Brain aerobic glycolysis and resilience in Alzheimer disease

The brain's "dark energy" puzzle upgraded: [18F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity

The brain's resting-state energy consumption is expected to be driven by spontaneous activity. We previously used 50 resting-state fMRI (rs-fMRI) features to predict [18F]FDG SUVR as a proxy of glucose metabolism. Here, we expanded on our effort by estimating [18F]FDG kinetic parameters Ki (irreversible uptake), K1 (delivery), k3 (phosphorylation) in a large healthy control group (n = 47). Describing the parameters' spatial distribution at high resolution (216 regions), we showed that K1 is the least redundant (strong posteromedial pattern), and Ki and k3 have relevant differences (occipital cortices, cerebellum, thalamus). Using multilevel modeling, we investigated how much spatial variance of [18F]FDG parameters could be explained by a combination of a) rs-fMRI variables, b) cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2) from 15O PET. Rs-fMRI-only models explained part of the individual variance in Ki (35%), K1 (14%), k3 (21%), while combining rs-fMRI and CMRO2 led to satisfactory description of Ki (46%) especially. Ki was sensitive to both local rs-fMRI variables (ReHo) and CMRO2, k3 to ReHo, K1 to CMRO2. This work represents a comprehensive assessment of the complex underpinnings of brain glucose consumption, and highlights links between 1) glucose phosphorylation and local brain activity, 2) glucose delivery and oxygen consumption.

A sex-biased target against demyelination in Alzheimer's disease

Aging and Alzheimer's disease (AD) exhibit sex differences in several biological processes, including demyelination. In a recent study, Lopez-Lee et al. uncover the contributions of sex chromosomes and gonadal hormones to sex differences in demyelination and identify Toll-like receptor 7 (TLR7) as a potential target to ameliorate tauopathy-induced demyelination in men.

Increased White Matter Aerobic Glycolysis in Multiple Sclerosis

OBJECTIVE

Despite treatments which reduce relapses in multiple sclerosis (MS), many patients continue to experience progressive disability accumulation. MS is associated with metabolic disruptions and cerebral metabolic stress predisposes to tissue injury and possibly impaired remyelination. Additionally, myelin homeostasis is metabolically expensive and reliant on glycolysis. We investigated cerebral metabolic changes in MS and when in the disease course they occurred, and assessed their relationship with microstructural changes.

METHODS

This study used combined fluorodeoxyglucose (FDG) positron emission tomography (PET) and magnetic resonance imaging (MRI) to measure cerebral metabolic rate of glucose and oxygen, thereby quantifying glycolysis. Twelve healthy controls, 20 patients with relapsing MS, and 13 patients with non-relapsing MS were studied. Relapsing patients with MS were treatment naïve and scanned pre- and post-initiation of high efficacy disease modifying therapy.

RESULTS

In normal appearing white matter, we observed increased glucose utilization and reduced oxygen utilization in newly diagnosed MS, consistent with increased glycolysis. Increased glycolysis was greater in patients with a longer disease duration course and higher disability. Among newly diagnosed patients, different treatments had differential impacts on glucose utilization. Last, whereas hypermetabolism within lesions was clearly associated with inflammation, no such relationship was found within normal appearing white matter.

INTERPRETATION

Increased white matter glycolysis is a prominent feature of cerebral metabolism in MS. It begins early in the disease course, increases with disease duration and is independent of microstructural evidence of inflammation in normal appearing white matter. Optimization of the metabolic environment may be an important component of therapies designed to reduce progressive disability. ANN NEUROL 2025;97:766-778.

Chronic stress enhances glycolysis and promotes tumorigenesis

Depression is a well-known risk factor for tumors, but the mechanisms other than inflammation are unclear. Aerobic glycolysis is considered to be a critical element in the reprogramming of energy metabolism in malignant tumors, and impaired glycolysis has been reported in the brains of chronic stress mice. Therefore, this study aimed to explore the role of glycolysis in which depression promotes tumorigenesis. We examined the impacts of chronic unpredictable mild stress (CUMS) on the growth and metastasis of breast cancer (BC) and lung cancer (LC). CUMS was used to construct a mouse depression model, BALB/c mice were injected with 4T1-Luc cells in the right subcutaneous mammary fat pad, and C57BL/6 mice were injected with Lewis-Luc cells in the tail vein. The experiments were conducted through behavioral experiments, live imaging techniques of small animals, Western blot, Glycolytic metabolites measurement, Hematoxylin and eosin staining (H&E staining), Nissl staining, and immunohistochemical (IHC) tests. The findings showed that both CUMS and tumors induced depressive-like behavior, neuronal damage, and impaired synaptic plasticity in mice, while CUMS also enhanced tumor development and metastasis in both BC and LC. In the brain, both CUMS and tumor alone and in combination less influence glycolytic products and enzyme levels. However, CUMS significantly enhanced the levels of aerobic glycolytic products and enzymes in tumor tissue. Collectively, our results provide insights into how glycolysis is regulated in the brain, leading to depression-like behavior, and how depression, in turn, enhanced glycolysis and promoted tumorigenesis.

Quantitative mapping of key glucose metabolic rates in the human brain using dynamic deuterium magnetic resonance spectroscopic imaging

Deuterium (2H) magnetic resonance spectroscopic imaging (DMRSI) is a newly developed technology for assessing glucose metabolism by simultaneously measuring deuterium-labeled glucose and its downstream metabolites (1) and has a potential to provide a powerful neurometabolic imaging tool for quantitative studies of cerebral glucose metabolism involving multiple metabolic pathways in the human brain. In this work, we developed a dynamic DMRSI method that combines advanced radiofrequency coil and postprocessing techniques to substantially improve the imaging signal-to-noise ratio for detecting deuterated metabolites and enable robust dynamic DMRSI of the human brain at 7 T with very high resolution (HR; 0.7 cc nominal voxel and 2.5 min/image) and whole-brain coverage. Utilizing this capability, we were able to map and differentiate metabolite contents and dynamics throughout the human brain following oral administration of deuterated glucose. Furthermore, by introducing a sophisticated kinetic model, we demonstrated that three key cerebral metabolic rates of glucose consumption (CMRGlc), lactate production (CMRLac), and tricarboxylic acid (TCA) cycle (V TCA), as well as the maximum apparent rate of forward glucose transport (T max) can be simultaneously imaged in the human brain through a single dynamic DMRSI measurement. The results clearly show that the glucose transport, neurotransmitter turnover, CMRGlc, and V TCA are significantly higher in gray matter than in white matter in the human brain; and the mean metabolic rates and their ratios measured in this study are consistent with the values reported in the literature. The HR dynamic DMRSI methodology presented herein is of great significance and value for the quantitative assessment of human brain glucose metabolism, aerobic glycolysis, and metabolic reprogramming under physiopathological conditions.

Exploring glycolytic enzymes in disease: potential biomarkers and therapeutic targets in neurodegeneration, cancer and parasitic infections

Glycolysis, present in most organisms, is evolutionarily one of the oldest metabolic pathways. It has great relevance at a physiological level because it is responsible for generating ATP in the cell through the conversion of glucose into pyruvate and reducing nicotinamide adenine dinucleotide (NADH) (that may be fed into the electron chain in the mitochondria to produce additional ATP by oxidative phosphorylation), as well as for producing intermediates that can serve as substrates for other metabolic processes. Glycolysis takes place through 10 consecutive chemical reactions, each of which is catalysed by a specific enzyme. Although energy transduction by glucose metabolism is the main function of this pathway, involvement in virulence, growth, pathogen-host interactions, immunomodulation and adaptation to environmental conditions are other functions attributed to this metabolic pathway. In humans, where glycolysis occurs mainly in the cytosol, the mislocalization of some glycolytic enzymes in various other subcellular locations, as well as alterations in their expression and regulation, has been associated with the development and progression of various diseases. In this review, we describe the role of glycolytic enzymes in the pathogenesis of diseases of clinical interest. In addition, the potential role of these enzymes as targets for drug development and their potential for use as diagnostic and prognostic markers of some pathologies are also discussed.

Individual-level metabolic connectivity from dynamic [18F]FDG PET reveals glioma-induced impairments in brain architecture and offers novel insights beyond the SUVR clinical standard

PURPOSE

This study evaluates the potential of within-individual Metabolic Connectivity (wi-MC), from dynamic [18F]FDG PET data, based on the Euclidean Similarity method. This approach leverages the biological information of the tracer's full temporal dynamics, enabling the direct extraction of individual metabolic connectomes. Specifically, the proposed framework, applied to glioma pathology, seeks to assess sensitivity to metabolic dysfunctions in the whole brain, while simultaneously providing further insights into the pathophysiological mechanisms regulating glioma progression.

METHODS

We designed an index (Distance from Healthy Group, DfHG) based on the alteration of wi-MC in each patient (n = 44) compared to a healthy reference (from 57 healthy controls), to individually quantify metabolic connectivity abnormalities, resulting in an Impairment Map highlighting significantly compromised areas. We then assessed whether our measure of metabolic network alteration is associated with well-established markers of disease severity (tumor grade and volume, with and without edema). Subsequently, we investigated disruptions in wi-MC homotopic connectivity, assessing both affected and seemingly healthy tissue to deepen the pathology's impact on neural communication. Finally, we compared network impairments with local metabolic alterations determined from SUVR, a validated diagnostic tool in clinical practice.

RESULTS

Our framework revealed how gliomas cause extensive alterations in the topography of brain networks, even in structurally unaffected regions outside the lesion area, with a significant reduction in connectivity between contralateral homologous regions. High-grade gliomas have a stronger impact on brain networks, and edema plays a mediating role in global metabolic alterations. As compared to the conventional SUVR-based analysis, our approach offers a more holistic view of the disease burden in individual patients, providing interesting additional insights into glioma-related alterations.

CONCLUSION

Considering our results, individual PET connectivity estimates could hold significant clinical value, potentially allowing the identification of new prognostic factors and personalized treatment in gliomas or other focal pathologies.

Simultaneous assessment of cerebral glucose and oxygen metabolism and perfusion in rats using interleaved deuterium (2H) and oxygen-17 (17O) MRS

Cerebral glucose and oxygen metabolism and blood perfusion play key roles in neuroenergetics and oxidative phosphorylation to produce adenosine triphosphate (ATP) energy molecules in supporting cellular activity and brain function. Their impairments have been linked to numerous brain disorders. This study aimed to develop an in vivo magnetic resonance spectroscopy (MRS) method capable of simultaneously assessing and quantifying the major cerebral metabolic rates of glucose (CMRGlc) and oxygen (CMRO2) consumption, lactate formation (CMRLac), and tricarboxylic acid (TCA) cycle (VTCA); cerebral blood flow (CBF); and oxygen extraction fraction (OEF) via a single dynamic MRS measurement using an interleaved deuterium (2H) and oxygen-17 (17O) MRS approach. We introduced a single-loop multifrequency radio-frequency (RF) surface coil that can be used to acquire proton (1H) magnetic resonance imaging (MRI) or interleaved low-γ X-nuclei 2H and 17O MRS. By combining this RF coil with a modified MRS pulse sequence, 17O-isotope-labeled oxygen gas inhalation, and intravenous 2H-isotope-labeled glucose administration, we demonstrate for the first time the feasibility of simultaneously and quantitatively measuring six important physiological parameters, CMRGlc, CMRO2, CMRLac, VTCA, CBF, and OEF, in rat brains at 16.4 T. The interleaved 2H-17O MRS technique should be readily adapted to image and study cerebral energy metabolism and perfusion in healthy and diseased brains.

EMATA: a toolbox for the automatic extraction and modeling of arterial inputs for tracer kinetic analysis in [18F]FDG brain studies

PURPOSE

PET imaging is a pivotal tool for biomarker research aimed at personalized medicine. Leveraging the quantitative nature of PET requires knowledge of plasma radiotracer concentration. Typically, the arterial input function (AIF) is obtained through arterial cannulation, an invasive and technically demanding procedure. A less invasive alternative, especially for [18F]FDG, is the image-derived input function (IDIF), which, however, often requires correction for partial volume effect (PVE), usually performed via venous blood samples. The aim of this paper is to present EMATA: Extraction and Modeling of Arterial inputs for Tracer kinetic Analysis, an open-source MATLAB toolbox. EMATA automates IDIF extraction from [18F]FDG brain PET images and additionally includes a PVE correction procedure that does not require any blood sampling.

METHODS

To assess the toolbox generalizability and present example outputs, EMATA was applied to brain [18F]FDG dynamic data of 80 subjects, extracted from two distinct datasets (40 healthy controls, 40 glioma patients). Additionally, to compare with the reference standard, quantification using both IDIF and AIF was carried out on a third open-access dataset of 18 healthy individuals.

RESULTS

EMATA consistently performs IDIF extraction across all datasets, despite differences in scanners and acquisition protocols. Remarkably high agreement is observed when comparing Patlak's Ki between IDIF and AIF (R2: 0.98 ± 0.02).

CONCLUSION

EMATA proved adaptability to different datasets characteristics and the ability to provide arterial input functions that can be used for reliable PET quantitative analysis.

Sevoflurane exposure accelerates the onset of cognitive impairment via promoting p-Drp1S616-mediated mitochondrial fission in a mouse model of Alzheimer's disease

Sevoflurane is an inhalational anesthetic widely used in clinical settings. Accumulating evidence has shown that sevoflurane exposure may impair cognitive function, potentially contributing to Alzheimer's disease (AD)-related changes. However, the underlying mechanism remains poorly understood. In the present study, 4-month-old 5xFAD mice were used to investigate the effect of sevoflurane exposure on cognitive decline by Y-maze test and novel object recognition test. We found that sevoflurane exposure promoted the appearance of cognitive impairment of 5xFAD mice, accompanied with the deterioration of Aβ accumulation, synaptic defects, and neuroinflammation. Additionally, sevoflurane was also found to aggravate mitochondrial fission of 5xFAD mice, as indicated by the further upregulated expression of p-Drp1S616. Moreover, sevoflurane significantly increased mitochondrial damage and dysfunction of AD models both in vitro and in vivo experiments. Seahorse XF analysis further indicated that sevoflurane exposure facilitated a metabolic shift from oxidative phosphorylation to glycolysis. Further rescue experiments revealed that a key mechanism underlying sevoflurane-induced cognitive impairment was the excessive mitochondrial fission, as supported by the result that the mitochondrial fission inhibitor Mdivi-1 counteracted the sevoflurane-mediated deteriorative effects in 5xFAD mice. These findings provided evidence for a new mechanism of sevoflurane exposure accelerating AD-related cognitive decline.

Brain Metabolism in Health and Neurodegeneration: The Interplay Among Neurons and Astrocytes

The regulation of energy in the brain has garnered substantial attention in recent years due to its significant implications in various disorders and aging. The brain's energy metabolism is a dynamic and tightly regulated network that balances energy demand and supply by engaging complementary molecular pathways. The crosstalk among these pathways enables the system to switch its preferred fuel source based on substrate availability, activity levels, and cell state-related factors such as redox balance. Brain energy production relies on multi-cellular cooperation and is continuously supplied by fuel from the blood due to limited internal energy stores. Astrocytes, which interface with neurons and blood vessels, play a crucial role in coordinating the brain's metabolic activity, and their dysfunction can have detrimental effects on brain health. This review characterizes the major energy substrates (glucose, lactate, glycogen, ketones and lipids) in astrocyte metabolism and their role in brain health, focusing on recent developments in the field.

The brain's "dark energy" puzzle upgraded: [18F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity

The brain's resting-state energy consumption is expected to be mainly driven by spontaneous activity. In our previous work, we extracted a wide range of features from resting-state fMRI (rs-fMRI), and used them to predict [18F]FDG PET SUVR as a proxy of glucose metabolism. Here, we expanded upon our previous effort by estimating [18F]FDG kinetic parameters according to Sokoloff's model, i.e.,K i (irreversible uptake rate),K 1 (delivery),k 3 (phosphorylation), in a large healthy control group. The parameters' spatial distribution was described at a high spatial resolution. We showed that whileK 1 is the least redundant, there are relevant differences betweenK i andk 3 (occipital cortices, cerebellum and thalamus). Using multilevel modeling, we investigated how much of the regional variability of [18F]FDG parameters could be explained by a combination of rs-fMRI variables only, or with the addition of cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2), estimated from 15O PET data. We found that combining rs-fMRI and CMRO2 led to satisfactory prediction of individualK i variance (45%). Although more difficult to describe,K i andk 3 were both most sensitive to local rs-fMRI variables, whileK 1 was sensitive to CMRO2. This work represents the most comprehensive assessment to date of the complex functional and metabolic underpinnings of brain glucose consumption.

Glycolytic metabolism: Food for immune cells, fuel for depression?

Inflammation is one biological pathway thought to impact the brain to contribute to major depressive disorder (MDD) and is reliably associated with resistance to standard antidepressant treatments. While peripheral immune cells, particularly monocytes, have been associated with aspects of increased inflammation in MDD and symptom severity, significant gaps in knowledge exist regarding the mechanisms by which these cells are activated to contribute to behavioral symptoms in MDD. One concept that has gained recent appreciation is that metabolic rewiring to glycolysis in activated myeloid cells plays a crucial role in facilitating these cells' pro-inflammatory functions, which may underlie myeloid contribution to systemic inflammation and its effects on the brain. Given emerging evidence from translational studies of depression that peripheral monocytes exhibit signs of glycolytic activation, better understanding the immunometabolic phenotypes of monocytes which are known to be elevated in MDD with high inflammation is a critical step toward comprehending and treating the impact of inflammation on the brain. This narrative review examines the extant literature on glycolytic metabolism of circulating monocytes in depression and discusses the functional implications of immunometabolic shifts at both cellular and systemic levels. Additionally, it proposes potential therapeutic applications of existing immunomodulators that target glycolysis and related metabolic pathways in order to reverse the impact of elevated inflammation on the brain and depressive symptoms.

The Astrocyte: Metabolic Hub of the Brain

Astrocytic metabolism has taken center stage. Interposed between the neuron and the vasculature, astrocytes exert control over the fluxes of energy and building blocks required for neuronal activity and plasticity. They are also key to local detoxification and waste recycling. Whereas neurons are metabolically rigid, astrocytes can switch between different metabolic profiles according to local demand and the nutritional state of the organism. Their metabolic state even seems to be instructive for peripheral nutrient mobilization and has been implicated in information processing and behavior. Here, we summarize recent progress in our understanding of astrocytic metabolism and its effects on metabolic homeostasis and cognition.

Predicting Alzheimer's Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data

Alzheimer's disease (AD) is an important research topic. While amyloid plaques and neurofibrillary tangles are hallmark pathological features of AD, cognitive resilience (CR) is a phenomenon where cognitive function remains preserved despite the presence of these pathological features. This study aimed to construct and compare predictive machine learning models for CR scores using RNA-seq data from the Religious Orders Study and Memory and Aging Project (ROSMAP) and Mount Sinai Brain Bank (MSBB) cohorts. We evaluated support vector regression (SVR), random forest, XGBoost, linear, and transformer-based models. The SVR model exhibited the best performance, with contributing genes identified using Shapley additive explanations (SHAP) scores, providing insights into biological pathways associated with CR. Finally, we developed a tool called the resilience gene analyzer (REGA), which visualizes SHAP scores to interpret the contributions of individual genes to CR. REGA is available at https://igcore.cloud/GerOmics/REsilienceGeneAnalyzer/.

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.

TMS-derived short afferent inhibition discriminates cognitive status in older adults without dementia

Aging is a complex and diverse biological process characterized by progressive molecular, cellular, and tissue damage, resulting in a loss of physiological integrity and heightened vulnerability to pathology. This biological diversity corresponds with highly variable cognitive trajectories, which are further confounded by genetic and environmental factors that influence the resilience of the aging brain. Given this complexity, there is a need for neurophysiological indicators that not only discern physiologic and pathologic aging but also closely align with cognitive trajectories. Transcranial Magnetic Stimulation (TMS) may have utility in this regard as a non-invasive brain stimulation tool that can characterize features of cortical excitability. Particularly, as a proxy for central cholinergic function, short-afferent inhibition (SAI) dysfunction is robustly associated with cognitive deficits in the latter stages of Alzheimer's Disease and Related Dementia (ADRD). In this study, we evaluated SAI in healthy young adults and older adults who, though absent clinical diagnoses, were algorithmically classified as cognitively normal (CN) or cognitively impaired (CI) according to the Jak/Bondi actuarial criteria. We report that SAI is preserved in the Old-CN cohort relative to the young adults, and SAI is significantly diminished in the Old-CI cohort relative to both young and CN older adults. Additionally, diminished SAI was significantly associated with impaired sustained attention and working memory. As a proxy measure for central cholinergic deficits, we discuss the potential value of SAI for discerning physiological and pathological aging.

Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain

PURPOSE

The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach.

METHODS

51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions.

RESULTS

Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning.

CONCLUSIONS

Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Deuterium Metabolic Imaging of Alzheimer Disease at 3-T Magnetic Field Strength: A Pilot Case-Control Study

Background Impaired glucose metabolism is characteristic of several types of dementia, preceding cognitive symptoms and structural brain changes. Reduced glucose uptake in specific brain regions, detected using fluorine 18 (18F) fluorodeoxyglucose (FDG) PET, is a valuable diagnostic marker in Alzheimer disease (AD). However, the use of 18F-FDG PET in clinical practice may be limited by equipment availability and high cost. Purpose To test the feasibility of using MRI-based deuterium (2H) metabolic imaging (DMI) at a clinical magnetic field strength (3 T) to detect and localize changes in the concentration of glucose and its metabolites in the brains of patients with a clinical diagnosis of AD. Materials and Methods Participants were recruited for this prospective case-control pilot study between March 2021 and February 2023. DMI was performed at 3 T using a custom birdcage head coil following oral administration of deuterium-labeled glucose (0.75 g/kg). Unlocalized whole-brain MR spectroscopy (MRS) and three-dimensional MR spectroscopic imaging (MRSI) (voxel size, 3.2 cm cubic) were performed. Ratios of 2H-glucose, 2H-glutamate and 2H-glutamine (2H-Glx), and 2H-lactate spectroscopic peak signals to 2H-water peak signal were calculated for the whole-brain MR spectra and for individual MRSI voxels. Results A total of 19 participants, including 10 participants with AD (mean age, 68 years ± 5 [SD]; eight males) and nine cognitively healthy control participants (mean age, 70 years ± 6; six males) were evaluated. Whole-brain spectra demonstrated a reduced ratio of 2H-Glx to 2H-glucose peak signals in participants with AD compared with control participants (0.41 ± 0.09 vs 0.58 ± 0.20, respectively; P = .04), suggesting an impairment of oxidative glucose metabolism in AD. However, there was no evidence of localization of these changes to the expected regions of metabolic impairment at MRSI, presumably due to insufficient spatial resolution. Conclusion DMI at 3 T demonstrated impairment of oxidative glucose metabolism in the brains of patients with AD but no evidence of regional signal differences. © RSNA, 2024 Supplemental material is available for this article.

Multi-Tracer Studies of Brain Oxygen and Glucose Metabolism Using a Time-of-Flight Positron Emission Tomography - Computed Tomography Scanner

The authors have developed a paradigm using positron emission tomography (PET) with multiple radiopharmaceutical tracers that combines measurements of cerebral metabolic rate of glucose (CMRGlc), cerebral metabolic rate of oxygen (CMRO2), cerebral blood flow (CBF), and cerebral blood volume (CBV), culminating in estimates of brain aerobic glycolysis (AG). These in vivo estimates of oxidative and non-oxidative glucose metabolism are pertinent to the study of the human brain in health and disease. The latest positron emission tomography-computed tomography (PET-CT) scanners provide time-of-flight (TOF) imaging and critical improvements in spatial resolution and reduction of artifacts. This has led to significantly improved imaging with lower radiotracer doses. Optimized methods for the latest PET-CT scanners involve administering a sequence of inhaled 15O-labeled carbon monoxide (CO) and oxygen (O2), intravenous 15O-labeled water (H2O), and 18F-deoxyglucose (FDG)-all within 2-h or 3-h scan sessions that yield high-resolution, quantitative measurements of CMRGlc, CMRO2, CBF, CBV, and AG. This methods paper describes practical aspects of scanning designed for quantifying brain metabolism with tracer kinetic models and arterial blood samples and provides examples of imaging measurements of human brain metabolism.

Futoquinol improves Aβ25-35-induced memory impairment in mice by inhibiting the activation of p38MAPK through the glycolysis pathway and regulating the composition of the gut microbiota

Futoquinol (Fut) is a compound extracted from Piper kadsura that has a nerve cell protection effect. However, it is unclear whether Fut has protective effects in Alzheimer's disease (AD). In this study, we aimed to explore the therapeutic effect of Fut in AD and its underlying mechanism. UPLC-MS/MS method was performed to quantify Fut in the hippocampus of mice brain. The cognition ability, neuronal and mitochondria damage, and levels of Aβ1-42, Aβ1-40, p-Tau, oxidative stress, apoptosis, immune cells, and inflammatory factors were measured in Aβ25-35-induced mice. The content of bacterial meta-geometry was predicted in the microbial composition based on 16S rDNA. The protein levels of HK II, p-p38MAPK, and p38MAPK were detected. PC-12 cells were cultured in vitro, and glucose was added to activate glycolysis to further explore the mechanism of action of Fut intervention in AD. Fut improved the memory and learning ability of Aβ25-35 mice, and reduced neuronal damage and the deposition of Aβ and Tau proteins. Moreover, Fut reduced mitochondrial damage, the levels of oxidative stress, apoptosis, and inflammatory factors. Fut significantly inhibited the expression of HK II and p-p38MAPK proteins. The in vitro experiment showed that p38MAPK was activated and Fut action inhibited after adding 10 mM glucose. Fut might inhibit the activation of p38MAPK through the glycolysis pathway, thereby reducing oxidative stress, apoptosis, and inflammatory factors and improving Aβ25-35-induced memory impairment in mice. These data provide pharmacological rationale for Fut in the treatment of AD.

Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.

Verifying and refining early statuses in Alzheimer's disease progression: a possibility from deep feature comparison

Defining the early status of Alzheimer's disease is challenging. Theoretically, the statuses in the Alzheimer's disease continuum are expected to share common features. Here, we explore to verify and refine candidature early statuses of Alzheimer's disease with features learned from deep learning. We train models on brain functional networks to accurately classify between amnestic and non-amnestic mild cognitive impairments and between healthy controls and mild cognitive impairments. The trained models are applied to Alzheimer's disease and subjective cognitive decline groups to suggest feature similarities among the statuses and identify informative subpopulations. The amnestic mild cognitive impairment vs non-amnestic mild cognitive impairments classifier believes that 71.8% of Alzheimer's disease are amnestic mild cognitive impairment. And 73.5% of subjective cognitive declines are labeled as mild cognitive impairments, 88.8% of which are further suggested as "amnestic mild cognitive impairment." Further multimodal analyses suggest that the amnestic mild cognitive impairment-like Alzheimer's disease, mild cognitive impairment-like subjective cognitive decline, and amnestic mild cognitive impairment-like subjective cognitive decline exhibit more Alzheimer's disease -related pathological changes (elaborated β-amyloid depositions, reduced glucose metabolism, and gray matter atrophy) than non-amnestic mild cognitive impairments -like Alzheimer's disease, healthy control-like subjective cognitive decline, and non-amnestic mild cognitive impairments -like subjective cognitive decline. The test-retest reliability of the subpopulation identification is fair to good in general. The study indicates overall similarity among subjective cognitive decline, amnestic mild cognitive impairment, and Alzheimer's disease and implies their progression relationships. The results support "deep feature comparison" as a potential beneficial framework to verify and refine early Alzheimer's disease status.

The Role of Insulin-like Growth Factor I in Mechanisms of Resilience and Vulnerability to Sporadic Alzheimer's Disease

Despite decades of intense research, disease-modifying therapeutic approaches for Alzheimer's disease (AD) are still very much needed. Apart from the extensively analyzed tau and amyloid pathological cascades, two promising avenues of research that may eventually identify new druggable targets for AD are based on a better understanding of the mechanisms of resilience and vulnerability to this condition. We argue that insulin-like growth factor I (IGF-I) activity in the brain provides a common substrate for the mechanisms of resilience and vulnerability to AD. We postulate that preserved brain IGF-I activity contributes to resilience to AD pathology as this growth factor intervenes in all the major pathological cascades considered to be involved in AD, including metabolic impairment, altered proteostasis, and inflammation, to name the three that are considered to be the most important ones. Conversely, disturbed IGF-I activity is found in many AD risk factors, such as old age, type 2 diabetes, imbalanced diet, sedentary life, sociality, stroke, stress, and low education, whereas the Apolipoprotein (Apo) E4 genotype and traumatic brain injury may also be influenced by brain IGF-I activity. Accordingly, IGF-I activity should be taken into consideration when analyzing these processes, while its preservation will predictably help prevent the progress of AD pathology. Thus, we need to define IGF-I activity in all these conditions and develop a means to preserve it. However, defining brain IGF-I activity cannot be solely based on humoral or tissue levels of this neurotrophic factor, and new functionally based assessments need to be developed.

Ketone bodies mediate alterations in brain energy metabolism and biomarkers of Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia. AD is a progressive neurodegenerative disorder characterized by cognitive dysfunction, including learning and memory deficits, and behavioral changes. Neuropathology hallmarks of AD such as amyloid beta (Aβ) plaques and neurofibrillary tangles containing the neuron-specific protein tau is associated with changes in fluid biomarkers including Aβ, phosphorylated tau (p-tau)-181, p-tau 231, p-tau 217, glial fibrillary acidic protein (GFAP), and neurofilament light (NFL). Another pathological feature of AD is neural damage and hyperactivation of astrocytes, that can cause increased pro-inflammatory mediators and oxidative stress. In addition, reduced brain glucose metabolism and mitochondrial dysfunction appears up to 15 years before the onset of clinical AD symptoms. As glucose utilization is compromised in the brain of patients with AD, ketone bodies (KBs) may serve as an alternative source of energy. KBs are generated from the β-oxidation of fatty acids, which are enhanced following consumption of ketogenic diets with high fat, moderate protein, and low carbohydrate. KBs have been shown to cross the blood brain barrier to improve brain energy metabolism. This review comprehensively summarizes the current literature on how increasing KBs support brain energy metabolism. In addition, for the first time, this review discusses the effects of ketogenic diet on the putative AD biomarkers such as Aβ, tau (mainly p-tau 181), GFAP, and NFL, and discusses the role of KBs on neuroinflammation, oxidative stress, and mitochondrial metabolism.

Patterns of Glucose Metabolism in [ 18 F]FDG PET Indicate Regional Variability and Neurodegeneration in the Progression of Alzheimer's Dementia

In disorders of cognitive impairment, such as Alzheimer's disease, neurodegeneration is the final common pathway of disease progression. Modulating, reversing, or preventing disease progression is a clinical imperative most likely to succeed following accurate and explanatory understanding of neurodegeneration, requiring enhanced consistency with quantitative measurements and expanded interpretability of complex data. The on-going study of neurodegeneration has robustly demonstrated the advantages of accumulating large amounts of clinical data that include neuroimaging, motiving multi-center studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Demonstrative advantages also arise from highly multivariate analysis methods, and this work reports advances provided by non-negative matrix factorization (NMF). NMF revealed patterns of covariance for glucose metabolism, estimated by positron emission tomography of [ 18 F]fluorodeoxyglucose, in 243 healthy normal participants of ADNI. Patterns for glucose metabolism provided cross-sectional inferences for 860 total participants of ADNI with and without cerebral amyloidosis and clinical dementia ratings (CDR) ranging 0-3. Patterns for glucose metabolism were distinct in number and topography from patterns identified in previous studies of structural MRI. They were also distinct from well-establish topographies of resting-state neuronal networks mapped by functional magnetic resonance imaging. Patterns for glucose metabolism identified significant topographical landmarks relating age, sex, APOE ε4 alleles, amyloidosis, CDR, and neurodegeneration. Patterns involving insular and orbitofrontal cortices, as well as midline regions of frontal and parietal lobes demonstrated the greatest neurodegeneration with progressive Alzheimer's dementia. A single pattern for the lateral parietal and posterior superior temporal cortices demonstrated preserved glucose metabolism for all diagnostic groups, including Alzheimer's dementia. Patterns correlated significantly with topical terms from the Neurosynth platform, thereby providing semantic representations for patterns such as attention, memory, language, fear/reward, movement and motor planning. In summary, NMF is a data-driven, principled, supervised statistical learning method that provides interpretable patterns of neurodegeneration. These patterns can help inform the understanding and treatment of Alzheimer's disease.

Highlights

▪ Data-driven non-negative matrix factorization (NMF) identified 24 canonical patterns of spatial covariance of cerebral glucose metabolism. The training data comprised healthy older participants (CDR = 0 without amyloidosis) cross-sectionally drawn from ADNI. ▪ In healthy participants, mean SUVRs for specific patterns in precuneus, lateral parietal cortex, and subcortical areas including superficial white matter and striatum, demonstrated increasing glucose metabolism with advancing age. ▪ In asymptomatic participants with amyloidosis , glucose metabolism increased compared to those who were asymptomatic without amyloid , particularly in medial prefrontal cortex, frontoparietal cortex, occipital white, and posterior cerebellar regions. ▪ In symptomatic participants with amyloidosis , insular cortex, medial frontal cortex, and prefrontal cortex demonstrated the most severe losses of glucose metabolism with increasing CDR. Lateral parietal and posterior superior temporal cortices retained glucose metabolism even for CDR > 0.5. ▪ NMF models of glucose metabolism are consistent with models arising from principal components, or eigenbrains, while adding additional regional interpretability. ▪ NMF patterns correlated with regions catalogued in Neurosynth. Following corrections for spatial autocorrelations, NMF patterns revealed meta-analytic identifications of patterns with Neurosynth topics of fear/reward, attention, memory, language, and movement with motor planning. Patterns varied with degrees of cognitive impairment.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

Shared metabolic shifts in endothelial cells in stroke and Alzheimer's disease revealed by integrated analysis

Since metabolic dysregulation is a hallmark of both stroke and Alzheimer's disease (AD), mining shared metabolic patterns in these diseases will help to identify their possible pathogenic mechanisms and potential intervention targets. However, a systematic integration analysis of the metabolic networks of the these diseases is still lacking. In this study, we integrated single-cell RNA sequencing datasets of ischemic stroke (IS), hemorrhagic stroke (HS) and AD models to construct metabolic flux profiles at the single-cell level. We discovered that the three disorders cause shared metabolic shifts in endothelial cells. These altered metabolic modules were mainly enriched in the transporter-related pathways and were predicted to potentially lead to a decrease in metabolites such as pyruvate and fumarate. We further found that Lef1, Elk3 and Fosl1 may be upstream transcriptional regulators causing metabolic shifts and may be possible targets for interventions that halt the course of neurodegeneration.

Cancer-associated somatic mutations in human phosphofructokinase-1 reveal a critical electrostatic interaction for allosteric regulation of enzyme activity

Metabolic reprogramming, including increased glucose uptake and lactic acid excretion, is a hallmark of cancer. The glycolytic 'gatekeeper' enzyme phosphofructokinase-1 (PFK1), which catalyzes the step committing glucose to breakdown, is dysregulated in cancers. While altered PFK1 activity and expression in tumors have been demonstrated, little is known about the effects of cancer-associated somatic mutations. Somatic mutations in PFK1 inform our understanding of allosteric regulation by identifying key amino acid residues involved in the regulation of enzyme activity. Here, we characterized mutations disrupting an evolutionarily conserved salt bridge between aspartic acid and arginine in human platelet (PFKP) and liver (PFKL) isoforms. Using purified recombinant proteins, we showed that disruption of the Asp-Arg pair in two PFK1 isoforms decreased enzyme activity and altered allosteric regulation. We determined the crystal structure of PFK1 to 3.6 Å resolution and used molecular dynamic simulations to understand molecular mechanisms of altered allosteric regulation. We showed that PFKP-D564N had a decreased total system energy and changes in the electrostatic surface potential of the effector site. Cells expressing PFKP-D564N demonstrated a decreased rate of glycolysis, while their ability to induce glycolytic flux under conditions of low cellular energy was enhanced compared with cells expressing wild-type PFKP. Taken together, these results suggest that mutations in Arg-Asp pair at the interface of the catalytic-regulatory domains stabilizes the t-state and presents novel mechanistic insight for therapeutic development in cancer.

Current Advances in Mitochondrial Targeted Interventions in Alzheimer's Disease

Alzheimer's disease is the most prevalent neurodegenerative disorder and affects the lives not only of those who are diagnosed but also of their caregivers. Despite the enormous social, economic and political burden, AD remains a disease without an effective treatment and with several failed attempts to modify the disease course. The fact that AD clinical diagnosis is most often performed at a stage at which the underlying pathological events are in an advanced and conceivably irremediable state strongly hampers treatment attempts. This raises the awareness of the need to identify and characterize the early brain changes in AD, in order to identify possible novel therapeutic targets to circumvent AD's cascade of events. One of the most auspicious targets is mitochondria, powerful organelles found in nearly all cells of the body. A vast body of literature has shown that mitochondria from AD patients and model organisms of the disease differ from their non-AD counterparts. In view of this evidence, preserving and/or restoring mitochondria's health and function can represent the primary means to achieve advances to tackle AD. In this review, we will briefly assess and summarize the previous and latest evidence of mitochondria dysfunction in AD. A particular focus will be given to the recent updates and advances in the strategy options aimed to target faulty mitochondria in AD.

Metabolic Reprogramming toward Aerobic Glycolysis and the Gut Microbiota Involved in the Brain Amyloid Pathology

Alzheimer's disease (AD) is characterized by the formation of senile plaques consisting of fibrillated amyloid-β (Aβ), dystrophic neurites, and the neurofibrillary tangles of tau. The oligomers/fibrillar Aβ damages the neurons or initiates an intracellular signaling cascade for neuronal cell death leading to Aβ toxicity. The Aβ is a 4 kDa molecular weight peptide originating from the C-terminal region of the amyloid precursor protein via proteolytic cleavage. Apart from the typical AD hallmarks, certain deficits in metabolic alterations have been identified. This study describes the emerging features of AD from the aspect of metabolic reprogramming in the main pathway of carbohydrate metabolism in the human brain. Particularly, the neurons in patients with AD favor glycolysis despite a normal mitochondrial function indicating a Warburg-like effect. In addition, certain dietary patterns are well known for their properties in preventing AD. Among those, a ketogenic diet may substantially improve the symptoms of AD. An effective therapeutic method for the treatment, mitigation, and prevention of AD has not yet been established. Therefore, the researchers pursue the development and establishment of novel therapies effective in suppressing AD symptoms and the elucidation of their underlying protective mechanisms against neurodegeneration aiming for AD therapy in the near future.

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

Background

Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored.

Methods

We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite.

Results

All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance.

Conclusions

Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences.

Funding

This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).