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The emergence of clinical data warehouses (CDWs), which contain the medical data of millions of patients, has paved the way for vast data sharing for research. The quality of MRIs gathered in CDWs differs greatly from what is observed in research settings and reflects a certain clinical reality. Consequently, a significant proportion of these images turns out to be unusable due to their poor quality. Given the massive volume of MRIs contained in CDWs, the manual rating of image quality is impossible. Thus, it is necessary to develop an automated solution capable of effectively identifying corrupted images in CDWs. This study presents an innovative transfer learning method for automated quality control of 3D gradient echo T1-weighted brain MRIs within a CDW, leveraging artefact simulation. We first intentionally corrupt images from research datasets by inducing poorer contrast, adding noise and introducing motion artefacts. Subsequently, three artefact-specific models are pre-trained using these corrupted images to detect distinct types of artefacts. Finally, the models are generalised to routine clinical data through a transfer learning technique, utilising 3660 manually annotated images. The overall image quality is inferred from the results of the three models, each designed to detect a specific type of artefact. Our method was validated on an independent test set of 385 3D gradient echo T1-weighted MRIs. Our proposed approach achieved excellent results for the detection of bad quality MRIs, with a balanced accuracy of over 87%, surpassing our previous approach by 3.5 percent points. Additionally, we achieved a satisfactory balanced accuracy of 79% for the detection of moderate quality MRIs, outperforming our previous performance by 5 percent points. Our framework provides a valuable tool for exploiting the potential of MRIs in CDWs., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:012

Two-dimensional (2D) hybrid sp-sp2 carbon systems are an appealing subject for science and technology. For these materials, topology and structure significantly affect electronic and vibrational properties. We investigate here by periodic density-functional theory (DFT) calculations we here investigate the Raman and IR spectra of 2D carbon crystals belonging to the family of graphdiynes (GDYs) and having different structures and topologies. By joining DFT calculations with symmetry analysis, we assign the IR and Raman modes in the spectra of all the investigated systems. On this basis, we discuss how the modulation of the Raman and IR active bands depends on the different interactions between sp and sp2 domains. The symmetry-based classification allows identifying the marker bands sensitive to the different peculiar topologies. These results show the effectiveness of vibrational spectroscopy for the characterization of new nanostructures, deepening the knowledge of the subtle interactions that take place in these 2D materials.

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to i) spend less time on data management and processing, ii) perform reproducible evaluations of their methods, and iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI and PET data), as well as tools for statistics, machine learning and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS and NIFD). Processed data include image-valued scalar fields (e.g. tissue probability maps), meshes, surface-based scalar fields (e.g. cortical thickness maps) or scalar outputs (e.g. regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-H\^opitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score \textgreater 90\%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score \textgreater 80\%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.

Graphdiynes (GDYs) as two-dimensional carbon structures based on sp2 hybridized aromatic rings connected by sp-hybridized acetylenic linear links are gathering an increasing popularity, both for their peculiar properties and for the promising applications. In these materials, structural features affect the degree of pi-electron conjugation resulting in different electronic, optical and vibrational properties. In particular, how topology, connectivity between sp and sp2 domains and system size are related with the final properties is fundamental to understand structure-property relationships and to tailor the properties by proper structure design. By using a computational approach based on density functional theory calculations, we here investigate structure-property relations in a class of 1D and 2D GDY molecular fragments as building block models of extended structures. By analysing how the structure can modulate the pi-electron conjugation in these systems, HOMO-LUMO gap is found to depend on the peculiar topology and connections between linear sp domains and aromatic units. A topological indicator is computed, showing a trend with the gap and with the frequency of the main vibrational mode occurring in Raman spectra. Our findings can contribute to guide the molecular design of new GDY-based sp-sp2 carbon materials, aiming at tuning their properties by precise control of the structure., Comment: 13 pages, 5 figures

Homozygous mutations in the progranulin gene (GRN) are associated with neuronal ceroid lipofuscinosis 11 (CLN11), a rare lysosomal-storage disorder characterized by cerebellar ataxia, seizures, retinitis pigmentosa, and cognitive disorders, usually beginning between 13 and 25 years of age. This is a rare condition, previously reported in only four families. In contrast, heterozygous GRN mutations are a major cause of frontotemporal dementia associated with neuronal cytoplasmic TDP-43 inclusions. We identified homozygous GRN mutations in six new patients. The phenotypic spectrum is much broader than previously reported, with two remarkably distinct presentations, depending on the age of onset. A childhood/juvenile form is characterized by classical CLN11 symptoms at an early age at onset. Unexpectedly, other homozygous patients presented a distinct delayed phenotype of frontotemporal dementia and parkinsonism after 50 years; none had epilepsy or cerebellar ataxia. Another major finding of this study is that all GRN mutations may not have the same impact on progranulin protein synthesis. A hypomorphic effect of some mutations is supported by the presence of residual levels of plasma progranulin and low levels of normal transcript detected in one case with a homozygous splice-site mutation and late onset frontotemporal dementia. This is a new critical finding that must be considered in therapeutic trials based on replacement strategies. The first neuropathological study in a homozygous carrier provides new insights into the pathological mechanisms of the disease. Hallmarks of neuronal ceroid lipofuscinosis were present. The absence of TDP-43 cytoplasmic inclusions markedly differs from observations of heterozygous mutations, suggesting a pathological shift between lysosomal and TDP-43 pathologies depending on the mono or bi-allelic status. An intriguing observation was the loss of normal TDP-43 staining in the nucleus of some neurons, which could be the first stage of the TDP-43 pathological process preceding the formation of typical cytoplasmic inclusions. Finally, this study has important implications for genetic counselling and molecular diagnosis. Semi-dominant inheritance of GRN mutations implies that specific genetic counseling should be delivered to children and parents of CLN11 patients, as they are heterozygous carriers with a high risk of developing dementia. More broadly, this study illustrates the fact that genetic variants can lead to different phenotypes according to their mono- or bi-allelic state, which is a challenge for genetic diagnosis.

Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM inference methods: nodewise edge selection and penalised likelihood maximisation. We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one. As a result, we propose a composite procedure that explores a family of graphs with an nodewise numerical scheme and selects a candidate among them with an overall likelihood criterion. We demonstrate that, when the number of observations is small, this selection method yields graphs closer to the truth and corresponding to distributions with better KL divergence with regards to the real distribution than the other two. Finally, we show the interest of our algorithm on two concrete cases: first on brain imaging data, then on biological nephrology data. In both cases our results are more in line with current knowledge in each field.

The Q & U Bolometric Interferometer for Cosmology, QUBIC, is an innovative experiment designed to measure the polarization of the Cosmic Microwave Background and in particular the signature left therein by the inflationary expansion of the Universe. The expected signal is extremely faint, thus extreme sensitivity and systematic control are necessary in order to attempt this measurement. QUBIC addresses these requirements using an innovative approach combining the sensitivity of Transition Edge Sensor cryogenic bolometers, with the deep control of systematics characteristic of interferometers. This makes QUBIC unique with respect to others classical imagers experiments devoted to the CMB polarization. In this contribution we report a description of the QUBIC instrument including recent achievements and the demonstration of the bolometric interferometry performed in lab. QUBIC will be deployed at the observation site in Alto Chorrillos, in Argentina at the end of 2019., Comment: Accepted for publication in the Journal of Low Temperature Physics

QUBIC (Q \& U Bolometric Interferometer for Cosmology) is an international ground-based experiment dedicated in the measurement of the polarized fluctuations of the Cosmic Microwave Background (CMB). It is based on bolometric interferometry, an original detection technique which combine the immunity to systematic effects of an interferometer with the sensitivity of low temperature incoherent detectors. QUBIC will be deployed in Argentina, at the Alto Chorrillos mountain site near San Antonio de los Cobres, in the Salta province. The QUBIC detection chain consists in 2048 NbSi Transition Edge Sensors (TESs) cooled to 350mK.The voltage-biased TESs are read out with Time Domain Multiplexing based on Superconducting QUantum Interference Devices (SQUIDs) at 1 K and a novel SiGe Application-Specific Integrated Circuit (ASIC) at 60 K allowing to reach an unprecedented multiplexing (MUX) factor equal to 128. The QUBIC experiment is currently being characterized in the lab with a reduced number of detectors before upgrading to the full instrument. I will present the last results of this characterization phase with a focus on the detectors and readout system., Comment: Conference proceedings submitted to the Journal of Low Temperature Physics for LTD18

Over 30 papers have proposed to use convolutional neural network (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review and found that more than half of the surveyed papers may have suffered from data leakage. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.

Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of AD. However, classification performance obtained with different approaches is difficult to compare and these studies are also difficult to reproduce. In the present paper, we first extend a previously proposed framework to diffusion MRI data for AD classification. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 0.05 up to 0.40 relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML., Comment: 51 pages, 5 figure and 6 tables