In vivo neuropil density from anatomical MRI and machine learning.

Brain energy budgets specify metabolic costs emerging from underlying mechanisms of cellular and synaptic activities. While current bottom-up energy budgets use prototypical values of cellular density and synaptic density, predicting metabolism from a person's individualized neuropil density would be ideal. We hypothesize that in vivo neuropil density can be derived from magnetic resonance imaging (MRI) data, consisting of longitudinal relaxation (T1) MRI for gray/white matter distinction and diffusion MRI for tissue cellularity (apparent diffusion coefficient, ADC) and axon directionality (fractional anisotropy, FA). We present a machine learning algorithm that predicts neuropil density from in vivo MRI scans, where ex vivo Merker staining and in vivo synaptic vesicle glycoprotein 2A Positron Emission Tomography (SV2A-PET) images were reference standards for cellular and synaptic density, respectively. We used Gaussian-smoothed T1/ADC/FA data from 10 healthy subjects to train an artificial neural network, subsequently used to predict cellular and synaptic density for 54 test subjects. While excellent histogram overlaps were observed both for synaptic density (0.93) and cellular density (0.85) maps across all subjects, the lower spatial correlations both for synaptic density (0.89) and cellular density (0.58) maps are suggestive of individualized predictions. This proof-of-concept artificial neural network may pave the way for individualized energy atlas prediction, enabling microscopic interpretations of functional neuroimaging data.

Brain energy metabolism: A roadmap for future research.

Although we have learned much about how the brain fuels its functions over the last decades, there remains much still to discover in an organ that is so complex. This article lays out major gaps in our knowledge of interrelationships between brain metabolism and brain function, including biochemical, cellular, and subcellular aspects of functional metabolism and its imaging in adult brain, as well as during development, aging, and disease. The focus is on unknowns in metabolism of major brain substrates and associated transporters, the roles of insulin and of lipid droplets, the emerging role of metabolism in microglia, mysteries about the major brain cofactor and signaling molecule NAD+, as well as unsolved problems underlying brain metabolism in pathologies such as traumatic brain injury, epilepsy, and metabolic downregulation during hibernation. It describes our current level of understanding of these facets of brain energy metabolism as well as a roadmap for future research.

Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning.

BACKGROUND: Deep learning-based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning-based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR). PURPOSE: To investigate a convolutional neural network-based SR (CNN-SR) approach for simultaneous frequency-and-phase correction (FPC) of single-voxel Meshcher-Garwood point-resolved spectroscopy (MEGA-PRESS) MRS data. STUDY TYPE: Retrospective. SUBJECTS: Forty thousand simulated MEGA-PRESS datasets generated from FID Appliance (FID-A) were used and split into the following: 32,000/4000/4000 for training/validation/testing. A 101 MEGA-PRESS medial parietal lobe data retrieved from the Big GABA were used as the in vivo datasets. FIELD STRENGTH/SEQUENCE: 3T, MEGA-PRESS. ASSESSMENT: Evaluation of frequency and phase offsets mean absolute errors were performed for the simulation dataset. Evaluation of the choline interval variance was performed for the in vivo dataset. The magnitudes of the offsets introduced were -20 to 20 Hz and -90° to 90° and were uniformly distributed for the simulation dataset at different signal-to-noise ratio (SNR) levels. For the in vivo dataset, different additional magnitudes of offsets were introduced: small offsets (0-5 Hz; 0-20°), medium offsets (5-10 Hz; 20-45°), and large offsets (10-20 Hz; 45-90°). STATISTICAL TESTS: Two-tailed paired t-tests for model performances in the simulation and in vivo datasets were used and a P-value <0.05 was considered statistically significant. RESULTS: CNN-SR model was capable of correcting frequency offsets (0.014 ± 0.010 Hz at SNR 20 and 0.058 ± 0.050 Hz at SNR 2.5 with line broadening) and phase offsets (0.104 ± 0.076° at SNR 20 and 0.416 ± 0.317° at SNR 2.5 with line broadening). Using in vivo datasets, CNN-SR achieved the best performance without (0.000055 ± 0.000054) and with different magnitudes of additional frequency and phase offsets (i.e., 0.000062 ± 0.000068 at small, -0.000033 ± 0.000023 at medium, 0.000067 ± 0.000102 at large) applied. DATA CONCLUSION: The proposed CNN-SR method is an efficient and accurate approach for simultaneous FPC of single-voxel MEGA-PRESS MRS data. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

A 3D atlas of functional human brain energetic connectome based on neuropil distribution.

The human brain is energetically expensive, yet the key factors governing its heterogeneous energy distributions across cortical regions to support its diversity of functions remain unexplored. Here, we built up a 3D digital cortical energy atlas based on the energetic costs of all neuropil activities into a high-resolution stereological map of the human cortex with cellular and synaptic densities derived, respectively, from ex vivo histological staining and in vivo PET imaging. The atlas was validated with PET-measured glucose oxidation at the voxel level. A 3D cortical activity map was calculated to predict the heterogeneous activity rates across all cortical regions, which revealed that resting brain is indeed active with heterogeneous neuronal activity rates averaging around 1.2 Hz, comprising around 70% of the glucose oxidation of the cortex. Additionally, synaptic density dominates spatial patterns of energetics, suggesting that the cortical energetics rely heavily on the distribution of synaptic connections. Recent evidence from functional imaging studies suggests that some cortical areas act as hubs (i.e., interconnecting distinct and functionally active regions). An inverse allometric relationship was observed between hub metabolic rates versus hub volumes. Hubs with smaller volumes have higher synapse density, metabolic rate, and activity rates compared to nonhubs. The open-source BrainEnergyAtlas provides a granular framework for exploring revealing design principles in energy-constrained human cortical circuits across multiple spatial scales.

Gene expression regulates metabolite homeostasis during the Crabtree effect: Implications for the adaptation and evolution of Metabolism.

A key issue in both molecular and evolutionary biology has been to define the roles of genes and phenotypes in the adaptation of organisms to environmental changes. The dominant view has been that an organism's metabolic adaptations are driven by gene expression and that gene mutations, independent of the starting phenotype, are responsible for the evolution of new metabolic phenotypes. We propose an alternate hypothesis, in which the phenotype and genotype together determine metabolic adaptation both in the lifetime of the organism and in the evolutionary selection of adaptive metabolic traits. We tested this hypothesis by flux-balance and metabolic-control analysis of the relative roles of the starting phenotype and gene expression in regulating the metabolic adaptations during the Crabtree effect in yeast, when they are switched from a low- to high-glucose environment. Critical for successful short-term adaptation was the ability of the glycogen/trehalose shunt to balance the glycolytic pathway. The role of later gene expression of new isoforms of glycolytic enzymes, rather than flux control, was to provide additional homeostatic mechanisms allowing an increase in the amount and efficiency of adenosine triphosphate and product formation while maintaining glycolytic balance. We further showed that homeostatic mechanisms, by allowing increased phenotypic plasticity, could have played an important role in guiding the evolution of the Crabtree effect. Although our findings are specific to Crabtree yeast, they are likely to be broadly found because of the well-recognized similarities in glucose metabolism across kingdoms and phyla from yeast to humans.

In vivo calibration of genetically encoded metabolite biosensors must account for metabolite metabolism during calibration and cellular volume.

Isotopic assays of brain glucose utilization rates have been used for more than four decades to establish relationships between energetics, functional activity, and neurotransmitter cycling. Limitations of these methods include the relatively long time (1-60 min) for the determination of labeled metabolite levels and the lack of cellular resolution. Identification and quantification of fuels for neurons and astrocytes that support activation and higher brain functions are a major, unresolved issues. Glycolysis is preferentially up-regulated during activation even though oxygen level and supply are adequate, causing lactate concentrations to quickly rise during alerting, sensory processing, cognitive tasks, and memory consolidation. However, the fate of lactate (rapid release from brain or cell-cell shuttling coupled with local oxidation) is long disputed. Genetically encoded biosensors can determine intracellular metabolite concentrations and report real-time lactate level responses to sensory, behavioral, and biochemical challenges at the cellular level. Kinetics and time courses of cellular lactate concentration changes are informative, but accurate biosensor calibration is required for quantitative comparisons of lactate levels in astrocytes and neurons. An in vivo calibration procedure for the Laconic lactate biosensor involves intracellular lactate depletion by intravenous pyruvate-mediated trans-acceleration of lactate efflux followed by sensor saturation by intravenous infusion of high doses of lactate plus ammonium chloride. In the present paper, the validity of this procedure is questioned because rapid lactate-pyruvate interconversion in blood, preferential neuronal oxidation of both monocarboxylates, on-going glycolytic metabolism, and cellular volumes were not taken into account. Calibration pitfalls for the Laconic lactate biosensor also apply to other metabolite biosensors that are standardized in vivo by infusion of substrates that can be metabolized in peripheral tissues. We discuss how technical shortcomings negate the conclusion that Laconic sensor calibrations support the existence of an in vivo astrocyte-neuron lactate concentration gradient linked to lactate shuttling from astrocytes to neurons to fuel neuronal activity.

A tribute to Leif Hertz: The historical context of his pioneering studies of the roles of astrocytes in brain energy metabolism, neurotransmission, cognitive functions, and pharmacology identifies important, unresolved topics for future studies.

Leif Hertz, M.D., D.Sc. (honoris causa) (1930-2018), was one of the original and noteworthy participants in the International Conference on Brain Energy Metabolism (ICBEM) series since its inception in 1993. The biennial ICBEM conferences are organized by neuroscientists interested in energetics and metabolism underlying neural functions; they have had a high impact on conceptual and experimental advances in these fields and on promoting collaborative interactions among neuroscientists. Leif made major contributions to ICBEM discussions and understanding of metabolic and signaling characteristics of astrocytes and their roles in brain function. His studies ranged from uptake of K+ from extracellular fluid and its stimulation of astrocytic respiration, identification, and regulation of enzymes specifically or preferentially expressed in astrocytes in the glutamate-glutamine cycle of excitatory neurotransmission, a requirement for astrocytic glycogenolysis for fueling K+ uptake, involvement of glycogen in memory consolidation in the chick, and pharmacology of astrocytes. This tribute to Leif Hertz highlights his major discoveries, the high impact of his work on astrocyte-neuron interactions, and his unparalleled influence on understanding the cellular basis of brain energy metabolism. His work over six decades has helped integrate the roles of astrocytes into neurotransmission where oxidative and glycogenolytic metabolism during neurotransmitter glutamate turnover are key aspects of astrocytic energetics. Leif recognized that brain astrocytic metabolism is greatly underestimated unless the volume fraction of astrocytes is taken into account. Adjustment for pathway rates expressed per gram tissue for volume fraction indicates that astrocytes have much higher oxidative rates than neurons and astrocytic glycogen concentrations and glycogenolytic rates during sensory stimulation in vivo are similar to those in resting and exercising muscle, respectively. These novel insights are typical of Leif's astute contributions to the energy metabolism field, and his publications have identified unresolved topics that provide the neuroscience community with challenges and opportunities for future research.

Neurovascular coupling is optimized to compensate for the increase in proton production from nonoxidative glycolysis and glycogenolysis during brain activation and maintain homeostasis of pH, pCO2 , and pO2.

During transient brain activation cerebral blood flow (CBF) increases substantially more than cerebral metabolic rate of oxygen consumption (CMRO2 ) resulting in blood hyperoxygenation, the basis of BOLD fMRI contrast. Explanations for the high CBF vs. CMRO2 slope, termed neurovascular coupling (NVC) constant, focused on maintainenance of tissue oxygenation to support mitochondrial ATP production. However, paradoxically the brain has a 3-fold lower oxygen extraction fraction (OEF) than other organs with high energy requirements, like heart and muscle during exercise. Here, we hypothesize that the NVC constant and the capillary oxygen mass transfer coefficient (which in combination determine OEF) are co-regulated during activation to maintain simultaneous homeostasis of pH and partial pressure of CO2 and O2 (pCO2 and pO2 ). To test our hypothesis, we developed an arteriovenous flux balance model for calculating blood and brain pH, pCO2 , and pO2 as a function of baseline OEF (OEF0 ), CBF, CMRO2 , and proton production by nonoxidative metabolism coupled to ATP hydrolysis. Our model was validated against published brain arteriovenous difference studies and then used to calculate pH, pCO2, and pO2 in activated human cortex from published calibrated fMRI and PET measurements. In agreement with our hypothesis, calculated pH, pCO2, and pO2 remained close to constant independently of CMRO2 in correspondence to experimental measurements of NVC and OEF0 . We also found that the optimum values of the NVC constant and OEF0 that ensure simultaneous homeostasis of pH, pCO2, and pO2 were remarkably similar to their experimental values. Thus, the high NVC constant is overall determined by proton removal by CBF due to increases in nonoxidative glycolysis and glycogenolysis. These findings resolve the paradox of the brain's high CBF yet low OEF during activation, and may contribute to explaining the vulnerability of brain function to reductions in blood flow and capillary density with aging and neurovascular disease.

The early days of ex vivo 1 H, 13 C, and 31 P nuclear magnetic resonance in the laboratory of Dr. Robert G. Shulman from 1975 to 1995.

This paper provides a brief description of the early use of ex vivo nuclear magnetic resonance (NMR) studies of tissue and tissue extracts performed in the laboratory of Dr. Robert G. Shulman from 1975 through 1995 at Bell Laboratories, then later at Yale University. During that period, ex vivo NMR provided critical information in support of resonance assignments and the quantitation of concentrations for magnetic resonance spectroscopy studies. The period covered saw rapid advances in magnet technology, starting with studies of microorganisms in vertical bore high-resolution NMR studies, then by 1981 studies of small mammals in a horizontal bore magnet, and then studies of humans in 1984. Ex vivo NMR played a critical role in all these studies. A general strategy developed in the lab for using ex vivo NMR to support in vivo studies is presented, as well as illustrative examples.

Measurement of neuro-energetics and neurotransmission in the rat olfactory bulb using 1 H and 1 H-[13 C] NMR spectroscopy.

The olfactory bulb (OB) plays a fundamental role in the sense of smell and has been implicated in several pathologies, including Alzheimer's disease. Despite its importance, high metabolic activity and unique laminar architecture, the OB is not frequently studied using MRS methods, likely due to the small size and challenging location. Here we present a detailed metabolic characterization of OB metabolism, in terms of both static metabolite concentrations using 1 H MRS and metabolic fluxes associated with neuro-energetics and neurotransmission by tracing the dynamic 13 C flow from intravenously administered [1,6-13 C2 ]-glucose, [2-13 C]-glucose and [2-13 C]-acetate to downstream metabolites, including [4-13 C]-glutamate, [4-13 C]-glutamine and [2-13 C]-GABA. The unique laminar architecture and associated metabolism of the OB, distinctly different from that of the cerebral cortex, is characterized by elevated GABA and glutamine levels, as well as increased GABAergic and astroglial energy metabolism and neurotransmission. The results show that, despite the technical challenges, high-quality 1 H and 1 H-[13 C] MR spectra can be obtained from the rat OB in vivo. The derived metabolite concentrations and metabolic rates demonstrate a unique metabolic profile for the OB. The metabolic model provides a solid basis for future OB studies on functional activation or pathological conditions.

Metabolic control analysis of hepatic glycogen synthesis in vivo.

Multiple insulin-regulated enzymes participate in hepatic glycogen synthesis, and the rate-controlling step responsible for insulin stimulation of glycogen synthesis is unknown. We demonstrate that glucokinase (GCK)-mediated glucose phosphorylation is the rate-controlling step in insulin-stimulated hepatic glycogen synthesis in vivo, by use of the somatostatin pancreatic clamp technique using [13C6]glucose with metabolic control analysis (MCA) in three rat models: 1) regular chow (RC)-fed male rats (control), 2) high fat diet (HFD)-fed rats, and 3) RC-fed rats with portal vein glucose delivery at a glucose infusion rate matched to the control. During hyperinsulinemia, hyperglycemia dose-dependently increased hepatic glycogen synthesis. At similar levels of hyperinsulinemia and hyperglycemia, HFD-fed rats exhibited a decrease and portal delivery rats exhibited an increase in hepatic glycogen synthesis via the direct pathway compared with controls. However, the strong correlation between liver glucose-6-phosphate concentration and net hepatic glycogen synthetic rate was nearly identical in these three groups, suggesting that the main difference between models is the activation of GCK. MCA yielded a high control coefficient for GCK in all three groups. We confirmed these findings in studies of hepatic GCK knockdown using an antisense oligonucleotide. Reduced liver glycogen synthesis in lipid-induced hepatic insulin resistance and increased glycogen synthesis during portal glucose infusion were explained by concordant changes in translocation of GCK. Taken together, these data indicate that the rate of insulin-stimulated hepatic glycogen synthesis is controlled chiefly through GCK translocation.

Reversibility of brain glucose kinetics in type 2 diabetes mellitus.

AIMS/HYPOTHESIS: We have previously shown that individuals with uncontrolled type 2 diabetes have a blunted rise in brain glucose levels measured by 1H magnetic resonance spectroscopy. Here, we investigate whether reductions in HbA1c normalise intracerebral glucose levels. METHODS: Eight individuals (two men, six women) with poorly controlled type 2 diabetes and mean ± SD age 44.8 ± 8.3 years, BMI 31.4 ± 6.1 kg/m2 and HbA1c 84.1 ± 16.2 mmol/mol (9.8 ± 1.4%) underwent 1H MRS scanning at 4 Tesla during a hyperglycaemic clamp (~12.21 mmol/l) to measure changes in cerebral glucose at baseline and after a 12 week intervention that improved glycaemic control through the use of continuous glucose monitoring, diabetes regimen intensification and frequent visits to an endocrinologist and nutritionist. RESULTS: Following the intervention, mean ± SD HbA1c decreased by 24.3 ± 15.3 mmol/mol (2.1 ± 1.5%) (p=0.006), with minimal weight changes (p=0.242). Using a linear mixed-effects regression model to compare glucose time courses during the clamp pre and post intervention, the pre-intervention brain glucose level during the hyperglycaemic clamp was significantly lower than the post-intervention brain glucose (p<0.001) despite plasma glucose levels during the hyperglycaemic clamp being similar (p=0.266). Furthermore, the increases in brain glucose were correlated with the magnitude of improvement in HbA1c (r = 0.71, p=0.048). CONCLUSION/INTERPRETATION: These findings highlight the potential reversibility of cerebral glucose transport capacity and metabolism that can occur in individuals with type 2 diabetes following improvement of glycaemic control. Trial registration ClinicalTrials.gov NCT03469492.

Mechanistic stoichiometric relationship between the rates of neurotransmission and neuronal glucose oxidation: Reevaluation of and alternatives to the pseudo-malate-aspartate shuttle model.

The ~1:1 stoichiometry between the rates of neuronal glucose oxidation (CMRglc-ox-N ) and glutamate (Glu)/γ-aminobutyric acid (GABA)-glutamine (Gln) neurotransmitter (NT) cycling between neurons and astrocytes (VNTcycle ) has been firmly established. However, the mechanistic basis for this relationship is not fully understood, and this knowledge is critical for the interpretation of metabolic and brain imaging studies in normal and diseased brain. The pseudo-malate-aspartate shuttle (pseudo-MAS) model established the requirement for glycolytic metabolism in cultured glutamatergic neurons to produce NADH that is shuttled into mitochondria to support conversion of extracellular Gln (i.e., astrocyte-derived Gln in vivo) into vesicular neurotransmitter Glu. The evaluation of this model revealed that it could explain half of the 1:1 stoichiometry and it has limitations. Modifications of the pseudo-MAS model were, therefore, devised to address major knowledge gaps, that is, submitochondrial glutaminase location, identities of mitochondrial carriers for Gln and other model components, alternative mechanisms to transaminate α-ketoglutarate to form Glu and shuttle glutamine-derived ammonia while maintaining mass balance. All modified models had a similar 0.5 to 1.0 predicted mechanistic stoichiometry between VNTcycle and the rate of glucose oxidation. Based on studies of brain ß-hydroxybutyrate oxidation, about half of CMRglc-ox-N may be linked to glutamatergic neurotransmission and localized in pre-synaptic structures that use pseudo-MAS type mechanisms for Glu-Gln cycling. In contrast, neuronal compartments that do not participate in transmitter cycling may use the MAS to sustain glucose oxidation. The evaluation of subcellular compartmentation of neuronal glucose metabolism in vivo is a critically important topic for future studies to understand glutamatergic and GABAergic neurotransmission.

MR spectroscopy frequency and phase correction using convolutional neural networks.

PURPOSE: To introduce a novel convolutional neural network (CNN)-based approach for frequency-and-phase correction (FPC) of MR spectroscopy (MRS) spectra to achieve fast and accurate FPC of single-voxel MEGA-PRESS MRS data. METHODS: Two neural networks (one for frequency and one for phase) were trained and validated using published simulated and in vivo MEGA-PRESS MRS dataset with wide-range artificial frequency and phase offsets applied. The CNN-based approach was subsequently tested and compared to the current deep learning solution: multilayer perceptrons (MLP). Furthermore, random noise was added to the original simulated dataset to further investigate the model performance at varied signal-to-noise ratio (SNR) levels (i.e., 10, 5, and 2.5). Additional frequency and phase offsets (i.e., small, moderate, large) were also applied to the in vivo dataset, and the CNN model was compared to the conventional approach SR and model-based SR implementation (mSR). RESULTS: The CNN model is more robust to noise compared to the MLP-based approach due to having smaller mean absolute errors in both frequency (0.01 ± 0.01 Hz at SNR = 10 and 0.01 ± 0.02 Hz at SNR = 2.5) and phase (0.12 ± 0.09° at SNR = 10 and -0.07 ± 0.44° at SNR = 2.5) offset prediction. Furthermore, better performance was demonstrated for FPC when compared to the MLP-based approach, and SR when applied to the in vivo dataset for both with and without additional offsets. CONCLUSION: A CNN-based approach provides a solution to the automated preprocessing of MRS data, and the experimental results demonstrate the quantitatively improved spectra quality compared to the state-of-the-art approach.

Extracellular pH mapping of liver cancer on a clinical 3T MRI scanner.

PURPOSE: To demonstrate feasibility of developing a noninvasive extracellular pH (pHe ) mapping method on a clinical MRI scanner for molecular imaging of liver cancer. METHODS: In vivo pHe mapping has been demonstrated on preclinical scanners (e.g., 9.4T, 11.7T) with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS), where the pHe readout by 3D chemical shift imaging (CSI) depends on hyperfine shifts emanating from paramagnetic macrocyclic chelates like TmDOTP5- which upon extravasation from blood resides in the extracellular space. We implemented BIRDS-based pHe mapping on a clinical 3T Siemens scanner, where typically diamagnetic 1 H signals are detected using millisecond-long radiofrequency (RF) pulses, and 1 H shifts span over ±10 ppm with long transverse (T2 , 102 ms) and longitudinal (T1 , 103 ms) relaxation times. We modified this 3D-CSI method for ultra-fast acquisition with microsecond-long RF pulses, because even at 3T the paramagnetic 1 H shifts of TmDOTP5- have millisecond-long T2 and T1 and ultra-wide chemical shifts (±200 ppm) as previously observed in ultra-high magnetic fields. RESULTS: We validated BIRDS-based pH in vitro with a pH electrode. We measured pHe in a rabbit model for liver cancer using VX2 tumors, which are highly vascularized and hyperglycolytic. Compared to intratumoral pHe (6.8 ± 0.1; P < 10-9 ) and tumor's edge pHe (6.9 ± 0.1; P < 10-7 ), liver parenchyma pHe was significantly higher (7.2 ± 0.1). Tumor localization was confirmed with histopathological markers of necrosis (hematoxylin and eosin), glucose uptake (glucose transporter 1), and tissue acidosis (lysosome-associated membrane protein 2). CONCLUSION: This work demonstrates feasibility and potential clinical translatability of high-resolution pHe mapping to monitor tumor aggressiveness and therapeutic outcome, all to improve personalized cancer treatment planning.

Elevated homocarnosine and GABA in subject on isoniazid as assessed through 1H MRS at 7T.

Typical magnetic resonance spectroscopy J-editing methods designed to quantify GABA suffer from contamination of both overlapping macromolecules and homocarnosine signal, introducing potential confounds. The aim of this study was to develop a novel method to assess accurately both the relative concentrations of homocarnosine as well as GABA free from overlapping creatine, homocarnosine and macromolecule signal. A novel method which utilized the combination of echo time STEAM and MEGA-sLASER magnetic resonance spectroscopy experiments at 7T were used to quantify the concentration of GABA and homocarnsoine independently, which are typically quantified in tandem. The metabolites GABA and homocarnosine were measured in brain of 6 healthy control subjects, and in a single subject medicated with isoniazid. It was found that (16.6±10.2)% of the supposed GABA signal in the brain originated from homocarnosine, and that isoniazid caused significantly elevated concentration of GABA and homocarnosine in a single subject compared to controls.

Reevaluation of Astrocyte-Neuron Energy Metabolism with Astrocyte Volume Fraction Correction: Impact on Cellular Glucose Oxidation Rates, Glutamate-Glutamine Cycle Energetics, Glycogen Levels and Utilization Rates vs. Exercising Muscle, and Na+/K+ Pumping Rates.

Accurate quantification of cellular contributions to rates of substrate utilization in resting, activated, and diseased brain is essential for interpretation of data from studies using [18F]fluorodeoxyglucose-positron-emission tomography (FDG-PET) and [13C]glucose/magnetic resonance spectroscopy (MRS). A generally-accepted dogma is that neurons have the highest energy demands of all brain cells, and calculated neuronal rates of glucose oxidation in awake, resting brain accounts for 70-80%, with astrocytes 20-30%. However, these proportions do not take cell type volume fractions into account. To evaluate the conclusion that neuron-astrocyte glucose oxidation rates are similar when adjusted for astrocytic volume fraction (Hertz, Magn Reson Imaging 2011; 29, 1319), the present study analyzed data from 31 studies. On average, astrocytes occupy 6.1, 9.6, and 15% of tissue volume in hippocampus, cerebral cortex, and cerebellum, respectively, and regional astrocytic metabolic rates are adjusted for volume fraction by multiplying by 17.6, 11.4, and 6.8, respectively. After adjustment, astrocytic glucose oxidation rates in resting awake rat brain are 4-10 fold higher than neuronal oxidation rates. Volume-fraction adjustment also increases brain glycogen concentrations and utilization rates to be similar to or exceed exercising muscle. Ion flux calculations to evaluate sodium/potassium homeostasis during neurotransmission are not correct if astrocyte-neuron volume fractions are assumed to be equal. High rates of glucose and glycogen utilization after adjustment for volume fraction indicate that astrocytic energy demands are much greater than recognized, with most of the ATP being used for functions other than glutamate processing in the glutamate-glutamine cycle, challenging the notion that astrocytes 'feed hungry neurons'.

Functional MRS with J-edited lactate in human motor cortex at 4 T.

While functional MRI (fMRI) localizes regions of brain activation, functional MRS (fMRS) provides insights into metabolic underpinnings. Previous fMRS studies detected task-induced lactate increase using short echo-time non-edited 1H-MRS protocols, where lactate changes depended on accurate exclusion of overlapping lactate and lipid/macromolecule signals. Because long echo-time J-difference 1H-MRS detection of lactate is less susceptible to this shortcoming, we posited if J-edited fMRS protocol could reliably detect metabolic changes in the human motor cortex during a finger-tapping paradigm in relation to a reliable measure of basal lactate. Our J-edited fMRS protocol at 4T was guided by an fMRI pre-scan to determine the 1H-MRS voxel placement in the motor cortex. Because lactate and ß-hydroxybutyrate (BHB) follow similar J-evolution profiles we observed both metabolites in all spectra, but only lactate showed reproducible task-induced modulation by 0.07 mM from a basal value of 0.82 mM. These J-edited fMRS results demonstrate good sensitivity and specificity for task-induced lactate modulation, suggesting that J-edited fMRS studies can be used to investigate the metabolic underpinning of human cognition by measuring lactate dynamics associated with activation and deactivation fMRI paradigms across brain regions at magnetic field lower than 7T.

In vivo 13 C and 1 H-[13 C] MRS studies of neuroenergetics and neurotransmitter cycling, applications to neurological and psychiatric disease and brain cancer.

In the last 25 years 13 C MRS has been established as the only noninvasive method for measuring glutamate neurotransmission and cell specific neuroenergetics. Although technically and experimentally challenging 13 C MRS has already provided important new information on the relationship between neuroenergetics and neuronal function, the high energy cost of brain function in the resting state and the role of altered neuroenergetics and neurotransmitter cycling in disease. In this paper we review the metabolic and neurotransmitter pathways that can be measured by 13 C MRS and key findings on the linkage between neuroenergetics, neurotransmitter cycling, and brain function. Applications of 13 C MRS to neurological and psychiatric disease as well as brain cancer are reviewed. Recent technological developments that may help to overcome spatial resolution and brain coverage limitations of 13 C MRS are discussed.

Glymphatic System Function in Relation to Anesthesia and Sleep States.

The brain is one of the most metabolically active organs in the body. The brain's high energy demand associated with wakefulness persists during rapid eye movement sleep, and even during non-rapid eye movement sleep, cerebral oxygen consumption is only reduced by 20%. The active bioenergetic state parallels metabolic waste production at a higher rate than in other organs, and the lack of lymphatic vasculature in brain parenchyma is therefore a conundrum. A common assumption has been that with a tight blood-brain barrier restricting solute and fluid movements, a lymphatic system is superfluous in the central nervous system. Cerebrospinal fluid (CSF) flow has long been thought to facilitate central nervous system tissue "detoxification" in place of lymphatics. Nonetheless, while CSF production and transport have been studied for decades, the exact processes involved in toxic waste clearance remain poorly understood. Over the past 5 years, emerging data have begun to shed new light on these processes in the form of the "glymphatic system," a novel brain-wide perivascular transit passageway dedicated to CSF transport and metabolic waste drainage from the brain. Here, we review the key anatomical components and operational drivers of the brain's glymphatic system, with a focus on its unique functional dependence on the state of arousal and anesthetic regimens. We also discuss evidence for why clinical exploration of this novel system may in the future provide valuable insight into new strategies for preventing delirium and cognitive dysfunction in perioperative and critical care settings.