Diffeomorphic Iterative Centroid Methods for Template Estimation on Large Datasets

International audienceA common approach for analysis of anatomical variability relies on the stimation of a template representative of the population. The Large Deformation Diffeomorphic Metric Mapping is an attractive framework for that purpose. However, template estimation using LDDMM is computationally expensive, which is a limitation for the study of large datasets. This paper presents an iterative method which quickly provides a centroid of the population in the shape space. This centroid can be used as a rough template estimate or as initialization of a template estimation method. The approach is evaluated on datasets of real and synthetic hippocampi segmented from brain MRI. The results show that the centroid is correctly centered within the population and is stable for different orderings of subjects. When used as an initialization, the approach allows to substantially reduce the computation time of template estimation

Fast Template-based Shape Analysis using Diffeomorphic Iterative Centroid

International audienceA common approach for the analysis of anatomical variability relies on the estimation of a representative template of the population, followed by the study of this population based on the parameters of the deformations going from the template to the population. The Large Deformation Diffeomorphic Metric Mapping framework is widely used for shape analysis of anatomical structures, but computing a template with such framework is computationally expensive. In this paper we propose a fast approach for template-based analysis of anatomical variability. The template is estimated using a recently proposed iterative approach which quickly provides a centroid of the population. Statistical analysis is then performed using Kernel-PCA on the initial momenta that define the deformations between the centroid and each subject of the population. This approach is applied to the analysis of hippocampal shape on 80 patients with Alzheimer's Disease and 138 controls from the ADNI database

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects

L'imagerie cérébrale au service de la rééducation

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Modelling morphological variability of the hippocampus using manifold learning and large deformations

International audienceThe hippocampus is a structure of the temporal lobe of the brain which plays an important role in memory processes and in many neuropsychiatric disorders, including Alzheimer's disease, epilepsy and schizophrenia. Here, we present a method to infer the anatomical variability of the hippocampus, based on the LDDMM (Large Deformation Diffeomorphic Metric Mapping) framework and a manifold learning approach to capture the geometry of the population in the space of shapes

Incomplete hippocampalInversion : a comprehensive MRI study of over 2000 subjects

The incomplete-hippocampal-inversion (IHI), also known as malrotation, is an atypical anatomical pattern of the hippocampus, which has been reported in healthy subjects in different studies. However, extensive characterization of IHI in a large sample has not yet been performed. Furthermore, it is unclear whether IHI are restricted to the medial-temporal lobe or are associated with more extensive anatomical changes. Here, we studied the characteristics of IHI in a community-based sample of 2008 subjects of the IMAGEN database and their association with extra-hippocampal anatomical variations. The presence of IHI was assessed on T1-weighted anatomical magnetic resonance imaging (MRI) using visual criteria. We assessed the association of IHI with other anatomical changes throughout the brain using automatic morphometry of cortical sulci. We found that IHI were much more frequent in the left hippocampus (left: 17%, right: 6%, χ2−test, p < 10−28). Compared to subjects without IHI, subjects with IHI displayed morphological changes in several sulci located mainly in the limbic lobe. Our results demonstrate that IHI are a common left-sided phenomenon in normal subjects and that they are associated with morphological changes outside the medial temporal lobe

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Neurofeedback (NF) allows to exert self-regulation over specific aspects of one's own brain activity by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, most commonly electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). EEG-fMRI-neurofeedback (EEG-fMRI-NF) is a new approach that consists in providing a NF based simultaneously on EEG and fMRI signals. By exploiting the complementarity of these two modalities, EEG-fMRI-NF opens a new spectrum of possibilities for defining bimodal NF targets that could be more robust, flexible and effective than unimodal ones. Since EEG-fMRI-NF allows for a richer amount of information to be fed back, the question arises of how to represent the EEG and fMRI features simultaneously in order to allow the subject to achieve better self-regulation. In this work, we propose to represent EEG and fMRI features in a single bimodal feedback (integrated feedback). We introduce two integrated feedback strategies for EEG-fMRI-NF and compare their early effects on a motor imagery task with a between-group design. The BiDim group (n=10) was shown a two-dimensional (2D) feedback in which each dimension depicted the information from one modality. The UniDim group (n=10) was shown a one-dimensional (1D) feedback that integrated both types of information even further by merging them into one. Online fMRI activations were significantly higher in the UniDim group than in the BiDim group, which suggests that the 1D feedback is easier to control than the 2D feedback. However subjects from the BiDim group produced more specific BOLD activations with a notably stronger activation in the right superior parietal lobe (BiDim > UniDim, p < 0.001, uncorrected). These results suggest that the 2D feedback encourages subjects to explore their strategies to recruit more specific brain patterns. To summarize, our study shows that 1D and 2D integrated feedbacks are effective but also appear to be complementary and could therefore be used in a bimodal NF training program. Altogether, our study paves the way to novel integrated feedback strategies for the development of flexible and effective bimodal NF paradigms that fully exploits bimodal information and are adapted to clinical applications

A Comprehensive MRI Study of Over 2000 Subjects

The incomplete-hippocampal-inversion (IHI), also known as malrotation, is an atypical anatomical pattern of the hippocampus, which has been reported in healthy subjects in different studies. However, extensive characterization of IHI in a large sample has not yet been performed. Furthermore, it is unclear whether IHI are restricted to the medial-temporal lobe or are associated with more extensive anatomical changes. Here, we studied the characteristics of IHI in a community-based sample of 2008 subjects of the IMAGEN database and their association with extra-hippocampal anatomical variations. The presence of IHI was assessed on T1-weighted anatomical magnetic resonance imaging (MRI) using visual criteria. We assessed the association of IHI with other anatomical changes throughout the brain using automatic morphometry of cortical sulci. We found that IHI were much more frequent in the left hippocampus (left: 17%, right: 6%, χ2−test, p < 10−28). Compared to subjects without IHI, subjects with IHI displayed morphological changes in several sulci located mainly in the limbic lobe. Our results demonstrate that IHI are a common left-sided phenomenon in normal subjects and that they are associated with morphological changes outside the medial temporal lobe

Hippocampal Shape Alterations Are Associated With Pre- And Postsurgical Verbal And Visual Memory Deficits In Temporal Lobe Epilepsy

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The Exopolysaccharide Matrix Modulates the Interaction between 3D Architecture and Virulence of a Mixed-Species Oral Biofilm

Virulent biofilms are responsible for a range of infections, including oral diseases. All biofilms harbor a microbial-derived extracellular-matrix. The exopolysaccharides (EPS) formed on tooth-pellicle and bacterial surfaces provide binding sites for microorganisms; eventually the accumulated EPS enmeshes microbial cells. The metabolic activity of the bacteria within this matrix leads to acidification of the milieu. We explored the mechanisms through which the Streptococcus mutans-produced EPS-matrix modulates the three-dimensional (3D) architecture and the population shifts during morphogenesis of biofilms on a saliva-coated-apatitic surface using a mixed-bacterial species system. Concomitantly, we examined whether the matrix influences the development of pH-microenvironments within intact-biofilms using a novel 3D in situ pH-mapping technique. Data reveal that the production of the EPS-matrix helps to create spatial heterogeneities by forming an intricate network of exopolysaccharide-enmeshed bacterial-islets (microcolonies) through localized cell-to-matrix interactions. This complex 3D architecture creates compartmentalized acidic and EPS-rich microenvironments throughout the biofilm, which triggers the dominance of pathogenic S. mutans within a mixed-species system. The establishment of a 3D-matrix and EPS-enmeshed microcolonies were largely mediated by the S. mutans gtfB/gtfC genes, expression of which was enhanced in the presence of Actinomyces naeslundii and Streptococcus oralis. Acidic pockets were found only in the interiors of bacterial-islets that are protected by EPS, which impedes rapid neutralization by buffer (pH 7.0). As a result, regions of low pH (<5.5) were detected at specific locations along the surface of attachment. Resistance to chlorhexidine was enhanced in cells within EPS-microcolony complexes compared to those outside such structures within the biofilm. Our results illustrate the critical interaction between matrix architecture and pH heterogeneity in the 3D environment. The formation of structured acidic-microenvironments in close proximity to the apatite-surface is an essential factor associated with virulence in cariogenic-biofilms. These observations may have relevance beyond the mouth, as matrix is inherent to all biofilms