichle and Gusnard,
2002; Raichle et al.,
2001). As noted above, during activation, oxygen consumption does
not increase commensurately with glucose utilization and blood flow
(Fox and Raichle, 1986;
Fox et al., 1988). Thus,
during activation, oxygen delivery to the activated area increases (as a
consequence of the increased arterial blood flow) while oxygen utilization
does so only marginally. This implies that the fractional oxygen extraction is
lower, meaning that what is referred to as the `oxygen extraction fraction
(OEF)', will decrease during activation. OEF has turned out to be a very
useful variable to define baseline: it is very stable at rest because there is
an excellent match between blood flow and oxygen utilization, and activation
or deactivation (to be discussed below) of a given area can be defined in
terms of its OEF. Thus an alternative way to define activation is an instance
where and when an area shows a transient decrease in OEF in comparison to the
mean OEF of the brain (Gusnard and
Raichle, 2001; Raichle et al.,
2001). Following this line of reasoning, an increase in OEF would
imply a decrease in the activity, or deactivation, of a given area.
Thus the fact that a baseline can be defined, vastly increases the dynamic
range of brain metabolism as an informative correlate of neural activity.
Interestingly, a thorough analysis of a large number of PET studies has shown
that decreases in activity could be observed in certain brain areas, notably
postero-medial and -lateral cortices and ventral- and dorso-medial prefrontal
cortices, during visual, auditory and motor tasks (for a review, see
Gusnard and Raichle, 2001).
Thus, this notion of baseline implies that during a particular task not only
activation is observed (e.g. activation of primary visual cortex during visual
stimulation) but also deactivation in certain areas. These data suggest that
there are areas that are activated during goal-directed protocols
(sensory-motor tasks for example), while others are actively engaged during
the `resting state'. The time constants of both types of activities are
markedly different: indeed, while the typical activations are transient, the
functional activity embedded in the baseline is sustained, suggesting the
involvement of mechanisms related to processing of the information acquired
during the transient activations. Available PET data indicate that these
sustained activities may cease during task-dependent transient activations and
resume during the intervening periods, thus providing a strong determinant of
baseline metabolism (Gusnard and Raichle,
2001).
To summarize this important new dimension of brain metabolism and related imaging studies it appears that two modes with different temporal dimensions operate:
).
Recognizing the potential significance of this task-independent activity
mode obviously raises questions, for example: `What is its function and
which are its cellular and molecular determinants?'. For the second
question, potential answers are at hand, based on the current knowledge that
we have about the cellular and molecular mechanisms of neurometabolic
coupling. Indeed, glucose utilization, which is one of the variables measured
in most of the PET studies that have resulted in the recognition of the
sustained baseline activity mode, is largely determined by glutamate-mediated
neurotransmission (Magistretti and
Pellerin, 1999). This is not surprising, since, as noted earlier,
90% of synapses are glutamatergic. A bottom-up analysis of the energy budget
of the cerebral cortex has indicated that most energy is devoted to action
potential propagation and restoration of ion gradients at excitatory synapses
(Attwell and Laughlin, 2001).
In this analysis, only 15% of total brain energy consumption can be accounted
for by maintenance of resting potential and glial cell activity. Similar
results have been obtained using a radically different approach, namely
magnetic resonance spectroscopy determination of glucose oxidation and
glutamate cycling at different levels of anesthesia and the corresponding
levels of cortical electrical activity
(Hyder et al., 2002;
Sibson et al., 1998). Also in
this analysis, over 80% of glucose utilization was linearly correlated with
glutamate-mediated neurotransmission. Thus, one is compelled to conclude from
these data that synaptic activity at glutamatergic circuits operates during
the task-independent baseline mode of activity reflected in the large glucose
utilization that characterizes this state. The mechanisms that couple
glutamate activity and glucose utilization are now largely known involving, as
noted earlier, a central role of astrocytes
(Magistretti et al., 1999;
Pellerin and Magistretti,
2003).
In contrast, the answer to the first part of the question namely `What
is its (baseline activity) function..', is much more elusive. One could
suggest that in general terms it may correspond to a `post-processing of
information' mode. Terms that have been used include `stimulus-independent
thoughts' (Teasdale et al.,
1995), `stream of consciousness'
(Andreasen et al., 1995),
`optimization of cognitive and behavioral serial programs'
(Ingvar, 1985) (for a review,
see Gusnard and Raichle,
2001). Another formulation could be that this baseline mode
reflects, at least in part, ongoing processes of synaptic plasticity. These
processes have been the object of intense attention over the recent years
(Bear, 2003;
Grossman et al., 2002). A
number of neurotransmitter-regulated mechanisms have been identified and have
provided new insights into the cellular and molecular mechanisms of learning
and memory (Malenka, 2003). In
other words, it is conceivable that a sustained task-independent activity that
is correlated with a high basal metabolic activity, does not simply correspond
to `neuronal noise' but actually reflects synaptic plasticity processes that
are related to the post-processing of incoming information.
Synaptic plasticity and metabolic plasticity
Given the tight coupling that exists between synaptic activity and energy
metabolism, it is likely that the processes that underlie synaptic plasticity
may also be reflected at the energy metabolism level, resulting in correlated
metabolic adaptations, which could be defined as `metabolic plasticity'. The
notion of metabolic plasticity has indeed found experimental validation. Thus
evidence has been obtained in a restricted number of experimental paradigms
for activity-dependent long-term metabolic adaptations
(Barrett et al., 2003;
Gonzalez-Lima and Garrosa,
1991; Hyden et al.,
2000; Maviel et al.,
2004; Room et al.,
1989; Welker et al.,
1992; Zhang and Wong-Riley,
1999). In this context, over the years our laboratory has gathered
evidence that plasticity of energy metabolism is regulated by a restricted set
of neurotransmitters. Such adaptations in metabolic pathways are mediated by
transcriptional mechanisms that modulate the expression of genes involved in
energy metabolism (Allaman et al.,
2003; Allaman et al.,
2000; Allaman et al.,
2004; Debernardi et al.,
2003; Pierre et al.,
2003; Sorg and Magistretti,
1992). Most of the data on metabolic plasticity that our
laboratory has collected relate to in vitro analyses at the molecular
level. We have recently begun to explore the mechanisms of metabolic
plasticity in a well-established paradigm of learning and memory, and during
the sleep–wake cycle, two conditions for which we have preliminary
evidence of such metabolic plasticity. Particular, although not exclusive,
attention has been focused on the possible correlation between
task-independent activity and metabolic plasticity.
|
A main line of research of our laboratory has been to explore the role of
certain neurotransmitters on the metabolic fluxes of glucose, with particular
reference to the regulation of glycogen metabolism in astrocytes. This line of
research constituted our initial interest on long-term metabolic regulation,
or in other words, metabolic plasticity. Thus, we have identified a number of
neuroactive molecules, in particular adenosine, noradrenaline and certain
cytokines, regulating the expression of key genes involved in glycogen
metabolism (Fig. 2), in
particular protein targeting to glycogen (PTG)
(Allaman et al., 2000;
Allaman et al., 2004;
Cardinaux et al., 2000). Thus
exposure to the above-mentioned transmitters results in the
cyclic-AMP-dependent induction of expression of the transcription factor
C/EBP, of glycogen synthase and of PTG. In search for an in vivo
physiological condition in which a certain degree of metabolic plasticity
could be identified, we explored the level of expression of the key enzyme in
glycogen metabolism, PTG. We have identified a circadian rhythm for the
expression of PTG mRNA and a reversible induction of its expression following
sleep deprivation (Petit et al.,
2002).
Brain energy metabolism during sleep–waking cycle
A proposed function for sleep is brain energy restoration. Numerous studies
using glucose uptake measurements in mouse, rat and cat with the
[14C]2-deoxyglucose technique, have reported that energy metabolism
exhibits a decrease during slow-wave sleep (SWS) and an increase during
paradoxical sleep (PS), depending on the brain areas (for a review, see
Franzini, 1992). It has also
been reported that the synthesis of glycogen is increased during SWS with a
50–70% rise when compared to the preceding waking period
(Karnovsky et al., 1983).
Taken together these data suggest that SWS might be a period of energy saving
while PS has a relatively high energy cost. More recently, investigations on
brain gene expression during sleep and wakefulness in rat indicate that
different genes encoding proteins involved in energy metabolism are modulated
by sleep deprivation (Tononi and Cirelli,
2001). In particular, genes encoding subunit 1 of the cytochrome
c oxidase and subunit 2 of the NADH dehydrogenase, which play a key
role in oxidative metabolism, are induced by a short period of total sleep
deprivation (TSD) during 3 h. After 8 h of sleep deprivation, other genes
related to energy metabolism such as glucose transporter type 1 are also
induced. These data suggest that a plasticity in the expression pattern of
energy-metabolism genes can be revealed by manipulations affecting the
sleep–wake cycle.
Glycogen metabolism and homeostatic regulation of sleep
The homeostatic regulation of sleep fits the brain energy restoration
hypothesis, as suggested in its most recent formulation
(Benington and Heller, 1995).
According to this hypothesis, adenosine, a neurotransmitter with inhibitory
properties, may be a link between sleep regulation and energy metabolism.
Adenosine concentrations, which partly derive from the ATP degradation, rise
during the spontaneous or forced-waking period and decrease following
subsequent sleep period (Huston et al.,
1996; Porkka-Heiskanen et al.,
1997). The second aspect of the hypothesis is that sleep, and in
particular SWS, might serve to replenish glycogen stores depleted during the
waking period. Recent experimental results have failed to verify or falsify
this hypothesis, since both (small) decreases and increases in brain glycogen
levels have been observed following sleep deprivation
(Franken et al., 2003;
Gip et al., 2002;
Kong et al., 2002).
In view of these in vivo and in vitro results and in
consideration of the long-standing interest of our laboratory in the
regulation of glycogen metabolism, we have explored the possibility that
regulation of the expression of genes encoding enzymes involved in glycogen
metabolism could take place during the sleep–waking cycle
(Fig. 3). In order to test this
hypothesis, we measured the variations of mRNA levels coding for three such
enzymes, namely protein targeting to glycogen (PTG), glycogen synthase (GS)
and glycogen phosphorylase (Gphos), throughout the sleep–waking cycle
and at the end of 6 h of sleep deprivation (SD). In addition, in order to
determine the functional impact of the regulation of these mRNAs on glycogen
synthesis, we assayed the activity of GS in the cortex of mice after 6 h SD as
well as 3 h later when animals had recovered sleep. The results of this study
indicate that prolonged waking induces a twofold increase of PTG mRNA.
Moreover, a parallel increase in GS activity suggests a functional role of the
increase in PTG. Indeed, the induction of PTG during waking may set cortical
glycogen metabolism in a `glycogen-synthesis mode' and possibly set the
appropriate metabolic conditions for sleep induction
(Petit et al., 2002).
|
It is now widely recognized that subtle mechanisms of neuronal plasticity,
resulting in functional and structural modifications at the synaptic level,
represent the cellular and molecular correlates of the processes of learning
and memory. A variety of behavioural paradigms are available to explore the
mechanisms of learning and memory in laboratory animals. Spatial learning is
one of the best established of such behavioural paradigms; in addition the
role of a particular brain area, the hippocampus in such spatial learning has
been extensively characterized. Indeed, the hippocampus is involved in coding,
consolidation as well as retrieval of spatial memory in rodents
(O'Keefe and Nadel, 1978;
Riedel et al., 1999). What is
less clear, however, is whether such tasks are accomplished by the hippocampus
as a uniform computational unit, or whether information processing occurs in
discrete steps distributed throughout distinct subregions with evolving
temporal patterns. Previous evidence from electrophysiology, partial
hippocampal lesion studies and, more recently, human brain imaging have
suggested the existence of functional specialization within the hippocampus
(Jung et al., 1994;
Lepage et al., 1998;
Moser and Moser, 1998;
Poucet and Buhot, 1994). In
addition, studies in rodents have shown that markers of activity (in
particular metabolic markers such as 2-deoxyglucose 2-DG), evolve spatially
and temporally over the different phases of the learning paradigm, including
during recall (Bontempi et al.,
1999).
We have mapped glucose utilization at different phases of a
well-established spatial learning task, the eight-arm radial maze. The results
obtained obtained indicate that indeed, the metabolic demand during the
various learning phases evolve spatially and temporally in the areas engaged
by the task, in particular in the hippocampus. In keeping with the initial
hypothesis, distinct patterns of metabolic activity were observed during the
learning and recall phases. Analysis of the metabolic activity in the
hippocampus revealed different patterns over the rostro-caudal axis in its
three major subregions, the CA1, CA3 and dentate gyrus (DG). Thus, as learning
proceeded, more areas of the CA1 and CA3 became engaged metabolically, moving
from the posterior and intermediate parts toward the anterior level. In
addition, during recall, increased metabolic activity could be observed only
in the anterior parts of the dentate gyrus
(Fig. 4B–D)
(Ros et al., 2006). This set
of data is in keeping with the notion that metabolic adaptations (plasticity)
are occurring as a correlate of learning and recall.
|
Significance
Understanding the cellular and molecular determinants of brain energy metabolism is extremely relevant, not only to unravel basic mechanisms of brain physiology, but because it may also contribute to the understanding of pathophysiological mechanisms of a variety of neurological and psychiatric disorders. In addition, the main techniques of functional brain imaging such as PET and fMRI rely upon signals that are directly related to the coupling between neuronal activity and metabolic responses. A new dimension of brain metabolism, namely its relation to mechanisms of neuronal plasticity, is now being explored. We believe that this approach may also shed new light on the determinants of one of the most striking features of the mammalian brain, namely its capacity to adapt to the environment and to be determined by experience, through the mechanisms of neural plasticity to which metabolic aspects, at least in part based in glial cells, may participate.
List of abbreviations
Acknowledgments
This work was supported by an FNRS grant to the author.
References
Allaman, I., Pellerin, L. and Magistretti, P. J. (2000). Protein targeting to glycogen mRNA expression is stimulated by noradrenaline in mouse cortical astrocytes. Glia 30,382 -391.[CrossRef][Medline]
Allaman, I., Lengacher, S., Magistretti, P. J. and Pellerin, L. (2003). A2B receptor activation promotes glycogen synthesis in astrocytes through modulation of gene expression. Am J. Physiol. 284,C696 -C704.
Allaman, I., Pellerin, L. and Magistretti, P. J. (2004). Glucocorticoids modulate neurotransmitter-induced glycogen metabolism in cultured cortical astrocytes. J. Neurochem. 88,900 -908.[CrossRef][Medline]
Andreasen, N. C., O'Leary, D. S., Cizadlo, T., Arndt, S., Rezai,
K., Watkins, G. L., Ponto, L. L. and Hichwa, R. D.
(1995). Remembering the past: two facets of episodic memory
explored with positron emission tomography. Am. J.
Psychiatry 152,1576
-1585.
Attwell, D. and Laughlin, S. B. (2001). An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21,1133 -1145.[Medline]
Barrett, D., Shumake, J., Jones, D. and Gonzalez-Lima, F.
(2003). Metabolic mapping of mouse brain activity after
extinction of a conditioned emotional response. J.
Neurosci. 23,5740
-5749.
Bear, M. F. (2003). Bidirectional synaptic plasticity: from theory to reality. Philos. Trans. R. Soc. Lond. B Biol. Sci. 358,649 -655.[CrossRef][Medline]
Benington, J. H. and Heller, H. C. (1995). Restoration of brain energy metabolism as the function of sleep. Prog. Neurobiol. 45,347 -360.[CrossRef][Medline]
Bittar, P. G., Charnay, Y., Pellerin, L., Bouras, C. and Magistretti, P. J. (1996). Selective distribution of lactate dehydrogenase isoenzymes in neurons and astrocytes of human brain. J. Cereb. Blood Flow Metab. 16,1079 -1089.[CrossRef][Medline]
Bontempi, B., Laurent-Demir, C., Destrade, C. and Jaffard, R. (1999). Time-dependent reorganization of brain circuitry underlying long-term memory storage. Nature 400,671 -675.[CrossRef][Medline]
Braitenberg, V. and Schuz, A. (1998). Cortex: Statistics and Geometry of Neuronal Connectivity. New York: Springer.
Cardinaux, J.-R., Allaman, I. and Magistretti, P. J. (2000). Pro-inflammatory cytokines induce the transcription factors C/EBP b and C/EBP d in primary astrocytes. Glia 29,91 -97.[CrossRef][Medline]
Clarke, D. D. and Sokoloff, L. (1994). Circulation and energy metabolism of the brain. In Basic Neurochemistry (ed. G. J. Siegel, B. W. Agranoff, R. W. Albers and P. B. Molinoff), pp. 645-680. New York: Raven Press.
Debernardi, R., Pierre, K., Lengacher, S., Magistretti, P. J. and Pellerin, L. (2003). Cell-specific expression pattern of monocarboxylate transporters in astrocytes and neurons observed in different mouse brain cortical cell cultures. J. Neurosci. Res. 73,141 -155.[CrossRef][Medline]
Fox, P. T. and Raichle, M. E. (1986). Focal
physiological uncoupling of cerebral blood flow and oxidative metabolism
during somatosensory stimulation in human subjects. Proc. Natl.
Acad. Sci. USA 83,1140
-1144.
Fox, P. T., Raichle, M. E., Mintun, M. A. and Dence, C.
(1988). Nonoxidative glucose consumption during focal physiologic
neural activity. Science
241,462
-464.
Franken, P., Gip, P., Hagiwara, G., Ruby, N. F. and Heller, H. C. (2003). Changes in brain glycogen after sleep deprivation vary with genotype. Am. J. Physiol. 285,R413 -R419.
Franzini, C. (1992). Brain metabolism and blood flow during sleep. J. Sleep Res. 1, 3-16.[Medline]
Gip, P., Hagiwara, G., Ruby, N. F. and Heller, H. C. (2002). Sleep deprivation decreases glycogen in the cerebellum but not in the cortex of young rats. Am. J. Physiol. 283,R54 -R59.
Gonzalez-Lima, F. and Garrosa, M. (1991). Quantitative histochemistry of cytochrome oxidase in rat brain. Neurosci. Lett. 123,251 -253.[CrossRef][Medline]
Grossman, A. W., Churchill, J. D., Bates, K. E., Kleim, J. A. and Greenough, W. T. (2002). A brain adaptation view of plasticity: is synaptic plasticity an overly limited concept? Prog. Brain Res. 138,91 -108.[Medline]
Gusnard, D. A. and Raichle, M. E. (2001). Searching for a baseline: functional imaging and the resting human brain. Nat. Rev. Neurosci. 2,685 -694.[CrossRef][Medline]
Huston, J. P., Haas, H. L., Boix, F., Pfister, M., Decking, U., Schrader, J. and Schwarting, R. K. W. (1996). Extracellular adenosine levels in neostriatum and hippocampus during rest and activity periods of rats. Neuroscience 73, 99-107.[CrossRef][Medline]
Hyden, H., Rapallino, M. V. and Cupello, A. (2000). Unraveling of important neurobiological mechanisms by the use of pure, fully differentiated neurons obtained from adult animals. Prog. Neurobiol. 60,471 -499.[CrossRef][Medline]
Hyder, F., Rothman, D. L. and Shulman, R. G.
(2002). Total neuroenergetics support localized brain activity:
implications for the interpretation of fMRI. Proc. Natl. Acad. Sci.
USA 99,10771
-10776.
Ingvar, D. H. (1985). `Memory of the future': an essay on the temporal organization of conscious awareness. Hum. Neurobiol. 4,127 -136.[Medline]
Jung, M. W., Wiener, S. I. and McNaughton, B. L. (1994). Comparison of spatial firing characteristics of units in dorsal and ventral hippocampus of the rat. J. Neurosci. 14,7347 -7356.[Abstract]
Karnovsky, M. L., Reich, P., Anchors, J. M. and Burrows, B. L. (1983). Changes in brain glycogen during slow-wave sleep in the rat. J. Neurochem. 41,1498 -1501.[CrossRef][Medline]
Kasischke, K. A., Vishwasrao, H. D., Fisher, P. J., Zipfel, W.
R. and Webb, W. W. (2004). Neural activity triggers
neuronal oxidative metabolism followed by astrocytic glycolysis.
Science 305,99
-103.
Kong, J., Shepel, P. N., Holden, C. P., Mackiewicz, M., Pack, A.
I. and Geiger, J. D. (2002). Brain glycogen decreases
with increased periods of wakefulness: implications for homeostatic drive to
sleep. J. Neurosci. 22,5581
-5587.
Lepage, M., Habib, R. and Tulving, E. (1998). Hippocampal PET activations of memory encoding and retrieval: the HIPER model. Hippocampus 8,313 -322.[CrossRef][Medline]
Loaiza, A., Porras, O. H. and Barros, L. F.
(2003). Glutamate triggers rapid glucose transport stimulation in
astrocytes as evidenced by real-time confocal microscopy. J.
Neurosci. 23,7337
-7342.
Magistretti, P. J. (1999). Brain energy metabolism. In Fundamental Neuroscience (ed. M. Zigmond, F. E. Bloom, S. Landis, J. Roberts and L. Squire), pp.389 -413. San Diego: Academic Press.
Magistretti, P. J. and Pellerin, L. (1999). Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Philos. Trans. R. Soc. Lond. B Biol. Sci. 354,1155 -1163.[CrossRef][Medline]
Magistretti, P. J., Pellerin, L., Rothman, D. L. and Shulman, R.
G. (1999). Energy on demand. Science
283,496
-497.
Malenka, R. C. (2003). The long-term potential of LTP. Nat. Rev. Neurosci. 4, 923-926.[CrossRef][Medline]
Maviel, T., Durkin, T. P., Menzaghi, F. and Bontempi, B.
(2004). Sites of neocortical reorganization critical for remote
spatial memory. Science
305, 96-99.
Mazziotta, J. C., Toga, A. W. and Frackowiak, R. S. J. (ed.) (2000). Brain Mapping. San Diego: Academic Press.
Moser, M. B. and Moser, E. I. (1998). Functional differentiation in the hippocampus. Hippocampus 8,608 -619.[CrossRef][Medline]
Mulligan, S. J. and MacVicar, B. A. (2004). Calcium transients in astrocyte endfeet cause cerebrovascular constrictions. Nature 431,195 -199.[CrossRef][Medline]
O'Keefe, J. and Nadel, L. (1978). The Hippocampus as a Cognitive Map. Oxford: Oxford University Press.
Pellerin, L. and Magistretti, P. J. (1994).
Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism
coupling neuronal activity to glucose utilization. Proc. Natl.
Acad. Sci. USA 91,10625
-10629.
Pellerin, L. and Magistretti, P. J. (2003). How
to balance the brain energy budget while spending glucose differently?
J. Physiol. 546,325
.
Pellerin, L. and Magistretti, P. J. (2004). Empiricism and rationalism: two paths toward the same goal. J. Cereb. Blood Flow Metab. 24,1240 -1241.[CrossRef][Medline]
Pellerin, L., Pellegri, G., Bittar, P. G., Charnay, Y., Bouras, C., Martin, J. L., Stella, N. and Magistretti, P. J. (1998). Evidence supporting the existence of an activity-dependent astrocyte-neuron lactate shuttle. Dev. Neurosci. 20,291 -299.[CrossRef][Medline]
Petit, J.-M., Tobler, I., Allaman, I., Borbély, A. A. and Magistretti, P. J. (2002). Sleep deprivation modulates brain mRNAs encoding genes of glycogen metabolism. Eur. J. Neurosci. 16,1163 -1167.[CrossRef][Medline]
Pierre, K., Debernardi, R., Magistretti, P. J. and Pellerin, L. (2003). Noradrenaline enhances MCT2 expression in cultured mouse cortical neurons via a translational regulation. J. Neurochem. 86,1468 -1476.[CrossRef][Medline]
Porkka-Heiskanen, T., Strecker, R. E., Thakkar, M., Bjorkum, A.
A., Greene, R. W. and McCarley, R. W. (1997).
Adenosine: a mediator of the sleep-inducing effects of prolonged wakefulness.
Science 276,1265
-1268.
Poucet, B. and Buhot, M. C. (1994). Effects of medial septal or unilateral hippocampal inactivations on reference and working spatial memory in rats. Hippocampus 4, 315-321.[CrossRef][Medline]
Raichle, M. E. (2003). Functional brain imaging
and human brain function. J. Neurosci.
23,3959
-3962.
Raichle, M. E. and Gusnard, D. A. (2002).
Appraising the brain's energy budget. Proc. Natl. Acad. Sci.
USA 99,10237
-10239.
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J.,
Gusnard, D. A. and Shulman, G. L. (2001). A default
mode of brain function. Proc. Natl. Acad. Sci. USA
98,676
-682.
Reiman, E. M., Caselli, R. J., Yun, L. S., Chen, K., Bandy, D.,
Minoshima, S., Thibodeau, S. N. and Osborne, D.
(1996). Preclinical evidence of Alzheimer's disease in persons
homozygous for the epsilon 4 allele for apolipoprotein E. N. Engl.
J. Med. 334,752
-758.
Riedel, G., Micheau, J., Lam, A. G., Roloff, E., Martin, S. J., Bridge, H., Hoz, L., Poeschel, B., McCulloch, J. and Morris, R. G. (1999). Reversible neural inactivation reveals hippocampal participation in several memory processes. Nat. Neurosci. 2,898 -905.[CrossRef][Medline]
Room, P., Tielemans, A. J., De Boer, T., Tonnaer, J. A., Wester, J., Van den Broek, J. H. and Van Delft, A. M. (1989). Local cerebral glucose uptake in anatomically defined structures of freely moving rats. J. Neurosci. Methods 27,191 -202.[CrossRef][Medline]
Ros, J., Pellerin, L., Magara, F., Dauguet, J., Schenk, F. and Magistretti, P. J. (2006). Metabolic activation pattern of distinct hippocampal subregions during spatial learning and memory retrieval. J. Cereb. Blood Flow Metab. 26,468 -477.[CrossRef][Medline]
Roy, C. S. and Sherrington, M. B. (1890). On
the regulation of the blood-supply of the brain. J. Physiol.
Lond. 11,85
-108.
Serres, S., Bezancon, E., Franconi, J. M. and Merle, M.
(2004). Ex vivo analysis of lactate and glucose metabolism in the
rat brain under different states of depressed activity. J. Biol.
Chem. 279,47881
-47889.
Shulman, G. L., Corbetta, M., Buckner, R. L., Raichle, M. E.,
Fiez, J. A., Miezin, F. M. and Petersen, S. E. (1997).
Top-down modulation of early sensory cortex. Cereb.
Cortex 7,193
-206.
Sibson, N. R., Dhankhar, A., Mason, G. F., Rothman, D. L.,
Behar, K. L. and Shulman, R. G. (1998). Stoichiometric
coupling of brain glucose metabolism and glutamatergic neuronal activity.
Proc. Natl. Acad. Sci. USA
95,316
-321.
Sokoloff, L., Reivich, M., Kennedy, C., Des Rosiers, M. H., Patlak, C. S., Pettigrew, K. D., Sakurada, O. and Shinohara, M. (1977). The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat. J. Neurochem. 28,897 -916.[Medline]
Sorg, O. and Magistretti, P. J. (1992). Vasoactive intestinal peptide and noradrenaline exert long-term control on glycogen levels in astrocytes: blockade by protein synthesis inhibition. J. Neurosci. 12,4923 -4931.[Abstract]
Takano, T., Tian, G. F., Peng, W., Lou, N., Libionka, W., Han, X. and Nedergaard, M. (2006). Astrocyte-mediated control of cerebral blood flow. Nat. Neurosci. 9, 260-267.[CrossRef][Medline]
Teasdale, J. D., Dritschel, B. H., Taylor, M. J., Proctor, L., Lloyd, C. A., Nimmo-Smith, I. and Baddeley, A. D. (1995). Stimulus-independent thought depends on central executive resources. Mem. Cognit. 23,551 -559.[Medline]
Tononi, G. and Cirelli, C. (2001). Modulation of brain gene expression during sleep and wakefulness: a review of recent findings. Neuropsychopharmacology 25,S28 -S35.[CrossRef][Medline]
Welker, E., Rao, S. B., Dorfl, J., Melzer, P. and van der Loos, H. (1992). Plasticity in the barrel cortex of the adult mouse: effects of chronic stimulation upon deoxyglucose uptake in the behaving animal. J. Neurosci. 12,153 -170.[Abstract]
Zhang, C. and Wong-Riley, M. (1999). Expression and regulation of NMDA receptor subunit R1 and neuronal nitric oxide synthase in cortical neuronal cultures: correlation with cytochrome oxidase. J. Neurocytol. 28,525 -539.[CrossRef][Medline]
Zonta, M., Angulo, M. C., Gobbo, S., Rosengarten, B., Hossmann, K. A., Pozzan, T. and Carmignoto, G. (2003). Neuron-to-astrocyte signaling is central to the dynamic control of brain microcirculation. Nat. Neurosci. 6, 43-50.[CrossRef][Medline]
Related articles in JEB:
This article has been cited by other articles:
|
|
 |
|
  C. L. Ludlow, J. Hoit, R. Kent, L. O. Ramig, R. Shrivastav, E. Strand, K. Yorkston, and C. M. Sapienza Translating Principles of Neural Plasticity Into Research on Speech Motor Control Recovery and Rehabilitation J Speech Lang Hear Res, February 1, 2008; 51(1): S240 - S258. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. D. Cahoy, B. Emery, A. Kaushal, L. C. Foo, J. L. Zamanian, K. S. Christopherson, Y. Xing, J. L. Lubischer, P. A. Krieg, S. A. Krupenko, et al. A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function J. Neurosci., January 2, 2008; 28(1): 264 - 278. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 W. S. Stone and L. J. Seidman Toward a Model of Memory Enhancement in Schizophrenia: Glucose Administration and Hippocampal Function Schizophr Bull, January 1, 2008; 34(1): 93 - 108. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. C. Nawroth, C. A. Greer, W. R. Chen, S. B. Laughlin, and G. M. Shepherd An Energy Budget for the Olfactory Glomerulus J. Neurosci., September 5, 2007; 27(36): 9790 - 9800. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Garland Jr and S. A. Kelly Phenotypic plasticity and experimental evolution J. Exp. Biol., June 15, 2006; 209(12): 2344 - 2361. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
