Unsupervised Domain Adaptation with Optimal Transport in multi-site segmentation of Multiple Sclerosis lesions from MRI data: Preprint

Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutional Neural Networks (CNNs) for this task. Although these methods provide accurate segmentation, their applicability in clinical settings remains limited due to a reproducibility issue across different image domains. MS images can have highly variable characteristics across patients, MRI scanners and imaging protocols; retraining a supervised model with data from each new domain is not a feasible solution because it requires manual annotation from expert radiologists. In this work, we explore an unsupervised solution to the problem of domain shift. We present a framework, Seg-JDOT, which adapts a deep model so that samples from a source domain and samples from a target domain sharing similar representations will be similarly segmented. We evaluated the framework on a multi-site dataset, MICCAI 2016, and showed that the adaptation towards a target site can bring remarkable improvements in a model performance over standard training

Perfusion Index: Physical Principles, Physiological Meanings and Clinical Implications in Anaesthesia and Critical Care

Short title: Clinical Usefulness of Perfusion IndexInternational audiencePhotoplethysmography (PPG) has been extensively used for pulse oximetry monitoring in anaesthesia, perioperative and intensive care. However, some components of PPG signal have been employed for other purposes, such as non-invasive haemodynamic monitoring. Perfusion index (PI) is derived from PPG signal and represents the ratio of pulsatile on non-pulsatile light absorbance or reflectance of the PPG signal. PI determinants are complex and interlinked, involving and reflecting the interaction between peripheral and central haemodynamic characteristics, such as vascular tone and stroke volume. Recently, several studies have shed light on the interesting performances of this variable, especially assessing regional or neuraxial block success, and haemodynamic monitoring in anaesthesia, perioperative and intensive care. Nevertheless, no review has yet been published concerning the interest of PI in these fields. In this narrative review will be exposed first the physiological and pathophysiological determinants of PI, and then the mean to measure this value as well as its potential limitations. In the second part, the existing data concerning usefulness of PI in different clinical settings such as operating theatres, intensive care units and emergency departments will be presented and discussed. Finally, the perspectives concerning the use of PI and mentioned aspects that should be explored regarding this tool will be underlined

Improving sensitivity and specificity in diffusion MRI group studies

Diffusion MRI provides a unique opportunity to study the brain tissue architecture at a microscopic level. More specifically, it allows to infer biophysical properties of the axons in the white matter in-vivo. Microstructural parameters are widely used in multi-subject studies to track pathological processes, follow normal development and aging, or investigate behaviour. This thesis aims to identify and potentially address the limitations and pitfalls in voxelwise comparison of diffusion MRI parameters across subjects. To allow for accurate matching of brain structures across subjects, non-linear transformation that spatially aligns the data is required. We demonstrate that using advanced registration methods, we can outperform the standard registration-projection approach both in terms of sensitivity and specificity. The coarse resolution of the data typically causes partial volume effects that bias the diffusion parameters and potentially mislead the interpretation of a group study outcome. We provide evidence that these effects can be addressed by constraining the diffusion model parameter space, which leads to marginally lower sensitivity, but allows an accurate interpretation of the results. Additionally, we suggest that additional information inferred with a data driven approach might mitigate the loss in sensitivity. Finally, we design an original tract-specific modelling framework that enables to estimate microstructural parameters unbiased by the presence of foreign fibre populations or tissues. We demonstrate the sensitivity of our method in a study relating microstructure and behaviour.</p

Improving sensitivity and specificity in diffusion MRI group studies

Diffusion MRI provides a unique opportunity to study the brain tissue architecture at a microscopic level. More specifically, it allows to infer biophysical properties of the axons in the white matter in-vivo. Microstructural parameters are widely used in multi-subject studies to track pathological processes, follow normal development and aging, or investigate behaviour. This thesis aims to identify and potentially address the limitations and pitfalls in voxelwise comparison of diffusion MRI parameters across subjects. To allow for accurate matching of brain structures across subjects, non-linear transformation that spatially aligns the data is required. We demonstrate that using advanced registration methods, we can outperform the standard registration-projection approach both in terms of sensitivity and specificity. The coarse resolution of the data typically causes partial volume effects that bias the diffusion parameters and potentially mislead the interpretation of a group study outcome. We provide evidence that these effects can be addressed by constraining the diffusion model parameter space, which leads to marginally lower sensitivity, but allows an accurate interpretation of the results. Additionally, we suggest that additional information inferred with a data driven approach might mitigate the loss in sensitivity. Finally, we design an original tract-specific modelling framework that enables to estimate microstructural parameters unbiased by the presence of foreign fibre populations or tissues. We demonstrate the sensitivity of our method in a study relating microstructure and behaviour.</p

Perfusion Index: Physical Principles, Physiological Meanings and Clinical Implications in Anaesthesia and Critical Care

Short title: Clinical Usefulness of Perfusion IndexInternational audiencePhotoplethysmography (PPG) has been extensively used for pulse oximetry monitoring in anaesthesia, perioperative and intensive care. However, some components of PPG signal have been employed for other purposes, such as non-invasive haemodynamic monitoring. Perfusion index (PI) is derived from PPG signal and represents the ratio of pulsatile on non-pulsatile light absorbance or reflectance of the PPG signal. PI determinants are complex and interlinked, involving and reflecting the interaction between peripheral and central haemodynamic characteristics, such as vascular tone and stroke volume. Recently, several studies have shed light on the interesting performances of this variable, especially assessing regional or neuraxial block success, and haemodynamic monitoring in anaesthesia, perioperative and intensive care. Nevertheless, no review has yet been published concerning the interest of PI in these fields. In this narrative review will be exposed first the physiological and pathophysiological determinants of PI, and then the mean to measure this value as well as its potential limitations. In the second part, the existing data concerning usefulness of PI in different clinical settings such as operating theatres, intensive care units and emergency departments will be presented and discussed. Finally, the perspectives concerning the use of PI and mentioned aspects that should be explored regarding this tool will be underlined

Unsupervised and weakly supervised approaches for answer selection tasks with scarce annotations

Addressing Answer Selection (AS) tasks with complex neural networks typically requires a large amount of annotated data to increase the accuracy of the models. In this work, we are interested in simple models that can potentially give good performance on datasets with no or few annotations. First, we propose new unsupervised baselines that leverage distributed word and sentence representations. Second, we compare the ability of our neural architectures to learn from few annotated examples in a weakly supervised scheme and we demonstrate how these methods can benefit from a pre-training on an external dataset. With an emphasis on results reproducibility, we show that our simple methods can reach or approach state-of-the-art performances on four common AS datasets

Statistical Analysis of White Matter Integrity for the Clinical Study of Specific Language Impairment in Children

International audienceChildren affected by Specific Language Impairment (SLI) fail to develop a normal language capability. To date, the etiology of SLI remains largely unknown. It induces difficulties with oral language which cannot be directly attributed to intellectual deficit or other developmental delay. Whereas previous studies on SLI focused on the psychological and genetic aspects of the pathology, few imaging studies investigated defaults in neuroanatomy or brain function. We propose to investigate the integrity of white matter in SLI thanks to diffusion Magnetic Res- onance Imaging. An exploratory analysis was performed without a priori on the impaired regions. A region of interest statistical analysis was performed based, first, on regions defined from Catani's atlas and, then, on tractography-based regions. Both the mean fractional anisotropy and mean apparent diffusion coefficient were compared across groups. To the best of our knowledge, this is the first study focusing on white matter integrity in specific language impairment. 22 children with SLI and 19 typically developing children were involved in this study. Overall, the tractography-based approach to group comparison was more sensitive than the classical ROI-based approach. Group differences between controls and SLI patients included decreases in FA in both the perisylvian and ventral pathways of language, comforting findings from previous functional studies

Evidence of a hormonal reshuffle in the cecal metabolome fingerprint of a strain of rats resistant to decompression sickness

International audienceOn one side, decompression sickness (DCS) with neurological disorders lead to a reshuffle of the fecal metabolome from rat caecum. On the other side, there is high inter-individual variability in terms of occurrence of DCS. One could wonder whether the fecal metabolome could be linked to the DCS-susceptibility. We decided to study male and female rats selected for their resistance to decompression sickness, and we hypothesize a strong impregnation concerning the fecal metabolome. The aim is to verify whether the rats resistant to the accident have a fecal metabolomic signature different from the stem generations sensitive to DCS. 39 DCS-resistant animals (21 females and 18 males), aged 14 weeks, were compared to 18 age-matched standard Wistar rats (10 females and 8 males), i.e., the same as those we used for the founding stock. Conventional and ChemRICH approaches helped the metabolomic interpretation of the 226 chemical compounds analyzed in the cecal content. Statistical analysis shows a panel of 81 compounds whose expression had changed following the selection of rats based on their resistance to DCS. 63 compounds are sex related. 39 are in common. This study shows the spectral fingerprint of the fecal metabolome from the caecum of a strain of rats resistant to decompression sickness. This study also confirms a difference linked to sex in the metabolome of non-selected rats, which disappear with selective breeding. Results suggest hormonal and energetic reshuffle, including steroids sugars or antibiotic compounds, whether in the host or in the microbial community