Fab-dimerized glycan-reactive antibodies are a structural category of natural antibodies.

Natural antibodies (Abs) can target host glycans on the surface of pathogens. We studied the evolution of glycan-reactive B cells of rhesus macaques and humans using glycosylated HIV-1 envelope (Env) as a model antigen. 2G12 is a broadly neutralizing Ab (bnAb) that targets a conserved glycan patch on Env of geographically diverse HIV-1 strains using a unique heavy-chain (VH) domain-swapped architecture that results in fragment antigen-binding (Fab) dimerization. Here, we describe HIV-1 Env Fab-dimerized glycan (FDG)-reactive bnAbs without VH-swapped domains from simian-human immunodeficiency virus (SHIV)-infected macaques. FDG Abs also recognized cell-surface glycans on diverse pathogens, including yeast and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike. FDG precursors were expanded by glycan-bearing immunogens in macaques and were abundant in HIV-1-naive humans. Moreover, FDG precursors were predominately mutated IgM+IgD+CD27+, thus suggesting that they originated from a pool of antigen-experienced IgM+ or marginal zone B cells.

Longitudinal flortaucipir, metabolism and volume differ between phonetic and prosodic speech apraxia.

Progressive apraxia of speech (PAOS) is a neurodegenerative motor-speech disorder that most commonly arises from a four-repeat tauopathy. Recent studies have established that progressive apraxia of speech is not a homogenous disease but rather there are distinct subtypes: the phonetic subtype is characterized by distorted sound substitutions, the prosodic subtype by slow and segmented speech and the mixed subtype by a combination of both but lack of predominance of either. There is some evidence that cross-sectional patterns of neurodegeneration differ across subtypes, although it is unknown whether longitudinal patterns of neurodegeneration differ. We examined longitudinal patterns of atrophy on MRI, hypometabolism on 18F-fluorodeoxyglucose-PET and tau uptake on flortaucipir-PET in a large cohort of subjects with PAOS that had been followed for many years. Ninety-one subjects with PAOS (51 phonetic, 40 prosodic) were recruited by the Neurodegenerative Research Group. Of these, 54 (27 phonetic, 27 prosodic) returned for annual follow-up, with up to seven longitudinal visits (total visits analysed = 217). Volumes, metabolism and flortaucipir uptake were measured for subcortical and cortical regions, for all scans. Bayesian hierarchical models were used to model longitudinal change across imaging modalities with PAOS subtypes being compared at baseline, 4 years from baseline, and in terms of rates of change. The phonetic group showed smaller volumes and worse metabolism in Broca's area and the striatum at baseline and after 4 years, and faster rates of change in these regions, compared with the prosodic group. There was also evidence of faster spread of hypometabolism and flortaucipir uptake into the temporal and parietal lobes in the phonetic group. In contrast, the prosodic group showed smaller cerebellar dentate, midbrain, substantia nigra and thalamus volumes at baseline and after 4 years, as well as faster rates of atrophy, than the phonetic group. Greater hypometabolism and flortaucipir uptake were also observed in the cerebellar dentate and substantia nigra in the prosodic group. Mixed findings were observed in the supplementary motor area and precentral cortex, with no clear differences observed across phonetic and prosodic groups. These findings support different patterns of disease spread in PAOS subtypes, with corticostriatal patterns in the phonetic subtype and brainstem and thalamic patterns in the prosodic subtype, providing insight into the pathophysiology and heterogeneity of PAOS.

Uncovering the distinct macro-scale anatomy of dysexecutive and behavioural degenerative diseases.

There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer's disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer's disease pathology (behavioural Alzheimer's disease) and 28 had amnestic Alzheimer's disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as 'eigenbrains'. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer's disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer's disease, then amnestic Alzheimer's disease. Dysexecutive Alzheimer's disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer's disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer's disease, temporo-parietal areas in amnestic Alzheimer's disease and frontotemporal areas in bvFTD and behavioural Alzheimer's disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer's disease patient for behavioural Alzheimer's disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

Metabolic network connectivity disturbances in Parkinson's disease: a novel imaging biomarker.

The diagnosis of Parkinson's Disease (PD) presents ongoing challenges. Advances in imaging techniques like 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have highlighted metabolic alterations in PD, yet the dynamic network interactions within the metabolic connectome remain elusive. To this end, we examined a dataset comprising 49 PD patients and 49 healthy controls. By employing a personalized metabolic connectome approach, we assessed both within- and between-network connectivities using Standard Uptake Value (SUV) and Jensen-Shannon Divergence Similarity Estimation (JSSE). A random forest algorithm was utilized to pinpoint key neuroimaging features differentiating PD from healthy states. Specifically, the results revealed heightened internetwork connectivity in PD, specifically within the somatomotor (SMN) and frontoparietal (FPN) networks, persisting after multiple comparison corrections (P < 0.05, Bonferroni adjusted for 10% and 20% sparsity). This altered connectivity effectively distinguished PD patients from healthy individuals. Notably, this study utilizes 18F-FDG PET imaging to map individual metabolic networks, revealing enhanced connectivity in the SMN and FPN among PD patients. This enhanced connectivity may serve as a promising imaging biomarker, offering a valuable asset for early PD detection.

Imaging of the brain-heart axis: prognostic value in a European setting.

BACKGROUND AND AIMS: Increasing data suggest that stress-related neural activity (SNA) is associated with subsequent major adverse cardiovascular events (MACE) and may represent a therapeutic target. Current evidence is exclusively based on populations from the U.S. and Asia where limited information about cardiovascular disease risk was available. This study sought to investigate whether SNA imaging has clinical value in a well-characterized cohort of cardiovascular patients in Europe. METHODS: In this single-centre study, a total of 963 patients (mean age 58.4 ± 16.1 years, 40.7% female) with known cardiovascular status, ranging from 'at-risk' to manifest disease, and without active cancer underwent 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography between 1 January 2005 and 31 August 2019. Stress-related neural activity was assessed with validated methods and relations between SNA and MACE (non-fatal stroke, non-fatal myocardial infarction, coronary revascularization, and cardiovascular death) or all-cause mortality by time-to-event analysis. RESULTS: Over a maximum follow-up of 17 years, 118 individuals (12.3%) experienced MACE, and 270 (28.0%) died. In univariate analyses, SNA significantly correlated with an increased risk of MACE (sub-distribution hazard ratio 1.52, 95% CI 1.05-2.19; P = .026) or death (hazard ratio 2.49, 95% CI 1.96-3.17; P < .001). In multivariable analyses, the association between SNA imaging and MACE was lost when details of the cardiovascular status were added to the models. Conversely, the relationship between SNA imaging and all-cause mortality persisted after multivariable adjustments. CONCLUSIONS: In a European patient cohort where cardiovascular status is known, SNA imaging is a robust and independent predictor of all-cause mortality, but its prognostic value for MACE is less evident. Further studies should define specific patient populations that might profit from SNA imaging.

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.

Evaluation of the 2023 Duke-International Society of Cardiovascular Infectious Diseases Criteria in a Multicenter Cohort of Patients With Suspected Infective Endocarditis.

BACKGROUND: Since publication of Duke criteria for infective endocarditis (IE) diagnosis, several modifications have been proposed. We aimed to evaluate the diagnostic performance of the Duke-ISCVID (International Society of Cardiovascular Infectious Diseases) 2023 criteria compared to prior versions from 2000 (Duke-Li 2000) and 2015 (Duke-ESC [European Society for Cardiology] 2015). METHODS: This study was conducted at 2 university hospitals between 2014 and 2022 among patients with suspected IE. A case was classified as IE (final IE diagnosis) by the Endocarditis Team. Sensitivity for each version of the Duke criteria was calculated among patients with confirmed IE based on pathological, surgical, and microbiological data. Specificity for each version of the Duke criteria was calculated among patients with suspected IE for whom IE diagnosis was ruled out. RESULTS: In total, 2132 episodes with suspected IE were included, of which 1101 (52%) had final IE diagnosis. Definite IE by pathologic criteria was found in 285 (13%), 285 (13%), and 345 (16%) patients using the Duke-Li 2000, Duke-ESC 2015, or the Duke-ISCVID 2023 criteria, respectively. IE was excluded by histopathology in 25 (1%) patients. The Duke-ISCVID 2023 clinical criteria showed a higher sensitivity (84%) compared to previous versions (70%). However, specificity of the new clinical criteria was lower (60%) compared to previous versions (74%). CONCLUSIONS: The Duke-ISCVID 2023 criteria led to an increase in sensitivity compared to previous versions. Further studies are needed to evaluate items that could increase sensitivity by reducing the number of IE patients misclassified as possible, but without having detrimental effect on specificity of Duke criteria.

Astroglial glucose uptake determines brain FDG-PET alterations and metabolic connectivity during healthy aging in mice.

PURPOSE: 2-Fluorodeoxyglucose-PET (FDG-PET) is a powerful tool to study glucose metabolism in mammalian brains, but cellular sources of glucose uptake and metabolic connectivity during aging are not yet understood. METHODS: Healthy wild-type mice of both sexes (2-21 months of age) received FDG-PET and cell sorting after in vivo tracer injection (scRadiotracing). FDG uptake per cell was quantified in isolated microglia, astrocytes and neurons. Cerebral FDG uptake and metabolic connectivity were determined by PET. A subset of mice received measurement of blood glucose levels to study associations with cellular FDG uptake during aging. RESULTS: Cerebral FDG-PET signals in healthy mice increased linearly with age. Cellular FDG uptake of neurons increased between 2 and 12 months of age, followed by a strong decrease towards late ages. Contrarily, FDG uptake in microglia and astrocytes exhibited a U-shaped function with respect to age, comprising the predominant cellular source of higher cerebral FDG uptake in the later stages. Metabolic connectivity was closely associated with the ratio of glucose uptake in astroglial cells relative to neurons. Cellular FDG uptake was not associated with blood glucose levels and increasing FDG brain uptake as a function of age was still observed after adjusting for blood glucose levels. CONCLUSION: Trajectories of astroglial glucose uptake drive brain FDG-PET alterations and metabolic connectivity during aging.

Quantitative brain [18F]FDG PET beyond normal blood glucose levels.

Introduction SUV measurements from static brain [18F]FDG PET acquisitions are a commonly used tool in preclinical research, providing a simple alternative for kinetic modelling, which requires complex and time-consuming dynamic acquisitions. However, SUV can be severely affected by the animal handling and preconditioning protocols, primarily by those that may induce changes in blood glucose levels (BGL). Here, we aimed at developing and investigating the feasibility of SUV-based approaches for a wide range of BGL far beyond normal values, and consequently, to develop and validate a new model to generate standardized and reproducible SUV measurements for any BGL. Material and methods We performed dynamic and static brain [18F]FDG PET acquisitions in 52 male Sprague-Dawley rats sorted into control (n = 10), non-fasting (n = 14), insulin-induced hypoglycemia (n = 12) and glucagon-induced hyperglycemia (n = 16) groups. Brain [18F]FDG PET images were cropped, aligned and co-registered to a standard template to calculate whole-brain and regional SUV. Cerebral Metabolic Rate of Glucose (CMRglc) was also estimated from 2-Tissue Compartment Model (2TCM) and Patlak plot for validation purposes. Results Our results showed that BGL=100±6 mg/dL can be considered a reproducible reference value for normoglycemia. Furthermore, we successfully established a 2nd-degree polynomial model (C1=0.66E-4, C2=-0.0408 and C3=7.298) relying exclusively on BGL measures at pre-[18F]FDG injection time, that characterizes more precisely the relationship between SUV and BGL for a wide range of BGL values (from 10 to 338 mg/dL). We confirmed the ability of this model to generate corrected SUV estimations that are highly correlated to CMRglc estimations (R2= 0.54 2TCM CMRgluc and R2= 0.49 Patlak CMRgluc). Besides, slight regional differences in SUV were found in animals from extreme BGL groups, showing that [18F]FDG uptake is mostly directed toward central regions of the brain when BGLs are significantly decreased. Conclusion Our study successfully established a non-linear model that relies exclusively on pre-scan BGL measurements to characterize the relationship between [18F]FDG SUV and BGL. The extensive validation confirmed its ability to generate SUV-based surrogates of CMRglu along a wide range of BGL and it holds the potential to be adopted as a standard protocol by the preclinical neuroimaging community using brain [18F]FDG PET imaging.

A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET.

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.

Evaluation of the Therapeutical Effect of Matricaria Chamomilla Extract vs. Galantamine on Animal Model Memory and Behavior Using 18F-FDG PET/MRI.

The memory-enhancing activity of Matricaria chamomilla hydroalcoholic extract (MCE) is already being investigated by behavioral and biochemical assays in scopolamine-induced amnesia rat models, while the effects of scopolamine (Sco) on cerebral glucose metabolism are examined as well. Nevertheless, the study of the metabolic profile determined by an enriched MCE has not been performed before. The present experiments compared metabolic quantification in characteristic cerebral regions and behavioral characteristics for normal, only diseased, diseased, and MCE- vs. Galantamine (Gal)-treated Wistar rats. A memory deficit was induced by four weeks of daily intraperitoneal Sco injection. Starting on the eighth day, the treatment was intraperitoneally administered 30 min after Sco injection for a period of three weeks. The memory assessment comprised three maze tests. Glucose metabolism was quantified after the 18F-FDG PET examination. The right amygdala, piriform, and entorhinal cortex showed the highest differential radiopharmaceutical uptake of the 50 regions analyzed. Rats treated with MCE show metabolic similarity with normal rats, while the Gal-treated group shows features closer to the diseased group. Behavioral assessments evidenced a less anxious status and a better locomotor activity manifested by the MCE-treated group compared to the Gal-treated group. These findings prove evident metabolic ameliorative qualities of MCE over Gal classic treatment, suggesting that the extract could be a potent neuropharmacological agent against amnesia.

Advanced imaging for earlier diagnosis and morbidity prevention in multiple myeloma: A British Society of Haematology and UK Myeloma Society Good Practice Paper.

This Good Practice Paper provides recommendations for the use of advanced imaging for earlier diagnosis and morbidity prevention in multiple myeloma. It describes how advanced imaging contributes to optimal healthcare resource utilisation by in newly diagnosed and relapsed myeloma, and provides a perspective on future directions of myeloma imaging, including machine learning assisted reporting.

18F-FDG PET/CT for early prediction of pathological complete response in breast cancer neoadjuvant therapy: a retrospective analysis.

BACKGROUND: Neoadjuvant treatment has been developed as a systematic approach for patients with early breast cancer and has resulted in improved breast-conserving rate and survival. However, identifying treatment-sensitive patients at the early phase of therapy remains a problem, hampering disease management and raising the possibility of disease progression during treatment. METHODS: In this retrospective analysis, we collected 2-deoxy-2-[F-18] fluoro-d-glucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) images of primary tumor sites and axillary areas and reciprocal clinical pathological data from 121 patients who underwent neoadjuvant treatment and surgery in our center. The univariate and multivariate logistic regression analyses were performed to investigate features associated with pathological complete response (pCR). An 18F-FDG PET/CT-based prediction model was trained, and the performance was evaluated by receiver operating characteristic curves (ROC). RESULTS: The maximum standard uptake values (SUVmax) of 18F-FDG PET/CT were a powerful indicator of tumor status. The SUVmax values of axillary areas were closely related to metastatic lymph node counts (R = 0.62). Moreover, the early SUVmax reduction rates (between baseline and second cycle of neoadjuvant treatment) were statistically different between pCR and non-pCR patients. The early SUVmax reduction rates-based model showed great ability to predict pCR (AUC = 0.89), with all molecular subtypes (HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2-) considered. CONCLUSION: Our research proved that the SUVmax reduction rate of 18F-FDG PET/CT contributed to the early prediction of pCR, providing rationales for utilizing PET/CT in NAT in the future.

Individual-specific metabolic network based on 18F-FDG PET revealing multi-level aberrant metabolisms in Parkinson's disease.

Metabolic network analysis in Parkinson's disease (PD) based on 18F-FDG PET has revealed PD-related metabolic patterns. However, alterations at the systemic metabolic network level and at the connection level between different brain regions still remain unknown. This study aimed to explore metabolic network alterations at multiple network levels among PD patients using an individual-specific metabolic network (ISMN) approach. 18F-FDG-PET images of patients with PD (n = 34) and healthy subjects (n = 47) were collected. Healthy subjects were further separated into reference group (n = 28) and control group (n = 19) randomly. Standardized uptake value normalized by lean body mass ratio (SULr) maps was calculated from the PET images. ISMNs were constructed based on SULr maps for PD patients and controls with reference to the reference group. Comparisons of nodal and edge features were performed between PD and control groups. Correlation analysis was conducted between multilevel network properties and clinical scales in PD group. A linear classifier was trained based on nodal or edge features to distinguish PD from controls. The distance from each patient's ISMN to the group-level difference network showed a negative correlation with Hoehn and Yahr stage (r = -0.390, p = .023). Eight nodes from ISMN were identified which exhibited significantly increased nodal degree in PD patients compared to controls (p < .05). Eleven edges were observed which demonstrated significant distinctions in Z-score values in comparisons to the control group (p < .05). Furthermore, the nodal and edge features showed comparable performances in PD diagnosis compared to the traditional SULr values, with area under the receiver operating characteristic curve larger than 0.91. The proposed ISMN approach revealed systemic metabolic deviations, as well as nodal and edge distinctions in PD, which might be supplementary to the existing findings on PD-related metabolic patterns.

Topographic metabolism-function relationships in Alzheimer's disease: A simultaneous PET/MRI study.

Disruptions of neural metabolism and function occur in parallel during Alzheimer's disease (AD). While many studies have shown diverse metabolic-functional relationships in specific brain regions, much less is known about how large-scale network-level functional activity is associated with the topology of metabolism in AD. In this study, we took the advantages of simultaneous PET/MRI and multivariate analyses to investigate the associations between AD-related stereotypical spatial patterns (topographies) of glucose metabolism, measured by fluorodeoxyglucose PET, and functional connectivity, measured by resting-state functional MRI. A total of 101 participants, including 37 patients with AD, 25 patients with mild cognitive impairment (MCI), and 39 cognitively normal controls, underwent PET/MRI scans and cognitive assessments. Three pairs of distinct but optimally correlated metabolic and functional topographies were identified, encompassing large-scale networks including the default-mode, executive and control, salience, attention, and subcortical networks. Importantly, the metabolic-functional associations were not only limited to one-to-one-corresponding regions, but also occur in remote and non-overlapping regions. Furthermore, both glucose metabolism and functional connectivity, as well as their linkages, exhibited various degrees of disruptions in patients with MCI and AD, and were correlated with cognitive decline. In conclusion, our results support distributed and heterogeneous topographic associations between metabolism and function, which are jeopardized by AD. Findings of this study may deepen our understanding of the pathological mechanism of AD through the perspectives of both local energy efficiency and long-term interactions between synaptic disruption and functional disconnection contributing to the clinical symptomatology in AD.

Clinical implications of non-breast cancer related findings on FDG-PET/CT scan prior to neoadjuvant chemotherapy in patients with breast cancer.

PURPOSE: Breast cancer (BC) patients undergoing FDG-PET/CT scans for neoadjuvant chemotherapy (NAC) may have additional non-BC related findings. The aim of this study is to describe the clinical implications of these findings. METHODS: We included BC patients who underwent an FDG-PET/CT scan in our institute between 2011-2020 prior to NAC. We focused on patients with an additional non-BC related finding (i.e. BC metastases were excluded) for which diagnostic work-up was performed. Information about the diagnostic work-up and the clinical consequences was retrospectively gathered. A revision of all FDG-PET/CT scans was conducted by an independent physician to assess the suspicion level of the additional findings. RESULTS: Of the 1337 patients who underwent FDG-PET/CT, 202 patients (15%) had an non-BC related additional finding for which diagnostic work-up was conducted, resulting in 318 examinations during the first year. The non-BC related findings were mostly detected in the endocrine region (26%), gastro-intestinal region (16%), or the lungs (15%). Seventeen patients (17/202: 8%, 17/1337: 1.3%) had a second primary malignancy. Only 8 patients (8/202: 4%, 8/1337: 0.6%) had a finding that was considered more prognosis-determining than their BC disease. When revising all FDG-PET/CT scans, 57 (202/57: 28%) of the patients had an additional finding categorized as low suspicious, suggesting no indication for diagnostic work-up. CONCLUSION: FDG-PET/CT scans used for dissemination imaging in BC patients detect a high number of non-BC related additional findings, often clinically irrelevant and causing a large amount of unnecessary work-up. However, in 8% of the patients undergoing diagnostic work-up for an additional finding, a second primary malignancy was detected, warranting diagnostic attention in selected patients.

The efficacy of screening with FDG-PET/CT for distant metastases in breast cancer patients scheduled for neoadjuvant systemic therapy.

PURPOSE: This study aims to identify which breast cancer patients benefit from the routine use of FDG-PET/CT in a large cohort of patients scheduled for neoadjuvant systemic therapy (NST). METHODS: A total of 1337 breast cancer patients eligible for NST were identified from a retrospective database between 2011 and 2020 at a single tertiary care hospital. All patients underwent staging with FDG-PET/CT prior to NST. The incidence and extent of asymptomatic distant metastases in different patient subgroups were determined, as well as the impact on treatment. Logistic regression analysis was used to identify prognostic patient and tumor characteristics. RESULTS: FDG-PET/CT detected distant metastases in 109 patients (8%). Initial clinical stage was a prognostic factor for the presence of distant metastases, with a significantly higher risk for stage 2b and 3 as opposed to lower stages (p < 0.001). The incidence of distant metastases was 3% (4/125) for stage 1, 2% (8/534) for stage 2a, 7% (24/354) for stage 2b and 23% (73/324) for stage 3. Other characteristics such as age, tumor subtype, histological type and grade were not correlated with the risk of distant metastases. Among the subset of patients with distant metastases, 46% received palliative treatment, while the remaining 54% were diagnosed with oligometastatic breast cancer and were treated with curative intent. CONCLUSION: The results of the current study support the routine use of FDG-PET/CT for the detection of distant metastases in breast cancer patients with initial clinical stage 2b and 3, regardless of tumor subtype.

Therapeutic impact of 18F-FDG PET/CT for initial staging in patients with clinical stage I and IIA, HER2-positive, or triple-negative breast cancer.

PURPOSE: While 18F-FDG PET/CT (FDG-PET/CT) is consensual for clinical stage ≥ IIB breast cancers (BC), its benefit for stage I or IIA HER2+ or triple-negative breast cancer (TNBC) patients lacks sufficient evidence. We reported a single-institution, retrospective study evaluating FDG-PET/CT impact on patient management and staging for stage I or IIA HER2+ or Triple-Negative BC. METHODS: Patients who underwent FDG-PET/CT staging before any treatment between January 2015 and December 2020 at Oscar Lambret Center were included. EXCLUSIONS: patients with symptoms or conventional imaging suggestive of metastatic dissemination, or with prior malignancies. Initial stage was determined from mammography, breast ultrasound, breast MRI, and clinical examination. Staging and therapeutic impact based on FDG-PET/CT findings collected, including intra- (modification of dose/site/strategy in a type of management previously indicated) and inter-modality (modification of planned treatment strategy) changes. RESULTS: The cohort included 287 female patients with clinical stage I or IIA, HER2+ , or TNBC. Therapeutic impact observed for 18% of patients (n = 52), with 2% (n = 7) undergoing inter-modality change with omission of planned surgery. The impact on patient management was higher for stage IIA patients (20%, 47/237) than for stage I patients (10%, 5/50). Among stage IIA disease, changes in management were more important for T2N0 patients (22%, 44/205) than for T1N1 patients (9%, 3/32). While not statistically significant, trends suggest usefulness of FDG-PET/CT for T2N0 patients. CONCLUSION: Considering substantial therapeutic implications, our study suggests the usefulness of FDG-PET/CT for patients with stage IIA, HER2-positive, or Triple-Negative BC with tumor size > 2 cm (T2N0).

Western diet increases brain metabolism and adaptive immune responses in a mouse model of amyloidosis.

Diet-induced increase in body weight is a growing health concern worldwide. Often accompanied by a low-grade metabolic inflammation that changes systemic functions, diet-induced alterations may contribute to neurodegenerative disorder progression as well. This study aims to non-invasively investigate diet-induced metabolic and inflammatory effects in the brain of an APPPS1 mouse model of Alzheimer's disease. [18F]FDG, [18F]FTHA, and [18F]GE-180 were used for in vivo PET imaging in wild-type and APPPS1 mice. Ex vivo flow cytometry and histology in brains complemented the in vivo findings. 1H- magnetic resonance spectroscopy in the liver, plasma metabolomics and flow cytometry of the white adipose tissue were used to confirm metaflammatory condition in the periphery. We found disrupted glucose and fatty acid metabolism after Western diet consumption, with only small regional changes in glial-dependent neuroinflammation in the brains of APPPS1 mice. Further ex vivo investigations revealed cytotoxic T cell involvement in the brains of Western diet-fed mice and a disrupted plasma metabolome. 1H-magentic resonance spectroscopy and immunological results revealed diet-dependent inflammatory-like misbalance in livers and fatty tissue. Our multimodal imaging study highlights the role of the brain-liver-fat axis and the adaptive immune system in the disruption of brain homeostasis in amyloid models of Alzheimer's disease.