Adipose segmentation in small animals at 7T: a preliminary study

<p>Abstract</p> <p>Background</p> <p>Small animal MRI at 7 Tesla (T) provides a useful tool for adiposity research. For adiposity researchers, separation of fat from surrounding tissues and its subsequent quantitative or semi- quantitative analysis is a key task. This is a relatively new field and a priori it cannot be known which specific biological questions related to fat deposition will be relevant in a specific study. Thus it is impossible to predict what accuracy and what spatial resolution will be required in all cases and even difficult what accuracy and resolution will be useful in most cases. However the pragmatic time constraints and the practical resolution ranges are known for small animal imaging at 7T. Thus we have used known practical constraints to develop a method for fat volume analysis based on an optimized image acquisition and image post processing pair.</p> <p>Methods</p> <p>We designed a fat segmentation method based on optimizing a variety of factors relevant to small animal imaging at 7T. In contrast to most previously described MRI methods based on signal intensity of T1 weighted image alone, we chose to use parametric images based on Multi-spin multi-echo (MSME) Bruker pulse sequence which has proven to be particularly robust in our laboratory over the last several years. The sequence was optimized on a T1 basis to emphasize the signal. T2 relaxation times can be calculated from the multi echo data and we have done so on a pixel by pixel basis for the initial step in the post processing methodology. The post processing consists of parallel paths. On one hand, the weighted image is precisely divided into different regions with optimized smoothing and segmentation methods; and on the other hand, a confidence image is deduced from the parametric image according to the distribution of relaxation time relationship of typical adipose. With the assistance of the confidence image, a useful software feature was implemented to which enhances the data and in the end results in a more reliable and flexible method for adipose evaluation.</p> <p>Results</p> <p>In this paper, we describe how we arrived at our recommended procedures and key aspects of the post-processing steps. The feasibility of the proposed method is tested on both simulated and real data in this preliminary research. A research tool was created to help researchers segment out fat even when the anatomical information is of low quality making it difficult to distinguish between fat and non-fat. In addition, tool is designed to allow the operator to make adjustments to many of the key steps for comparison purposes and to quantitatively assess the difference these changes make. Ultimately our flexible software lets the researcher define key aspects of the fat segmentation and quantification.</p> <p>Conclusions</p> <p>Combining the full T2 parametric information with the optimized first echo image information, the research tool enhances the reliability of the results while providing more flexible operations than previous methods. The innovation in the method is to pair an optimized and very specific image acquisition technique to a flexible but tuned image post processing method. The separation of the fat is aided by the confidence distribution of regions produced on a scale relevant to and dictated by practical aspects of MRI at 7T.</p

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Traitement cérébral d'odeurs biologiquement signifiantes, révélé chez le rat par imagerie RMN fonctionnelle du manganèse

L'objectif de cette thèse est d'utiliser MEMRI (manganese-enhanced magnetic resonance imaging) pour étudier le traitement d'odeurs signifiantes dans le cortex olfactif primaire de rats dans les conditions les plus proches de la perception naturelle. MEMRI est une méthode fondée sur la détection d'un agent de contraste fonctionnel et rémanent de l'activité neuronale, le manganèse, qui a prouvé son efficacité pour montrer le traitement différencié d'odeurs dans le bulbe olfactif chez l'animal vigile. Cependant, cette technique a été surtout utilisée pour tracer les voies neuronales, mais relativement peu pour explorer des fonctions sensorielles. C'est pourquoi nous avons conduit deux études visant l'une à définir les conditions d'application du manganèse et l'autre à optimiser le traitement des images MEMRI, avant d'aborder la question biologique proprement dite. S'appuyant sur ces développement méthodologiques, nous avons ensuite utilisé MEMRI pour étudier les variations du traitement d'odeurs signifiantes (odeurs de nourriture et de prédateur comparées à une situation de contrôle) dans le cortex olfactif primaire de rats. Nous avons montré que le traitement cérébral d'une odeur de prédateur est différent de celui de la situation de contrôle dans le cortex olfactif primaire. Nous avons confirmé ce résultat par immunomarquage Fos dans le cortex piriforme. Mis ensemble, les résultats de MEMRI et Fos suggèrent que le traitement cérébral d'une odeur peut varier en terme de taille de populations de neurone recrutés ainsi qu'en termes d'intensité de l'activation de ces neurones. Enfin, les résultats MEMRI montrent qu'un message olfactif crucial, pour la survie, est traité asymétriquement dans le cerveau. Les avancées méthodologiques et scientifiques qu'apporte cette thèse ouvrent la voie à une meilleure compréhension du traitement cérébral des odeurs.The aim of this thesis was to use MEMRI (manganese-enhanced magnetic resonance imaging) for studying the processing of behaviorally significant odors in the rat primary olfactory cortex, under conditions close to natural perception in awake animals. MEMRI is a method based on the detection of o functional and remanent contrast agent, manganese, which has proved to be valuable dor studying odor processing in the olfactory bulb. However , this method has mainly been used to trace neuronal pathways, but seldom to explore sensory functions. Here, we have conducted two studies to define the conditions of application of manganese and to optimize processing of MEMRI images. Based on these methodological developments, we have then used MEMRI to investigate the activation of central olfactory structures following exposure of awake rats to biologically relevant odors (food and predator odors compared to a control situation). MEMRI revealed that a predator is processed differently from the control situation in the primary olfactory cortex. Fos immunolabeling in the anterior piriform cortex corroborated this result. Altogether, MEMRI and Fos results suggest that olfactory processing may rely on both the intensity of activation and the size of neuronal populations recruited. Finally, MEMRI revealed that the olfactory message, crucial for survival, is asymmetrically processed in the brain. Methodological and scientific advances brought by this thesis will be useful for better understanding brain olfactory processing.CLERMONT FD-Bib.électronique (631139902) / SudocSudocFranceF

Incertitude de la borne de Cramér-Rao : conséquences en spectroscopie RMN quantitative

Incertitude de la borne de Cramér-Rao : conséquences en spectroscopie RMN quantitative. 20. GERM Congres

CEST-MRI to reveal new contrasts: application in preclinical imaging and food science

MRI is one of the most performant non-invasive imaging technique. The images are contrasted thanks to local differences in water properties (T1, T2, ADC,…). One of the main limitation of MRI deals with its lack of sensitivity to image other molecules than water. One solution to circumvent this limitation is based on imaging the chemical exchange between solute (containing exchangeable protons) which is present in a too low concentration to be observable by MRI and the readily imaged water molecules. For that, several images are recorded with a signal saturation at different offsets from the water frequency. The water signal intensity is measured in function of the offset in a so-called z-spectrum. When exchangeable protons are irradiated, the water attenuation is more important than expected. The quantification of this attenuation provides an indirect way for imaging this low-concentration population. This approach has been implemented on our high-field MR imagers and is currently used in two projects. The first one consists in evaluating CEST imaging to characterize simultaneously both hypoxia and proteoglycan concentration in an animal model of chondrosarcoma (i.e., cartilage cancer). The hypoxia (pH) is imaged from the CEST effect of the amide functions (~3-4 ppm from the water frequency) while the proteoglycan concentration is deduced from the chemical exchange between hydroxyl groups and water (~1 ppm from the water resonance). As both properties are important in this pathology, CEST-MRI presents the advantage to assess both of them within a single acquisition. The second project focusses on imaging the gluten network formation in baking products. Indeed, the gluten network is made thanks to the formation of disulfide bonds from thiol moieties. First, we demonstrated that it is possible to monitor chemical exchange between thiol function and water. Applying CEST-MRI to baking products requires taking into account the magnetization transfer effects, which are predominant in the z-spectrum. We will present the progress of both projects

A robust and prior knowledge independent method to interpret non-negative least squares (NNLS) T2 relaxation results

The fitting of an NMR signal decay in a weighted sum of exponentials is an ill-posed problem, i.e. different sets of relaxation times and amplitudes will lead to the same least-squares distance between the model and the experimental noisy data. To analyze such data, the classical pipe consists in performing a non-negative least squares (NNLS) algorithm combined with a regularization to smooth the T2 distribution. However, a critical step of this approach deals with the choice of the operator and then of the corresponding regularization parameter which significantly affects the T2 distribution. These parameters are usually chosen based on the operator experience as well as prior knowledge on the sample. In this work, we propose to analyze NNLS results without regularization to circumvent these drawbacks. Our approach is based on the analysis of NNLS outputs by cumulative distribution functions (cdf) and not by probability density functions (pdf) as it is usually done. This concept is validated in different simulations for which the true T2 distributions are built from discrete to continuous functions. Simulation results showed that the T2 amplitude measured at a plateau of the cdf is unbiased and (almost) independent of both the decomposition basis and the signal-to-noise ratio. This observation allows to quantitatively interpret the NNLS inversions, especially when the true distribution is continuous. We suggest that NNLS by itself suffices in many situations, provided that cdf plateau can be discernable. The degrees of freedom to adjust in the method are then limited to the decomposition basis. To exemplify, this pragmatic and fruitful approach is applied on real NMR data obtained by spectroscopy and imaging