Metabolic and amyloid PET network reorganization in Alzheimer's disease: differential patterns and partial volume effects

Alzheimer’s disease (AD) is a neurodegenerative disorder, considered a disconnection syndrome with regional molecular pattern abnormalities quantifiable by the aid of PET imaging. Solutions for accurate quantification of network dysfunction are scarce. We evaluate the extent to which PET molecular markers reflect quantifiable network metrics derived through the graph theory framework and how partial volume effects (PVE)-correction (PVEc) affects these PET-derived metrics 75 AD patients and 126 cognitively normal older subjects (CN). Therefore our goal is twofold: 1) to evaluate the differential patterns of [18F]FDG- and [18F]AV45-PET data to depict AD pathology; and ii) to analyse the effects of PVEc on global uptake measures of [18F]FDG- and [18F]AV45-PET data and their derived covariance network reconstructions for differentiating between patients and normal older subjects. Network organization patterns were assessed using graph theory in terms of “degree”, “modularity”, and “efficiency”. PVEc evidenced effects on global uptake measures that are specific to either [18F]FDG- or [18F]AV45-PET, leading to increased statistical differences between the groups. PVEc was further shown to influence the topological characterization of PET-derived covariance brain networks, leading to an optimised characterization of network efficiency and modularisation. Partial-volume effects correction improves the interpretability of PET data in AD and leads to optimised characterization of network properties for organisation or disconnection

Machine learning methods for tracer kinetic modelling.

Tracer kinetic modelling based on dynamic PET is an important field of Nuclear Medicine for quantitative functional imaging. Yet, its implementation in clinical routine has been constrained by its complexity and computational costs. Machine learning poses an opportunity to improve modelling processes in terms of arterial input function prediction, the prediction of kinetic modelling parameters and model selection in both clinical and preclinical studies while reducing processing time. Moreover, it can help improving kinetic modelling data used in downstream tasks such as tumor detection. In this review, we introduce the basics of tracer kinetic modelling and present a literature review of original works and conference papers using machine learning methods in this field

Iodine-124 PET quantification of organ-specific delivery and expression of NIS-encoding RNA

Background!#!RNA-based vaccination strategies tailoring immune response to specific reactions have become an important pillar for a broad range of applications. Recently, the use of lipid-based nanoparticles opened the possibility to deliver RNA to specific sites within the body, overcoming the limitation of rapid degradation in the bloodstream. Here, we have investigated whether small animal PET/MRI can be employed to image the biodistribution of RNA-encoded protein. For this purpose, a reporter RNA coding for the sodium-iodide-symporter (NIS) was in vitro transcribed in cell lines and evaluated for expression. RNA-lipoplex nanoparticles were then assembled by complexing RNA with liposomes at different charge ratios, and functional NIS protein translation was imaged and quantified in vivo and ex vivo by Iodine-124 PET upon intravenous administration in mice.!##!Results!#!NIS expression was detected on the membrane of two cell lines as early as 6 h after transfection and gradually decreased over 48 h. In vivo and ex vivo PET/MRI of anionic spleen-targeting or cationic lung-targeting NIS-RNA lipoplexes revealed a visually detectable rapid increase of Iodine-124 uptake in the spleen or lung compared to control-RNA-lipoplexes, respectively, with minimal background in other organs except from thyroid, stomach and salivary gland.!##!Conclusions!#!The strong organ selectivity and high target-to-background acquisition of NIS-RNA lipoplexes indicate the feasibility of small animal PET/MRI to quantify organ-specific delivery of RNA

In vivo imaging of the immune response upon systemic RNA cancer vaccination by FDG-PET

Abstract Background [18F]Fluoro-2-deoxy-2-d-glucose positron emission tomography (FDG-PET) is commonly used in the clinic for diagnosis of cancer and for follow-up of therapy outcome. Additional to the well-established value in tumor imaging, it bears potential to depict immune processes in modern immunotherapies. T cells enhance their glucose consumption upon activation and are crucial effectors for the success of such novel therapies. In this study, we analyzed the T cell immunity in spleen after antigen-specific stimulation of T cells via highly innovative RNA-based vaccines using FDG-PET/MRI. For this purpose, we employed systemic administration of RNA-lipoplexes encoding the endogenous antigen of Moloney murine leukemia virus (gp70) which have been previously shown to induce potent innate as well as adaptive immune mechanisms for cancer immunotherapy. Feasibility of clinical imaging of increased splenic FDG uptake was demonstrated in a melanoma patient participating in a clinical phase 1 trial of a tetravalent RNA-lipoplex cancer vaccine. Results We observed exclusive increase of glucose uptake in spleen compared to other organs thanks to liposome-mediated RNA targeting to this immune-relevant organ. In vivo and ex vivo FDG uptake analysis in the spleen of vaccinated mice correlated well with antigen-specific T cell activation. Moreover, the use of an irrelevant (antigen non-specific) RNA also resulted in enhanced FDG uptake early after vaccination through the activation of several other splenic cell populations. The glucose uptake was also dependent on the dose of RNA administered in line with the activation and frequencies of proliferating antigen-specific T cells as well as the general activation pattern of splenic cell populations. Conclusions Our preclinical results show rapid and transient vaccination-induced increase of FDG uptake within the spleen reflecting immune activation preceding T cell proliferation. FDG-PET/CT in patients is also capable to image this immune activation resulting in a new potential application of FDG-PET/CT to image immune processes in new immunological therapies

GABAergic dysfunction in essential tremor : an 11C-flumazenil PET study

Essential tremor is the most common movement disorder, but the underlying pathophysiology is not well understood. A primary overactivity of cerebellothalamic output pathways is the most conspicuous finding, as indicated by animal and human studies. It has been argued that this overactivity may be due to impaired central inhibition, and converging evidence points toward a potential role of gamma-aminobutyric acid (GABA) dysfunction in tremor generation

Clinical Applications of Radiomics in Nuclear Medicine

Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice

Clinical applications of radiomics in nuclear medicine

Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice

Quantification of the Cannabinoid Type 1 Receptor Availability in the Mouse Brain

INTRODUCTION: The endocannabinoid system is involved in several diseases such as addictive disorders, schizophrenia, post-traumatic stress disorder, and eating disorders. As often mice are used as the preferred animal model in translational research, in particular when using genetically modified mice, this study aimed to provide a systematic analysis of in vivo cannabinoid type 1 (CB1) receptor ligand-binding capacity using positron emission tomography (PET) using the ligand [18F]MK-9470. We then compared the PET results with literature data from immunohistochemistry (IHC) to review the consistency between ex vivo protein expression and in vivo ligand binding. METHODS: Six male C57BL/6J (6–9 weeks) mice were examined with the CB1 receptor ligand [18F]MK-9470 and small animal PET. Different brain regions were evaluated using the parameter %ID/ml. The PET results of the [18F]MK-9470 accumulation in the mouse brain were compared with immunohistochemical literature data. RESULTS: The ligand [18F]MK-9470 was taken up into the mouse brain within 5 min after injection and exhibited slow kinetics. It accumulated highly in most parts of the brain. PET and IHC classifications were consistent for most parts of the telencephalon, while brain regions of the diencephalon, mesencephalon, and rhombencephalon were rated higher with PET than IHC. CONCLUSIONS: This preclinical [18F]MK-9470 study demonstrated the radioligand’s applicability for imaging the region-specific CB1 receptor availability in the healthy adult mouse brain and thus offers the potential to study CB1 receptor availability in pathological conditions

Chronic social stress-induced hyperglycemia in mice couples individual stress susceptibility to impaired spatial memory

Stress-associated mental disorders and diabetes pose an enormous socio-economic burden. Glucose dysregulation occurs with both psychosocial and metabolic stress. While cognitive impairments are common in metabolic disorders such as diabetes and are accompanied by hyperglycemia, a causal role for glucose has not been established. We show that chronic social defeat (CSD) stress induces lasting peripheral and central hyperglycemia and impaired glucose metabolism in a subgroup of mice. Animals exhibiting hyperglycemia early post-CSD display spatial memory impairments that can be rescued by the antidiabetic empagliflozin. We demonstrate that individual stress vulnerability to glucose homeostasis can be identified early after insult and that stress-induced hyperglycemia directly impinges on cognitive integrity. Our findings further bridge the gap between stress-related pathologies and metabolic disorders.Stringent glucose demands render the brain susceptible to disturbances in the supply of this main source of energy, and chronic stress may constitute such a disruption. However, whether stress-associated cognitive impairments may arise from disturbed glucose regulation remains unclear. Here we show that chronic social defeat (CSD) stress in adult male mice induces hyperglycemia and directly affects spatial memory performance. Stressed mice developed hyperglycemia and impaired glucose metabolism peripherally as well as in the brain (demonstrated by PET and induced metabolic bioluminescence imaging), which was accompanied by hippocampus-related spatial memory impairments. Importantly, the cognitive and metabolic phenotype pertained to a subset of stressed mice and could be linked to early hyperglycemia 2 days post-CSD. Based on this criterion, ∼40% of the stressed mice had a high-glucose (glucose >150 mg/dL), stress-susceptible phenotype. The relevance of this biomarker emerges from the effects of the glucose-lowering sodium glucose cotransporter 2 inhibitor empagliflozin, because upon dietary treatment, mice identified as having high glucose demonstrated restored spatial memory and normalized glucose metabolism. Conversely, reducing glucose levels by empagliflozin in mice that did not display stress-induced hyperglycemia (resilient mice) impaired their default-intact spatial memory performance. We conclude that hyperglycemia developing early after chronic stress threatens long-term glucose homeostasis and causes spatial memory dysfunction. Our findings may explain the comorbidity between stress-related and metabolic disorders, such as depression and diabetes, and suggest that cognitive impairments in both types of disorders could originate from excessive cerebral glucose accumulation