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

Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns

International audienceRepeated failures in clinical trials for Alzheimer's disease (AD) have raised a strong interest for the prodromal phase of the disease. A better understanding of the brain alterations during this early phase is crucial to diagnose patients sooner, to estimate an accurate disease stage, and to give a reliable prognosis. According to recent evidence, structural alterations in the brain are likely to be sensitive markers of the disease progression. Neuronal loss translates in specific spatiotemporal patterns of cortical atrophy, starting in the enthorinal cortex and spreading over other cortical regions according to specific propagation pathways. We developed a digital model of the cortical atrophy in the left hemisphere from prodromal to diseased phases, which is built on the temporal alignment and combination of several short-term observation data to reconstruct the long-term history of the disease. The model not only provides a description of the spatiotemporal patterns of cortical atrophy at the group level but also shows the variability of these patterns at the individual level in terms of difference in propagation pathways, speed of propagation, and age at propagation onset. Longitudinal MRI datasets of patients with mild cognitive impairments who converted to AD are used to reconstruct the cortical atrophy propagation across all disease stages. Each observation is considered as a signal spatially distributed on a network, such as the cortical mesh, each cortex location being associated to a node. We consider how the temporal profile of the signal varies across the network nodes. We introduce a statistical mixed-effect model to describe the evolution of the cortex alterations. To ensure a spatiotemporal smooth propagation of the alterations, we introduce a constrain on the propagation signal in the model such that neighboring nodes have similar profiles of the signal changes. Our generative model enables the reconstruction of personalized patterns of the neurodegenerative spread, providing a way to estimate disease progression stages and predict the age at which the disease will be diagnosed. The model shows that, for instance, APOE carriers have a significantly higher pace of cortical atrophy but not earlier atrophy onset

Unified analysis of shape and structural connectivity of neural pathways

International audienceAn abnormal brain development due to a neuropsychiatric disorder can influence the shape and the anatomical organization of both white and grey matter structures. An example is the syndrome of Gilles de la Tourette (GTS) which is thought to be associated with dysfunctions of the cortico-striato-pallido-thalamic circuits [6]. These anatomical complexes should be studied as a whole, analysing both the shape and the relative position of their structures.Atlas constructions permit to estimate an average shape complex of a given population, called template, and its deformations towards the shape complexes of each subject. The template represents the morphological invariants of the population whereas the deformations capture its variability.Previous works defined these deformations as single diffeomorphisms acting on the entire 3D space, so that ending points of fiber bundles could not move independently of grey matter structures [1,2,4,5]. This implicitly assumes that fiber bundles connect the same areas of grey matter structures across subjects. This assumption is not compatible with the aforementioned hypothesis about GTS [6] which relates the syndrome to atypical configurations of neural circuits. We propose a new atlas construction method which can handle both fibers and surfaces and which is based on a double diffeomorphism. This permits to analyse the morphological variations of each structure and the changes in the relative position between fiber bundles and grey matter structures, namely the variations in structural connectivity

An Automated Pipeline for the Analysis of PET Data on the Cortical Surface

We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run

Comparison of DTI Features for the Classification of Alzheimer's Disease: A Reproducible Study

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Advances in the Clinica software platform for clinical neuroimaging studies

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A pipeline for the analysis of 18F-FDG PET data on the cortical surface and its evaluation on ADNI

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NODDI Highlights Promising New Markers In Presymptomatic C9orf72 Carriers

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New advances in the Clinica software platform for clinical neuroimaging studies

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PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets