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Joint registration of a stack of 2D histological sections to recover 3D structure (``3D histology reconstruction'') finds application in areas such as atlas building and validation of \emph{in vivo} imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as ``banana effect'' (straightening of curved structures) and ``z-shift'' (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest., Comment: Medical Image Analysis (Accepted on 07/10/2021). 22 pages, 11 figures

International audience; Atrial fibrillation (AF) is a complex cardiac disease impact-ing an ever-growing population and increases 6-fold the risk of thrombusformation. However, image based bio-markers to predict thrombosis inpresence of AF are not well known. This lack of knowledge comes fromthe difficulty to analyse and compare the shape of the Left Atrium (LA)as well as the insufficiency of data that limits the complexity of modelswe can use. Conducting data analysis in cardiology exacerbates the smalldataset problem because the heart cycle renders impossible to compareimages taken at systole and diastole time. To address these issues, wefirst propose a graph representation of the LA, to focus on the impactof pulmonary veins (PV) and LA Appendage (LAA) positions, givinga simple object easy to analyse. Secondly, we propose a meta-learningframework for heterogeneous datasets based on the consistent represen-tation of each dataset in a common latent space. We show that sucha model is analogous to a meta-classifier, where each dataset is charac-terised by specific projection in a common latent space, while sharing thesame separating boundary. We apply this model to the graph represen-tation of the LA and interpret the model to give novel time-dependantbio-markers related to PV and LAA configurations for the prediction ofthrombosis.

Disease progression modeling (DPM) of Alzheimer's disease (AD) aims at revealing long term pathological trajectories from short term clinical data. Along with the ability of providing a data-driven description of the natural evolution of the pathology, DPM has the potential of representing a valuable clinical instrument for automatic diagnosis, by explicitly describing the biomarker transition from normal to pathological stages along the disease time axis. In this work we reformulated DPM within a probabilistic setting to quantify the diagnostic uncertainty of individual disease severity in an hypothetical clinical scenario, with respect to missing measurements, biomarkers, and follow-up information. We show that the staging provided by the model on 582 amyloid positive testing individuals has high face validity with respect to the clinical diagnosis. Using follow-up measurements largely reduces the prediction uncertainties, while the transition from normal to pathological stages is mostly associated with the increase of brain hypo-metabolism, temporal atrophy, and worsening of clinical scores. The proposed formulation of DPM provides a statistical reference for the accurate probabilistic assessment of the pathological stage of de-novo individuals, and represents a valuable instrument for quantifying the variability and the diagnostic value of biomarkers across disease stages.

International audience; The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, neuroimaging datasets do not provide a sufficient representation of heart features for the modeling of cardiovascular factors in brain disorders. To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features, along with a Gaussian Process emulator that can faithfully reproduce personalised cardiovas-cular dynamics. Experimental results on UK Biobank show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information, e.g. systolic and diastolic blood pressures only, while jointly estimating the emulated parameters of the lumped model. This allows a novel exploration of the heart-brain joint relationship through simulation of realistic cardiac dynamics corresponding to different conditions of brain anatomy.

International audience; Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our results demonstrate the impact of such external features in the cardiovascular model personalisation by learning more informative parameter-space constraints. Moreover, physiologically plausible mechanisms are captured through these personalised models as well as significant differences associated to specific clinical conditions.

In this study we investigate a novel quantitative instrument for the development of intervention strategies for disease modifying drugs in Alzheimer’s disease. Our framework is based on the modeling of the spatio-temporal dynamics governing the joint evolution of imaging and clinical biomarkers along the history of the disease, and allows the simulation of the effect of intervention time and drug dosage on the biomarkers’ progression. When applied to multi-modal imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative our method enables to generate hypothetical scenarios of amyloid lowering interventions. The results quantify the crucial role of intervention time, and provide a theoretical justification for testing amyloid modifying drugs in the pre-clinical stage. Our experimental simulations are compatible with the outcomes observed in past clinical trials, and suggest that anti-amyloid treatments should be administered at least 7 years earlier than what is currently being done in order to obtain statistically powered improvement of clinical endpoints.

International audience; Albeit spectral-domain OCT (SDOCT) is now in clinical use for glaucoma management, published clinical trials relied on time-domain OCT (TDOCT) which is characterized by low signal-to-noise ratio, leading to low statistical power. For this reason, such trials require large numbers of patients observed over long intervals and become more costly. We propose a probabilistic ensemble model and a cycle-consistent perceptual loss for improving the statistical power of trials utilizing TDOCT. TDOCT are converted to synthesized SDOCT and segmented via Bayesian fusion of an ensemble of GANs. The final retinal nerve fibre layer segmentation is obtained automatically on an averaged synthesized image using label fusion. We benchmark different networks using i) GAN, ii) Wasserstein GAN (WGAN) (iii) GAN + perceptual loss and iv) WGAN + perceptual loss. For training and validation, an independent dataset is used, while testing is performed on the UK Glaucoma Treatment Study (UKGTS), i.e. a TDOCT-based trial. We quantify the statistical power of the measurements obtained with our method, as compared with those derived from the original TDOCT. The results provide new insights into the UKGTS, showing a significantly better separation between treatment arms, while improving the statistical power of TDOCT on par with visual field measurements.

While data in healthcare is produced in quantities never imagined before, the feasibility of clinical studies is often hindered by the problem of data access and transfer, especially regarding privacy concerns. Federated learning allows privacy-preserving data analyses using decentralized optimization approaches keeping data securely decentralized. There are currently initiatives providing federated learning frameworks , which are however tailored to specific hardware and modeling approaches, and do not provide natively a deployable production-ready environment. To tackle this issue, herein we propose an open-source fed-erated learning frontend framework with application in healthcare. Our framework is based on a general architecture accommodating for different models and optimization methods. We present software components for clients and central node, and we illustrate the workflow for deploying learning models. We finally provide a real-world application to the federated analysis of multi-centric brain imaging data.

International audience; Objective: The Multidomain Alzheimer Preventive Trial (MAPT) was designed to assess the effect of omega-3 supplementation and a multidomain intervention (physical activity, cognitive training and nutritional advice) on cognitive decline of people with subjective memory complaint. In term of cognitive testing, no significant effect on cognitive decline was found over the 3-year follow-up. Yet, in the context of dementia-related conditions, brain morphological changes can be used to foretell the cognitive evolution. In this paper, we evaluate the effect of the interventions on the evolution of the brain morphology using the MR images acquired during MAPT. Methods: Subjects in the MAPT cohort with two MRI acquisitions, at baseline and at 36 months, were included , resulting in a subset of 376 subjects distributed in the 4 intervention groups: multidomain intervention plus omega-3, multidomain intervention plus placebo, omega-3 alone, and placebo alone. The morphological changes were assessed from volume measurements of regions of interest and a voxel-wise deformation-based approach. The primary outcome is the longitudinal deformation observed between the baseline image and the 3-year follow-up. Results: The multi-domain intervention is associated with a significant effect on the 3-year morphological evolution. The effect is similar within the two groups undergoing the intervention regardless of the omega-3 or placebo treatment. The voxel-wise deformation-based approach shows that the differences are mainly located in the left peri-ventricular area next to the temporoparietal junction (TPJ). These morphological changes correspond to a slower morphological evolution and are correlated with a better performance in cognitive assessments. These results could not be observed using the volumetric morphometry approach. No effect of omega-3 was observed.Discussion: In this study, we found that the multidomain intervention has a significant effect on morphological changes that are usually associated with the cognitive decline. This result suggests that effects at the level of cognitive decline may be visible in the long term, and that the cognitive scores may not be powerful enough to detect changes after 3 years. We argue that the use of neuroimaging could help define whether early intervention strategies are effective to delay cognitive decline and dementia.

Accurately monitoring the efficacy of disease-modifying drugs in glaucoma therapy is of critical importance. Albeit high resolution spectral-domain optical coherence tomography (SDOCT) is now in widespread clinical use, past landmark glaucoma clinical trials have used time-domain optical coherence tomography (TDOCT), which leads, however, to poor statistical power due to low signal-to-noise characteristics. Here, we propose a probabilistic ensemble model for improving the statistical power of imaging-based clinical trials. TDOCT are converted to synthesized SDOCT images and segmented via Bayesian fusion of an ensemble of generative adversarial networks (GANs). The proposed model integrates super resolution (SR) and multi-atlas segmentation (MAS) in a principled way. Experiments on the UK Glaucoma Treatment Study (UKGTS) show that the model successfully combines the strengths of both techniques (improved image quality of SR and effective label propagation of MAS), and produces a significantly better separation between treatment arms than conventional segmentation of TDOCT.

International audience; Cerebrovascular diseases have been associated with a variety of heart diseases like heart failure or atrial fibrillation, however the mech-anistic relationship between these pathologies is largely unknown. Until now, the study of the underlying heart-brain link has been challenging due to the lack of databases containing data from both organs. Current large data collection initiatives such as the UK Biobank provide us with joint cardiac and brain imaging information for thousands of individuals , and represent a unique opportunity to gain insights about the heart and brain pathophysiology from a systems medicine point of view. Research has focused on standard statistical studies finding correlations in a phenomenological way. We propose a mechanistic analysis of the heart and brain interactions through the personalisation of the parameters of a lumped cardiovascular model under constraints provided by brain-volumetric parameters extracted from imaging, i.e: ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We applied this framework in a cohort of more than 3000 subjects and in a pathological subgroup of 53 subjects diagnosed with atrial fibrillation. Our results show that the use of brain feature constraints helps in improving the parameter estimation in order to identify significant differences associated to specific clinical conditions.

The last decade has seen a great proliferation of supervised learning pipelines for individual diagnosis and prognosis in Alzheimer's disease. As more pipelines are developed and evaluated in the search for greater performance, only those results that are relatively impressive will be selected for publication. We present an empirical study to evaluate the potential for optimistic bias in classification performance results as a result of this selection. This is achieved using a novel, resampling-based experiment design that effectively simulates the optimisation of pipeline specifications by individuals or collectives of researchers using cross validation with limited data. Our findings indicate that bias can plausibly account for an appreciable fraction (often greater than half) of the apparent performance improvement associated with the pipeline optimisation, particularly in small samples. We discuss the consistency of our findings with patterns observed in the literature and consider strategies for bias reduction and mitigation., Graphical Abstract, Highlights • Demonstration and measurement of selection bias in AD classification experiments • Bias accounts for much of the performance improvement seen with pipeline optimisation. • Assessment of key risk factors and guidance on best research practices • Evidence of selection bias in results collected by a recent literature review

International audience; We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparametrized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis.

Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we tackle this problem by presenting a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer’s disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure.In this paper we propose a spatiotemporal analysis pipeline based Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects.We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

Apathy and anhedonia are common behavioral and psychological symptoms across brain disorders. Despite mainly common in presence of pathological conditions, apathy and anhedonia can also be present in healthy people. A comprehensive description of lack of interest and anhedonia in the general population is missing. The UK Biobank (UKB) includes 502,655 community-dwelling participants aged 37—73y, recruited between 2006 and 2010 in the UK. Extensive data were collected, including genetics, brain imaging, blood samples, family and medical history. No participant in the UKB received an apathy diagnosis (ICD10). However, self-report questions on mental health may be used as apathy proxies.

At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, cannot always be shared directly due to privacy and legal concerns, thus limiting the full exploitation of big data in the study of brain disorders. Here we propose a federated learning framework for securely accessing and meta-analyzing any biomedical data without sharing individual information. We illustrate our framework by investigating brain structural relationships across diseases and clinical cohorts. The framework is first tested on synthetic data and then applied to multi-centric, multi-database studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of the approach for further applications in distributed analysis of multi-centric cohorts, Comment: Federated learning, distributed databases, PCA, SVD, meta-analysis, brain disease

State-of-the-art data analysis methods in genetics and related fields have advanced beyond massively univariate analyses. However, these methods suffer from the limited amount of data available at a single research site. Recent large-scale multi-centric imaging-genetic studies, such as ENIGMA, have to rely on meta-analysis of mass univariate models to achieve critical sample sizes for uncovering statistically significant associations. Indeed, model parameters, but not data, can be securely and anonymously shared between partners. We propose here partial least squares (PLS) as a multivariate imaging-genetics model in meta-studies. In particular, we propose an online estimation approach to partial least squares for the sequential estimation of the model parameters in data batches, based on an approximation of the singular value decomposition (SVD) of partitioned covariance matrices. We applied the proposed approach to the challenging problem of modeling the association between 1,167,117 genetic markers (SNPs, single nucleotide polymorphisms) and the brain cortical and sub-cortical atrophy (354,804 anatomical surface features) in a cohort of 639 individuals from the Alzheimer's Disease Neuroimaging Initiative. We compared two different modeling strategies (sequential- and meta-PLS) to the classic non-distributed PLS. Both strategies exhibited only minimal approximation errors of model parameters. The proposed approaches pave the way to the application of multivariate models in large scale imaging-genetics meta-studies, and may lead to novel understandings of the complex brain phenotype-genotype interactions.

We present a disease progression model with single vertex resolution that we apply to cortical thickness data. Our model works by clustering together vertices on the cortex that have similar temporal dynamics and building a common trajectory for vertices in the same cluster. The model estimates optimal stages and progression speeds for every subject. Simulated data show that it is able to accurately recover the vertex clusters and the underlying parameters. Moreover, our clustering model finds similar patterns of atrophy for typical Alzheimer’s disease (tAD) subjects on two independent datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and a cohort from the Dementia Research Centre (DRC), UK. Using a separate set of subjects with Posterior Cortical Atrophy (PCA) from the DRC dataset, we also show that the model finds different patterns of atrophy in PCA compared to tAD. Finally, our model provides a novel way to parcellate the brain based on disease dynamics.

International audience; We propose a framework for developing a comprehensive biophysical model that could predict and simulate realistic longitudinal MRIs of patients with Alzheimer's Disease (AD). The framework includes three major building blocks: i) Atrophy generation ii) Brain deformation iii) Realistic MRI generation. Within this framework, this paper focuses on a detailed implementation of the brain deformation block with a carefully designed biomechanics-based tissue loss model. For a given baseline brain MRI, the model yields a deformation field imposing the desired atrophy at each voxel of the brain parenchyma while allowing the CSF to expand as required to globally compensate for the locally prescribed volume loss. Our approach is inspired by biomechanical principles and involves a system of equations similar to Stokes equations in fluid mechanics but with the presence of a non-zero mass source term. We use this model to simulate longitudinal MRIs by prescribing complex patterns of atrophy. We present experiments that provide an insight into the role of different biomechan-ical parameters in the model. The model allows simulating images with exactly the same tissue atrophy but with different underlying deformation fields in the image. We explore the influence of different spatial distributions of atrophy on the image appearance and on the measurements of atrophy reported by various global and local atrophy estimation algorithms. We also present a pipeline that allows evaluating atrophy estimation algorithms by simulating longitudinal MRIs from large number of real subject MRIs with complex subject-specific atrophy patterns. The proposed framework could help understand the implications of different model assumptions, regularization choices and spatial priors for the detection and measurement of brain atrophy from longitudinal brain MRIs.

International audience; We propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced. We investigate the group-wise differences between the patients with Alzheimer's disease and the healthy control group, and show that the proposed pipeline increases the sensitivity with no decrease in the specificity of the statistical study done on the longitudinal deformations.

In this work we evaluate the ability of PLS in generalizing to unseen clinical cohorts when applied to the analysis of the joint variation between genotype and phenotype in Alzheimer's disease (AD). The model is trained on single-nucleotide polymorphisms (SNPs) and brain volumes obtained from the ADNI database for a large cohort of healthy individuals and AD patients, and validated on the ADNI MCI and ENIGMA cohorts. The experimental results confirm the ability of PLS in providing a meaningful description of the joint dynamics between brain atrophy and genotype data in AD, while providing important generalization results when tested on clinically heterogeneous cohorts.

Introduction It has been extensively shown that the insurgence of dementia and cognitive decline is strongly tied to the presence of stroke and brain vascular damage. However, while hypertension is a major risk factor for stroke, the relationship between cardiac abnormalities such as high blood pressure or arrhythmias, with dementia still needs to be elucidated. Currently, large biobank studies offer the chance of jointly analyzing the common variation between cardiovascular pathologies and neurodegeneration. Methods From the UK Biobank database, we have image information from 10,000 individuals. From these, we have selected 4424 participants, which had all the complete image information, cardiac and brain images. For each individual, we extracted cardiovascular and brain related phenotypes. The cardiovascular information includes : systolic blood pressure, diastolic blood pressure, cardiac output and stroke volume. As brain volumetric indicators, we took into account grey matter (GM) and white matter (WM) volumes, and the ventricles volume. All the volumes were normalized by the total head volume. The relationship among the different variables was studied through canonical correlation analysis (CCA). Results and discussion Fig. 1 shows the comparison between the canonical loadings obtained from CCA, as well as the loadings obtained when regressing out age from the model. When age is not taken into account, the main predictor of the correlation is the systolic blood pressure, followed by diastolic blood pressure. These variables are correlated with the shrinkage of the brain volume (WM and GM) and with the increase of ventricles volume. When age is explicitly accounted for, we observe that cardiac output and stroke volume become the dominant factors, being mostly associated with GM loss and ventricles volumes increase. We hypothesize that in the first scenario age was strongly implicitly associated with blood pressures and brain changes. After removing the influence of age, we can observe how GM decreases as cardiac output and stroke volume increase, while WM shrinkage has disappeared. This indicates an underlying relationship among cardiovascular indicators and brain volumes, which should be further explored. We plan to explore this relationship in the future, developing unsupervised multivariate methods and biophysical models that allow us to obtain additional features that can help us to characterize this behavior and hopefully identify pathological subjects.

To study the relationship between gray matter atrophy and amyloid deposition in Alzheimer disease (AD).Volumetric magnetic resonance (MR) and [11C]-PIB PET were acquired from 23 patients with AD and 17 healthy older persons. Standardized [11C]-PIB uptake values were coregistered to MR scans in a standard space. Decreased density of and increased [11C]-PIB uptake in the gray matter of patients with AD vs controls were assessed with both voxel-based (p0.05 corrected) and region-of-interest (ROI) analyses. The relationship between decreased density of and increased [11C]-PIB uptake in the gray matter was investigated with voxel-based Pearson r maps (thresholded at p0.05) and ROI linear regression plots.Atrophy mapped to the hippocampus and increased [11C]-PIB uptake to large frontal, parietal, and posterior cingulate cortical areas. ROI analysis showed the largest effect size for atrophy in the hippocampus (2.01) and amygdala (1.27) and the highest effect size for [11C]-PIB uptake in frontal (2.66), posterior cingulate/retrosplenial (2.43), insular (2.41), and temporal (2.23) regions. In the hippocampus, [11C]-PIB uptake was significantly increased, but effect size was milder (1.72). Significant correlations between atrophy and increased [11C]-PIB uptake were found in the hippocampal (r = -0.54) and amygdalar ROIs (r = -0.40) but not in the frontal, temporal, posterior cingulate/retrosplenial, insular, and caudate ROIs (r between 0.04 and 0.25).The medial temporal lobe might be highly susceptible to amyloid toxicity, whereas neocortical areas might be more resilient.

International audience; The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug ecacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect significant di↵erences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neu-rodegeneration and represents a valuable instrument to model presymp-tomatic anatomical changes occurring in specific brain regions. In this study we propose a computational framework to model group-wise spatio-temporal shape di↵erences, and to statistically evaluate the e↵ects of time and pathological components on the modeled variability. The proposed approach leverages the geodesic regression framework based on varifolds, and models the spatio-temporal shape variability via dimensionality reduction of the subject-specific " residual " transformations normalised in a common reference frame through parallel transport. The proposed approach is applied to patients with genetic variants of fronto-temporal dementia, and shows that shape di↵erences in the posterior part of the thalamus can be observed several years before the appearance of clinical symptoms.

International audience; This paper proposes a framework to simulate patient specific structural Magnetic Resonance Images (MRIs) from the available MRI scans of Alzheimer’s Disease(AD) subjects. We use a biophysical model of brain deformation due to atrophy that can generate biologically plausible deformation for any given desired volume changes at the voxel level of the brain MRI. Large number of brain regions are segmented in 45 AD patients and the atrophy rates per year are estimated in these regions from the available two extremal time-point scans. Assuming linear progression of atrophy, the volume changes in scans closest to the half way time period is computed. These atrophy maps are prescribedto the baseline images to simulate the middle time-point images by using the biophysical model of brain deformation. From the baseline scans,the volume changes in real middle time-point scans are compared to the ones in simulated middle time-point images. This present framework also allows to introduce desired atrophy patterns at different time-points to simulate non-linear progression of atrophy. This opens a way to use abiophysical model of brain deformation to evaluate methods that study the temporal progression and spatial relationships of atrophy of differentregions in the brain with AD.

PURPOSE: The aim of this study was to determine why colorectal tumors confined to submucosa rarely metastasize. Under normal conditions, the submucosa contains many large lymphatic vessels with thin walls that would presumably favor the spread of cancer cells through the lymphatic system. METHODS: Specimens of colorectal cancer tissue, the border between tumor and normal tissue, and normal tissue were obtained from patients undergoing radical resection of colorectal cancer. The material was embedded in methacrylate resin for light microscopy and Epon® for transmission electron microscopy examination. Light microscopy observations were routinely performed on serial sections. RESULTS: No lymphatic vessels were ever found in the tumor mass. The border area contained peritumoral inflammatory infiltrate of variable thickness. Where submucosal lymphatic vessels came into contact with peritumoral inflammatory infiltrate, they were profoundly altered: their endothelium was fragmented, and their walls were disrupted. These altered lymphatic vessels were almost always accompanied by mast cells, which were observed in the process of degranulating toward the lymphatic endothelium. No such alterations were detected in blood vessels. CONCLUSION: Our results suggest that mast cells, probably influenced by inflammatory infiltrate and/or colorectal cancer cells, destroy lymphatic vessels, which prevents cancer cells from spreading through the lymphatic system.

In this work we propose a novel Gaussian process-based spatio-temporal model of time series of images. By assuming separability of spatial and temporal processes we provide a very efficient and robust formulation for the marginal likelihood computation and the posterior prediction. The model adaptively accounts for local spatial correlations of the data, and the covariance structure is effectively parameterised by the Kronecker product of covariance matrices of very small size, each encoding only a single direction in space. We provide a simple and flexible framework for within- and between-subject modelling and prediction. In particular, we introduce the Hoffman-Ribak method for efficient inference on posterior processes and its uncertainty. The proposed framework is applied in the context of longitudinal modelling in Alzheimer's disease. We firstly demonstrate the advantage of our non-parametric method for modelling of within-subject structural changes. The results show that non-parametric methods demonstrably outperform conventional parametric methods. Then the framework is extended to optimize complex parametrized covariate kernels. Using Bayesian model comparison via marginal likelihood the framework enables to compare different hypotheses about individual change processes of images.

International audience; This paper proposes a model of brain deformation triggered by atrophy in Alzheimer's Disease (AD). We introduce a macroscopic biophysical model assuming that the density of the brain remains constant, hence its volume shrinks when neurons die in AD. The deformation in the brain parenchyma minimizes the elastic strain energy with the prescribed local volume loss. The cerebrospinal fluid (CSF) is modelled differently to allow for fluid readjustments occuring at a much faster time-scale. PDEs describing the model is discretized in staggered grid and solved using Finite Difference Method. We illustrate the power of the model by showing different deformation patterns obtained for the same global atrophy but prescribed in gray matter (GM) or white matter (WM) on a generic atlas MRI, and with a realistic AD simulation on a subject MRI. This well-grounded forward model opens a way to study different hypotheses about the distribution of brain atrophy, and to study its impact on the observed changes in MR images.

Background Over the last 15 years, many studies demonstrated the myogenic regenerative potential of bone marrow mesenchymal stem cells (BM-MSC), making them an attractive tool for the regeneration of damaged tissues. In this study, we have developed an animal model of esophagogastric myotomy (MY) aimed at determining the role of autologous MSC in the regeneration of the lower esophageal sphincter (LES) after surgery. Methods Syngeneic BM-MSC were locally injected at the site of MY. Histological and functional analysis were performed to evaluate muscle regeneration, contractive capacity, and the presence of green fluorescent protein–positive BM-MSC (BM-MSC-GFP+) in the damaged area at different time points from implantation. Key Results Treatment with syngeneic BM-MSC improved muscle regeneration and increased contractile function of damaged LES. Transplanted BM-MSC-GFP+ remained on site up to 30 days post injection. Immunohistochemical analysis demonstrated that MSC maintain their phenotype and no differentiation toward smooth or striated muscle was shown at any time point. Conclusions & Inferences Our data support the use of autologous BM-MSC to both improve sphincter regeneration of LES and to control the gastro-esophageal reflux after MY.

Alpha-fetoprotein (AFP) is normally produced by primary hepatic neoplasms and germ cell tumors. There have, however, been reports of its production in cases of gastrointestinal tract adenocarcinoma. Gastric hepatoid carcinomas constitute a clinicopathological entity of recent acquisition and have certain common characteristics, which include the presence of hepatoid foci and frequent liver metastases, even in cases of early gastric cancer, and increasing serum AFP levels. In this study the case of one patient who underwent gastric resection and presented clinical, humoral, histological and immunohistochemical characteristics typical of hepatoid gastric carcinoma is reported. More biological studies, as well as precise criteria for pathological definition and therapy, are still necessary for a better understanding of this pathology.

International audience; Anatomical changes like brain atrophy or growth are usually not homogeneous in space and across spatial scales, since they map di erently depending on the anatomical structures. Thus, the accurate analysis of volume changes from medical images requires to reliably localize and distinguish the spatial changes occurring at di erent scales, from voxel to regional level. We propose here a framework for the sparse probabilistic scale-space analysis of volume changes encoded by deformations. Our framework is based on the Helmoltz decomposition of vector fields. By scale-space analysis of the scalar pressure map associated to the irrotational component of the deformation, we robustly identify the areas of maximal volume changes, and we de ne a consistent sparse decomposition of the irrotational component. We show the e ectiveness of our framework in the challenging problem of detecting the progression of tumor growth, and in the group-wise analysis of the longitudinal atrophy in Alzheimer's disease.

This randomised clinical trial, involving patients with advanced colorectal cancer, was carried out to compare the effectiveness of accelerated folinic acid (FA) plus 5-fluorouracil (5-FU) with that of the conventional regimen of 5-FU alone. Both regimens were administered with simulataneous supportive care. 185 patients were eligible: 94 were randomly allocated to receive FA 200 mg/m2 i.v. plus 5-FU 400 mg/m2 i.v. on days 1-5 every 3 weeks; and 91 to receive 5-FU 400 mg/m2 i.v. on days 1-5 every 4 weeks. The response rate was 33.3% in the accelerated FA/5-FU and 18.6% in the 5-FU arm (P = 0.045). Median survival was 13.5 months in the FA/5-FU arm and 7.5 months in the 5-FU arm (P = 0.039). Toxicity was mild and slightly more pronounced in the FA/5-FU arm (P = 0.078). This study indicates that, in patients with advanced colorectal cancer, accelerated chemotherapy with FA and 5-FU and simultaneous supportive care is capable of achieving a higher response rate and longer survival than conventional 5-FU alone, without severe toxicity.