Anatomical connections in the human visual cortex: validation and new insights using a DTI Geodesic Connectivity Mapping method

Various approaches have been introduced to infer the organization of white matter connectivity using Diffusion Tensor Imaging (DTI). In this study, we validate a recently introduced geometric tractography technique, Geodesic Connectivity Mapping (GCM), able to overcome the main limitations of geometrical approaches. Using the GCM technique, we could successfully characterize anatomical connections in the human low-level visual cortex. We reproduce previous findings regarding the topology of optic radiations linking the LGN to V1 and the regular organization of splenium fibers with respect to their origin in the visual cortex. Moreover, our study brings further insights regarding the connectivity of the human MT complex (hMT+) and the retinotopic areas

Human Retinotopic Mapping Using fMRI

We present in this report a new method for the retinotopic mapping of the human visual cortex using fMRI. This fast method allow s to delineate any human's occipital retinotopic visual areas after 30 minutes in an MR scanner. Based on the known retinotopic properties o f the visual cortex and on the procedures described in the literature, we first detail the experimental protocol we used. We then present th e functional data analysis we perform to get the retinotopic angular maps. The algorithm to get a model of the cortical surface from the ana tomical MR image is also rapidly presented. We then show the retinotopic maps projected on the latter model and compare them with the litera ture. Lastly, we present the choices we made to delineate these areas and extract regions of interest that can be used for further studying the human visual cortical system

Human Retinotopic Mapping Using fMRI

We present in this report a new method for the retinotopic mapping of the human visual cortex using fMRI. This fast method allow s to delineate any human's occipital retinotopic visual areas after 30 minutes in an MR scanner. Based on the known retinotopic properties o f the visual cortex and on the procedures described in the literature, we first detail the experimental protocol we used. We then present th e functional data analysis we perform to get the retinotopic angular maps. The algorithm to get a model of the cortical surface from the ana tomical MR image is also rapidly presented. We then show the retinotopic maps projected on the latter model and compare them with the litera ture. Lastly, we present the choices we made to delineate these areas and extract regions of interest that can be used for further studying the human visual cortical system

Prototype of multi-hazard early warning from EUNADICS-AV systems to trigger model forecasts of European airspace

info:eu-repo/semantics/nonPublishe

Système visuel cortical de bas niveau et perception du mouvement: une caractérisation par IRM

The evolution of cerebral imaging technologies combined with specific image processing algorithms contribute to improving our knowledge of the brain functioning, in particular regarding visual perception. This thesis contributes to current understanding implied in visual motion perception in humans, based on complementary information brought by different Magnetic Resonance Imaging (MRI) modalities.The first part of this work focuses on functional MRI (fMRI) identification of low-level visual areas. We detail the fMRI retinotopic mapping procedure we developed, from the stimulus design to the final anatomo-functional analysis. A specific functional localization of the hMT/V5+ complex is also obtained with a block design. These methods, optimized according to some stimulation parameters, allow the extraction of individually defined and homogeneous Regions Of Interest (ROI).In the second part, we characterize functionally these previously identified low-level visual areas. Based on the recent fMR-Adaptation paradigm, which allows to investigate the sensitivity of a cortical region to quantitative variations of a given feature, we demonstrate a functional differentiation across areas regarding their relative sensitivity to visual direction of motion.Lastly, we combine fMRI and Diffusion Tensor MRI (DTI) to study the anatomical connectivity within the low-level visual cortex. Based on state of the art white matter fibers mapping algorithms, this characterization gives insights on the network of areas implied, among others, in visual motion processing.L´évolution des technologies d´imagerie cérébrale alliée aux développement d´algorithmes spécifiques de traitement d´images permettent d´améliorer nos connaissances sur le fonctionnement du cerveau, en particulier s´agissant de la perception visuelle. L´objectif de ce travail de thèse est de contribuer à la compréhension des aires corticales impliquées dans la perception visuelle du mouvement chez l´homme, en analysant l´information des signaux de différentes modalités complémentaires d´Imagerie par Resonance Magnétique (IRM).Une première partie concerne l´identification individuelle des aires visuelles de bas niveau. Nous détaillons la méthode de cartographie rétinotopique par IRM fonctionnelle (IRMf) que nous avons developpée, depuis la conception des stimuli visuels à l´analyse anatomo-fonctionnelle finale. Par ailleurs, une localisation fonctionnelle du complexe hMT/V5+ est obtenue par un paradigme en bloc. Ces méthodes, optimisées suivant certains paramètres de la stimulation, permettent d´extraire pour tout individu des Régions d´Intérêt homogènes.Dans un deuxième temps, nous proposons une caractérisation fonctionnelle des différentes aires visuelles primaires. En se fondant sur le paradigme récent d´IRM d´adaptation qui permet d´étudier la sensibilité d´une région cérébrale à des variations quantitatives d´un paramètre de la stimulation, nous démontrons une différenciation de la sensibilité à la direction du mouvement dans les aires etudiées.Enfin, nous décrivons une expérience combinant les modalités d´IRMf et d´IRM de diffusion (IRMd) dans le but d´étudier la connectivité anatomique au sein du cortex visuel primaire. Cette caractérisation, établie en s´appuyant sur des algorithmes récents de cartographie des fibres de matière blanche, donne des indices sur le réseau d´aires notamment impliquées dans le traitement du mouvement visuel

Système visuel cortical de bas-niveau et perception du mouvement (une étude par IRM)

L'évolution des technologies d'imagerie cérébrale alliée au développement d'algorithmes spécifiques de traitement d'images permettent d'améliorer nos connaissances sur le fonctionnement du cerveau, en particulier s'agissant de la perception visuelle. L'objectif de ce travail de thèse est de contribuer à la compréhension des aires corticales impliquées dans la perception visuelle du mouvement chez l'homme, en analysant l'information des signaux de différentes modalités complémentaires d'Imagerie par Résonance Magnétique (IRM). Une première partie concerne l'identification individuelle des aires visuelles de bas-niveau. Nous détaillons la méthode de cartographie rétinotopique par IRM fonctionnelle (IRMf) que nous avons développée, depuis la conception des stimuli visuels à l'analyse anatomo-fonctionnelle finale. Par ailleurs, une localisation fonctionnelle du complexe hMT/V5+ est obtenue par un paradigme en bloc. Ces méthodes, optimisées suivant certains paramètres de la stimulation, permettent d'extraire pour tout individu des Régions d'Intérêt homogènes. Dans un deuxième temps, nous proposons une caractérisation fonctionnelle des différentes aires visuelles primaires. En se fondant sur le paradigme récent d'IRM d'adaptation qui permet d'étudier la sensibilité d'une région cérébrale à des variations quantitatives d'un paramètre de la stimulation, nous démontrons une différenciation de la sensibilité à la direction du mouvement dans les aires étudiées. Enfin, nous décrivons une expérience combinant les modalités d'IRMf et d'IRM de diffusion (IRMd) dans le but d'étudier la connectivité anatomique au sein du cortex visuel primaire. Cette caractérisation, établie en s'appuyant sur des algorithmes récents de cartographie des fibres de matière blanche, donne des indices sur le réseau d'aires notamment impliquées dans le traitement du mouvement visuel.The evolution of cerebral imaging technologies combined with specific image processing algorithms contribute to improving our knowledge of the brain functioning, in particular regarding visual perception. This thesis contributes to current understanding implied in visual motion perception in humans, based on complementary information brought by different Magnetic Resonance Imaging (MRI) modalities. The first part of this work focuses on functional MRI (fMRI) identification of low-level visual areas. We detail the fMRI retinotopic mapping procedure we developed, from the stimulus design to the final anatomo-functional analysis. A specific functional localization of the hMT/V5+ complex is also obtained with a block design. These methods, optimized according to some stimulation parameters, allow the extraction of individually defined and homogeneous Regions Of Interest (ROI). In the second part, we characterize functionally these previously identified low-level visual areas. Based on the recent fMR-Adaptation paradigm, which allows to investigate the sensitivity of a cortical region to quantitative variations of a given feature, we demonstrate a functional differentiation across areas regarding their relative sensitivity to visual direction of motion. Lastly, we combine fMRI and Diffusion Tensor MRI (DTI) to study the anatomical connectivity within the low-level visual cortex. Based on state of the art white matter fibers mapping algorithms, this characterization gives insights on the network of areas implied, among others, in visual motion processing.NICE-BU Sciences (060882101) / SudocSudocFranceF

Control Theory and Fast Marching Methods for Brain Connectivity Mapping

We propose a novel, fast and robust technique for the computation of anatomical connectivity in the brain. Our approach exploits the information provided by Diffusion Tensor Magnetic Resonance Imaging (or DTI) and models the white matter by using Riemannian geometry and control theory. We show that it is possible, from a region of interest, to compute the geodesic distance to any other point and the associated optimal vector field. The latter can be used to trace shortest paths coinciding with neural fiber bundles. We also demonstrate that no explicit computation of those 3D curves is necessary to assess the degree of connectivity of the region of interest with the rest of the brain. We finally introduce a general local connectivity measure whose statistics along the optimal paths may be used to evaluate the degree of connectivity of any pair of voxels. All those quantities can be computed simultaneously in a Fast Marching framework, directly yielding the connectivity maps. Apart from being extremely fast, this method has other advantages such as the strict respect of the convoluted geometry of white matter, the fact that it is parameter-free, and its robustness to noise. We illustrate our technique by showing results on real and synthetic datasets. Our GCM (Geodesic Connectivity Mapping) algorithm is implemented in C++ and will be soon available on the web

Control Theory and Fast Marching Techniques for Brain Connectivity Mapping

We propose a novel, fast and robust technique for the computation of anatomical connectivity in the brain. Our approach exploits the information provided by Diffusion Tensor Magnetic Resonance Imaging (or DTI) and models the white matter by using Riemannian geometry and control theory. We show that it is possible, from a region of interest, to compute the geodesic distance to any other point and the associated optimal vector field. The latter can be used to trace shortest paths coinciding with neural fiber bundles. We also demonstrate that no explicit computation of those 3D curves is necessary to assess the degree of connectivity of the region of interest with the rest of the brain. We finally introduce a general local connectivity measure whose statistics along the optimal paths may be used to evaluate the degree of connectivity of any pair of voxels. All those quantities can be computed simultaneously in a Fast Marching framework, directly yielding the connectivity maps. Apart from being extremely fast, this method has other advantages such as the strict respect of the convoluted geometry of white matter, the fact that it is parameter-free, and its robustness to noise. We illustrate our technique by showing results on real and synthetic datasets. Our GCM (Geodesic Connectivity Mapping) algorithm is implemented in C++ and will be soon available on the web. 1

Variational, Geometric and Statistical Methods for Modeling Brain Anatomy and Function

We survey the recent activities of the Odyss ee Laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function. We start with the problem of reconstructing sources in MEG and EEG and discuss the variational approach we have developed for solving these inverse problems. This motivates the need for geometric models of the head. We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical MR images. Anatomical connectivity can be extracted from Diffusion Tensor Magnetic Resonance Images but, in the current state of the technology, it must be preceded by a robust estimation and regularization stage. We discuss our work based on variational principles and show how the results can be used to track fibers in the white matter as geodesics in some Riemannian space. We then go to the statistical modeling of fMRI signals from the viewpoint of their decomposition in a pseudo-deterministic and stochastic part which we then use to perform clustering of voxels in a way that is inspired by the theory of Support Vector Machines and in a way that is grounded in information theory. Multimodal image matching is discussed next in the framework of image statistics and Partial Differential Equations with an eye on registering fMRI to the anatomy. The paper ends with a discussion of a new theory of random shapes that may prove useful in building anatomical and functional atlases