FDG-PET Imaging of Dementia and Neurodegenerative Disease

Astrocytic Ca2+ activation by chemogenetics mitigates the effect of kainic acid-induced excitotoxicity on the hippocampus

Astrocytes play a multifaceted role regulating brain glucose metabolism, ion homeostasis, neurotransmitters clearance, and water dynamics being essential in supporting synaptic function. Under different pathological conditions such as brain stroke, epilepsy, and neurodegenerative disorders, excitotoxicity plays a crucial role, however, the contribution of astrocytic activity in protecting neurons from excitotoxicity-induced damage is yet to be fully understood. In this work, we evaluated the effect of astrocytic activation by Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) on brain glucose metabolism in wild-type (WT) mice, and we investigated the effects of sustained astrocyte activation following an insult induced by intrahippocampal (iHPC) kainic acid (KA) injection using 2-deoxy-2-[18F]-fluoro-D-glucose (18F-FDG) positron emission tomography (PET) imaging, along with behavioral test, nuclear magnetic resonance (NMR) spectroscopy and histochemistry. Astrocytic Ca2+ activation increased the 18F-FDG uptake, but this effect was not found when the study was performed in knock out mice for type-2 inositol 1,4,5-trisphosphate receptor (Ip3r2-/-) nor in floxed mice to abolish glucose transporter 1 (GLUT1) expression in hippocampal astrocytes (GLUT1ΔGFAP). Sustained astrocyte activation after KA injection reversed the brain glucose hypometabolism, restored hippocampal function, prevented neuronal death, and increased hippocampal GABA levels. The findings of our study indicate that astrocytic GLUT1 function is crucial for regulating brain glucose metabolism. Astrocytic Ca2+ activation has been shown to promote adaptive changes that significantly contribute to mitigating the effects of KA-induced damage. This evidence suggests a protective role of activated astrocytes against KA-induced excitotoxicity.

Supplementation of Medium-Chain Triglycerides Combined with Docosahexaenoic Acid Inhibits Amyloid Beta Protein Deposition by Improving Brain Glucose Metabolism in APP/PS1 Mice

The deterioration of brain glucose metabolism predates the clinical onset of Alzheimer's disease (AD). Medium-chain triglycerides (MCTs) and docosahexaenoic acid (DHA) positively improve brain glucose metabolism and decrease the expression of AD-related proteins. However, the effects of the combined intervention are unclear. The present study explored the effects of the supplementation of MCTs combined with DHA in improving brain glucose metabolism and decreasing AD-related protein expression levels in APP/PS1 mice. The mice were assigned into four dietary treatment groups: the control group, MCTs group, DHA group, and MCTs + DHA group. The corresponding diet of the respective groups was fed to mice from the age of 3 to 11 months. The results showed that the supplementation of MCTs combined with DHA could increase serum octanoic acid (C8:0), decanoic acid (C10:0), DHA, and β-hydroxybutyrate (β-HB) levels; improve glucose metabolism; and reduce nerve cell apoptosis in the brain. Moreover, it also aided with decreasing the expression levels of amyloid beta protein (Aβ), amyloid precursor protein (APP), β-site APP cleaving enzyme-1 (BACE1), and presenilin-1 (PS1) in the brain. Furthermore, the supplementation of MCTs + DHA was significantly more beneficial than that of MCTs or DHA alone. In conclusion, the supplementation of MCTs combined with DHA could improve energy metabolism in the brain of APP/PS1 mice, thus decreasing nerve cell apoptosis and inhibiting the expression of Aβ.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease

The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy.

Glucose metabolic crosstalk and regulation in brain function and diseases

Brain glucose metabolism, including glycolysis, the pentose phosphate pathway, and glycogen turnover, produces ATP for energetic support and provides the precursors for the synthesis of biological macromolecules. Although glucose metabolism in neurons and astrocytes has been extensively studied, the glucose metabolism of microglia and oligodendrocytes, and their interactions with neurons and astrocytes, remain critical to understand brain function. Brain regions with heterogeneous cell composition and cell-type-specific profiles of glucose metabolism suggest that metabolic networks within the brain are complex. Signal transduction proteins including those in the Wnt, GSK-3β, PI3K-AKT, and AMPK pathways are involved in regulating these networks. Additionally, glycolytic enzymes and metabolites, such as hexokinase 2, acetyl-CoA, and enolase 2, are implicated in the modulation of cellular function, microglial activation, glycation, and acetylation of biomolecules. Given these extensive networks, glucose metabolism dysfunction in the whole brain or specific cell types is strongly associated with neurologic pathology including ischemic brain injury and neurodegenerative disorders. This review characterizes the glucose metabolism networks of the brain based on molecular signaling and cellular and regional interactions, and elucidates glucose metabolism-based mechanisms of neurological diseases and therapeutic approaches that may ameliorate metabolic abnormalities in those diseases.