A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression

The difference between the chronological and biological brain age of a subject can be an important biomarker for neurodegenerative diseases, thus brain age estimation can be crucial in clinical settings. One way to incorporate multimodal information into this estimation is through population graphs, which combine various types of imaging data and capture the associations among individuals within a population. In medical imaging, population graphs have demonstrated promising results, mostly for classification tasks. In most cases, the graph structure is pre-defined and remains static during training. However, extracting population graphs is a non-trivial task and can significantly impact the performance of Graph Neural Networks (GNNs), which are sensitive to the graph structure. In this work, we highlight the importance of a meaningful graph construction and experiment with different population-graph construction methods and their effect on GNN performance on brain age estimation. We use the homophily metric and graph visualizations to gain valuable quantitative and qualitative insights on the extracted graph structures. For the experimental evaluation, we leverage the UK Biobank dataset, which offers many imaging and non-imaging phenotypes. Our results indicate that architectures highly sensitive to the graph structure, such as Graph Convolutional Network (GCN) and Graph Attention Network (GAT), struggle with low homophily graphs, while other architectures, such as GraphSage and Chebyshev, are more robust across different homophily ratios. We conclude that static graph construction approaches are potentially insufficient for the task of brain age estimation and make recommendations for alternative research directions.Comment: Accepted at GRAIL, MICCAI 202

Atlas-Based Interpretable Age Prediction

Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting the discrepancy between chronological and biological age. To gain a comprehensive understanding of age-related changes observed in various body parts, we investigate them on a larger scale by using whole-body images. We utilise the Grad-CAM interpretability method to determine the body areas most predictive of a person's age. We expand our analysis beyond individual subjects by employing registration techniques to generate population-wide interpretability maps. Furthermore, we set state-of-the-art whole-body age prediction with a model that achieves a mean absolute error of 2.76 years. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance

Body Fat Estimation from Surface Meshes using Graph Neural Networks

Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estimation are the body mass index (BMI), waist circumference, or the waist-hip-ratio. However, those are rather imprecise measures that do not allow for a discrimination between different types of fat or between fat and muscle tissue. The estimation of visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volume has shown to be a more accurate measure for named risk factors. In this work, we show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area. We furthermore envision this method to be applicable to cheaper and easily accessible medical surface scans instead of expensive medical images

Biological nitrogen fixation of legumes crops under organic farming as driven by cropping management: A review

International audienceOrganic agriculture is widely acknowledged as a strategy to reduce the environmental impacts of food production while helping achieving global climate and biodiversity targets. Yet, recent global studies and meta-analyses have highlighted the strong nitrogen (N) limitation of many organic cropping systems. Sustaining crop yields thanks to biological N fixation (BNF) in organic cropping systems is key. However, we lack a systematic estimate of BNF operated by legume crops under organic management, and how it varies according to crop species and cropping practices. OBJECTIVE The objective of this study was to provide systematic estimate of the BNF operated by legume crops for organically managed leguminous crops according to different cropping practices. METHODS We performed a systematic review of the literature by collecting information on the N2 fixation under organic farming – mainly as N2 fixation absolute value (Ndfa, in kgN. ha−1 yr−1) and as the percentage of above-ground biomass N derived from the atmosphere (%Ndfa). RESULTS AND CONCLUSION We show that significant differences exist in the BNF among crop types and cropping practices. Best performances were found for both fodder crop species compared to pulses and for crop species characterized by long growing periods. We show a strong positive relationship between Ndfa and above-ground biomass production. We also found a strong variability in the performances of single crop species and cropping practices within different geographical sites. SIGNIFICANCE The BNF estimates provided here are key for designing more agroecological organic cropping systems that better rely on the BNF service provided by legume crops. They also represent an important base for exploring strategies that enhance N sourcing in organic cropping systems

PySAP-MRI: a Python Package for MR Image Reconstruction

International audienceTarget audience: It is expected that the audience has preliminary knowledge of classical MRI acquisition and reconstruction techniques. Pysap-mri is aimed at researchers who need fast MR image reconstruction algorithms for under-sampled k-space data. It has been fully tested on Linux Ubuntu 16.04/18.04 LTS and Mac OS operating systems.Purpose: We present the open-source MRI plugin, called pysap-mri, of the software package PySAP (Python Sparse data Analysis Package). PySAP offers a large set of fast wavelet transforms and a range of integrated optimization algorithms in Python. The plugin pysap-mri provides methods, tools and examples for MR image reconstruction in various acquisition setups (2D and 3D imaging, Cartesian and non-Cartesian readout, parallel imaging, etc.) in the context of accelerated acquisitions using compressed sensing. This plugin is available on Pypi as pysap-mri 0.1.1. Test data are available in pysap-data

Première identification archéologique d’un écobuage médiéval : le site de Vaudes « Les Trappes » (Aube)

International audienceA preventive excavation was carried out by Inrap at Vaudes « Les Trappes » (Aube) over a surface area of about 8000 m2 after the discovery of rubefied and blackened features, with the aim to identify if these features were natural or of human origin. Without either domestic or artisanal context, they are similar to features, which are often discovered in archaeological trial trenches and recorded as being tree root hole or tree stump fires. Their important density at Vaudes and the fact that they form a discontinued stratigraphic level over almost 350 m, appear significant in characterising their formation. The excavation and geo-archaeological surveys (stratigraphy, granulometry, elementary analysis, magnetic susceptibility, colorimetry and micromorphology) and palaeo-environmental studies (malacology, anthracology and carpology) confirm that these features are the result of in situ combustion in an open and relatively damp environment. Comparing the results with technical and ethnographic documentation on natural or agricultural fires has led us to interpret them as furnace residues as employed in paring and burning process. They date to the 11th-12th centuries and attest that this method existed well before its first mention in historical documents. This study underlines the necessity of showing greater importance for these somewhat enigmatic features despite the apparent difficulties in characterising them.La mise au jour en 2014 de structures rubéfiées et noircies à Vaudes « Les Trappes » (Aube) a conduit à la réalisation de fouilles préventives par l’Inrap sur une surface d’environ 8 000 m² afin d’identifier leur origine, anthropique ou naturelle. La morphologie de ces vestiges, dépourvus d’indices de contexte domestique ou artisanal, rappelle celle de faits fréquemment découverts lors de diagnostics archéologiques, enregistrés a priori comme le résultat de feux liés à des chablis ou des souches d’arbres. L’importante densité des structures, formant un niveau stratigraphique discontinu sur près de 350 mètres apparaissait ici particulièrement pertinente pour caractériser plus précisément les modalités de leur formation. La fouille archéologique, couplée à des caractérisations géoarchéologiques (étude stratigraphique, granulométrie, analyse élémentaire, susceptibilité magnétique, colorimétrie, micromorphologie) et paléoenvironnementales (malacologie, anthracologie, carpologie) confirme que ces structures résultent d’un phénomène de combustion in situ complexe, et cela dans un milieu relativement ouvert et humide. La confrontation de ces résultats à la documentation technique et ethnographique des feux naturels et agro-sylvo-pastoraux nous permet d’interpréter ces structures comme les résidus de fourneaux de combustion tels qu’employés dans un écobuage au sens classique. Leur datation autour des xie-xiie siècles atteste ainsi cette pratique agricole méconnue plusieurs siècles avant sa première mention historique et démontre la nécessité d’une plus grande considération de ce type de découverte, en dépit des difficultés pouvant apparaitre pour enregistrer et caractériser des vestiges parfois énigmatiques