A four-dimensional probabilistic atlas of the human brain

The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype-phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders

Statistical Computing on Non-Linear Spaces for Computational Anatomy

International audienceComputational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings

Anatomical Global Spatial Normalization

Anatomical global spatial normalization (aGSN) is presented as a method to scale high-resolution brain images to control for variability in brain size without altering the mean size of other brain structures. Two types of mean preserving scaling methods were investigated, “shape preserving” and “shape standardizing”. aGSN was tested by examining 56 brain structures from an adult brain atlas of 40 individuals (LPBA40) before and after normalization, with detailed analyses of cerebral hemispheres, all gyri collectively, cerebellum, brainstem, and left and right caudate, putamen, and hippocampus. Mean sizes of brain structures as measured by volume, distance, and area were preserved and variance reduced for both types of scale factors. An interesting finding was that scale factors derived from each of the ten brain structures were also mean preserving. However, variance was best reduced using whole brain hemispheres as the reference structure, and this reduction was related to its high average correlation with other brain structures. The fractional reduction in variance of structure volumes was directly related to ρ2, the square of the reference-to-structure correlation coefficient. The average reduction in variance in volumes by aGSN with whole brain hemispheres as the reference structure was approximately 32%. An analytical method was provided to directly convert between conventional and aGSN scale factors to support adaptation of aGSN to popular spatial normalization software packages

Inter- and intra-individual data fusion in medical imagning applied to the anatomical modeling of the human brain

Many research efforts in 3D medical imaging have been directed towards the definition of efficient and fast display and processing tools . Some very promising results are already available allowing a better access and a better use of the contents of medical images . The objective of this paper is to extend the use of existing data fusion methods toward their application in medicine (radiation therapy, epilepsy surgery, conventional neurosurgery etc.). Data fusion facilitates a better use of 3D image data by providing methods for the fusion of data from multiple modalities e.g ., multimodal registration and fusion between anatomical and functional data, the fusion of data from different patients or with a priori knowledge (models and/or atlases) and the recognition of complex anatomical structures and their symbolic identifications, when they are not explicitly described by the image contents. Three aspects of data fusion are considered in this paper with a particular emphasis on brain imaging. The first one concerns the combination of images and/or generic data; specifically, methods for multimodality registration and matching of data from different individuals by means of warping models . The second aspect concerns the identification of anatomical structures . Finally, the paper presents a state of the art 3D display technique to render the combined data. Perspectives are presented concerning the links between these numerical fusion procedures and their complementary symbolic procedures (data bases and knowledge representation systems).Beaucoup d'efforts de recherche en imagerie 3D médicale ont été dirigés vers la définition d'outils de traitement et de visualisation efficaces et rapides. Des résultats très encourageants sont disponibles aujourd'hui pour améliorer l'accès et l'utilisation médicale du contenu des images. Notre objectif dans ce papier est d'étendre le champ d'utilisation des méthodes de fusion de données à des fins d'applications précises (radiothérapie, chirurgie de l'epilepsie, neurochirurgie conventionnelle, etc.). L'amélioration de l'utilisation des données 3D passe par un effort de recherche plus poussé dans le domaine de la fusion de données. Cela concerne notamment la comparaison d'informations multi-capteurs (fusion multi-modalités, fusion d'informations anatomo-fonctionnelles,...), la fusion d'informations multi-patients ou venant de connaissances a priori (modèles) et enfin la reconnaissance de structures anatomiques complexes et leur identification symbolique, lorsqu'elles ne sont pas explicitement décrites par le contenu des images. Le problème de la fusion de données peut se traduire sous la forme i) d'une fusion de données multi-capteurs d'informations anatomiques et/ou fonctionnelles et ii) d'une fusion de données multi-individus qui, circonscrits au domaine cérébral, passe par l'utilisation de modèles d'anamorphose. Ces deux aspects mis bout à bout forment la trame méthodologique nécessaire à la modélisation anatomique des structures cérébrales. C'est dans ce cadre que se situent les travaux présentés dans ce papier. Le problème de fusion de données est abordé à la fois sous l'angle de la combinaison d'images et/ou de données génériques: problème du recalage multi-modalités et de la mise en correspondance de données entre individus (modèles de déformation appliqués au cerveau humain), sous l'angle de l'identification de structures anatomiques présentes sur les images (segmentation et étiquetage semantique) et enfin sous l'angle de la visualisation 3D des différentes informations. Des perspectives seront données pour ce qui concerne le lien entre ces procédures de fusion numériques et leurs compléments symboliques (bases de données et de connaissances

Chain expansion and chain localisation in the homogenous regime of blends of liquid low molar mass polysiloxanes as revealed by neutron scattering investigations

Using small angle neutron scattering and neutron spin echo spectroscopy, two isotopic blends of low molar mass polydimethylsiloxane (PDMS) and polyethylmethylsiloxane (PEMS), and the corresponding binary blends d-PDMS/ p-PEMS and p-PDMS/d-PEMS (d: deuterated, p: protonated) were studied at the critical composition in the homogeneous regime. From the scattering data it becomes evident that coil dimensions and collective dynamics of these both blend systems behave significantly different. Compared to the isotopic mixtures, which exhibit the expected unperturbed chain dimensions and the typical Rouse relaxation, in the binary blends, which differ by a large shift with respect to the critical temperature, considerable coil expansion and spatially restricted Rouse relaxation occur. Both these structural and dynamic effects are in agreement with the model of droplet formation and chain localization, resulting from the existence of microscopic heterogeneities within the spinodal boundaries of the phase diagram. In addition, the observation of Rouse relaxation, spatially restricted to microscopic length scales, provides a new access to the molecular understanding of the critical slowing down of the mutual diffusion process, observed by photon correlation spectroscopy