1,622 research outputs found
Recommended from our members
Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative.
IntroductionWe characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative.MethodsWe apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference.ResultsWe find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis.DiscussionThe latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms
Méthodes numériques et statistiques pour l'analyse de trajectoire dans un cadre de geométrie Riemannienne.
This PhD proposes new Riemannian geometry tools for the analysis of longitudinal observations of neuro-degenerative subjects. First, we propose a numerical scheme to compute the parallel transport along geodesics. This scheme is efficient as long as the co-metric can be computed efficiently. Then, we tackle the issue of Riemannian manifold learning. We provide some minimal theoretical sanity checks to illustrate that the procedure of Riemannian metric estimation can be relevant. Then, we propose to learn a Riemannian manifold so as to model subject's progressions as geodesics on this manifold. This allows fast inference, extrapolation and classification of the subjects.Cette thèse porte sur l'élaboration d'outils de géométrie riemannienne et de leur application en vue de la modélisation longitudinale de sujets atteints de maladies neuro-dégénératives. Dans une première partie, nous prouvons la convergence d'un schéma numérique pour le transport parallèle. Ce schéma reste efficace tant que l'inverse de la métrique peut être calculé rapidement. Dans une deuxième partie, nous proposons l'apprentissage une variété et une métrique riemannienne. Après quelques résultats théoriques encourageants, nous proposons d'optimiser la modélisation de progression de sujets comme des géodésiques sur cette variété
Building disease progression models from longitudinal biomarkers
International audienc
Learning the clustering of longitudinal shape data sets into a mixture of independent or branching trajectories
Given repeated observations of several subjects over time, i.e. a longitudinal data set, this paper introduces a new model to learn a classification of the shapes progression in an unsupervised setting: we automatically cluster a longitudinal data set in different classes without labels. Our method learns for each cluster an average shape trajectory (or representative curve) and its variance in space and time. Representative trajectories are built as the combination of pieces of curves. This mixture model is flexible enough to handle independent trajectories for each cluster as well as fork and merge scenarios. The estimation of such non linear mixture models in high dimension is known to be difficult because of the trapping states effect that hampers the optimisation of cluster assignments during training. We address this issue by using a tempered version of the stochastic EM algorithm. Finally, we apply our algorithm on different data sets. First, synthetic data are used to show that a tempered scheme achieves better convergence. We then apply our method to different real data sets: 1D RECIST score used to monitor tumors growth, 3D facial expressions and meshes of the hippocampus. In particular, we show how the method can be used to test different scenarios of hip-pocampus atrophy in ageing by using an heteregenous population of normal ageing individuals and mild cog-nitive impaired subjects
Riemannian metric learning for progression modeling of longitudinal datasets
International audienceExplicit descriptions of the progression of biomarkers across time usually involve priors on the shapes of the trajectories. To circumvent this limitation, we propose a geometric framework to learn a manifold representation of longitudinal data. Namely, we introduce a family of Riemannian metrics that span a set of curves defined as parallel variations around a main geodesic, and apply that framework to disease progression modeling with a mixed-effects model, where the main geodesic represents the average progression of biomarkers and parallel curves describe the individual trajectories. Learning the metric from the data allows to fit the model to longitudinal datasets and provides few interpretable parameters that characterize both the group-average trajectory and individual progression profiles. Our method outperforms the 56 methods benchmarked in the TADPOLE challenge for cognitive scores prediction
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
International audienceWe introduce a mixed-effects model to learn spatiotempo-ral patterns on a network by considering longitudinal measures distributed on a fixed graph. The data come from repeated observations of subjects at different time points which take the form of measurement maps distributed on a graph such as an image or a mesh. The model learns a typical group-average trajectory characterizing the propagation of measurement changes across the graph nodes. The subject-specific trajectories are defined via spatial and temporal transformations of the group-average scenario, thus estimating the variability of spatiotemporal patterns within the group. To estimate population and individual model parameters, we adapted a stochastic version of the Expectation-Maximization algorithm, the MCMC-SAEM. The model is used to describe the propagation of cortical atrophy during the course of Alzheimer's Disease. Model parameters show the variability of this average pattern of atrophy in terms of trajectories across brain regions, age at disease onset and pace of propagation. We show that the personaliza-tion of this model yields accurate prediction of maps of cortical thickness in patients
Longitudinal Variational Autoencoders learn a Riemannian progression model for imaging data
International audienceInterpretable progression models for longitudinal neuroimaging data are crucial to understanding neurodegenerative diseases. Well validated geometric progression models for biomarkers do not scale for such high dimensional data. In this work, we analyse a recent approach that combines a Variational Autoencoder with a latent linear mixed-effects model, and demonstrate that imposing a Euclidean prior on the latent space allows the network to learn the geometry of the observation manifold, and model non linear dynamics
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
In this study we propose a deformation-based framework to jointly model the
influence of aging and Alzheimer's disease (AD) on the brain morphological
evolution. Our approach combines a spatio-temporal description of both
processes into a generative model. A reference morphology is deformed along
specific trajectories to match subject specific morphologies. It is used to
define two imaging progression markers: 1) a morphological age and 2) a disease
score. These markers can be computed locally in any brain region. The approach
is evaluated on brain structural magnetic resonance images (MRI) from the ADNI
database. The generative model is first estimated on a control population,
then, for each subject, the markers are computed for each acquisition. The
longitudinal evolution of these markers is then studied in relation with the
clinical diagnosis of the subjects and used to generate possible morphological
evolution. In the model, the morphological changes associated with normal aging
are mainly found around the ventricles, while the Alzheimer's disease specific
changes are more located in the temporal lobe and the hippocampal area. The
statistical analysis of these markers highlights differences between clinical
conditions even though the inter-subject variability is quiet high. In this
context, the model can be used to generate plausible morphological trajectories
associated with the disease. Our method gives two interpretable scalar imaging
biomarkers assessing the effects of aging and disease on brain morphology at
the individual and population level. These markers confirm an acceleration of
apparent aging for Alzheimer's subjects and can help discriminate clinical
conditions even in prodromal stages. More generally, the joint modeling of
normal and pathological evolutions shows promising results to describe
age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres
Modeling and inference of spatio-temporal protein dynamics across brain networks
Models of misfolded proteins (MP) aim at discovering the bio-mechanical
propagation properties of neurological diseases (ND) by identifying plausible
associated dynamical systems. Solving these systems along the full disease
trajectory is usually challenging, due to the lack of a well defined time axis
for the pathology. This issue is addressed by disease progression models (DPM)
where long-term progression trajectories are estimated via time
reparametrization of individual observations. However, due to their loose
assumptions on the dynamics, DPM do not provide insights on the bio-mechanical
properties of MP propagation. Here we propose a unified model of
spatio-temporal protein dynamics based on the joint estimation of long-term MP
dynamics and time reparameterization of individuals observations. The model is
expressed within a Gaussian Process (GP) regression setting, where constraints
on the MP dynamics are imposed through non--linear dynamical systems. We use
stochastic variational inference on both GP and dynamical system parameters for
scalable inference and uncertainty quantification of the trajectories.
Experiments on simulated data show that our model accurately recovers
prescribed rates along graph dynamics and precisely reconstructs the underlying
progression. When applied to brain imaging data our model allows the
bio-mechanical interpretation of amyloid deposition in Alzheimer's disease,
leading to plausible simulations of MP propagation, and achieving accurate
predictions of individual MP deposition in unseen data
- …