Hum Brain Mapp

White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.Etude de cohorte sur la santé des étudiantsStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseLaboratoire pour les applications en imagerie biomédicaleTranslational Research and Advanced Imaging LaboratoryInitiative d'excellence de l'Université de Bordeau

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm(3) and 0.95 for PVSs larger than 15 mm(3). We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.Stopping cognitive decline and dementia by fighting covert cerebral small vessel diseas

Neuroinformatics

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92 %. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96 % of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.Laboratoire pour les applications en imagerie biomédical

Reproducibility of Rock-Eval® Thermal Analysis for Soil Organic Matter Characterization

International audienceRock-Eval® (RE) is a thermal analysis technique increasingly used to characterise soil organic matter. To interpret the results, particularly when investigating differences between samples, it is necessary to know the expected ranges of analytical error associated with the RE measurements. Moreover, the RE analyzer is now at its seventh version (RE7) while most literature results were produced using the previous version (RE6). Thus, a characterization of the reproducibility of RE measurements is necessary. We measured the reproducibility of RE measurements using fifteen samples from French croplands and forests that were analysed on five different RE instruments, located in different laboratories and belonging to both generations RE6 and RE7. From each RE analysis, we extracted RE parameters commonly used for soil organic matter characterization and we performed the prediction of the active and stable soil organic carbon fractions using a machine learning model (PartySOC) that uses RE parameters. We obtained a measure of the expected relative errors in RE parameters and PartySOC predictions per instrument, across instruments of the same generation and across generations. We found that the parameters total organic carbon (TOC), mineral carbon (MinC) and R-index are well reproducible, even across the RE6 and RE7 generations. Instead, the hydrogen index (HI) and oxygen index (OI) are more sensitive to signal variations, even within the same generation, especially when TOC is low. The PartySOC predictions were well reproducible across RE6 instruments but not across RE generations. In the future, the results of this study will help discriminate relevant differences between soil samples characterised using RE thermal analysis

The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1870 young healthy adults, aged 18-35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early ageing.Laboratoire pour les applications en imagerie biomédical

Meet-U: Educating through research immersion.

We present a new educational initiative called Meet-U that aims to train students for collaborative work in computational biology and to bridge the gap between education and research. Meet-U mimics the setup of collaborative research projects and takes advantage of the most popular tools for collaborative work and of cloud computing. Students are grouped in teams of 4-5 people and have to realize a project from A to Z that answers a challenging question in biology. Meet-U promotes "coopetition," as the students collaborate within and across the teams and are also in competition with each other to develop the best final product. Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give research seminars. This very unique combination of education and research is strongly motivating for the students and provides a formidable opportunity for a scientific community to unite and increase its visibility. We report on our experience with Meet-U in two French universities with master's students in bioinformatics and modeling, with protein-protein docking as the subject of the course. Meet-U is easy to implement and can be straightforwardly transferred to other fields and/or universities. All the information and data are available at www.meet-u.org

Examples of strategies and results for the 2016–2017 edition.

<p>Left panel: Team B implemented an efficient sampling algorithm using a grid representation of the proteins to be docked and FFT. For the scoring, they used evolutionary information extracted from multiple sequence alignments of homologs of the two partners. Right panel: Team D used biological knowledge during the sampling step to filter out conformations early and drastically reduce the search space. The results obtained by the students (Teams B and D) on two complexes (barnase–barstar complex, Protein Data Bank [PDB] code: 1AY7, and an antibody–antigen complex, PDB code: 1JPS, respectively) are comparable to those obtained from state-of-the-art methods, namely ZDOCK [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005992#pcbi.1005992.ref010" target="_blank">10</a>] and ATTRACT [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005992#pcbi.1005992.ref011" target="_blank">11</a>]. ZDOCK relies on efficient sampling using FFT and on an optimized energy function [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005992#pcbi.1005992.ref010" target="_blank">10</a>]. ATTRACT proceeds through minimization steps using an empirical, coarse-grained molecular mechanics potential [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005992#pcbi.1005992.ref011" target="_blank">11</a>]. Candidate conformations for the complexes are represented as cartoons and superimposed onto the known crystallographic structures. The receptor is in black, the ligand from the candidate conformation is colored (in orange for Meet-U students, blue for ZDOCK, and purple for ATTRACT), and that from the crystallographic structure is in grey. With each candidate conformation are associated its rank, according to the scoring function of the method, and its deviation (in Å) from the crystallographic structure. FFT, Fast Fourier Transform; PDB, protein data bank.</p