Feasibility of direct mapping of cerebral fluorodeoxy-D-glucose metabolism in situ at subcellular resolution using soft X-ray fluorescence

Glucose metabolism is difficult to image with cellular resolution in mammalian brain tissue, particularly with (18) fluorodeoxy-D-glucose (FDG) positron emission tomography (PET). To this end, we explored the potential of synchrotron-based low-energy X-ray fluorescence (LEXRF) to image the stable isotope of fluorine (F) in phosphorylated FDG (DG-6P) at 1 μm(2) spatial resolution in 3-μm-thick brain slices. The excitation-dependent fluorescence F signal at 676 eV varied linearly with FDG concentration between 0.5 and 10 mM, whereas the endogenous background F signal was undetectable in brain. To validate LEXRF mapping of fluorine, FDG was administered in vitro and in vivo, and the fluorine LEXRF signal from intracellular trapped FDG-6P over selected brain areas rich in radial glia was spectrally quantitated at 1 μm(2) resolution. The subsequent generation of spatial LEXRF maps of F reproduced the expected localization and gradients of glucose metabolism in retinal Müller glia. In addition, FDG uptake was localized to periventricular hypothalamic tanycytes, whose morphological features were imaged simultaneously by X-ray absorption. We conclude that the high specificity of photon emission from F and its spatial mapping at ≤1 μm resolution demonstrates the ability to identify glucose uptake at subcellular resolution and holds remarkable potential for imaging glucose metabolism in biological tissue. © 2012 Wiley Periodicals, Inc.

Elucidating the role of neuron-astrocyte metabolic coupling in learning and memory

Monika Tadi

The brain has very high energy demands that are mainly met by the circulating blood glucose to ensure its proper functioning. Thus, it is not surprising that though the human brain weighs only 2- 3% of the body weight, it consumes approximately 25% of total body glucose. “Neuro-energetic coupling" is a unique feature of the brain and refers to the existence of the tight coupling between (neuronal) synaptic brain activity and energy metabolism. The main excitatory neurotransmitter in the brain is glutamate and most of the energy requirements at the cortical level are met through glutamate-mediated neurotransmission, suggesting that there is a close relationship between brain activity, glutamatergic neurotransmission and energy metabolism. Until the 80s, blood-borne glucose was thought to be the sole energy substrate of the brain cells and it was assumed that glucose is metabolized by neurons and astrocytes (a type of glial cell) independently. It is only during the last two decades that the concept of compartmentalization of energy metabolism between neurons and astrocytes has become more obvious. The transfer of lactate from astrocytes to neurons termed as the “astrocyte-neuron lactate shuttle (ANLS)” model proposed by Dr. Pellerin and Prof. Magistretti posits that glutamate released during brain activation activates glycolysis (one of glucose metabolic pathways) in astrocytes leading to lactate (a monocarboxylate) release in the extracellular space, and this astrocytic lactate is then captured and used as an energy substrate by the activated neurons to meet their energy requirements. It includes a complex chain of events involved in neuro-metabolic coupling and supports the preferential use of lactate by activated neurons under conditions of increased energy demands. Higher brain functions such as learning a new task are associated with structural changes in brain and are metabolically demanding necessitating the need of an additional energy supply to meet the neurons increased energy requirement. In this context, the current thesis project aimed to elucidate the contribution of metabolic coupling between astrocytes and neurons, known as “neuron-astrocyte metabolic coupling” to learning and memory formation. Using an in vivo approach combined with behavioral, molecular and gene manipulation techniques in mice, we set out to investigate the role of neuron-astrocyte metabolic coupling, particularly ANLS in cognition. The project was divided into two main parts: In the first part, the expression of glucose metabolism-related genes was investigated during long term memory (LTM) formation in fear-motivated inhibitory avoidance (IA) learning paradigm. Using (14C) 2-Deoxyglucose (2-DG) technique, we first defined the brain areas that are involved in IA LTM formation. This technique provides an indirect measurement of brain activity by measuring glucose uptake in different brain areas during the task. The results of brain metabolic mapping show an increase in glucose utilization in the hippocampus, amygdala, anterior cingulate cortex and pre-limbic and infra-limbic cortical areas. Results from the quantitative analysis of mRNA levels in the dorsal hippocampus at different time point’s following IA task highlight a time dependent, learning specific change in the expression of genes related to glucose metabolism. Specific set of genes involved in the synthesis and degradation of glycogen (brain glucose reserve present only in astrocytes), pyruvate metabolism and pentose phosphate shunt as well as the ANLS were induced following the IA task. These early observations indicate that the metabolic interactions between astrocytes and neurons undergo modifications during a learning process and suggest that these adaptations may play a key role in the establishment of long-term memory. In the second part of research project, we focused on investigating the behavioral consequences of down regulating the expression of Monocarboxylate Transporter 1 (MCT1), a key ANLS related gene, on cognition and higher brain functions. Mice heterozygous for the MCT1 gene (MCT1+/-) were characterized in numerous behavioral paradigms such as general wellbeing, reflexive and motor capabilities. After having established that MCT1+/- mice have no alterations in general emotional state or in locomotion, a battery of behavioral tests was performed to study cognition in these mice. Compared with wild-type control mice, the MCT1+/- mice display normal muscle strength and motor coordination. However, when tested for higher brain functions, the MCT1+/- mice exhibit learning and memory deficits in several learning paradigms/tasks that are hippocampus and / or amygdala dependent. The cognitive impairment observed in the MCT1 heterozygous mice further highlights the importance of ANLS in memory and suggest that disruption of lactate transfer from astrocytes to neurons via the MCT1 results in cognitive impairment. The overall results in this thesis demonstrate that cerebral energy metabolism, and in particular the transfer of lactate from astrocytes to neurons not only provides temporal adaptations in the learning process, but it also plays a fundamental role in memory formation.

EPFL2013

Mathematical Modeling of Brain Energy Metabolism, Measured with PET and MRS in Rodents

Bernard Lanz

Positron emission tomography (PET) and nuclear magnetic resonance spectroscopy (MRS) are two biomedical measurement techniques developed in the end of the XXth century, which drastically improved the amount of accessible information available in vivo. PET became popular through the most widely used tracer, fluorodeoxyglucose (FDG), which enables the measurement of the local glucose utilization and is nowadays routinely applied in clinical practice. Nuclear magnetic resonance is used in a large array of applications such as in analytical chemistry or chemical structure determination and is essentially known for its versatile medical imaging capabilities, grouped under the name of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). In vivo MRS measures concentrations of metabolites by interacting with various nuclei such as 1H, 13C or 15N, with the major asset that MRS enables the identification of the chemical position at which the detected nuclei is located in the measured molecule. When coupled with labeled substrate infusions, dynamic MRS gives the opportunity to probe specific biochemical reactions directly in vivo, which opens the way to a wide range of metabolic studies. However, the correct interpretation of the elaborate dynamic labeling data acquired either with MRS or PET tracer experiments requires the application of adapted metabolic models to derive quantitative metabolic rates characterizing the biochemical processes under study. The work of this thesis involves both PET and MRS studies of brain energy metabolism in rodents and focuses on the development of adapted metabolic models to derive reliable metabolic fluxes characterizing various brain metabolic processes, such as glucose consumption, glial and neuronal oxidative metabolism and neurotransmission. FDG-PET enables the measurement of the cerebral metabolic rate of glucose (CMRGlc) using a three-compartment metabolic model for FDG transport across the blood-brain barrier and FDG phosphorylation to FDG-6P. Although this method is well established, a main drawback of CMRGlc measurement with FDG-PET is the necessity to characterize the available FDG concentration in the plasma over the experiment duration, the so-called arterial input function. This constraint is a strong limitation for preclinical studies in rodents, due to their low blood volume. We developed therefore a new method to extract the FDG input function directly from the PET images in rats and mice, using the time-activity curve of a voxel located in the inferior vena cava. The method was validated by comparison with a dedicated external blood counter and manual blood sampling. Using this method, a CMRGlc of ~0.22 μmol/g/min was determined in the mouse cortex. Glial oxidative metabolism can be specifically assessed using glial specific substrates, in particular [1-11C] acetate. However, no existing metabolic model described the 11C tissue activity curve in terms of neuroglial energy metabolism. In order to extract quantitative metabolic fluxes characterizing the glial TCA cycle rate and glutamate/glutamine cycling, we adapted the neuroglial two-compartment modeling approach previously used in 13C MRS cerebral metabolic studies to interpret positron emission data following 11C-acetate injection in the rat. The precision and accuracy of the estimated metabolic parameters was tested and a composite glial metabolic flux Vgtg=0.136 ± 0.042 μmol/g/min and a neurotransmission flux Vnt=0.170 ± 0.103 μmol/g/min were obtained. This approach enabled a direct comparison of the metabolic fluxes measured using 11C PET with the values determined with 13C MRS. In the field of 13C MRS, we took advantage of the higher sensitivity and spectral resolution available at high magnetic field (14.1T) to measure glial and neuronal oxidative metabolism, glutamatergic neurotransmission and pyruvate carboxylation in more details and with higher precision using both [1,6-13C2] glucose and [2-13C] acetate infusions in rats. In the acetate study, the glial specific uptake of acetate enabled a quantification of glial oxidative metabolism with unrivalled precision. We could estimate for the first time separately the transmitochondrial flux Vx in the glial and neuronal compartment, a biochemical pathway which has been subject of strong controversy. The glial and neuronal Vx were determined with high precision, as calculated through Monte Carlo simulation, and their value was on the same order of magnitude than the respective glial and neuronal TCA cycle flux. The model was extended to estimate the fraction of glutamate located in the glial compartment. A glial glutamate concentration of 0.6 ± 0.1 μmol/g was found. The values of the different metabolic fluxes characterizing the neuroglial system (Vtcag=0.27, Vxg=0.17, VPC=0.087, VNT=0.15, Vtcan=0.37 and Vxn=0.46 μmol/g/min) were in very good agreement with the values found using [1,6-13C2] glucose infusion. In addition, the effect of the MRS temporal resolution, experiment duration and signal- to-noise ratio on the precision of the derived metabolic fluxes was analyzed. Previous simulation studies showed that two-compartment modeling of neuroglial metabolism using 13C glucose infusion could lead to unreliable parameter estimations. However, using polarization transfer 13C MRS at 14.1T, the carbon positions C4, C3 and C2 of glutamate and glutamine as well as the positions C3 and C2 of aspartate could be measured with high SNR. The C2 positions were used in two-compartment modeling for the first time, enabling a precise measurement of the apparent glutamatergic neurotransmission and glial metabolism, including pyruvate carboxylation, as confirmed by Monte Carlo simulation. The glial dilution at the level of acetyl-CoA, which is related to the metabolism of other substrates than glucose, could also be characterized. Brain metabolism under hyperammonemic conditions was probed with 15N MRS under labeled ammonia infusion and modeled using a modified neuroglial two- compartment model describing the dynamics of [5-15N] glutamine, [2-15N] glutamate and [2-15N] glutamine. Due to detoxification processes, the total glutamine concentration, measured with 1H MRS, was steadily increasing. This non-steady- state metabolic condition resulting from the hyperammonemic stress was taken into account in the model, resulting in the determination of the apparent neurotransmission (0.26 ± 0.030 μmol/g/min), the glutamate dehydrogenase (0.029 ± 0.002 μmol/g/min) and net glutamine accumulation (0.033 ± 0.001 μmol/g/min). Finally, compartmental modeling was applied to 13C labeling studies in the awake rat, fed during 5, 24 or 48 h with a [1-13C]-labeled glucose solution, to determine brain glycogen and NAA turnover times (τGlyc=5.3 ± 3.2 h and τNAA=15.6 ± 6.5 h), using 13C spectra of brain extracts. A group of rats followed a mild brain activation protocol over 5 hours, which resulted in a decreased glycogen turnover time (τGlyc =2.9 ± 1.2 h).

EPFL2012

EEG-fMRI integration for the study of human brain function

Patricia Figueiredo, João Pedro Forjaco Jorge, Wietske Van der Zwaag

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have proved to be extremely valuable tools for the non-invasive study of human brain function. Moreover, due to a notable degree of complementarity between the two modalities, the combination of EEG and fMRI data has been actively sought in the last two decades. Although initially focused on epilepsy, EEG-fMRI applications were rapidly extended to the study of healthy brain function, yielding new insights into its underlying mechanisms and pathways. Nevertheless, EEG and fMRI have markedly different spatial and temporal resolutions, and probe neuronal activity through distinct biophysical processes, many aspects of which are still poorly understood. The remarkable conceptual and methodological challenges associated with EEG-fMRI integration have motivated the development of a wide range of analysis approaches over the years, each relying on more or less restrictive assumptions, and aiming to shed further light on the mechanisms of brain function along with those of the EEG-fMRI coupling itself. Here, we present a review of the most relevant EEG-fMRI integration approaches yet proposed for the study of brain function, supported by a general overview of our current understanding of the biophysical mechanisms coupling the signals obtained from the two modalities.